To research the molecular mechanism of compound Danshen tablets in the treatment of hepatic fibrosis through network pharmacology.  Traditional Chinese medicine systems pharmacology (TCMSP) and online Mendelian inheritance in man (OMIM) databases were searched for compound Danshen tablets' active ingredients o and hepatic fibrosis-related genes. The network enrichment of the targets of “herb-compound-target” was visualized and analyzed using Cytoscape software. Then, the screened target genes were used to construct a protein-protein interaction network. The DAVID enrichment database  was adopted for GO (Gene Ontology) enrichment and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichment of vital nodes. β1, EGF, CXCL16, CCL21, SERPINB5, SERPINA1, SOD2, and PPIG) for reversing hepatic fibrosis; and four core targets . In addition, KEGG enrichment analysis showed that compound Danshen tablets mainly involved FoxO and MAPK signaling pathways, as the key signaling pathways in the treatment of hepatic fibrosis.  The results yielded 234 targets of compound Danshen tablets; ten important targets (TNF, IL-10, TGF- TNF, IL-10, TGF-1, and EGF and FOXO and MAPK signaling pathways play a key role in the pathogenesis of hepatic fibrosis. Based on the results of this study, the mechanism of action of compound Danshen tablets in the treatment of hepatic fibrosis may be associated with the regulation of FoxO and MAPK signaling pathways and inhibition of TNF, IL-10, TGF-1, and EGF. Hepatic fibrosis is associated with high morbidity and mortality. It is a complex compensatory process for tissue repair in which large amounts of extracellular matrix produced by activated hepatic stellate cells (HSCs) are deposited excessively in the liver . HepaticTraditional Chinese medicines have been shown to have effective therapeutic effects with minimal side effects and have been used for the treatment of a wide range of diseases including hepatopathy . They haTraditional Chinese medicines may also be an effective prevention approach for hepatic fibrosis . Additiohttps://pubchem.ncbi.nlm.nih.gov/), and then the target proteins corresponding to the compounds screened from the Pharmmapper and PubMed databases were standardized in UniProt. Then, Cytoscape 3.7.2 software was used to construct the “active compound of compound Danshen tablets-target” network.Compound Danshen tablets contain three traditional Chinese medicines. The Traditional Chinese Medicine Systems Pharmacology (TCMSP) database (Target information was mainly from Drug Bank) was searched for active ingredients of the three traditional Chinese medicines using the “compound Danshen tablets” as the keyword. Oral bioavailability (OB) and drug-likeness (DL) of compound Danshen tablets were used as the screening parameters of the collected active ingredients. Then, the collected active ingredients were screened according to the screening conditions and integrated model of drug absorption, distribution, metabolism, and excretion (ADME). Subsequently, based on the active ingredients screening feedback suggestions in the document “parameter information” of the Chinese TCMSP document database, the following compound screening threshold conditions were determined: bioavailability ≥ 30% and congener medicine property ≥ 0.18. The compounds' structures were searched in PubChem (https://omim.org/), using “hepatic fibrosis” as the keyword. The targets of Homo sapiens were selected, and “uncompressed” was chosen to download the EXL form. After reduplicated data were removed, the EXL form was added to “OMIM hepatic fibrosis database.” Then, the gene ID in “OMIM hepatic fibrosis database” was converted to pronKB by querying the UniProt database and using the plotting function. After downloading the EXL, protein name and gene name columns were sorted, and the key information was screened to obtain “hepatic fibrosis-target.” Then, the gene names in the “active compound of compound Danshen tablets-target” and “hepatic fibrosis-target” were converted into gene symbols one at a time through querying the UniProt database. After that, the target information was input in the UniProt KB column, and human was chosen from popular organisms. Then, the gene name whose weight was ranked first was used to construct the UniProt-gene name database. Subsequently, according to the type file formed, the processed data were imported into Cytoscape 3.7.2 to construct the “compound Danshen tablets-compound-target-hepatic fibrosis” network.Known hepatic fibrosis genes were searched through the online Mendelian inheritance in man (OMIM) database ,” and the processed gene symbol data were imported into the Cytoscape 3.7.2 software for network visualization display. After that, topology analysis was conducted according to the network diagram composited by gene symbols, and the top 10 genes were selected as key targets in the compound Danshen tablets-hepatic fibrosis network according to the ranking of the weighting exponent of the node degree. Then, the STRING database was used to analyze the target interaction information between the top 10 genes to obtain the core targets.https://david.ncifcrf.gov) was adopted to perform Gene Ontology (Go) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis for common signaling targets of compound Danshen tablets-hepatic fibrosis. “Homo sapiens” was chosen in the gene-enriched species column. After screening, the gene enrichment results of p < 0.05 or FDR < 0.01 were used as output for data analysis and plotting.DAVID database  were obtained. Among the targets, the core targets were TNF, IL-10, TGF-β1, and EGF and the main active ingredients of compound Danshen tablets exerting antihepatic fibrosis effect included quercetin , β-sitosterol (TGFB1), ginsenoside Rh2 (TNF) in radix notoginseng, and luteolin  and cryptotanshinone (TNF) in radix salviae miltiorrhiza , cell component (CC), and molecular function (MF). The data from the STRING database were searched for items of  Figures . The aboβ1, EGF, CXCL16, CCL21, SERPINB5, SERPINA1, SOD2, and PPIG were the important targets, and TNF, IL-10, TGF-β1, and EGF were the major core targets for compound Danshen tablets in reversing hepatic fibrosis. Besides, the corresponding drug-active ingredients of compound Danshen tablets included quercetin , β-sitosterol (TGFB1), and ginsenoside Rh2 (TNF) in radix notoginseng and luteolin  and cryptotanshinone (TNF) in radix salviae miltiorrhiza. The above core targets promoted hepatic fibrosis by cytokine-cytokine receptor interaction, regulation of antigenic stimulation on chronic inflammatory reaction, biosynthesis of the receptors, endothelial cell apoptosis process, chemokine signaling pathway, and regulation of hyaluronan biosynthesis [α can trigger multiple signaling pathways involved in inflammation; further, TNFα production perpetuates the inflammation phase which results in the activation of resident HSCs into fibrogenic myofibroblasts [Network pharmacology and system biology can explain the effects of drugs on biological network disruption from the perspective of macroscopic or holistic regulation . Similarynthesis , 23. Furynthesis . TNFα caroblasts . IL-10 eroblasts . EGF, throblasts , FoxO1 iroblasts . The MAPTaken together, these results suggest that, possibly, inhibition of the four core targets  and FOXO and MAPK signaling pathways by the compound of Danshen tablets could result in reversal effects on hepatic fibrosis.TNF, IL-10, TGF-1, and EGF and FOXO and MAPK signaling pathways play a key role in the pathogenesis of hepatic fibrosis. Therefore, the mechanism of action of compound Danshen tablets in the treatment of hepatic fibrosis may be associated with the regulation of FoxO and MAPK signaling pathways and inhibition of TNF, IL-10, TGF-1, and EGF. Further, the development of TNF, IL-10, TGF-ß1, and EGF receptor antagonists could be an effective therapeutic approach to hepatic fibrosis.


The PI3K/AKT/mTOR and RAF/MEK/ERK pathways are commonly activated by mutations and chromosomal translocation in vital targets. The PI3K/AKT/mTOR signaling pathway is dysregulated in nearly all kinds of neoplasms, with the component in this pathway alternations. RAF/MEK/ERK signaling cascades are used to conduct signaling from the cell surface to the nucleus to mediate gene expression, cell cycle processes and apoptosis. RAS, B-Raf, PI3K, and PTEN are frequent upstream alternative sites. These mutations resulted in activated cell growth and downregulated cell apoptosis. The two pathways interact with each other to participate in tumorigenesis. PTEN alterations suppress RAF/MEK/ERK pathway activity via AKT phosphorylation and RAS inhibition. Several inhibitors targeting major components of these two pathways have been supported by the FDA. Dozens of agents in these two pathways have attracted great attention and have been assessed in clinical trials. The combination of small molecular inhibitors with traditional regimens has also been explored. Furthermore, dual inhibitors provide new insight into antitumor activity. This review will further comprehensively describe the genetic alterations in normal patients and tumor patients and discuss the role of targeted inhibitors in malignant neoplasm therapy. We hope this review will promote a comprehensive understanding of the role of the PI3K/AKT/mTOR and RAF/MEK/ERK signaling pathways in facilitating tumors and will help direct drug selection for tumor therapy. With the discovery of oncogenes and antioncogenes in the 1980s, the novel targeted treatment of imatinib was first approved in 2001 by the FDA . The reqPI3K/AKT/mTOR signaling is a crucial intracellular pathway in regulating fundamental cellular functions, including but not limited to regulating cell growth, motility, survival, metabolism, and angiogenesis. Hyperactivation of the PI3K/AKT/mTOR pathway occurs in nearly all malignant neoplasms . A previAKT could serve as the targeted effector to the cell surface for activation. Two sites (T308 and S473) of AKT could be phosphorylated. In addition to phosphotidylinositide-dependent kinases (PDKs), AKT can be phosphorylated by mTOR. This signaling activation contributed to the growth of cells by suppressing autophagy via the activation of mechanistic target of rapamycin (mTOR) , 12. MTOThe RAF/MEK/ERK pathway is a critical signaling pathway in transmitting signals from membranes to the nucleus . The RAFBoth pathways share common inputs and can also be activated via RAS. In addition, when one pathway is suppressed, the other pathway may offer compensatory effectiveness . MTOR, aPI3K/AKT/mTOR and RAF/MEK/ERK contain various kinases regulated by phosphorylation and dephosphorylation via relevant kinases. These two signaling pathways are considered vital oncogenic signaling pathways in tumorigenesis. In the current review, we describe the critical role of the PI3K/AKT/mTOR and RAF/MEK/ERK signaling pathways in carcinoma initiation and tumor development as potential strategies for tumor therapy, summarizing the recent doubts about targeting the pathway in monotherapy and combination therapy.The signaling pathway composed of PI3K, protein kinase B (AKT), and mTOR is a part of a complicated signaling cascade comprising distinct upstream regulators and downstream effectors, which play critical roles in the formation processes of human cancers . Prior eVital mouse models serving as key genetic evidence to identify the imperative roles of the PI3K/AKT/mTOR signaling pathway in promoting tumorigenesis have been constructed in recent years, contributing to the comprehension of the signaling by the recognition of whole modules via various methods and supplying valuable information for patients to confirm activation of the pathways in human carcinomas by deep sequencing, proteomics, reversed-phase protein arrays (RPPA) and bioinformatics approaches . DifferePI3Ks promoted the transfer from PIP2 to PIP3 via phosphorylation of phosphatidylinositol. PIP3 is the basis of multiple downstream targets of the PI3K/AKT/mTOR pathway . PI3Ks cReferencing previous studies, the frequency of the PIK3CA mutated gene varies from 10 to 15% in human cancers . PIK3CA AKT is a ser/thr kinase in downstream effectors of the PI3K/AKT/mTOR signaling pathway and includes three subtypes: AKT1, AKT2 and AKT3 . AKT1 isMutations in Akt2 and Akt3 disrupt the role of pleckstrin homology (PH) and kinase domain (KD), resulting in AKT oncogenic activation . Akt1 amThe mTOR activation mutations increase its kinase activity, contributing to overactive downstream pro-proliferation signaling pathways . The mutPTEN, the most common tumor suppressor gene, is the major PIP3 kinase that antagonizes PI3K phosphorylation, and its loss could contribute to uncontrolled PI3K signal transduction , 61. PTE+/− mice, RAS mutations was commonly observed in Pten+/+ mice in according to chemically induced skin neoplasms. Lacking RAS mutations results in second Pten allele loss. The results demonstrated RAS and PTEN may have synergistic effect in carcinogenesis.PTEN mutations plays an important role in PI3K pathway activation in tumor occurrence and contributes to a majority of activates in tumor prognosis . PTEN muP29S negatively mediate the transcription and function of PTEN . V. V124]. Buparlisib (BKM120) is an ATP-competitive pan class l PI3K inhibitor that can also affect mTOR and Vps34 at higher doses . BuparliBased on the safety dose in solid tumors treated with chemotherapy combined with BKM120, the combination of BKM120 at a safe dose and radiotherapy was applied to advanced non-small cell lung carcinoma . This thCopanlisib (BAY 80 − 6946) is a panclass I PI3K inhibitor that can inhibit all four PI3K class-I isoform activations . The maxIn addition to buparlisib (BKM120) and copanlisib (BAY 80 − 6946), LY294002, pictilisib (GDC 0941), pilaralisib , SF1126, ZSTK474, rigosertib (ON-01910) and CH5132799 are pan class l PI3K inhibitors with ongoing clinical studies . CopanliCompared with pan-PI3K inhibitors, isoform-selective PI3K inhibitors target one of the isoforms in PI3K, which decreases AEs and enhances targets . PatientConsidering that both PI3K and mTOR are members of the same PIKK super family of kinases accompany with similar structural isoforms and responses, inhibitors inhibiting both PI3K and mTOR targets were found through research on mTOR inhibitors . The admIn a phase Ib dose-finding study of BEZ235, diarrhea (58%), mucositis (58%), and nausea (42%) were the most frequent toxicities of all grades. Mucositis was the most common grade 3 AE . No gradVoxtalisib  is a potent inhibitor targeting class-I PI3Ks, mTORC1 and mTORC2 . In vitrCompared with voxtalisib and BEZ235, PQR309  showed a better ability to transfer the brain blood barrier (BBB) . PQR309 GDC-0084 is another oral and brain-penetrant dual PI3K/mTOR inhibitor. The usual PI3K/mTOR-related AEs were observed after GDC-0084 treatment. The first phase I study of GDC-0084 in patients with progressive or recurrent glioma identified that GDC-0084 could cross the blood‒brain barrier . The resMany dual inhibitors have been developed and applied in xenograft mouse models and malignant tumor cell lines with promising effects on tumors. PKI-587 improved the radiosensitivity and oxaliplatin sensitivity of HC via the PI3K/AKT/mTOR pathways to reduce DNA damage repair . The appIn addition to the mentioned small molecule inhibitors, dual PI3K/mTOR inhibitors such as GSK2126458, SF1126, LY294002, PF-04691502, LY302341, and PWT33597 have shown favorable antitumor efficacy in various malignant neoplasms . In a ceAKT is a kind of effector in the PI3K/AKT/mTOR pathway of tumors and is a potential target in treating cancer . The AKTPhosphorylation and dephosphorylation of AKT regulate the activation of Akt-dependent behavior . ATP-comIn the first human study of capivasertib, capivasertib monotherapy showed clinical significance in Akt1 E17K-mutant metastatic breast cancer patients with estrogen receptor (ER) positivity . GastroiIpatasertib (GDC-0068), which targets AKT signaling, showed acceptable AEs and a favorable prognosis in AKT-activated tumors . No diffSPY 2 is a phase II study elevating the response of MK-2206 in combination with standard taxane- and anthracycline-based neoadjuvant therapy . In the The mechanistic target of rapamycin (mTOR) is a ser/thr kinase that belongs to the PIKK family . MTOR coA phase I trial of temsirolimus demonstrated a higher overall response rate (ORR) of advanced RCC . A phaseAZD2014 is another second-generation mTOR inhibitor that attenuates myeloid-derived suppressor cell recruitment and blocks cell proliferation in ovarian cancer . AZD2014RAF/MEK/ERK transfer signals from receptors on the cytomembrane to regulate transcription, which promotes protein synthesis and other functions Fig.  226]. T. T226]. The complexity of this pathway was gradually observed with the academic paper published, including many factors in the transcription factor, apoptosis-promoting, and caspase executioner families . TranscrRAF included three isoforms: A, B and C. The supporting evidence for C-Raf serving as a cellular oncogene was not sufficient. After stimulation of receptors on the cell surface for RAF activation, C-Raf was phosphorylated at the S43, S259 and S621 sites, which maintained kinase activity. A-Raf is rarely found to be mutated in neoplasms and is one of the weakest effectors in promoting MEK1. B-Raf is the strongest isoform that promotes MEK activity –242. ERKCollectively, the signaling pathway is a critical pathway in tumors and could act as a promising approach in therapy. Inhibitors in the pathway have been developed and are undergoing clinical trials .Gene alterations in the signaling cascade can be divided into two categories: driver of neoplasms (RAF mutations) or indicator of worse survival (MEK and ERK mutations) . In thisAs mentioned above, V-Raf serves as a ser/thr protein kinase, of which C-Raf is rarely mutated in tumors . The rolB-Raf is considered the primary event but is not enough for tumor formation. BRAF mutations may lead to abnormal signaling activation and protein overexpression, which contribute to cell cycle blockade . In hemaMEK and ERK mutations are not frequently observed in tumors. Furthermore, MEK and ERK mutations did not co-occur with RAF mutations, which may demonstrate that the role of MEK and ERK mutations in neoplasms was not similar to that of RAF . MEK mutThe RAF/MEK/ERK signaling pathway is critical in tumor cell growth, cell apoptosis, cell differentiation and cellular metabolism . GTP-bouA series of RAF inhibitors are undergonig clinicals, and some are in preclinical research Table . GeneralGSK2118436 , PLX-4032 , encorafenib (Braftovi and LGX818), and sorafenib (BAY 43-9006) have been approved. It has been reported that sorafenib is approved for treating RCC and HC, while it is not a pure RAF inhibitor , 263. SoPLX-4720 and PLX-4032 (vemurafenib and RG7204) are two inhibitors produced by Plexxikon/Roche . PLX-472Trametinib  and cobimetinib  are approved for tumors with the BRAF V600E mutation and could be used as monotherapy or plus other therapies . These iTrametinib was approved in 2017 for treating unresectable BRAF V600E or V600K mutation melanoma patients . The effSome agents targeting the terminal kinase ERK have been developed, but most have been in preclinical trials, and few have been developed in clinical trials. Their effect in RAF or MEK-mutated tumors is unclear , 282. SiOverall, the application of RAF/MEK/ERK inhibitors contributes to targeted therapy for tumors, and the effect of RAF/MEK/ERK inhibitors facilitates the understanding of RAF mutations in tumor therapy. Comprehensive investigation of the relationship of RAF/MEK/ERK mutations and wild-type counterparts provides new insight into novel allosteric targets to help the treatment of neoplasms.Apoptosis induced by the RAF/MEK/ERK and PI3K/AKT/mTOR signaling pathways is regulated by key factors that are regulated by the phosphorylation of ERK or AKT . HER2 ovCell growth and protein production were suppressed by inhibiting MEK and mTOR simultaneously in human NSCLC cells . InhibitWith the development of RAF/MEK/ERK and PI3K/AKT/mTOR pathways, combinations of RAF and PI3K/AKT/mTOR or MEK and PI3K/AKT/mTOR inhibitors are undergoing clinical trials. The combination application of the agents in advanced solid neoplasms was well tolerated and was a single agent. The clinical response and long-term survival were observed. The signals from the RAF/MEK/ERK signaling pathway negated PI3K/AKT/mTOR activation. Both signaling pathways are regulated by RAS and RTK on the surface and stimulated by second messengers. ERK phosphorylation was promoted by mTORC1 dephosphorylation of its residues in malignant mammary patients after therapy. The RAF/MEK/ERK signaling pathway was activated in patients with TNBC treated with buparlisib. The MEK inhibitor combined with buparlisib demonstrated a superior antitumor effect, indicating that inhibiting the PI3K/AKT/mTOR and MAPK/MEK/ERK pathways could have synergistic action , 294. InBoth the PI3K/AKT/mTOR and RAF/MEK/ERK pathways are frequently involved in cancer therapy. Both PI3K/AKT/mTOR and RAF/MEK/ERK can be regulated by p53, which contributes to cell proliferation, drug resistance, cell cycle progression and tumor metastasis . Many onIn the RAF/MEK/ERK pathway, MEK inhibitors initially demonstrated the most specificity . Some neThus, the concepts of the two pathways were summarized. First, with the wide understanding of the PI3K/AKT/mTOR and RAF/MEK/ERK pathways, hundreds of inhibitors have been developed. Some of them demonstrated promising effects and moderate toxicity in particular cancers, while some were abolished for certain reasons. Some potential mechanisms that are unknown with further exploration are essential. Second, crosstalk between PI3K/AKT/mTOR and RAF/MEK/ERK is complex and not fully discussed. The targets in the two pathways dependent or independently regulated cell proliferation, apoptosis, and other characteristics. Therefore, multiple target inhibitors are desirable. Third, some inhibitors were abolished, and some clinical trials were withdrawn because of their toxicity and AEs. How to solve this is another crucial question.The PI3K/AKT/mTOR and RAF/MEK/ERK pathways are intriguing aspects of human cancer therapy and are two complex cascades containing many targets. Further studies on reducing toxicity and improving effectiveness should be conducted. Accumulated studies on these two pathways will provide new hope for cancer patients.


Improper use of strychnine can cause death. The aim of this study was to identify and evaluate toxic mechanisms of action associated with active compounds in strychnine using a network toxicology approach, and explore potential pathogenic targets.In the present study, strychnine target and central nervous system-related gene set were established using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and four disease gene databases . An “ingredient-target” interactive active network map was constructed using Cytoscape software (version 3.8.0). Functional enrichment analysis was performed based on the hub genes. A protein-protein interaction network was constructed using STRING database. The pharmacokinetics (ADMET) properties of strychnine were evaluated using SwissADME tool. Molecular docking was performed using Autodock Vina to explore the interactions between the active compounds and the target protein.Five strychnine toxicity-related components and a gene set of 40 genes were obtained. GO and KEGG analyses showed that Strychnine acts on the central nervous system through G protein-coupled receptor signaling pathway. Analysis of “ADMET” related parameters showed a high gastrointestinal tract absorption of (S)-stylopine and isobrucine and the compounds could cross the blood brain barrier. CHRM1 was selected as a key gene in strychnine toxicity. Molecular docking results showed that the co-crystalized ligands did not form hydrogen bond with CHRM1. (S)-stylopine had the highest binding affinity  compared with the other two compounds.Network toxicology and molecular docking reveal the toxicity mechanisms of strychnine active compounds. The findings showed that CHRM1 is a potential neurotoxic target. (S)-stylopine showed stronger neurotoxic effect compared with the other ligands. It is used as a herbal remedy for rheumatism, musculoskeletal injuries and limb paralysis after processing to reduce its toxicity [maqianzi exhibits excellent anti-tumor effect on various tumors [toxicity . In addis tumors , 3. Alths tumors . A previs tumors . The vicStudies report that alkaloids are the major bioactive components of maqianzi plant. The plant contains 1.0–1.4% of strychnine and brucine which are responsible for the toxicity effects . These ahttps://tcmspw.com/) [http://ctdbase.org/,CTD) for toxicity analysis. A search of the related target genes was conducted for each compound in TCMSP. Pharmacokinetics “ADMET” properties of strychnine were evaluated using SwissADME tool (http://www.swissadme.ch/) [Data on the main ingredients of strychnine were retrieved from the TCMSP database (pw.com/)  includindme.ch/) .https://www.genecards.org/) [https://omim.org/) [https://www.pharmgkb.org/) [http://db.idrblab.net/ttd/) [Neurological injury-related genes were retrieved from four databases namely: Genecards database (ds.org/) , OMIM daim.org/) , PharmGkkb.org/) , and TTDet/ttd/) . FurtherStrychnine toxic component target and neural-associated gene sets were obtained by selecting the intersection between the target genes set for toxic components of strychnine set and the neural-associated gene set.https://david.ncifcrf.gov) was used to perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis [The intersecting components obtained between the active components of strychnine and neurological-related targets were imported into Cytoscape3.8.0 , for conanalysis . The “gghttps://string-db.org/) (version 11.0), to explore the interactive relationships among these genes. The results were imported into the Cytoscape for visualization of the network. The MCODE plugin was used to perform sub-network analysis using the following parameters: “Degree=2, Node score cutoff=0.2, k-core=2” to identify core targets in the network.The Strychnine toxic component target and neural-associated gene sets were uploaded into the STRING database (https://pubchem.ncbi.nlm.nih.gov/). Further, the receptor protein encoded by the target gene was obtained from the UniProt database (https://www.uniprot.org/) and the 3-D structures and co-crystallized ligand, CHRM1 (PDB-ID: 6WJC) were retrieved from the Protein Data Bank (PDB) database (https://www.rcsb.org/).Key genes obtained from the sub-network were used for molecular docking analysis. The 3D structures for the molecule ligands ((S)-stylopine, isobrucine and stigmasterol) were retrieved from the PubChem database (The protein structures were optimized using Pymol software (vision: 2.4.0) by removing water molecules and other small molecule ligands before conducting molecular docking. Autodock Tools (vision:1.5.6) software was hired to remove water molecules, remove ligands and added hydrogen atoms to receptor target. The target protein was then used as a receptor, and the active pharmaceutical ingredients were used as ligand molecules. The active binding site of the molecular docking were identified based on the coordinates of the co-crystallized ligand. The Grid-box coordinates and size were set according to the active pocket of the target protein. Autodock Vina was used for docking of the receptor protein with the small molecule ligands. Analysis and visualization were performed using PyMol software.A total of 13 strychnine compounds were identified from TCMSSP database based on the criteria OB ≥ 30%, DL ≥ 30.18. Toxicity information for five strychnine compounds including (S)-stylopine, isobrucine, brucine N-oxide, stigmasterol and (+)-catechin was obtained from the CTD database. A total of 65 drug target genes were selected from the CTD database. Further, neurological-related genes were retrieved from Genecards, OMIM, PharmGkb, and TTD databases. Duplicates were removed and the results were combined with the above results, and a total of 16,786 neurological-related genes were obtained Fig. A. The fiThe five toxic active ingredients Table  and targThe “ADMET” properties of the active ingredients were evaluated to determine the mechanism of strychnine toxicity. The results showed that the gastrointestinal tract absorption (GI absorption) of (S)-stylopine and isobrucine was higher compared with stigmasterol, co-crystallized ligand 1 and Co-crystallized ligand 2. In addition, (S)-stylopine was able to cross the blood brain barrier permeant (BBB permeant). In addition, the findings showed that (S)-stylopine was an inhibitor of cytochrome P450 enzyme  , cell components (CC), molecular function (MF) and KGGG of 40 genes were evaluated to gain further insight on the biological functions of the identified target genes related to the toxic active ingredient of strychnine. GO results showed a total of 576 significantly enriched terms. The top 10 terms, using The most important gene, CHRM1, was selected for molecular docking. Three active compounds targeting the CHRM1 protein were obtained by TCMSP dataset analysis. The three compounds were (S)-stylopine, isobrucine and stigmasterol. Results from molecular docking analysis showed that all three active compounds were binding at the active pocket of the CHRM1 protein. Further, the active ingredient of strychnine showed good binding affinity to the key CNS target protein CHRM1. (S)-stylopine formed an interaction with Arg218 of CHRM1 on the A chain through a hydrogen bond. Isobrucine compound showed an interaction with Gln185 of the A chain of CHRM1 through a hydrogen bond. Stigmasterol formed a hydrogen bond with Ser388 in the A chain of CHRM1 protein. The 2D interactions picture was incomplete, so a 3D image was generated Fig. A-C. The Molecular docking results of the co-crystalized ligands showed no hydrogen bonds Fig. A-C and tMaqianzi herb is used in Chinese folk medicine. It is commonly used for anaesthesia and treatment of traumatic injuries, pains, paralysis and tumors , 16–18. In the present study, 13 active compounds extracted from maqianzi were retrieved from TCMSP database. Toxicity information for the 5 strychnine components was obtained from the CTD database. A strychnine target neurological-related gene set that comprised 40 target genes was obtained by analyzing the active components from the 5 ingredients of strychnine. A toxic active ingredient-target network for strychnine toxicity was constructed, and 7 genes were identified as the most important genes in the network. The results showed that different components were linked to different targets, indicating a multi-component, multi-target action of strychnine.Key genes from the toxic active ingredient-target network and the hub genes from the PPI were further analyzed and the findings showed that CHRM1 was the target gene for strychnine toxicity.chrm1 gene was performed.The results from functional enrichment analysis showed that the identified target genes in the toxic active ingredient of strychnine were mainly implicated in neuroactive ligand-receptor interaction, and calcium signaling pathway. This implies that the compounds directly increase neuronal activity and excitability, leading to increased muscular activity. These findings are consistent with findings from previous studies. The top 5 most significant GO (BP) terms indicated that the identified target genes are involved in G protein-coupled receptor signaling pathway and phospholipase C-activating G protein-coupled receptor signaling pathway. Cholinergic muscarinic receptors are G protein-coupled receptors (GPCRs) that modulate several vital functions of the central and peripheral nervous systems. These receptors are classified into M1–M5 receptors , 20, andThree molecules namely; (S)-stylopine, isobrucine and stigmasterol, were selected as potential ligands for CHRM1 protein. This finding indicates that CHRM1 is a potential neurotoxic target. ADME prediction using SwissADME web server showed that the three molecules exhibited wide distribution and poor metabolic excretion.. Several studies report that strychnine is an antagonist of glycine thus it is effective as a pesticide. The active compounds bind noncovalently to the same receptor (acetylcholine binding protein), preventing the inhibitory effects of glycine on the postsynaptic neuron. The motor neurons are more easily activated, when the inhibitory signals are prevented, resulting in spastic muscle contractions, and ultimately death by asphyxiation . These rComparison between predicted ligands and natural co-crystalized ligands is an effective means to study and predict the toxic effect of ligands. Therefore, the two co-crystalized ligands were redocked to CHRM1 protein, and the docking results were compared with the results of the three predicted ligands. The findings showed that (S)-stylopine had the highest binding affinity with a binding energy of − 8.5 kcal/mol, followed by stigmasterol and isobrucine. These results indicate that the active ingredients from maqianzi have a good binding affinity to key target protein molecules. The three active compounds formed hydrogen bonds with the target protein whereas co-crystalized ligands did not show hydrogen bond interactions with CHRM1. The molecular docking results were consistent with the network pharmacology screening results, and reliability of network pharmacology was verified by molecular docking.Further, the ADMET related parameters of the three compounds  were compared with those of the two co-crystalized ligands of CHRM1. The findings showed that (S)-stylopine had stronger neurotoxic effect compared with the other ligands.2+ signaling in response to Ach in striatal cells [2+ stores such as the endoplasmic reticulum (ER) by binding the IP3 receptor and CHRM1 regulates Ca2+ release through ER [Previous studies report that the CHRM1 is the most important muscarinic receptor in the CNS and is highly expressed in neurons. CHRM1 mediates various cellular responses. It is implicated in cellular responses such as adenylate cyclase inhibition, phosphoinositide degeneration, potassium channel mediation, and modulates several effects of acetylcholine in the central and peripheral nervous system. In addition, CHRM1 is involved in modulation of vagally-induced bronchoconstriction and in secretion of acid in gastrointestinal tract. CHRM1 is a member of neuromuscular presynaptic modulating receptors . Lee et.al cells . Phosphorough ER . Giesselrough ER . These frough ER . HoweverIn the present study, the toxic active components and molecular mechanisms of strychnine-induced neurological symptoms were explored using network toxicology approaches. The results of the study provide a basis for subsequent studies on strychnine toxicity and provide information on the toxicity mechanism of herbal medicines in forensic toxicology. However, the findings of the present should be validated through experimental studies.


Chronic atrophic gastritis (CAG) is a common chronically digestive disease which is notoriously characterized by atrophy of the epithelium and glands of the gastric mucosa, reduced number, thinning of the gastric mucosa, thickening of the mucosal base, or pyloric glandular hyperplasia and intestinal glandular hyperplasia, or with atypical hyperplasia. Banxia Xiexin decoction (BXD) has been applied for two thousand years and is considered an effective therapy for functional dyspepsia, gastroesophageal reflux disease and colon cancer. In this current study, to probe into the underlying mechanism of BXD on CAG, network pharmacology was conducted to collect druggable ingredients and predicted targets of BXD and the CAG-associated targets were harvested to take intersection with druggable ingredients from BXD predicted targets to obtain potential critical action targets. Subsequently, GO enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were conducted to elucidate the underlying mechanisms and roles from the perspective of overall pathways and cellular functions. Eventually, molecular docking integrated with molecular dynamics simulations was conducted to further investigate the mechanism of action of BXD active ingredients on CAG from drug molecule-target interactions and to provide a theoretical basis for BXD drug development. Chronic atrophic gastritis (CAG) is a common chronically digestive disease which is notoriously characterized by atrophy of the epithelium and glands of the gastric mucosa, reduced number, thinning of the gastric mucosa, thickening of the mucosal base, or pyloric glandular hyperplasia and intestinal glandular hyperplasia, or with atypical hyperplasia , 2. The Pinelliae Rhizoma (Ban Xia), Coptidis Rhizoma (Huang Lian), Scutellariae Radix (Huang Qin), Zingiberis Rhizoma (Gan Jiang), Ginseng Radix (Ren Shen), Jujubae Fructus (Da Zao), and Glycyrrhizae Radix (Gan Cao). Drawing on previous studies, BXD has yielded various anti-inflammatory and antioxidative pharmacological effects, and the therapeutic effects on CAG and Ulcerative colitis (UC) are confirmed evidence [Traditional Chinese medicine (TCM) has been prevalently acknowledged as the treasure trove of natural compounds with pronounced clinical efficacy . As repoevidence , 13. Phaevidence . NeverthNetwork pharmacology is an emerging method to shed light on the process of disease development from the perspective of scientific biology and the equilibrium of biological network, to understand drug-organism interactions from a holistic viewpoint of promoting or restoring the equilibrium of biological network, and to guide the discovery of new drugs, which coincides with the theory of Traditional Chinese Medicine and is widely utilized for the exploration of mechanisms of Traditional Chinese Medicine , 16. CurIn our current study, network pharmacology was initially conducted to screen out potential druggable ingredients of BXD and probe into the underlying biological mechanism by which BXD on the treatment of CAG from a systemic perspective and at the molecular level. Subsequently, the herb-biological function network of BXD was established to illustrate the regulatory effect of BXD for the treatment of CAG. Key targets were selected to perform the molecular docking procedure for predicting binding energy and screening potential therapeutic ingredients. Eventually, the target-compound complexes were conducted molecular dynamics simulation and performed binding free energy calculations via Gromacs 2020 software and MMPBSA procedure.http://tcmspw.com/tcmsp.php) was conducted to screen potential active compounds from BXD by, respectively, importing Pinelliae Rhizoma (Ban Xia), Coptidis Rhizoma (Huang Lian), Scutellariae Radix (Huang Qin), Zingiberis Rhizoma (Gan Jiang), Ginseng Radix (Ren Shen), Jujubae Fructus (Da Zao), and Glycyrrhizae Radix (Gan Cao) as keywords [https://www.ncbi.nlm.nih.gov/) was conducted as supplementary compounds of BXD. Eventually, all obtained compounds with the predicted drug properties were uploaded on ProTox-II (https://tox-new.charite.de/protox_II/index.php?site=home) which is an online database for predicting the toxicity of compounds [http://www.swisstargetprediction.ch/index.php) and PharmMapper Server (http://www.lilab-ecust.cn/pharmmapper/) which were prevalently utilized for potential target identification [https://bioinfogp.cnb.csic.es/tools/venny/index.html) to combine duplicate item.Bioinformatics was conducted to identify and comprehensively collect underlying therapeutic druggable compounds of BXD. The whole procedure was described as follows. Initially, the online database Traditional Chinese Medicine Systems Pharmacology Analysis Database and Analysis Platform , OMIM (http://omim.org/) DisGeNET (http://www.disgenet.org/), NCBI Gene (https://www.ncbi.nlm.nih.gov), and GENECARD (https://www.genecards.org/) were utilized by importing the keyword of chronic atrophic gastritis [To comprehensively collect CAG-associated targets, six resources of disease online databases including CTD (astritis –24. Conshttp://metascape.org) by importing key targets which refer to the intersection of CAG-related targets and BXD predicted targets [http://www.cytoscape.org/) [Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted on the Metascape database ( targets . GO- (Gepe.org/) .https://string-db.org/) and Metascape online database servers were performed to obtain the associated analysis results of predicted protein interactions [To explore the protein and protein interactions among the key targets and illustrate the underlying mechanism, the PPI network was performed and visualized utilizing the Cytoscape software and Metascape online serve, which contains six PPI online serve sourceSTRING  and instadock (https://hassanlab.org/instadock) are gratuitous and brilliant software for drug discovery with excellent docking accuracy and speed that combines predictive physics-based methods with machine learning techniques to accelerate drug discover [https://pubchem.ncbi.nlm.nih.gov/) were prepared and optimized using ChemOffice and OpenBabel software for structure optimization, format conversion, charge correction, and energy minimization [https://www.rcsb.org/) and were predicted using the AlphaFold database, which is a state-of-the-art AI system developed by DeepMind that computationally predicts protein structures with unprecedented accuracy and speed  [Computer-aided drug design, development, and target prediction have recently emerged as an important method for understanding biological regulatory mechanisms, which provides a theoretical basis for the design and discovery of new drug targets. AutoDock 1.5.6 (discover –29. The mization . The PDB.ac.uk/) . The flihttp://bio2byte.be/acpype) to generate gro and itp files [In the validation of the Binding Capacity Between Active Ingredients and Key Targets by Molecular Docking Simulation parameters, during the protein-ligand complex simulation, all proteins were utilized amber99sb-ildn force field to process protein, and small molecules were processed with acpype  simulations were conducted utilizing GROMACS 2020.6 software; TIP3P was selected as the force field of water molecules. The simulation box was set at 1.0 nm to ensure the distance between the atoms of the protein-ligand complex, and the edge of the box is appropriate for periodicity conditions. Fill the entire box with water using the solvate command, and add SOL items to the molecules section of the top file after running and add ions, default add NA and CL; -neutral command was performed to balance the simulation system to neutral. Then, the steepest descent method was performed to simulate and optimize the system for energy reduction; subsequently, the energy minimization and duration 10 ns constraint dynamics was conducted. Eventually, the index file was created, and md simulation was performed for 200 ns. All MD simulations were performed under an isothermal and isostatic ensemble with a temperature of 298.15 K and a pressure of 1 atmosphere. The temperature and pressure were controlled by the V-rescale and Parrinello-Rahman methods, respectively, and the temperature and pressure coupling constants were 0.1 and 0.5 ps, respectively. Except for the protein prodrug ligand simulation time of 50 ns, the MD simulation time of the other three systems was 200 ns.It can be applied to each compound.Eventually, the MMPBSA process in GROMACS was utilized to calculate the binding energy, and the following equation was utilized: ΔGbind = Gcomplex − Gfree‐protein − Gfree‐ligand.MM is van der Waals and electrostatic energy (ΔGvdw + ΔGele); PB is polar solvation (ΔGpolar); SA is nonpolar solvation (ΔGnonpolar).In the MM/GBSA calculations, the protein-ligand binding free energy is calculated based on MD simulation trajectories and compared with experimental binding data to build and validate the new model. The free energy changes during ligand-protein binding to form complexes are described by the original MM/GBSA according to the equations as the sum of different interactions. The basic principle is to calculate the difference between the bound and unbound free energies of two solventized molecules or to compare the free energies of different solventized conformations of the same molecule.https://tcmspw.com/index.php) were utilized for online searching. Based on the four predicted parameters of drugs including oral bioavailability (OB), drug-likeness (DL), toxicity prediction, and Caco-2 parameters obtained through ADME, ninety-two ingredients were selected as potential druggable active compounds of BXD, and the detailed information and structures were presented at . Subsequently, the Venn diagram of ingredients was conducted. We found that there are a total of 182 potential active druggable ingredients in BXD, 15 ingredients in Banxia, 22 ingredients in Renshen, 5 ingredients in Shengjiang, 92 ingredients in Gancao, 14 ingredients in Huanglian and 36 ingredients in Huangqin which were identified out. The composition and content ratio of druggable potential in BXD were confirmed by a previous study which performed an LC-MS analysis to identify the principal characteristic peaks associated with a BXD fingerprint. Saponins, flavonoids and alkaloids were identified as the master abundant compounds that mainly exist in Renshen, Gamcao, Huangqin, and Huanglian, which is consistent with our results , and the parameters of binding pocket of targets were obtained from the PyMOL 2.2.0 software. Eventually, the molecular docking procedure was performed by utilizing Vina software. The hot map , which is beneficial for evaluating the stability of the ligand and protein complex. Based on the docking scores between 152 druggable compounds from BXD and selected key targets coupled with the visualization of binding pattern, three complexes including NR3C1-coptisine, NOS2-palmidin A, and IL-2-licocoumarone were selected, and their conformational behaviour was analyzed via molecular dynamics simulations and MMPBSA calculations  demonstrated in vivo pharmacological effects in the treatment of H. pylori gastritis infection. Berberine and baicalin also have been demonstrated in the treatment of CAG [TNF signaling pathway, PI3K-Akt signaling pathway, and NF-kappa B signaling pathway are common inflammatory associated pathways that CAG is involved in, and many traditional Chinese medicines have been reported to modulate these inflammatory pathways. For example, NF-kappa B and TNF signaling pathway have been reported to be associated with the helicobacter pylori gastrointestinal infection in CAG, and many traditional Chinese medicines have accomplished good results in the anti-inflammatory treatment of gastritis which encompassing Gancao, Huanglian and Huangqin –47. Addit of CAG –50. ConsThe interactions of proteins are of crucial importance in normal biological processes. Cells receive signals from exogenous or endogenous sources and regulate their gene expression through their specific signaling pathways in order to maintain their normal biological properties. Proteins play an important role in this process, as they can internally regulate and mediate many biological activities of the cell. Although some proteins can function as monomers, most proteins act with chaperone molecules or form complexes with other proteins to perform their functions . AccordiThere are some limitations in our study. First, we did not perform the relevant assays to determine the effect of BXD on CAG. Besides, the relation of BXD and signaling pathways and protein complexes was not verified by experiments. All these limitations will be perfected in the future study.In conclusion, our study elucidated the potential mechanisms of BXD underlying CAG via network pharmacology, molecular docking, and molecular dynamics simulations, which might provide theoretical value for the treatment of CAG.


Hepatocellular carcinoma (HCC) is a complex and refractory malignant tumor, ranking the third cause of cancer-related deaths worldwide. Lenvatinib is currently employed to treat advanced, unresectable HCC as a first-line drug. The purpose of this study was to explore the pharmacological mechanisms of lenvatinib acting on HCC through the analysis of differential expressed genes based on network pharmacology. The target genes of lenvatinib were collected from PubChem, SwissTargetPrediction, PharmMapper, and BATMAN-TCM online public databases. In addition, related gene targets for HCC were obtained using NCBI Gene Expression Omnibus (NCBI-GEO) database. Afterward, the protein-protein interaction (PPI) network was established to visualize and understand the interaction relationships of overlapping gene targets from both lenvatinib and HCC. Furthermore, according to the data obtained, Gene Ontology (GO) analysis indicated that these intersectant genes were mainly enriched in response to xenobiotic stimulus, gland development, ion channel complex, membrane raft, and steroid binding. Besides, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis suggested that the therapeutic effects of lenvatinib on HCC probably involved bile secretion, MAPK signaling pathway, cGMP-PKG signaling pathway, PI3K-Akt signaling pathway, and Ras signaling pathway. Moreover, a total of six key differential genes, namely, ALB, CCND1, ESR1, AR, CCNA2, and AURKA, were identified as most significant targets associated with lenvatinib treating HCC and further verified by molecular docking, which demonstrated that lenvatinib had a strong binding efficiency with these six key gene-encoded proteins. Taken together, this study systematically provided new insights for researchers to determine the intervention mechanisms of lenvatinib in HCC therapy. Liver cancer remains a global health challenge. GLOBOCAN statistics showed that there were about 841,080 new liver cancer cases and 781,631 liver cancer-associated deaths worldwide in 2018 . In prevLenvatinib is an oral small molecule inhibitor of multiple receptor tyrosine kinases approved for first-line therapy in patients with unresectable HCC in the United States, the European Union, Japan, and China , 8. In tTherefore, the exact mechanisms of lenvatinib in the treatment of HCC and individualized researches on differentially expressed genes in HCC need to be investigated in depth. In this study, relevant target information was acquired from publicly available online databases for differential crossover genes screening, GO function enrichment and pathway enrichment analyses, and final molecular docking for the aim of exploring the potential mechanisms of lenvatinib acting on HCC.https://pubchem.ncbi.nlm.nih.gov/), the key terms were searched and the structural formula of lenvatinib was downloaded. Afterwards, according to the structural formula obtained, the following three online databases for finding drug targets were utilized: SwissTargetPrediction (http://www.swisstargetprediction.ch/), PharmMapper (http://www.lilab-ecust.cn/pharmmapper/), and BATMAN-TCM (http://bionet.ncpsb.org.cn/batman-tcm/). In the SwissTargetPrediction database, for the query molecule, the standard filtering probability of taking the protein as target was set to be >0. In the BATMAN-TCM database, action targets were screened by setting score cutoff ≥5 and P-value ≤0.05. In the PharmMapper database, norm fit score was set as ≥0.7. The target data were extracted from the PharmMapper database and then transformed into the corresponding genes using the UniProt database (http://www.uniprot.org/).By means of the data in PubChem (https://www.ncbi.nlm.nih.gov/geo/) database, and previous published data sets GSE45267, GSE62232, and GSE101685, were selected to further obtain gene expression data. Based on the GEO2R (http://www.ncbi.nlm.nih.gov/geo/geo2r/) analysis, differentially expressed genes with the cutoff criteria of |Log2FC|> 1 and adjusted P-value <0.01 were obtained and visualized by volcano plot. Based on normalized sequencing data and deleted duplicates, a total of 1682 differentially expressed genes for HCC were obtained in comparison with normal liver tissues. Subsequently, heatmap was drawn with the R software packages to reveal the top 20 significantly upregulated and downregulated differentially expressed gene targets for HCC.For identifying the targets for HCC, the keywords “hepatocellular carcinoma” were searched in NCBI Gene Expression Omnibus . In drawing operations, 260 target genes of lenvatinib and 1682 differentially expressed genes in HCC were input, and 45 intersectant targets were obtained. The overlapping target genes from both lenvatinib and HCC were located in the intersected part following merging and deleting the duplicates.Taking the intersection of the target genes of lenvatinib and therapeutic targets for HCC, Venn diagram was depicted using VENNY 2.1 (https://string-db.org/) by setting the minimum required interaction score to threshold 0.4. Only interactions with score above the threshold were selected for the construction of PPI network relationship. Subsequently, by means of Cytoscape 3.8.2 software (https://cytoscape.org/), PPI data obtained were imported to visualize PPI network. Further, a total of the top six core gene were acquired based on degree calculated by CytoNca plugin in Cytoscape software.The PPI data of overlapping targets in Venn diagram were downloaded from the STRING database (http://metascape.org/gp/index.html#/main/step1) and visualized by the representative enriched terms using the Bioinformatics (http://www.bioinformatics.com.cn). GO enrichment analysis covered the three domains: biological process (BP), cellular component (CC), and molecular function (MF). These results were presented in bar graph for GO analysis and bubble chart for KEGG analysis.In order to investigate the biological functions of the overlapping target genes, GO and KEGG enrichment analyses were adopted using the Metascape database (https://pubchem.ncbi.nlm.nih.gov/) and converted to mol2 format by using OpenBabel 2.4.1 software. The receptor proteins coded by the selected key target genes were searched using the UniProt database (http://www.uniprot.org/). Besides, the Protein Data Bank (PDB) database (https://www.rcsb.org/) was utilized to download 3D structures of the protein receptors. All the protein molecules were prepared for confirming the protein active pockets by removing water molecules using PyMol software and adding hydrogen atoms with AutoDockTools 1.5.6 software. Subsequently, docking simulation and visualization was conducted using AutoDock Vina software as previously described [For the purpose of assessing the reliability of the prediction results, molecular docking validations were carried out at the protein level using lenvatinib and the major relevant targets. The two-dimensional (2D) structure of small molecule ligand lenvatinib was downloaded from the PubChem database  is shown in A detailed workflow of the present study on network pharmacology-based analysis is shown in Concurrently, the list of overlapping target gene names between disease genes and drug genes is also shown in The PPI network is known to be involved in various forms of life processes, such as biological signaling, gene expression regulation, energy metabolism, and cell cycle regulation, which is composed of interactive proteins. In order to explore protein interactions across these 45 overlapping targets from lenvatinib and HCC, the detail of genes was uploaded to the STRING database, forming a graphic of PPI network, as shown in As a widely used method in bioinformatics, GO enrichment analysis contains biological process (BP), cellular component (CC), and molecular function (MF). In order to figure out how these 45 overlapping genes are involved in the pathogenesis of HCC, GO analysis was carried out using the Metascape database and, respectively, visualized in BP, CC, and MF with top 10 representative enriched terms . As showKEGG is a database resource for understanding advanced functions and utilities of biological systems, such as cells, organisms, and ecosystems, from genomic and molecular level information. In order to further investigate the potential pathways of lenvatinib affecting HCC, the aforementioned 45 overlapping genes were analyzed using the Metascape database and the top 20 significant results of KEGG pathway enrichment analysis are shown in the bubble chart of Molecular docking is a widely used method for drug discovery based on rational structure. The docking algorithm predicts the noncovalent interaction between a target macromolecule (receptor) and a drug molecule (ligand), starting from unbound 3D structure of both components . AlthougIn the current study, according to core target genes in the PPI network, the key genes with degree values in the top six were selected, namely ALB, CCND1, ESR1, AR, CCNA2, and AURKA, to perform molecular docking analyses of these gene-encoded proteins with lenvatinib. When the binding energy is less than 0 kcal/mol, the spontaneous binding and interaction of both molecules are considered, and the lower the binding energy is, the more stable the molecular conformation is. Of note, if the binding force is less than −5.0 kcal/mol, it means that the two components have a good combination. The results of molecular docking diagrams and corresponding binding energy values are shown in HCC is the fifth most common cancer worldwide and also the third leading cause of cancer-related deaths, with an increasing incidence annually. HCC usually emerges in patients with underlying chronic liver injury diseases resulting from hepatitis virus infection, alcohol abuse, drug toxicity, and metabolic dysfunctions, which progressively develop into liver fibrosis and cirrhosis, ultimately leading to HCC , 13. DueLenvatinib is an oral multitarget tyrosine kinase inhibitor (TKI) that has been demonstrated to exhibit potent antiangiogenic and tumor growth inhibitory activity in the treatment of a variety of solid tumors and approved as a monotherapy or combination therapy for differentiated thyroid, hepatocellular carcinoma, and renal cell carcinoma . As a neIn the present research, results of GO function and KEGG pathway enrichment analyses were so similar since they were all related to the processes of material synthesis and catabolism or cell division and proliferation. In terms of KEGG analysis, the antitumor effects of lenvatinib on HCC were possibly connected with bile secretion, MAPK signaling pathway, cGMP-PKG signaling pathway, PI3K-Akt signaling pathway, and Ras signaling pathway. Among these enriched pathways, bile secretion is closely related to the development of HCC. In the liver, bile is produced and released into small intestine to enhance the digestion and absorption of fat. The major metabolites of bile acids by enzymes perform an integral function in maintaining healthy gut microbiota that has been demonstrated to be tightly correlated with the risk, development, and progression of HCC . Activatβ-catenin signaling pathway, which is associated with tumorigenesis and poor prognosis in HCC. Downregulation of cyclin D1 can cause cell cycle arrest and further inhibit proliferation and induce apoptosis in HCC cells [in vitro and in vivo experiments in the follow-up study.Furthermore, a total of six key target genes were obtained in accordance with the above analysis of network pharmacology. Among these genes, ALB-encoded albumin performs an integra function in HCC progression as a tumor suppressor and inhibition of albumin enhanced the migration and invasion of HCC cells by upregulating MMP2, MMP9, and uPAR . CCND1-eCC cells . ESR1 enCC cells . AR encoCC cells . Cyclin CC cells . Aurora CC cells . TherefoIn summary, the network pharmacology-based analyses of lenvatinib in HCC therapy offered a comprehensive understanding of potential molecular mechanisms by which lenvatinib restrains HCC and also a theoretical basis for the clinical application of lenvatinib in patients with advanced, unresectable HCC.


The mechanism of action for most cancer drugs is not clear. Large-scale pharmacogenomic cancer cell line datasets offer a rich resource to obtain this knowledge. Here, we present an analysis strategy for revealing biological pathways that contribute to drug response using publicly available pharmacogenomic cancer cell line datasets.BRAFV600. We apply our approach to reveal determinants of drug resistance to microtubule inhibitors.We present a custom machine-learning based approach for identifying biological pathways involved in cancer drug response. We test the utility of our approach with a pan-cancer analysis of ML210, an inhibitor of GPX4, and a melanoma-focused analysis of inhibitors of Our method implicated lipid metabolism and Rac1/cytoskeleton signaling in the context of ML210 and BRAF inhibitor response, respectively. These findings are consistent with current knowledge of how these drugs work. For microtubule inhibitors, our approach implicated Notch and Akt signaling as pathways that associated with response.Our results demonstrate the utility of combining informed feature selection and machine learning algorithms in understanding cancer drug response.The online version contains supplementary material available at 10.1186/s12859-022-04720-z. Drug resistance and off-target toxicity are two major obstacles for precision cancer treatment. Experimental approaches to understand these areas of research depend on the use of genetic screens or drug perturbation experiments paired with -omics profiling. However, such experiments require large commitments of resources including cell culture, genetic screening constructs, sequencing costs, and personnel. Analysis of publicly available pharmacogenomic datasets is a vastly less expensive option to understand the biology of cancer drugs. The difficulty with using in silico approaches is that meaningful signals may be weak and not easily detectable. Considering this challenge, Machine learning (ML) algorithms has become an increasingly popular strategy to build predictive models that utilize molecular patients of tumor or cancer cells to predict and understand patients’ or cell lines’ response to drugs –11.V600 inhibitors vemurafenib (VEM) and dabrafenib. We also used our approach to identify pathways that inform response to anti-tubulin drugs.Existing strategies for building drug response classifiers are incredibly diverse, utilizing various combinations of inputs, feature selection approaches, and algorithms. Here, we built a machine learning algorithm focused on informing the biological processes that drive cancer drug response. We do so by integrating prior knowledge of biological pathways and protein–protein interaction data. We tested our approach on two compounds: ML210, a selective covalent inhibitor of glutathione peroxidase 4 (GPX4) and the selective BRAFp, sample j, and gene i, is given by,z is the normalized gene expression. The number of genes to use for each pathway, or k, is determined using a greedy search strategy. That is, compute the t-score for each gene for a given pathway. Rank genes in increasing order if average t-scores are less than zero or in decreasing order, otherwise. Iterate over i until the maximum k is the smallest number that maximizes the t-score for All analysis was performed in R using custom scripts. First, consider KEGG pathways belonging to Metabolism, Genetic Information Processing, Environmental Information Processing, and Cellular Processes. This list contains ~ 150 pathways. For each pathway, compute the pathway activity scores. The pathway activity score is defined as the t-score of the pathway activities across drug-sensitive and drug-resistant cell lines. Specifically, the pathway activity for pathway mentclasspt{minimaFor the BRAFi analysis, pathways are determined to be significant using a null distribution generated by permuting the cell line labels. For the ML210 and Paclitaxel analysis, pathways with pathway activity scores within the bottom or top 10th or 20th percentiles, respectively, were retained for further analysis. Significance thresholds were designed to return ~ 20% of the initial number of input KEGG pathways.Next, take all genes from the pathways deemed significant. Bin these genes into mutually exclusive network modules. Genes are grouped together into mutually exclusive network modules through hierarchical clustering of the dissimilarity between genes. Dissimilarity is computed as 1 minus the standard topological overlap measure described in . The adjThen, determine the most informative genes in each module, separately, using Boruta, a random-forest-based feature selection algorithm, with default parameters . Genes whttps://github.com/johncolby/SVM-RFE. RFE involves running the SVM iteratively while removing the least informative feature at each iteration. The rank of the feature is inversely related to the iteration it was removed by the SVM algorithm. For our analysis, the rank for a feature is given as an average of a feature’s rank across ten-fold cross validation for the ML210 and Paclitaxel analysis or leave-one-out-cross validation for the BRAFi analysis. The ranking of each feature determines the importance of the module it belongs to. The biological representation of each module was determined using  Gene Ontology pathways enrichment analysis implemented by the limma R package [Finally, take all the informative genes from the previous step and build a classifier using the support vector machine learning algorithm with recursive feature elimination (RFE). We used the implementation provided at  package .th percentile of the AUC for VEM or Dabrafenib in the GDSC. Two BRAF inhibitors were used to compensate for missing data.To perform our machine learning analysis, we used RMA-normalized microarray gene expression from Genomics of Drug Sensitivity in Cancer (GDSC). We used ML210 and PTX drug response data from the Cancer Therapeutics Response Portal V2 (CTRP v2). We used VEM and Dabrafenib response data from GDSC. We used area under the curve (AUC) as the metric for drug response. The cutoff for ML210 resistance was set at an AUC of 9, which qualitatively separated two modes of the AUC distribution  than others. Pathway activity feature selection returned pathways listed in Additional file BRAFV600 cutaneous melanoma [Next, we tested our approach on selective inhibitors of melanoma , 30.Fig.For our last case study, we wondered if our approach could identify new insights for drugs where the mechanisms of response are less understood. We took an -omics approach and looked for drugs with heterogeneous response. To this end, we ranked drugs in CTRPv2 with respect to the mean absolute deviation of the AUC. In addition to ML210 discussed previously, three anti-tubulin drugs (paclitaxel (PTX), docetaxel, vincristine) were among those with the most variable response Fig. A. SensitIn the era of precision oncology, anti-tubulin drugs are considered “non-targeted”, but unexpectedly we observed that the response to anti-tubulin drugs was highly disparate across different cancer cell lines. This suggests that there may be cancer cell intrinsic features that dictate sensitivity to these drugs. To explain this variation, we applied our analysis approach on PTX. Pathway activity feature selection retained 3232 genes. Boruta feature selection retained 822 genes across 49 modules. Pan-cancer analysis suggested that Notch, Akt, and adhesion signaling may be involved in PTX-response Fig. A. Notch on or Yapoff cancers [on cancers, we found that cancers with low Yap signature was more sensitive to PTX inhibition. Conversely, we found that cancer cell lines that had a high PI3K/AKT signature tended to more sensitive to PTX  Minimum feature ranking for each module. B) GO Biological Processes pathway enrichment of genes contained within modules presented in A). P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method.Additional file 3. Supplementary Table S1. KEGG pathways that passed pathway activity selection for ML210 analysis.Additional file 4. Supplementary Table S2. Top 20 enriched GO Biological Processes of genes returned by Boruta. P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method.Additional file 5. Supplementary Table S3. Top 20 Enriched GO Biological Processes of t-test derived genes for ML210 analysis. P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method.Additional file 6. Supplementary Table S4. Top 20 Enriched GO Biological Processes enrichment of elastic-net derived genes for ML210 analysis. P-values shown are uncorrected for multiple hypothesis testing.Additional file 7. Supplementary Table S5. KEGG pathways that passed pathway activity selection for BRAFi analysis.Additional file 8. Supplementary Table S6. Top 20 GO Biological Processes enriched in important modules for BRAFi analysis. P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method.Additional file 9. Supplementary Table S7. Top 20 GO Biological Processes enrichment of t-test derived genes for BRAFi analysis. P-values shown are uncorrected for multiple hypothesis testing.Additional file 10. Supplementary Table S8. Top 20 GO Biological Processes enrichment of elastic-net derived genes for BRAFi analysis. P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method.Additional file 11. Supplementary Table S9. Top 20 GO Biological Processes enrichment of t-test derived genes for PTX analysis. P-values shown are corrected for multiple hypothesis testing using the Holm-Bonferroni method.Additional file 12. Supplementary Table S10. Top 20 GO Biological Processes enrichment of elastic-net derived genes for PTX analysis. P-values shown are uncorrected for multiple hypothesis testing.Additional file 13. Supplementary Table S11. Top 20 GO Biological Processes enrichment of t-test derived genes for PTX analysis without blood cancers. P-values shown are uncorrected for multiple hypothesis testing.Additional file 14. Supplementary Table S12. Top 20 GO Biological Processes enrichment of elastic-net derived genes for PTX analysis without blood cancers. P-values shown are uncorrected for multiple hypothesis testing.


To study the mechanism of 25 ingredient decoction for setting a fracture (TDSF) in fracture treatment using network pharmacology. The TCMSP, BATMAN-TCM, HERB, and Uniprot protein databases were used to identify the active ingredients and targets of TDSF. Fracture-related targets were collected from the gene cards and the online mendelian inheritance in man databases. The acquisition of common genes of active compounds of TDSF and disease fractures was carried out using the Venny software. The Cytoscape 3.7.1 software and String database were used to construct a network diagram of drug-active ingredient-target-disease and the main core targets were obtained by protein interaction analysis. The Metascape platform was used to perform gene oncology functional and Kyoto encyclopedia of genes and genomes pathway enrichment analyses for common drug-disease targets. A total of 311 active ingredients and 348 targets were associated with TDSF, with 5197 targets related to fractures and 224 common targets between the 2 keywords. Key targets included serine/threonine protein kinase 1, tumor necrosis factor, interleukin 6, tumor protein 53, and vascular endothelial growth factor. Important roles of the following pathway were identified: cancer, lipid, and atherosclerosis; AGE-RAGE signaling pathway in diabetic complications; chemical carcinogenesis – receptor activation; PI3K -Akt signaling pathway; platinum drug resistance; cAMP signaling pathway; transcriptional mis regulation in cancer; serotonergic synapse; and malaria. TDSF mainly treats fractures by acting on multiple targets, such as serine/threonine protein kinase 1, tumor necrosis factor, interleukin 6, tumor protein 53, and vascular endothelial growth factor, and regulating the PI3K/AKT and cAMP signaling pathways. Despite economic growth and advancement in science and technology, the incidence of nonunion is still 5% to 10% in patients with fractures who can receive active treatment. Patients with nonunion mostly require surgery. Although the surgical procedure may result in the healing of the fracture, the patient limb function may be impacted and the patient economical and psychological conditions can be challenged. Therefore, selecting a method that can promote fracture healing, shorten the fracture healing duration, and reduce the incidence of nonunion is an attractive and popular topic in trauma orthopedics.The incidence of fractures between 1989 and 2013 was 2% to 3%, and fractures are the most common trauma and the main cause of disability.,5 Twenty-five ingredient decoction for setting a fracture (TDSF) is a classic prescription for fracture treatment recorded by Wei Yilin, a physician in the Yuan Dynasty, in “Shiyi Dexiao Prescription,” which is the representative prescription of “Shaolin” traumatology school to treat fractures. TDSF is made of Angelicae Dahuricae Radix, Lycii Cortex, Diverse Wormwood Herbs, Angelicae Sinensis Radix, Paeoniae Radix Rubra, Paeoniae Radix Alba, Pharbitidis Semen, Rehmanniae Radix, Chuanxiong Rhizoma, Achyranthis Bidentatae Radix, Olibanum, Myrrha, Psoraleae Fructus, Akebiae Caulis, Aucklandiae Radix, Pygosternonis Herba, Pyritum, Equiseti Hiemalis Herba, Cinnamomi Cortex, Notopterygii Rhizoma Et Radix, Angelicae Pubescentis Radix, Pinelliae Rhizoma, Drynariae Rhizoma, Aconiti Kusnezoffii Radix, and Aconiti Radix. This prescription uses blood-activating and qi-regulating medicines in combination, mainly for activating blood, healing wounds, activating qi, and relieving pain. Further, the prescription expels wind and dampness, nourishes the liver and kidneys, and strengthens muscles and bones.,7According to traditional Chinese medicine, fracture healing is a process consisting of “removal of blood stasis, regeneration, and osseointegration” and the treatment of fractures is divided into 3 stages. In the initial stage, fracture causes displacement and soft tissue damage, characterizes by local bleeding to form hematoma, congested tissues, and edema, and results in swelling. In the Qi stagnation and blood stasis stage, treatment focuses on promoting blood circulation and Qi, removing blood stasis, and relieving pain. In the middle stage, the local hematoma is absorbed, edema is relieved, soft tissues are repaired, and the callus starts to form again. These changes decrease local swelling and pain. In the late stage, treatment mainly focuses to strengthen the bones and tendons and nourishing the liver and kidneys. the mechanism of action of TDSF in the treatment of fractures is still unclear. This study used network pharmacology to screen out the main active ingredients and potential targets in prescriptions, predict and explore the mechanism of action of TDSF in the treatment of fractures, and provide theoretical support for future development and utilization.Although TDSF has achieved good results in clinical applications,http://tcmspw.com/tcmsp.php), BATMAN-TCM (http://bionet.ncpsb.org/batman-tcm/), and HERB (http://herb.ac.cn/) databases. The obtained protein targets were entered into the Uniprot protein database (https://www.uniprot.org/) and BATMAN-TCM database for gene name specifications, with species set as “Homo sapiens.”After setting the core screening parameters of pharmacokinetics , with oral bioavailability of ≥ 30% and drug-likeness of ≥ 0.18, active ingredients of TDSF were collected using the TCMSP (http://www.genecards.org) and the online mendelian inheritance in man (OMIM) (http://www.omim.org) databases were searched to collect information about fracture targets, summarize all targets, and remove duplicate items to obtain fracture-related targets.Using “fracture” as the keyword, the Gene Cards  was obtained. The Cytoscape 3.7.1 software was used to correlate active ingredients, intersect targets and diseases, and build a network diagram of active ingredients-targets-diseases.https://string-db.org/), with species and minimum interaction threshold set as “Homo sapiens” and “medium confidence > 0.4,” respectively, to construct a protein-protein interaction (PPI) network graph. The Cytoscape 3.7.1 software was used for topology analysis and the “CytoNCA” plug-in was used to analyze the degree value, betweenness centrality (BC), closeness centrality (CC), and local average connectivity (LAC). Further, the main core targets were screened according to their medians.The drug-disease common targets were uploaded to the String database (http://metascape.org), and the species “homo sapiens” was selected for gene oncology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses, and the obtained results were visualized.The drug-disease intersection targets were imported into the Metascape platform , Lycii Cortex (13), Diverse Wormwood Herb (5), Angelicae Sinensis Radix (2), Paeoniae Radix Rubra (29), Paeoniae Radix Alba (13), Pharbitidis Semen (22), Rehmanniae Radix (3), Chuanxiong Rhizoma (7), Achyranthis Bidentatae Radix (20), Olibanum (8), Myrrha (44), Psoraleae Fructus (12), Akebiae Caulis (8), Aucklandiae Radix (6), Pygosternonis Herba (11), Pyritum (1), Equiseti Hiemalis Herba (9), Cinnamomi Cortex (10), Notopterygii Rhizoma Et Radix (15), Angelicae Pubescentis Radix (9), Pinelliae Rhizoma (13), Drynariae Rhizoma (18), Aconiti Kusnezoffii Radix (8), and Aconiti Radix (3). Among them, 29 active ingredients shared by the drugs  and a Venn diagram was prepared  are important regulators of fracture healing, and angiogenesis is a key process in bone healing. Yang et al used miR-29b-3pKD-BMSCs in vitro with a human umbilical cord in a co-culture of venous endothelial cells. Activation of the PI3K/AKT pathway was found to promote human umbilical cord in a co-culture of venous endothelial cells proliferation, migration, angiogenesis, and ultimately fracture healing. Xu et al studied the regulatory effect of low-dose IL-34 on osteoblastogenesis and osteoclastogenesis and found that low-dose IL-34 regulates the osteogenesis of hBMSCs by activating the PI3K/AKT pathway and enhances fracture healing. Recent studies have shown that the PI3K/AKT signaling pathway not only regulates cell proliferation and growth but also enhances communication between bone cells and promotes fracture healing.Recent studies have confirmed that the PI3K/AKT and cAMP signaling pathways play important regulatory roles in fracture healing. After PI3K is activated, it can bind to the PH domain-containing signaling protein AKT and phosphoinositide-dependent protein kinase (PDK1) in cells through the secondary messenger PIPa and promoting the activation of AKT, thereby regulating cell proliferation, movement, growth, survival, and metabolic function. studied the relationship between the flavonoid extract of Epimedium Herba (TFE) and peak bone mass in young rats and found that the extract promotes bone formation by activating the cAMP pathway in vivo. Xie et al also confirmed that the activation of the cAMP signaling pathway affects the differentiation of osteoblasts. Dong Wantao et al conducted in vivo experiments with Xiaoding ointment, observed pathological changes in callus tissue under a light microscope, and detected the expression of the cAMP protein in callus tissue by immunohistochemistry. They found that the Xiaoding ointment promoted fracture healing by regulating the cAMP signaling pathway.The cAMP signaling pathway channels extracellular signals to G protein-coupled receptors, resulting in changes in the level of intracellular secondary messenger cAMP and eliciting cellular responses. cAMP plays a role in the control of various cellular processes. Xi et alIn conclusion, through a preliminary analysis of network pharmacology, we identified the effective active ingredients, key targets, and pathways of TDSF during fracture treatment. The treatment of fractures using TDSF is a complex process involving multiple components, targets, and pathways. TDSF may be involved in the therapeutic process of fractures via the PI3K/AKT and cAMP signaling pathways through key targets, such as AKT1, TNF, IL6, TP53, and VEGFA.Conceptualization: Yanke Hao, Mingliang Wang.Data curation: Zenghui Tian, Mingliang Wang.Formal analysis: Yanke Hao.Investigation: Xiaoying Wang, Yingying Li.Methodology: Zenghui Tian, Mingliang Wang.Project administration: Yanke Hao, Mingliang Wang.Resources: Yingying Li, Pengfei Hou.Software: Zenghui Tian, Mingliang Wang, Dengwan LV.Supervision: Kaiying Cui, Pengfei Hou.Validation: Dengwan Lv, Kaiying Cui, Jie Shi.Writing – original draft: Zenghui Tian, Mingliang Wang.Writing – review & editing: Yanke Hao, Mingliang Wang.


Pathway enrichment analysis (PEA) is a common method for exploring functions of hundreds of genes and identifying disease-risk pathways. Moreover, different pathways exert their functions through crosstalk. However, existing PEA methods do not sufficiently integrate essential pathway features, including pathway crosstalk, molecular interactions, and network topologies, resulting in many risk pathways that remain uninvestigated.To overcome these limitations, we develop a new crosstalk-based PEA method, CTpathway, based on a global pathway crosstalk map (GPCM) with >440,000 edges by combing pathways from eight resources, transcription factor-gene regulations, and large-scale protein-protein interactions. Integrating gene differential expression and crosstalk effects in GPCM, we assign a risk score to genes in the GPCM and identify risk pathways enriched with the risk genes.Analysis of >8300 expression profiles covering ten cancer tissues and blood samples indicates that CTpathway outperforms the current state-of-the-art methods in identifying risk pathways with higher accuracy, reproducibility, and speed. CTpathway recapitulates known risk pathways and exclusively identifies several previously unreported critical pathways for individual cancer types. CTpathway also outperforms other methods in identifying risk pathways across all cancer stages, including early-stage cancer with a small number of differentially expressed genes. Moreover, the robust design of CTpathway enables researchers to analyze both bulk and single-cell RNA-seq profiles to predict both cancer tissue and cell type-specific risk pathways with higher accuracy.http://www.jianglab.cn/CTpathway/. The stand-alone program can be accessed here https://github.com/Bioccjw/CTpathway.Collectively, CTpathway is a fast, accurate, and stable pathway enrichment analysis method for cancer research that can be used to identify cancer risk pathways. The CTpathway interactive web server can be accessed here The online version contains supplementary material available at 10.1186/s13073-022-01119-6. Over 15 years, significant efforts have been made to annotate the functions of individual genes and construct higher order functional knowledgebases, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) . HoweverFC) or significant P-values and treat each selected gene equally. Consequently, these methods achieve highly inconsistent results with small changes  in thresholds.PEA methods could be categorized into four generations . First-gFC. Enrichment scores (ESs) are then calculated for predefined gene sets  by considering how well the gene sets are enriched at the top or bottom of the ranked gene lists, which indicate their activation or repression, respectively. Therefore, FCS methods address the limitations of ORA methods.To address these limitations, the second-generation methods called functional class scoring (FCS) were developed . FCS metPrevious studies hypothesized that genes with different topological properties have different weights for the linked pathways –14. BecaFrom the perspective of systems biology, genes may iteratively affect many other genes that exist in multiple pathways, causing pathway crosstalk that accounts for the phenotypes, such as crosstalk between ERK and WNT signaling in tumorigenesis . The latAs more biological knowledge was gained, protein-protein interaction (PPI) network and gene expression data were used to detect crosstalk between pathways , 20. Rechttp://www.jianglab.cn/CTpathway/) and the stand-alone program (https://github.com/Bioccjw/CTpathway) [2FC or P-values to identify risk pathways in a specific condition  by performing the CTpathway method.In this study, we provided a new NT method for gene enrichment analysis called CTpathway: a crosstalk-based PEA method in a global pathway crosstalk map (GPCM) Fig. . To obtapathway) , which aWe collected eight knowledgebases of human pathways including KEGG , PANTHERWe obtained experimentally validated TF-gene regulations from the TRANSFAC Professional database (release: February 2014) . TF-geneWe obtained PPIs from 12 sources  databasTo test whether CTpathway could be applied in scRNA-seq data, we downloaded breast cancer (BRCA) scRNA-seq data from the GEO database GSE118389 )  is available or not, α (or β) equals to 1 or 0. DE is the differential expression score, represented as a vector:L is the intersection number of genes in the expression profile and genes in the GPCM.When G = , where a v ∈ V represents a gene and a e ∈ E represents an edge. First, one gene in the expression file was taken as a seed , and given an initial weight score of 1. Then, a random walk with restart (RWR) algorithm [Ci from one to others.r is the restart coefficient, t is iteration times, W was a |N| × |N| column-normalized adjacent matrix of graph G, and Ni is a |N| × 1 vector with ith element equal to 1 and others all equal to 0. Next, with respect to all the genes in the expression file, we iterated over each gene as a seed. This process was called a multiple random walk with restart (multi-RWR) algorithm  as follows:Here in GPCM, for one gene, we calculated a risk score integrating all the nodes (genes) impact on this node (gene). The GPCM was defined as a simple undirected graph lgorithm  was usedentclass1pt{minimaRS of gene j impacted by gene i as follows:j impacted by gene i and DEi represents the differential expression score of gene i.For example, we calculated an j, we integrated scores impacted by all genes as the final gene risk score (RSj) as follows:L is the intersection number of genes in the expression profile and genes in the GPCM.For gene RS of each gene in GPCM. For a pathway k, we calculated a pathway enrichment score PSk as the average of RS values for the genes in pathway k. The formula is as follows:n represents the number of genes in pathway k.We obtained h permutations. For a pathway, the empirical P-value was defined as the proportion of random pathway enrichment scores (PSrandom) larger than the real pathway enrichment score (PS): P-value = h was 1000. However, because of the limited number of permutations, it often produces a P-value of 0. To solve this problem, we estimated the exact P-value by using the generalized Pareto distribution (GPD) [FDR) to account for false positives [FDR < 0.01 were considered as significant pathways. In addition, CTpathway automatically clusters significant pathways into non-redundant groups. Pairwise similarities between any two significant pathways are computed based on a Jaccard similarity coefficient. According to user’s input cutoff of Jaccard similarity coefficient, a pathway similarity network is constructed. A default coefficient of 0.3 was set up in this study, which could be customized by the users using our web server. The Markov Cluster (MCL) algorithm [FDR) pathway within each cluster to represent the cluster. To obtain a better visualization, CTpathway shows the top 20 non-redundant pathways or clusters with low FDR, if there are more than 20 clusters or pathways. For each cluster, the top 10 pathways with lower FDR are shown in the enrichment map if there are more than 10 pathways which are within one cluster.We performed the permutation analysis to estimate the significance of the pathway. First, we shuffled genes in the differential expression profile. Then, we calculated the pathway enrichment score for each pathway. The background distribution was generated after performing on (GPD) . Becauseositives . The patlgorithm  was emplDR value was calculated to represent the rank difference (before and after optimization) of the target pathway as follows:m before optimization and m after optimization. M is the number of data sets. K is the number of total KEGG pathways. Here, M is 24 and K is 299.DT value was calculated to represent the running time difference (before and after optimization) as follows:m before the optimization, and m after the optimization.RR value was used as the criteria to compare the accuracy of different tools. Each data set m had an RR value for its target pathway, represented as RRm, which was the rank ratio of the target pathway in data set m, and calculated as follows:M were described as above. To make it comparable between different methods, we used KEGG pathways as candidate pathways.S value was used as the criteria to compare the stability or reproducibility of different tools. First, for eight data sets of four cancer types, each of which has microarray data and RNA-seq data, we identified risk pathways by using different methods. For each cancer type, we compared shared significant pathways identified from microarray data and RNA-Seq data. Because compared methods just identified few pathways or no pathways when using routine FDR or adjusted P-value as a cutoff, pathways with P-value < 0.05 were identified as significant pathways for all methods here. S value was calculated as follows:Jd represents the Jaccard similarity coefficient of different data sets of cancer type d and D represents the number of all cancer types. In this study, D is 4.P-value < 0.05 was considered to be statistically significant for DEGs. For CTpathway, permutation analysis was performed to estimate the significance of the pathway; FDR < 0.01 was considered as significant. For GSEA, FDR < 0.01 was considered as significant. For other compared methods, P-value < 0.05 was considered as significant because few pathways or no pathways were identified when using routine FDR or adjusted P-value as a cutoff.Differential expression analysis was performed by R package limma for the GEO data set and R package DESeq2 for the TCGA data set. RR values for each method using 24 gold standard data sets  based on different sources (TCGA RNA-seq data and GEO microarray data) of eight gene expression data  of the genes participate in more than one pathway  to reflect the disturbed level of gene expression  to calculate a risk score (RS) as the impact of the gene on the pathways  data set   from the Cancer Gene Census (CGC) [2FC| and RS from high to low, separately. Next, we evaluated if CCGs were located in the top of the rank list by the GSEA method [RS rank list for all 8 data sets at a significance level of FDR <0.1, whereas all |log2FC|-based FDRs were >0.1  . We obtaA method . The resPS, by integrating the RS of all nodes in the pathway. We took the average of the RS values in a pathway k as PSk. By permutation, we identified the significant dysregulated pathways  without compromising the quality of the results (rank difference = 0.018)  , GSEA s” sectionRR values of the target pathways obtained from different tools , indicating that our method was more accurate. Moreover, the comparisons of ROC curves and AUC values also indicated CTpathway had the best performance   patients, 9/10 target pathways of cancer types could be identified by CTpathway at a significance level of P-value < 0.05, whereas all but one other method could either not identify any or only identified one target pathway in early-stage patients for one cancer type  [FDR = 5.06×10−6) [FDR = 6.47×10−6) [FDR = 1.61×10−6) [FDR = 6.21×10−7) [RR value for the “B cell receptor signaling pathway” in B cell . Cell t389 . Cel48×10−6) ; “Neurop06×10−6) ; “Toll-l47×10−6) ; “ECM-re61×10−6) ; “TCR” w21×10−7) . CompareJaccard similarity coefficient for shared genes among all significant pathway pairs. MCL clustering algorithm [Jaccard similarity coefficient on the web server.Redundancy is a frequently neglected problem for most PEA methods. Pathways sharing genes lead to functional similarities. As a result, it is difficult to extract representative pathways from redundant information , 102. CTlgorithm  was empl2FC and P-value or either. By selecting several parameters, input Email address, and clicking the “run” button  by using multiple sources of pathways and priori knowledge in human.FC and P-value for each gene from differential expression analysis as gene differential expression score (DE). Next, we obtained a crosstalk effect matrix by the multi-RWR algorithm and calculated a final risk score (RS) by integrating the DE and crosstalk effects. By enrichment analysis of the CGC genes, we demonstrated that RS was a better index for identifying risk genes, and identified important genes with a high RS and low |log2FC| that were overlooked by other methods that relied on |log2FC|. Finally, we calculated a pathway enrichment score by averaging RS for genes in the pathway and identified significantly dysregulated pathways by permutation. Our optimization process reduced ~86.3% of the original running time. Furthermore, the performance of CTpathway is significantly better compared with existing methods in terms of accuracy , reproducibility, and running time. In addition, by applying CTpathway to cancer patient samples, we determined that CTpathway could identify critical pathways, which were not identified by other methods. For the data sets with a small number of DEGs, CTpathway was also useful and outperformed the other methods. Notably, CTpathway outperformed other methods in identifying target pathways in early-stage cancer tissues and blood samples. For scRNA-seq data, which can have small DEG numbers, CTpathway could effectively identify cell type-related pathways. Our results demonstrate that CTpathway could be applied in disease analysis, and especially for data sets with fewer DEGs, early cancer diagnosis, which may lead to starting treatment earlier, and scRNA-seq data. We also developed an online web tool to allow users to easily and freely perform PEA with CTpathway.First, we collected TF-gene regulation, PPI, and gene-gene interaction and constructed a GPCM. The topological property analysis showed that the degree distribution approximately displayed a power law distribution, which was similar to most biological networks. Then, we integrated This study provides a new useful PEA method, CTpathway, for over 2500 pathways in eight pathway databases, and showed that CTpathway performed better than other widely used methods. We evaluated CTpathway performance using the commonly used standard data sets. However, these data sets are limiting because there are only 24 target pathways for 24 diseases, indicating a need in the field for more gold standard data sets for the evaluation of pathway enrichment analysis methods. If the data sets contained additional known risk pathways for diseases, the methods could be evaluated more precisely using the precision-recall curve and AUPRC. In addition, CTpathway still has limitations related to reproducibility, which is consistent with PEA methods overall. For example, when different data sets belonging to the same disease serve as input, the results may differ. While differences in samples and sample handling and processing between different labs contribute to reproducibility challenges, CTpathway was more reproducible than the other methods, showing ~35% overlap between different data sets tested.Homo sapiens in this version. Through constructing GPCM for other species, CTpathway could be used to identify risk pathways of other species. Although future studies will be needed to investigate these areas, CTpathway provides a new publicly available method that should result in new discoveries in multiple fields of biology and disease research.Of note, the NT methods are highly dependent on the information of interactions, such as TF-gene regulations, PPIs, and gene-gene interactions, and thus, incomplete information will limit the development of these methods. In this study, TF-gene regulations come from the TRANSFAC database. Recently, several other resources of TF-gene regulation have been provided , 104. AdThis study presents a novel pathway crosstalk-based method, CTpathway, for performing pathway enrichment analysis. CTpathway outperformed existing methods on accuracy, reproducibility, and speed. CTpathway exclusively identified critical pathways in several cancer types. Furthermore, CTpathway was useful even for data sets with few differentially expressed genes and could identify target pathways in early-stage cancer patient samples, which could lead to earlier treatment, and identify cell type-related pathways for scRNA-seq data. Finally, we provide an interactive and easy-to-use web server so users can conveniently perform pathway enrichment analysis and discover disease-risk pathways.Additional file 1: Table S1. Information of protein-protein (PPI) included in this study. Table S2. Summary of data sets analyzed in this study. Table S3. Summary of 12 gold standard data sets. Table S4. The degree of genes in the GPCM. Table S5. List of CGC genes of four cancer types. Table S6. GSEA enrichment results of RS or |log2FC| for CGC genes. Tables S7-S14. Significant (FDR < 0.01) pathways identified by CTpathway for GEO COAD, TCGA COAD, GEO LIHC, TCGA LIHC, GEO LUAD, TCGA LUAD, GEO OV, TCGA OV. Tables S15-S18. Comparative results show significant KEGG pathways identified by CTpathway with other methods from GEO LUAD, TCGA LUAD, GEO OV, TCGA OV data. Table S19. Significant (FDR < 0.01) KEGG pathways for GEO OV data only identified by CTpathway. Table S20. EMT genes in the top 100 of RS rank list.Additional file 2: Figure S1. Crosstalk effect consideration detects genes known to be associated with LUAD. Figure S2. CGC genes are in top of the RS rank list, but not top of the |log2FC| rank list. Figure S3. The proportion of risk genes, overlooked by other methods. Figure S4. Comparison of DEG proportions between pathways specifically identified by CTpathway and non-specific pathways. Figure S5. Significant pathways in data sets with a small number of DEGs, identified by CTpathway. Figure S6. Comparison of enrichment result  of target pathways for TCGA cancer stages , obtained by different methods. Figure S7. Enrichment result  comparison of target pathways for B cell by different methods. Figure S8. An enrichment map constructed from early-stage COAD data set. Figure S9. The publicly available CTpathway web tool.