Background: Several clinical trials have demonstrated that glucagon-like peptide-1 (GLP-1) receptor agonists (GLP-1RAs) reduce the incidence of non-fatal myocardial infarction (MI) in patients with type 2 diabetes mellitus (T2DM). However, the underlying mechanism remains unclear. In this study, we applied a network pharmacology method to investigate the mechanisms by which GLP-1RAs reduce MI occurrence in patients with T2DM.Methods: Targets of three GLP-1RAs , T2DM, and MI were retrieved from online databases. The intersection process and associated targets retrieval were employed to obtain the related targets of GLP-1RAs against T2DM and MI. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genes (KEGG) enrichment analyses were performed. The STRING database was used to obtain the protein-protein interaction (PPI) network, and Cytoscape was used to identify core targets, transcription factors, and modules.Results: A total of 198 targets were retrieved for the three drugs and 511 targets for T2DM with MI. Finally, 51 related targets, including 31 intersection targets and 20 associated targets, were predicted to interfere with the progression of T2DM and MI on using GLP-1RAs. The STRING database was used to establish a PPI network comprising 46 nodes and 175 edges. The PPI network was analyzed using Cytoscape, and seven core targets were screened: AGT, TGFB1, STAT3, TIMP1, MMP9, MMP1, and MMP2. The transcription factor MAFB regulates all seven core targets. The cluster analysis generated three modules. The GO analysis for 51 targets indicated that the terms were mainly enriched in the extracellular matrix, angiotensin, platelets, and endopeptidase. The results of KEGG analysis revealed that the 51 targets primarily participated in the renin-angiotensin system, complement and coagulation cascades, hypertrophic cardiomyopathy, and AGE-RAGE signaling pathway in diabetic complications.Conclusion: GLP-1RAs exert multi-dimensional effects on reducing the occurrence of MI in T2DM patients by interfering with targets, biological processes, and cellular signaling pathways related to atheromatous plaque, myocardial remodeling, and thrombosis. Over the past four decades, the number of people living with diabetes has increased from 108 million in 1980 to 537 million in 2021, of which the overwhelming majority (over 90%) were diagnosed as type 2 diabetes mellitus (T2DM). In 2021, 6.7 million deaths were caused by diabetes or its complications . Among tGlucagon-like peptide-1 receptor agonists (GLP-1RAs), such as liraglutide and dulaglutide, are widely used to treat patients with T2DM and obesity. They exert beneficial effects, including inhibition of glucagon secretion, delayed gastric emptying, decreased appetite, rare occurrence of hypoglycemia, and controlled weight gain . In receNetwork pharmacology is a big data integration method based on numerous databases and statistical algorithms . It aimshttps://pubchem.ncbi.nlm.nih.gov). As dulaglutide does not have a defined chemical structure, it was excluded from our study. Next, the following four target prediction databases were selected to retrieve targets for the GLP-1RAs: (1) The Binding Database (http://www.bindingdb.org/bind/ByTargetNames.jsp), a public and web-accessible database containing 2,096,653 binding data points for 8,185 proteins and over 920,703 drug-like molecules (https://sea.bkslab.org/), which can be rapidly used to search large compounds and to build cross-target similarity maps (http://www.swisstargetprediction.ch/) that allows estimating the most probable protein targets of a small molecule (http://targetnet.scbdd.com/home/index/), an open web server that can be used to predict the binding of multiple targets for any given molecule across 623 proteins by establishing a high-quality model for each human protein (https://www.uniprot.org/)  of three GLP-1Ras  were retrieved from PubChem, an open chemistry database at the National Institutes of Health  Theity maps ; (3) Themolecule , and (4) protein . All tarot.org/)  for subshttps://www.pharmgkb.org/), TTD (http://db.idrblab.net/ttd/), GeneCards (https://www.genecards.org), DrugBank (https://go.drugbank.com/), and OMIM (https://www.omim.org) databases. The PharmGKB database is a pharmacogenomic knowledge resource containing clinical information  to obtain a Venn diagram showing the intersection targets of GLP-1RAs against T2DM and MI. Then, the GeneMANIA database was applied to find extra targets, highly associated with the intersection targets, using a massive set of functional association data  to build and visualize a drug-target-disease network presented by nodes and edges. Nodes represent drugs, diseases, and target genes, whereas edges represent the existing correlations between any two nodes.https://string-db.org/) and processed in a multiple protein analysis pattern to obtain the PPI network data. The STRING database focuses on researching the interactive relationships between proteins, which helps identify core regulatory genes , the top five GO and KEGG analysis terms were listed and marked in the scatter plots.To further uncover the underlying biological process and involved signaling pathways in related targets, the KEGG pathway and GO enrichment analysis, including biological process (BP), cellular component (CC), and molecular function (MF), were conducted using Enrichr web tools , and theShen Jiayu et al., the maximal clique centrality (MCC), edge percolated component (EPC), maximum neighborhood component (MNC), and degree algorithms (The “TSV” file of the PPI network data was input to the Cytoscape software to identify hub targets and clusters using the Cytohubba (Version 0.1) and MCODE (Version 2.0.0) plugins, respectively. The Cytohubba plugin provides 11 methods for exploring virtual nodes in biological networks . Referrigorithms  were selgorithms . The MCOgorithms . All theWe collected the molecular structures of three GLP-1RAs  from the PubChem database. The detailed information is listed in When collecting disease targets, T2DM-related keywords were input into the five databases, and a total of 5004 targets were obtained: 4486 from the GeneCards database, 282 from the OMIM database, 24 from the PharmGkb database, 109 from the TTD database, and 103 from the DrugBank database. Finally, 4623 targets remained after removing repetitions. Parallelly, we also retrieved the MI-related targets from the five databases, and 727 targets were identified, including 313 from the GeneCards database, 18 from the OMIM database, 121 from the PharmGkb database, 47 from the TTD database, and 133 from the DrugBank database. A total of 511 unique targets were identified after removing duplicates. Interestingly, 511 targets of MI were all covered into targets of T2DM. The online Venn diagram tool generated 31 intersection targets between drugs and diseases .The GeneMANIA database provided additional 20 targets , highly The three GLP-1RAs, T2DM and MI, and 51 related targets were input into Cytoscape to construct a drug-disease-target network . The netThe TSV file of the PPI network was loaded into Cytoscape software, and the MMC, MNC, EPC, and Degree algorithms in the “Cytohubba” plugin were used to calculate the core targets. Seven core targets were identified, including AGT, TGFB1, STAT3, TIMP1, MMP9, MMP1, and MMP2 , suggestNext, the Iregulon plugin was applied to find the direct transcription factors of seven core targets, and the normalized enrichment score (NES) was calculated and used for ranking purposes . The higThe MCODE plugin was then used to predict the modules in the PPI network. The targets were clustered into three modules. Each module represents a densely connected region of the molecular interaction network . DetaileThe 51 related targets were uploaded to Enrichr for KEGG and GO enrichment analyses, and the top five terms ranked by q-value were selected for display. For the GO enrichment analysis, we identified the top five terms from BP, CC, and MF. The results are shown in detail in The top five terms for global KEGG enrichment analysis are shown in Complications related to diabetes result in 1.5 million deaths per year, and cardiovascular events are the primary cause of death . SeveralResearchers have proposed various hypotheses to elucidate the therapeutic mechanism of GLP-1RAs in cardiovascular diseases. Many studies have consistently reported that several GLP-1RAs exert inhibitory effects on plaque formation, development, and rupture . HoweverThe other three core targets identified were AGT, STAT3, and TGFB1. AGT-encoding angiotensinogen is an essential component of the RAS and participates in the regulation of blood pressure, body fluids, and electrolyte balance. Angiotensinogen undergoes two cleavages to form angiotensin II (Ang II), which has well-known adverse effects on the myocardium. A recent study showed that the mRNA expression of GLP-1R was considerably associated with the components of the renin-angiotensin-aldosterone system (RAAS) detected in epicardial and pericardial fat in patients with severe coronary artery disease . HoweverShiraishi et al. demonstrated that GLP-1 induces macrophage transformation into the M2 phenotype, contributing to the beneficial effects of GLP-1 against diabetes  values, systolic blood pressure, and body weight . The medOur study provides a comprehensive investigation and analysis of the multi-dimensional effects of GLP-1RAs on preventing MI in patients with T2DM, which may be mainly mediated by interfering with specific targets, biological processes, and cellular signaling pathways related to atheromatous plaque, myocardial remodeling, and thrombosis. However, this study has some limitations as it lacks a series of experiments to prove the proposed hypothesis. Accordingly, further experiments and multi-omics studies are warranted to understand the comprehensive mechanism of GLP-1RAs.


Fumaria indica is a herbaceous annual plant used in various regions of Asia to treat a variety of ailments, including liver cancer. Several in vitro investigations have revealed the effectiveness of F. indica in the treatment of liver cancer; however, the exact molecular mechanism is still unrevealed. In this study, the network pharmacology technique was utilized to characterize the mechanism of F. indica on liver cancer. Furthermore, we analyzed the active ingredient-target-pathway network and uncovered that Fumaridine, Lastourvilline, N-feruloyl tyramine, and Cryptopine conclusively contributed to the development of liver cancer by affecting the MTOR, MAPK3, PIK3R1, and EGFR gene. Afterward, molecular docking was used to verify the effective activity of the active ingredients against the prospective targets. The results of molecular docking predicted that several key targets of liver cancer  bind stably with the corresponding active ingredient of F. indica. We concluded through network pharmacology methods that multiple biological processes and signaling pathways involved in F. indica exerted a preventing effect in the treatment of liver cancer. The molecular docking results also provide us with sound direction for further experiments. In the framework of this study, network pharmacology integrated with docking analysis revealed that F. indica exerted a promising preventive effect on liver cancer by acting on liver cancer-associated signaling pathways. This enables us to understand the biological mechanism of the anti liver cancer activity of F. indica.Liver cancer (LC), a frequently occurring cancer, has become the fourth leading cause of cancer mortality. The small number of reported data and diverse spectra of pathophysiological mechanisms of liver cancer make it a challenging task and a serious economic burden in health care management.  Liver cancer is the fourth major cause of cancer-related deaths globally. According to a report from Cancer Research UK, liver cancer will be one of the fastest-growing malignancies by 2035 . It is aFumaria indica  is a wild plant that has been used in traditional ayurvedic texts to treat a wide range of illnesses, including skin diseases, tropical diseases, cardiovascular disorder, circulatory disease, fever, and headache, as well as many other things [F. indica possesses anticancer, antidiabetic, antimicrobial, and antiinflammatory properties due to the presence of natural bioactive compounds [F. indica is a fundamental and a unique source of a variety of potential phytochemicals, making it a useful and versatile plant with a wide range of medicinal properties [F. indica against N-nitrosodiethylamine and CCl4-induced hepatocellular carcinoma in Wistar rats. Their findings powerfully support that treatment with F. indica significantly reduced the liver injury and restored the entire liver cancer markersr things . The latompounds . The Fumoperties . Hussainoperties  evaluateNetwork pharmacology (NP) is a field of study that is based on systems biology and multi-directional pharmacology . Rather F. indica. To the best of our knowledge, this is the first study to combine bioinformatics analysis with NP to reveal the mechanism of F. indica for liver cancer treatment. This research study will add to our understanding of the molecular mechanism of F. indica’s anti-liver effect and help to speed up the drug discovery process. The bioactive components and putative molecular network mechanism of F. indica against liver cancer are examined from a systematic and molecular level using network pharmacology along with molecular docking analysis. In the near future, wet lab research is also proposed to explore the additional pharmacological potentials.In the current study, a comprehensive NP-based approach was used to explore the active phytochemicals of Fumaria indica were found in the literature. Out of these, 15  components met the screening standards of the DL index ≥ 0.18 and OB ≥ 30%. Furthermore, from the SwissTargetPrediction database, we found 1500 genes that could be the target of the 15 active ingredients , and the STAT pathways [The functional annotation and enrichment analysis unveiled the potential biological roles of d so on. . The KEGpathways , includipathways . KEGG paThe top four targets EGFR, MTOR, MAPK3, and PIK3R1 were chosen for molecular docking after a thorough analysis of the PPI network. PDB structure was used to find the 3D structure of the target protein (EGFR (PDB id: 1IVO), MTOR (PDB id: 1FAP), MAPK3 (PDB id: 2ZOQ), and PIK3R1 (PDB id: 1HPO)). All the PDB structures were selected on the basis of their resolution. Low numeric values in Å mean the resolution of the structure is good and can be considered for further analysis. In the framework of the current study, EGFR had a resolution of 3.30 Å, while other targets MTOR, MAPK3, and PIK3R1 had resolution of 2.70 Å, 2.39 Å, and 2.50 Å, respectively. Furthermore, the CPort tool was used for active site prediction of the selected proteins. The compounds interacted with the active site of the EGFR receptor via forming a bond with the following amino acid residues: Tyr B251, Gln A8, Leu A38, Ala A62, Asn A86, Thr B249, Pro B248, and Lys A407. In the case of MTOR, the active compounds bound with the Ser B2035, Glu A54, Tyr A26, Phe A46, Asp A37, Arg A42, Thr B2098, Asp B2102, Lys B2095, Trp B2101, Phe B2039, Tyr B2105, Phe B2108, and Leu B2031 residues. On the other hand, MAPK3 bound with active compounds via Arg A41, Arg A64, Thr B347, Glu B194, Pro B193, Asn B161, Phe A371, Arg A370, Pro A373, and Asp A105. Lastly, the selected compounds bound with PIK3R1 by forming a bond with Arg A8,Ala A28,Gly B27,Asp A25,Ile A84,Asp B25,Gly B48,Ile B47,Asp B29,Ile A50,Gly A4, and Ile B50 . The druF. indica. Fumaridine and N-feruloyl tyramine showed good binding affinity with all the targets, except EGFR, having docking scores between −13.86 kcal/mol and −10.69 kcal/mol. However, EGFR showed top binding with the compounds Lastourvilline and Cryptopine, having docking scores of −12.69 kcal/mol and −10.29 kcal/mol, respectively. Similarly, all the compounds also showed strong hydrogen bond interactions with interacting residues of MTOR. Thus, docking analysis strengthened our findings that predicted stable target bonds with active compounds of ADMET analysis is a challenging process in drug discovery. The SwissADME tool was applied to forecast different types of pharmacokinetic properties. The pharmacokinetic factor may be used to predict the absorption, distribution, metabolism and elimination (ADME), and toxicity of the top therapeutic novel compounds. ADMET profiling of all the top selected drug candidates showed that there are no negative consequences of the pharmacokinetic properties, first and foremost, of the potential compounds . The ADMNatural product research has received a lot of interest in recent years . The netFumaria indica is a medicinal plant of the fumitory family that is rich with phytochemical constituents, which have huge medicinal value [F. indica has antipyretic, antidiarrheal, and hypoglycemic effects, according to pharmacological investigations [F. indica against liver diseases. However, the exact molecular mechanism remains unclarified [F. indica bioactive compounds, as well as a novel therapeutic concept for future research into F. indica processes for liver cancer therapy. The hallmark of this age will be the identification of potential bioactive ingredients that stop the pathophysiology of disorders and disease. al value ,33. F. iigations ,35. Varilarified . This stIn the current study we uncover several target genes that are revealed to be involved in various pathways in cancer. The pathogenesis of disease can be avoided by targeting the genes that cause disruption in pathways in cancer. A slew of studies strengthened our findings, such as most people that are suffering from liver cancer include chronic infections with HBV or HCV, or cirrhosis, and certain people inherit liver diseases and diabetes . It is iF. indica were mainly involved in protein phosphorylation, peptidyl-tyrosine phosphorylation phosphatidylinositol 3-kinase complex, class IA, and GO protein serine/threonine/tyrosine kinase activity. KEGG pathway studies revealed that targets were involved in liver cancer-related pathways. The KEGG pathway enrichment results revealed that the putative targets were significantly enriched in hepatitis B and viral carcinogenesis, and the cAMP signaling pathway, PI3K-Akt signaling route, MAPK signaling pathway, estrogen signaling pathway, p53 signaling pathway, and cell cycle signaling pathway were all found to be enriched in cancer pathways. The biological information of target genes was obtained using GO enrichment analysis. According to GO functional analysis, anti-liver cancer targets of It is noteworthy that our core genes are mainly enriched in the cyclic adenosine monophosphate (cAMP) signaling pathway. Previous studies demonstrated that the cAMP signaling pathway controls a variety of cellular activities such as lipid, metabolism, inflammation, cell differentiation, and injury and regulates gene-protein expression and function . Hence, F. indica and to validate its medicinal applications.According to topological parameters of the compound–genes–pathway network, four major targets named EGFR, MAPK3, MTOR, and PIK3R1 were identified as the core targets. Furthermore, these core targets were validated using molecular docking, which revealed that Fumaridine, N-feruloyl tyramine, Lastourvilline, and Cryptopine bound stably with these core targets. The findings of docking analysis indicated that these four compounds can be used for treatment of liver cancer because of their ability to bind stably with core targets. In the light of current network pharmacology, this research predicted the active compounds, their prospective targets, and associated pathways for the treatment of liver cancer, thereby providing a theoretical foundation for future experimental research. Given the limitation of network pharmacology, the basic pharmacological mechanism for liver cancer treatment can only be discovered by data mining. The mining of active compounds is particularly based on different databases. Although the information in databases are curated, a lot of inconsistencies may, however, occur due to variety of information resources and experimental data. In this regard, modern high throughput techniques including chromatography can be used; liquid chromatography and mass spectrometry can help to solve this problem. Even though we have given some intriguing evidence, more research and clinical trials are required to fully investigate the potential of Fumaria indica was collected from the literature using different databases such as PubMed and Google scholar and KNApSAcK. PubChem Explore Chemistry [F. indica were virtually screened by applying bioavailability (OB) and drug likeness (DL) parameters, which are crucial in the characteristics of absorption, distribution, metabolism, and excretion (ADME) characteristics of drugs. Compounds were only retained if DL ≥ 0.18 and OB ≥ 30% to satisfy ADME criteria. Biologically active compounds that did not match these conditions were discarded and were not investigated further. In this regard, DL and OB of all active constituents were calculated using Molsoft [The information on active phytocompounds of hemistry  was usedhemistry  and Chemhemistry  were use Molsoft  and Swis Molsoft .The potential target genes of screened active constituents were retrieved by entering their Canonical SMILES to SwissTarget Prediction tool . Therefop-value ≤ 0.01 was selected, and top 10 GO enrichments and top 10 KEGG pathways were chosen for subsequent analysis.To perform gene enrichment analysis and KEGG pathway analysis, Database for annotation, visualization, and integrated discovery (DAVID)  was usedF. indica in liver cancer was performed by network analysis. The software Cytoscape 3.8.0, which is a freely available, graphical user interface for importing, visually exploring, and analyzing bimolecular interaction networks, was used to construct and visualize the network [The mechanism of  network . Active Protein–protein interaction (PPI) data were obtained from the Search Tool for the retrieval of Interacting Genes (STRING) database with a confidence score of >0.7 to construct PPI network by uploading common genes on a database . The PPIF. indica contains multi-targeting compounds that function on numerous disease-related pathways; hence, they might be considered as novel therapeutic options against liver cancer. Furthermore, our studies revealed that the EGFR, MAPK3, MTOR, and PIK3R1 genes are effective and potential therapeutic agents for lowering the incidence of liver cancer and potentially exhibiting therapeutic effects on liver cancer. However, the current study has significant limitations since further phytochemical and pharmacological research is needed to verify these findings.This research establishes a scientific foundation for determining the efficacy of multicomponent, multi-target drug treatment as well as finding novel anti liver cancer therapeutic targets. In this study, network pharmacology along with molecular docking was employed to explore the underlying mechanism for the treatment of liver cancer. According to network analysis,


Neonatal hyperbilirubinemia is caused by the excessive production of bilirubin and decreased excretion ability in the neonatal period. It leads to a concentration of blood bilirubin that exceeds a certain threshold. Yinzhihuang oral liquid (YZH) is a traditional Chinese medicine mixture used in the treatment of neonatal hyperbilirubinemia in China. This article systematically explores the pharmacological mechanisms by which YZH acts in the treatment of neonatal hyperbilirubinemia through network pharmacology at the molecular level.  We adopted the method of network pharmacology, which includes active component prescreening, target gene prediction, gene enrichment analysis, and network analysis.  According to the network pharmacological analysis, 8 genes  may be targets of YZH in the treatment of neonatal hyperbilirubinemia. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses showed that YZH may regulate antioxidation, modulate lipid metabolism, and have anti-infective properties.  In this study, the pharmacological action and molecular mechanisms of YZH were predicted as a whole. It was found that YZH is a promising drug for treating oxidative stress due to bilirubin, as it reduces immunosuppression and helps to eliminate virus infection. Serum bilirubin concentrations higher than 17 mg per deciliter in full-term infants are no longer considered physiologic. Significantly elevated serum bilirubin levels allow the entry of bilirubin into the brain, causing irreversible damage. This is termed as kernicterus [Neonatal jaundice is one of the most common conditions in the neonatal period. Approximately 60% of term and 80% of preterm infants develop jaundice in the first week of life, and approximately 10% of breast-fed babies are still jaundiced at the age of 1 month [nicterus . Severe nicterus . Therefonicterus . Howevernicterus . Some stArtemisiae Scopariae Herba, Gardeniae Fructus, Scutellariae Radix, and Lonicerae Japonicae Flos. For infants with hyperbilirubinemia in China, especially those for whom phototherapy monotherapy fails, YZH can reduce bilirubin and promote the regression of jaundice [Yinzhihuang oral liquid (YZH) is a compound formula of traditional Chinese medicine that is composed of jaundice . For exajaundice . Animal jaundice . NeverthTraditional Chinese medicine (TCM), especially compound formulae, has the characteristics of multicomponents, multitargets, and synergistic effects, with unclear mechanisms of action and other problems. Therefore, it is difficult to verify the potential mechanisms through traditional experimental methods, and it is not easy to establish a scientific and appropriate assessment system to evaluate efficacy .Network pharmacology, which is a novel research field based on pharmacology and pharmacodynamics, generates complicated interaction networks based on target molecules, biological functions, and bioactive compounds . This apIn this study, a comprehensive network pharmacological method was established to explore the potential mechanism of action of YZH by molecular docking and network analysis. The aim was to determine drug action-related targets and key pathways and provide a direction for further drug research.https://old.tcmsp-e.com/tcmsp.php), which is a unique systems pharmacology database of Chinese herbal medicines that is used for collecting the chemical components and targets of TCM [Artemisiae Scopariae Herba, 41 specimens of Scutellariae Radix, 41 specimens of Lonicerae Japonicae Flos, and 15 specimens of Gardeniae Fructus  [Scutellariae Radix, 26 specimens of Lonicerae Japonicae Flos, 15 specimens of Artemisiae Scopariae Herba, and 10 specimens of Gardeniae Fructus. A total of 32 components were excluded because no corresponding protein sequence or genetic information was found  . The tarhttps://go.drugbank.com/) [https://omim.org/) [https://www.genecards.org/) [https://www.pharmgkb.org/) [http://db.idrblab.net/ttd/) [Genes related to neonatal hyperbilirubinemia were searched and downloaded from public databases, including DrugBank (nk.com/) , OMIM (him.org/) , GeneCards.org/) , PharmGkkb.org/) , and TTDet/ttd/) . In totahttps://cn.string-db.org/) and used to predict protein-protein interactions [PPI data were obtained from STRING (ractions . Target Cytoscape software (version 3.9.1) was used to visually analyse the active components of YZH and the common targets of neonatal hyperbilirubinemia . The nodp < 0.05.Functional annotations and the involved pathways of the genes were studied by GO and KEGG enrichment analyses in R (version4.1.2 for Windows). A difference was considered to be statistically significant at We intersected the target genes of the drug and disease, and a total of 135 common genes were obtained . To furtp value <0.05, we obtained 2356 significantly enriched GO terms. The results of the GO analysis showed that YZH treats neonatal hyperbilirubinemia through a variety of biological processes. The top 5 processes were the response to lipopolysaccharide (GO:0032496), response to molecule of bacterial origin (GO:0002237), response to oxidative stress (GO:0006979), response to xenobiotic stimulus (GO:0009410), and response to nutrient levels (GO:0031667). The main CC terms were cytoplasmic vesicle lumen (GO:0060205), vesicle lumen (GO:0031983), secretory granule lumen (GO:0034774), membrane raft (GO:0045121), and membrane microdomain (GO:0098857). The top five MF terms were DNA-binding transcription factor binding (GO:0140297), RNA polymerase II-specific DNA-binding transcription factor binding (GO:0061629), tetrapyrrole binding (GO:0046906), peptide binding (GO:0042277), and heme binding (GO:0020037) , cellular component (CC), and molecular function (MF) terms of the 123 target genes. By setting the filter as an adjusted q value <0.05 and 0020037)  (Supplem0020037) . In concp value <0.05. Overall, these target genes are involved in the pathways of lipid metabolism (hsa05417), hepatitis B virus infection (hsa05161), Kaposi sarcoma-associated herpesvirus infection (hsa05167), measles (hsa05162), and human cytomegalovirus infection (hsa05163). In summary, the related pathways are mainly involved in lipid metabolism and virus infection, including hepatitis B virus and human cytomegalovirus ” jaundice, hepatitis, heatstroke, and allergic inflammatory dermatitis. Randomized controlled clinical trials have reported that this drug preparation can significantly shorten the recovery time of serum total bilirubin and regression time of jaundice in the treatment of neonatal jaundice [Scutellariae Radix is the root of Scutellaria baicalensis Georgi (Lamiaceae). The small molecule components extracted from it have antiviral, antitumor, antibacterial, antioxidation, and anti-inflammation activities and protect hepatocytes and nerve cells [Scutellaria baicalensis Georgi may treat lipopolysaccharide-induced liver injury in mice by inhibiting the cytokines TNF-α, IL-1β, IL-6, COX-2, iNOS, and NF-kB [Lonicerae Japonicae Flos has been widely used in China as an edible herbal medicine. Its extract has anti-inflammatory, bacteriostatic, antiviral, antioxidant, and hepatoprotective effects [Gardenia jasminoides is another TCM, and the preparation can be used to treat acute or chronic hepatic diseases, icteric hepatitis, itching skin, eczema, diabetes, and depression [Gardeniae Fructus can reduce thioacetamide-induced liver fibrosis in mice through the AMPK/SIRT1/NF-κB and Nrf2 signal pathways [YZH is mainly composed of four components:Ajaundice . In TCM,ve cells . Animal nd NF-kB . Lonicer effects . The new effects . Gardenipression . Animal pathways . The anaκB signaling to regulate the inflammatory response [κB transcription factor family and the core of DNA damage response pathway. It serves as a “master regulator” of the cellular inflammatory and stress responses. The RELA protein network can be phosphorylated to different degrees according to the type of DNA damage. Thus, it can affect different pathways to regulate DNA damage repair [κB protein production by inhibiting RELA phosphorylation [Bilirubin is an effective antioxidant, and low levels of bilirubin have antioxidant effects. However, when the blood bilirubin level exceeds a certain threshold, it can cause oxidative stress and lead to DNA damage . Bilirubresponse . RELA ise repair . Nonetherylation . In our γ, TNF-α, IL-2, and IL-21. However, it has been reported that high levels of BR induce apoptosis in reactive CD4+ T cells [Bilirubin has been shown to possess potent immunomodulatory properties through the innate and adaptive immune systems . It inhi T cells . The fin T cells . Further T cells . These f T cells . In addi T cells . Akt can T cells . Hence, In summary, we studied the potential therapeutic mechanism by which YZH acts in the treatment of neonatal hyperbilirubinemia by means of network pharmacology. The results showed that YZH could improve the oxidative stress and immunosuppression caused by excess bilirubin in neonates with hyperbilirubinemia. It was also found that this drug targets the treatment of hepatitis virus infection, offering a new strategy for the treatment of hyperbilirubinemia caused by hepatitis virus infection. However, the mechanisms of the active components of YZH still need to be clarified through further experiments.


Ardisiae Japonicae Herba (AJH), the dried whole herb of Ardisia japonica (Thunb.) Blume [Primulaceae], has been used in treating chronic obstructive pulmonary disease (COPD) in China. However, the material basis and molecular mechanisms of AJH against COPD remain unclear. Therefore, in this study, we attempt to establish a systematic approach to elucidate the material basis and molecular mechanisms through compound identification, network analysis, molecular docking, and experimental validation.Ultra-high performance liquid chromatography-Orbitrap Fusion mass spectrometry (UPLC-Orbitrap Fusion MS) was used to characterize the chemical compounds of AJH. The SwissTargetPrediction, String and Metascape databases were selected for network pharmacology analysis, including target prediction, protein-protein interaction (PPI) network analysis, GO and KEGG pathway enrichment analysis. Cytoscape 3.7.2 software was used to construct a component-target-pathway network to screen out the main active compounds. Autodock Vina software was used to verify the affinity between the key compounds and targets. TNF-α-stimulated A549 cell inflammation model was built to further verify the anti-inflammatory effects of active compounds.Altogether, 236 compounds were identified in AJH, including 33 flavonoids, 21 Phenylpropanoids, 46 terpenes, 7 quinones, 27 steroids, 71 carboxylic acids and 31 other compounds. Among them, 41 compounds were selected as the key active constituents, which might exhibit therapeutic effects against COPD by modulating 65 corresponding targets primarily involved in inflammation/metabolism/immune-related pathways. The results of molecular docking showed that the key compounds could spontaneously bind to the receptor proteins with a strong binding ability. Finally, the anti-inflammatory effects of the three active compounds were validated with the decreased levels of Interleukin-6 (IL-6) and Matrix Metalloproteinase 9 (MMP9) in TNF-α-induced A549 cells model.This study clarified that AJH may exert therapeutic actions for COPD via regulating inflammation/immune/metabolism-related pathways using UPLC-Orbitrap Fusion MS technology combined with network pharmacology for the first time. This study had a deeper exploration of the chemical components and pharmacological activities in AJH, which provided a reference for the further study and clinical application of AJH in the treatment of COPD. Chronic obstructive pulmonary disease (COPD) is a serious chronic inflammatory disease mainly characterized by persistent respiratory symptoms and irreversible airflow restriction caused by significant exposure to harmful gases or particles . COPD isArdisiae Japonicae Herba (AJH) is the whole herb of Ardisia japonica (Thunb.) Blume [Primulaceae], which has anti-inflammatory , anti-caIn this study, we developed a comprehensive method to clarify the material basis and molecular mechanisms of AJH for treating COPD by combining UPLC-Orbitrap Fusion MS analysis and network pharmacology. UPLC-Orbitrap Fusion MS technology was performed to characterize the chemical profile of AJH. Network pharmacology and molecular docking were used to conduct data mining on its chemical components to screen the bioactive components of AJH and predict the underlying mechanisms relevant to COPD treatment, and the effects of potentially bioactive components were further validated in TNF-α-induced A549 cells in vitro. The protocol is shown in Methanol and acetonitrile (HPLC-grade) were obtained from TEDIA , formic acid (MS-grade) was obtained from Thermo Fisher . All other chemicals and reagents were analytical grade. Ultra-pure water was obtained through a Milli-QPOD water purification system . Ardisiae Japonicae Herba pieces were obtained from Jiangxi Zhangshu Tianqitang Traditional Chinese Medicine Co., Ltd , and authenticated by Professor Sui-Qing Chen, Henan University of Chinese Medicine, Zhengzhou, China. The voucher specimens were stored at Scientific Research Center, Henan University of Chinese Medicine, Zhengzhou, China.As reference standards, twenty-nine pure compounds were used. Twenty-six reference standards, including Methyl salicylate (CHB190925), Palmitic acid (CHB181119), Kaempferol (CHB190127), Embelin (CHB190829), Hydroxygenkwanin (CHB180611), Rapanone (CHB190723), Epigallocatechin gallate (CHB180307), Myricitrin (CHB180611), Isoeugenol (CHB190830), Gallic acid (CHB180114), Ethyl gallate (CHB180116), Caryophyllene oxide (CHB180624), Myristic acid (CHB190102), Taxifolin (CHB190105), Myricetin (CHB180614), Astilbin (CHB190107), Scopoletin (CHB180210), Fraxetin (CHB180112), Cardamomin (CHB180312), Eriodictyol (CHB190123), Cianidanol (CHB180809), Gallocatechin (CHB200915), Arctigenin (CHB180131), Epicatechin gallate (CHB180305), Rutin (CHB190110), Nictoflorin (CHB190102) were obtained from Chengdu Cloma Biological Technology Co., LTD ; Quercetin (MUST-20101104) and Luteolin (MUST-14050411) were obtained from Chengdu Must Biological Technology Co., Ltd. ; Bergenin (111532–201604) was obtained from China Food and Drug Testing Institute , and the purity of each compound was ≥ 98% by HPLC analysis.Roswell Park Memorial Institute (RPMI) 1640 medium, Radio Immunoprecipitation Assay (RIPA) Lysis Buffer, Thiazolyl Blue Tetrazolium Bromide (MTT) and trypsin-EDTA were obtained from Beijing solarbio science & technology co., ltd , Fetal bovine serum (FBS) was purchased from Zhejiang Tianhang Biotechnology Co., Ltd. , the ELISA kits for IL-6 and MMP9  were obtained from BOSTER Biological Technology co., ltd .AJH powder (1.0 g) was accurately weighed and added in a 150 mL round-bottom flask with 100 mL methanol, mixed well, reflux-heated for 1h twice and filtered. Obtained filtrates were combined and dried by centrifugation. The residue was redissolved with methanol (HPLC-grade) and diluted to 10 mL volumetric flask. The solution was filtered using 0.22 μm microporous membrane and stored at 4°C before analysis.Accurately weigh an appropriate amount of each reference standard into a 10 mL volumetric flask, add methanol to dissolve and dilute to the mark, single-component reference stock solutions were obtained and stored at 4°C before use. Then, proper amounts of reserve solutions were added to a 25 mL volumetric flask, methanol (HPLC-grade) was added to reach scale, the mixed reference solution was obtained. The solutions were filtered through 0.22 μm microporous membranes and stored at 4°C before use.TMC18 column  was applied at a constant flow rate of 0.2 mL/min at 30°C. The mobile phase was made up of methanol (A) and 0.1% formic acid aqueous solution (B), and the gradient elution was as follows: 0~2 min (93~75% B), 2~10 min (75~20% B), 10~15 min (16~12% B), 15~20 min (12~10% B), 22~24 min (10~0% B). The injection volume was 5 μL.Chemical identification was conducted on an ultra-performance liquid chromatography-orbitrap fusion mass (UPLC-Orbitrap Fusion MS)  equipped with an electrospray ionization source (ESI). Thermo Scientific AccucoreThe acquisition parameters of orbitrap fusion were set as follows: Vaporizer temperature: 275°C; Ion Transfer Tube temperature: 300°C; Carrier gas (N2); Sheath gas and Aux gas flow were set as 35 arb and 5 arb, respectively; spray voltage were set at 3.5 kV, 2.8 kV ; collision energy was 35–55 eV. The sample was analyzed in both positive and negative ions Full MS/dd-MS2 modes with the first-level full scan  and the second-level scan , and the mass range was recorded from m/z 100–1200.http://lsp.nwu.edu.cn/tcmsp.php), Traditional Chinese medicine integrative database  and SciFinder (https://scifinder.cas.org/) database. Then, a database of AJH ingredients was established. AJH was identified by the optimized UPLC-Orbitrap Fusion MS method. The possible chemical composition (with an error of less than 5 ppm) was determined using Xcalibur based on map data, precise molecular weight and resulting fragment ions. The data was analyzed using Compound Discoverer software to integrate ion peak information, attribution information, ChemSpider, mzCloud and other databases of characteristic fragments integrated with existing chemical composition information reports. The structure of compounds was inferred using the cracking prediction of MassFrontier and its cracking rule.The chemical compounds of AJH were collected from existing databases, including the Traditional Chinese Medicine Systems Pharmacology Database and the Analysis Platform  and saved in SDF format. These SDF documents were imported to SwissTargetPrediction database (http://www.swisstargetprediction.ch/) for target prediction. For more accurate prediction, relevant parameters were set (probability ≥ 0.1). In addition, the COPD-related targets were selected from GeneCards database (https://www.genecards.org/) by using “Chronic Obstructive Pulmonary Disease” as the keywords. Then, the overlapping of AJH prediction targets and COPD-related targets was selected as potential targets.The molecular structures of components identified in AJH were downloaded from PubChem database  network was built. Cytoscape 3.7.2 was used for topological analysis of PPI network to realize its visualization. Using three topological parameters, betweenness centrality (Bc), closeness centrality (Cc) and degree, a topology analysis of the PPI network was performed to determine hub genes for further analysis.https://metascape.org/gp/index.html) for GO and KEGG analysis to obtain the information of pathways. Bioinformatics Data analysis and Visualization online platform (http://www.bio-informatics.com.cn/) was used to conduct GO and KEGG pathway analysis and visualization. In the process, the organism was set as “Homo sapiens”, and significance level was p Ho0.01.Potential targets identified above were uploaded into Metascape database . Pymol 1.8 software was used to remove water molecules and separate the primary ligand. After saving, the structure was imported into Autodock Tools 1.5.6 and saved in “pdbqt” format. Chem 3D software was used to download the mol2 files of the top 10 core compounds, and they were imported into Autodock Tools 1.5.6 and saved in pdbqt format. Finally, docking was carried out through Autodock vina 1.1.2. Discovery Studio 4.5 Client was used to visualize docking results and establish a docking interaction model diagram.The affinity between the core compounds and targets was verified by Autodock Vina software The 3D structures of core targets in the first five degrees of AJH in COPD treatment were obtained from the RCSB PDB database . Cells were cultured in RPMI 1640 complete medium (containing 10% FBS) and kept in an incubator at 37°C and 5% CO2. When the cells were overgrown, they were digested with 0.25% trypsin-EDTA, collected, subcultured or tested.4 cells/well) were planted into 96-well plates (n = 6), and cultured at 37°C and 5% CO2 for 24 hours. Then the cells were treated with PBS or different dosages of bergenin , luteolin  and kaempferol . After incubation for 24 h, added 10 μl MTT (0.5 mg/mL) to each well, and cultured for 4h. Using DMSO (100 μL/well) to replace cell culture. The absorbance was measured at 570 nm using a microplate reader.A549 cells (1 × 106 cells/plate) were incubated in 6-well plates (n = 6) for 24 h, incubated with TNF-α (10ng/mL) for 24 h. Then treated with bergenin , luteolin , and kaempferol , and incubated at 37°C with 5% CO2 in a humidified atmosphere for 48 h. Supernatant was collected, centrifuged and stored at -80°C for testing. RIPA Lysis Buffer 300 μL/well was added to lysate cells. Ten minutes later, the lysate was collected, the supernatant was collected by centrifugation, and levels of IL-6, MMP9 in the supernatant were determined according to the instructions of ELISA kits.A549 cells . Analysis of variance (ANOVA) was used to analyze the differences between multiple groups. P-values < 0.05 was considered to be statistically significant.+ and 74 in ESI-) were identified in AJH, including 33 flavonoids (F), 21 Phenylpropanoids (P), 46 terpenes (T), 7 quinones (Q), 27 steroids (S), 71 carboxylic acids (C) and 31 others (O) based on the reference standards, ingredients database, mass fragment mode and mass spectrum library with UPLC-Orbitrap Fusion MS system. Among them, 29 compounds  were identified by comparing with the reference standards. The details of 236 components, including compounds name, formula, retention time (tR), and fragments ions are presented in S1 Table.Total ion chromatograms (TICs) of AJH are presented in S2 Table). Only 207 compounds had relevant targets when the probability ≥ 0.1. In addition, 6412 COPD-related genes were collected from the GeneCards database. To ensure the precision of target collection, based on the parameter of “Relevance score,” the index above the third median value was selected as the key index, and 436 COPD-related targets were obtained (S2 Table). After taking the intersection of 884 predicted targets and 436 COPD-related targets, 108 common targets were obtained which means that they could be the main potential targets of AJH in COPD treatment After removing repeated targets, 884 targets were collected based on 236 primarily identified compounds using the SwissTargetPrediction database (3B). Using the three parameters degree, Bc and Cc, the index above the median value was selected as the key index, the threshold value of screening was degree ≥ 44, Bc cnd Cc, the index above the median value was selected as the Fig 3). In addition, genes in the top ten degree were selected , the results showed that AKT Serine/Threonine Kinase 1 (AKT1), Interleukin-6 (IL6), Tumor necrosis factor (TNF), Tumor Protein P53 (TP53) and Matrix Metalloproteinase 9 (MMP9) were the most vital targets of PPI, which may be the main targets of AJH in COPD treatment.After inputting these common targets into STRING database, we obtained a PPI network comprising 108 nodes and 2410 edges . Moreover, the term/pathway-target networks were constructed , cellular component (CC) and molecular function (MF). The top 10 important terms in BP, CC and MF are shown in The component-target-pathway network  was consS2 Table), indicating the ligands molecules could bind to receptor proteins spontaneously. In addition, the binding energy was smaller than -5.0 kJ/mol, which further proved the strong binding energy. The heat map revealed the difference of binding energy between different compounds and targets, as shown in The top ten core compounds were docking with the corresponding five key targets, and the results were analyzed. The basic information about ligands and proteins is shown in Luteolin has the best binding ability with five core targets. Visualization of the docking results was performed using Discovery Studio 4.5 Client software. The docking of the luteolin, kaempferol and bergenin with target proteins was primarily through thirteen forces, including conventional hydrogen bond, unfavorable donor-donor, unfavorable acceptor- acceptor, unfavorable bump, carbon hydrogen bond, amide-pi stacked, pi-donor hydrogen bond, pi-sigma, pi-pi stacked, pi-sulfur, sulfur-x, pi-alkyl, van der waals . LuteoliAccording to the network pharmacology, molecular docking analysis and references, three bioactive components including luteolin, kaempferol, bergenin had the better molecular docking results. To further evaluate the results obtained by systematic pharmacologic analysis, these components were selected from AJH to examine the potential effects of anti-inflammatory by using TNF-α-stimulated A549 cells. We employed ELISA assay for IL-6, MMP9 to confirm the predicted anti-inflammatory effects of three bioactive components. Morever, MTT assay and cytokine expression assay were performed at concentrations that did not affect cell activity by comparing references and conducting preliminary experiments.The MTT kits were used to assess the cytotoxicity to select proper concentrations of bergenin, luteolin and kaempferol. As shown in In this study, the chemical components of AJH were identified through a comprehensive approach, including rapid analysis of chemical components, network pharmacology analysis and experiment in vitro, and the molecular mechanisms of its anti-COPD against COPD was discussed. Firstly, the total ion chromatograms of AJH were obtained by UPLC-Orbitrap Fusion MS. Different mobile phase systems and elution gradients were investigated to optimize reliable and effective chromatography conditions. It was found that the elution effect of methanol was better than that of acetonitrile, and the separation effect of each chromatographic peak was better. Adding 0.1% formic acid can improve the peak shape and the mass spectrum response, so we used methanol-0.1% formic acid water as the mobile phase. In addition, due to the different response modes of various compounds in AJH, positive and negative ion-scanning modes were selected for simultaneous detection line monitoring. By using the Compound Discoverer 3.0 software to match the original MS raw data to mzCloud, Chemspider and other built-in databases, a total of 236 chemical constituents (162 in ESI+ and 74 in ESI-) were clarified or preliminarily characterized, mainly including flavonoids, phenylpropanoids and terpenes, which is consistent with the literature . PreviouNetwork pharmacology can reflect the contact among components/targets and pathways, and the molecular mechanisms of drug therapy can be clarified by constructing a “component-target-pathway” network. In the present study, 111 of the 236 components characterized of AJH were thought to have potentially critical anti-COPD effects through acting on 108 related targets. To further explore the key pharmaceutically bioactive compounds and underlying mechanisms of AJH against COPD, a multidimensional component-target-pathway network was further constructed and visualized using Cytoscape v3.7.2. Based on three topological parameters, forty-one key active components were screened out, including twelve flavonoids, eleven steroids, seven carboxylic acids, six terpenes, two phenylpropanoids, one quinone and two other compounds, which proved that AJH had a multicomponent synergistic effect in the treatment of COPD. Of these, flavonoids have the activities of anti-inflammation and anti-oxidation by inhibiting the expression of TNF-α, IL-6 and COX-2, which may have greater potential effects on COPD –49. It hThe results of network pharmacology showed that these active components may effectively improve the pathologic status of COPD patients by modulating sixty-five corresponding targets primarily involved in inflammation and oxidation related pathways. Among the predicted potential targets, AKT1, IL6, TNF, TP53, MMP9, CASP3 and ALB had high frequency, and they were involved in several biological processes, including positive regulation of MAPK cascade, positive regulation of cell motility, response to oxidative stress, regulation of inflammatory response, which were closely related to the pathogenesis of COPD. KEGG enrichment analysis showed that the potential targets were highly related to human disease for cancer, infectious diseases, metabolic diseases, immune system and signal transduction, including the pathways in cancer, AGE-RAGE signaling pathway in diabetic complications, endocrine resistance, EGFR tyrosine kinase inhibitor resistance, HIF-1, TNF, MAPK and PI3K-Akt signaling pathway, etc. Combined with the COPD pathogenesis and common signaling pathways, it is inferred that AJH exerted therapeutic effects on COPD through the following aspects: anti-inflammatory, antioxidant and immune regulation. TNF-α is one of the key signal molecules of high secretion of airway mucus, and it is also a biological indication of COPD grade and curative efficacy . TNF-α cWe selected molecular docking to validate the binding of core compounds and targets related to inflammation and oxidative stress. The binding free energy of compounds to the key targets was smaller than -5.0 kJ/mol, which indicated that the ligand molecules could spontaneously bind to receptor proteins with strong binding ability. Studies have shown that alveolar epithelial cells A549 are one of the mediators for COPD detection ,63. InflIn summary, this study clarified the components of AJH systematically, and explored the molecular mechanisms of AJH against COPD using an integrative strategy for the first time. Totally, 236 components were characterized using UPLC-Orbitrap Fusion MS, of which forty-one key bioactive components screened in AJH may effectively improve the pathologic status of COPD patients by modulating sixty-five corresponding targets primarily involved in inflammation/ metabolism/immune-related pathways. This study provided a comprehensive exploration into the chemical characteristics, pharmacological effects of AJH, and provided a reference for the further study and clinical application of AJH in the treatment of COPD. However, more experiments are needed to further demonstrate the deeper mechanisms, which will be the focus of our research in the future.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S1 File(DOCX)Click here for additional data file.


Xiezhuo Huayu Yiqi Tongluo Formula (XHYTF) consists of 14 Chinese herbal medicines. In this study, we investigated the potential mechanism of XHYTF in the treatment of uric acid nephropathy (UAN) through network pharmacology, molecular docking, and in vivo methods.  Using various pharmacological databases and analysis platforms, information on the active ingredients and targets of Chinese herbal medicine was collected, and UAN disease targets were retrieved using OMIM, Gene Cards, and NCBI. Then common target proteins were integrated. A Drug-Component-Target (D-C-T) map was constructed to screen core compounds and build a protein-protein interaction (PPI) network. Further, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed for common targets, and a Drug-Component-Target-Pathway (D-C-T-P) network diagram was constructed. The molecular docking simulation was performed to verify the binding affinity between core components and hub targets. Subsequently, the UAN rat model was established, followed by the collection of serum and renal tissues. The expression levels of indicators in the serum were determined using an enzyme-linked immunosorbent assay. The pathological changes of renal tissues were detected using H & E staining and Masson staining. The expression of related proteins in renal tissue was detected by western blot. β-sitosterol, and stigmasterol were observed to be the key active ingredients of XHYTF that were effective against UAN. The analysis of the PPI network revealed TNF, IL6, AKT1, PPARG, and IL1β as the 5 key targets. GO enrichment analysis revealed that the pathways were mainly concentrated in cell killing, regulation of signaling receptor activity, and other activities. Subsequently, KEGG pathway analysis revealed that multiple signaling pathways, including the HIF-1, PI3K-Akt, IL-17, and other signaling pathways, were closely related to the action of XHYTF. All 5 key targets were confirmed to interact with all core active ingredients. In vivo experiments indicated that XHYTF significantly reduced blood uric acid and creatinine levels, alleviated inflammatory cell infiltration in kidney tissues, reduced the levels of serum inflammatory factors such as TNF-α and IL1β, and ameliorated renal fibrosis in rats with UAN. Finally, western blot revealed decreased levels of PI3K and AKT1 proteins in the kidney, which confirmed the hypothesis.  In the study, 216 active ingredients and 439 targets in XHYTF were screened, and 868 targets were identified as being related to UAN. Among them, 115 were common targets. Based on the D-C-T network, quercetin, luteolin,  Collectively, our observations demonstrated that XHYTF significantly protects kidney function, including alleviation of inflammation and renal fibrosis via multiple pathways. This study provided novel insights into the treatment of UAN using traditional Chinese medicines. Uric acid nephropathy (UAN) is caused by disrupted purine metabolism, resulting in excessive blood uric acid levels, excessive or reduced renal excretion, and supersaturated deposition of uric acid in the serum, resulting in deposits in the medulla, interstitium, or distal collecting duct, causing kidney damage. Patients with hyperuricemia had a 2.14-fold higher risk of developing chronic kidney disease (CKD) than patients with normal UA levels [Rhizoma smilacis glabrae has antioxidant, anti-inflammatory, and immune regulation properties, lowering uric acid levels, reducing inflammatory cell infiltration of kidney tissue, protecting kidneys, and so on [Chinese rhubarb, can inhibit inflammatory damage and has a protective effect on UAN [Traditional Chinese medicine (TCM) has its own unique understanding of the pathogenesis of UAN. Numerous studies indicated that TCM may be beneficial in treating UAN in a multitarget and multipathway manner –7. Xiezhnd so on . Rhein, t on UAN . The theNetwork pharmacology is an emerging research approach, which acts as a catalyst for revealing the biological basis of herbal formulations . By intehttps://lsp.nwsuaf.edu.cn/tcmsp.php). The threshold was set to oral bioavailability (OB) ≥30% and drug likeness (DL) ≥0.18 for further filtering to obtain the active ingredients; this was supplemented by a literature search to continue including the active ingredients that had been deleted because of OB and DL thresholds but had research significance in the follow-up study. Pheretima could not be retrieved in TCMSP; therefore, the active ingredients could be collected by searching the CNKI database (https://www.cnki.net/) and using the PubChem database (https://Pubchem.ncbi.nlm.nih.gov/). SMILE structures of potential active ingredients were collected and imported into the SwissTargetPrediction database (https://www.swisstargetprediction.ch/) to predict potential targets. Selection probability >0 and TOP10 target genes were used as targets of the Pheretima. All targets were normalized to gene ID using the annotation file of the UniProt database (https://www.uniprot.org/). The screened active ingredients and potential targets of XHYTF were imported into Cytoscape 3.7.1  to construct a network diagram of Drug-Component-Target (D-C-T).The active ingredients and targets of XHYTF were mainly mined from the online Traditional Chinese Medicine Systems Pharmacology (TCMSP) database (https://www.genecards.org/), the Online Mendelian Inheritance in Man , and the NCBI database (https://www.ncbi.nlm.nih.gov/gene). The screening condition of the GeneCards database was “Relevance Score” >10. The online tool Venny 2.1 (https://bioinfogp.cnb.csic.es/tools/venny/index.html) was used to screen the common targets of XHYTF against UAN.The known targets related to UAN were obtained using the key word “urine acid nephropathy” from the GeneCards database . The targets had the gene type Homo sapiens and selective PPI networks with a medium confidence level ≥0.400, and the free nodes were hidden. The PPI network information was imported into Cytoscape 3.7.1, using “Network Analyzer” to calculate the topology parameters of the protein interaction network. The node size and color reflect degree value, and the edge thickness reflects the combined score. Finally, the protein interaction network was obtained and visualized. The targets with degree values in the top 5 were selected as the core targets.The obtained intersection targets were uploaded to the STRING database (https://metascape.org/gp/index.html#/main/Step1) to perform KEGG pathway analysis. The results with min overlap = 3, min enrichment = 1.5, and P < 0.01, were screened for the analysis of enrichment and sorted as per ascending P values. The top 15 pathways ranked by KEGG were obtained. Additionally, the OmicShare Tools online platform (https://www.omicshare.com/tools/) was used to map the bubble chart of the KEGG enrichment analysis.Gene ontology (GO) functional annotation was conducted using the ClueGO plug of Cytoscape3.7.1, and the modules Biological Process (BP), Cell Compound (CC), and Molecular Function (MF) were selected. The ClueGO network was created using Kappa statistics. Representative GO terms and pathways were selected using predefined filters. The importance of these pathways and their similarity in related genes is automatically mapped onto the network and illustrated in different interchangeable visual styles . SimultaThe 15 pathways obtained as mentioned in https://www1.rcsb.org/). Autodock Vina molecular docking was conducted on the SailVina platform. Molecular docking was performed by preparing the receptor, using Biopython functional correction, preparing the ligand, preparing the docking site, etc. Finally, docking scores were extracted. The binding activity was evaluated from the docking score value, and the molecular docking results were obtained. Further, Open Babel software  was used to convert “PDBQT” format to “PDB” format, and the results were visualized using PyMOL software . Simultaneously, the binding sites were determined on the PLIP website (https://projects.biotec.tu-dresden.de/plip-web/plip/index); the PyMOL results file format was downloaded; and binding site residues were tagged and visually processed using PyMOL software.2D structures of the key active ingredients of XHYTF were downloaded from the PubChem database, and crystal structures corresponding to core targets were obtained from the RCSBPDB platform (α (batch No. RJ16622) and IL-1β (batch No. RJ15465) were purchased from Renjie Biotechnology Co., Ltd. . RIPA lysate (batch No. AR0102), protease inhibitor (batch No. AR1178), polyvinylidene difluoride (PVDF) membranes (batch No. AR0136-02), bicinchoninic acid (BCA) protein assay kit (batch No. AR0198), SDS-PAGE kit (batch No. AR0138), HRP Goat Anti-Rabbit IgG (H + L) (batch No. SA00001-2), HRP Goat Anti-Mouse IgG (H + L) (batch No. SA00001-1), 5× protein buffer (batch No. AR1112), chemiluminescence liquid (batch No. AR1111), and electrophoresis buffer solution (batch No. AR0139) were purchased from BOSTER Biological Technology Co., Ltd. . Rapid membrane transfer fluid (batch No. SS0405A) was purchased from Shuohua Biotechnology Co., Ltd. . Antibodies against PI3K (batch No. 20584-1-AP), Akt (batch No. 60203-2-Ig), and GAPDH (batch No. 60004-1-Ig) were purchased from Proteintech Group, Inc. .Paraformaldehyde (batch No. G1101), hematoxylin-eosin , etc. were purchased from Servicebio Biotechnology Co., Ltd. . Ethyl carbamate (batch No. 20210521), absolute ethanol (batch No. 100092683), xylene (batch No. 10023418), and neutral gum (batch No. 10004160) were purchased from Sinopharm Chemical Reagent Co., Ltd. (China). Allopurinol (batch No. X20N10Y103527), ethambutol (batch No. R16S10T97786), and adenine (batch No. J10G151411) were purchased from Yuanye Biotechnology Co., Ltd. . Carboxymethyl cellulose (batch No. C12339554) was purchased from Macklin Biotechnology Co., Ltd. . Enzyme-linked immunosorbent assay (ELISA) kits for serum TNF-Adenine + Ethambutol mixed suspension: first, 500 mg sodium carboxymethylcellulose was dissolved in 100 mL distilled water and configured into 5% sodium carboxymethyl cellulose solution for reserve. The suspensions of adenine (100 mg/kg·d) and ethambutol (250 mg/kg·d) were prepared by suspending 100 mg adenine and 250 mg ethambutol in 5% carboxymethylcellulose sodium solution, stored at −20°C, and brought to room temperature before use. Allopurinol solution was prepared by mixing 50 mg with normal saline to obtain 100 mL solution .The components of XHYTF used in the study were Coicis Semen 30 g, Smilacis Glabrae Rhizoma 30 g, Persicae Semen 10 g, Pseudobulbus Cremastrae seu Pleiones 9 g, Spatholobus suberectus Dunn 20 g, Hedysarum multijugum Maxim 30 g, Phellodendri chinensis cortex 12 g, Pheretima 15 g, Codonopsis Radix 15 g, Radix Salviae 20 g, Radix Rhei Et Rhizome 3 g, Cyathulae Radix 15 g, Plantaginis Semen 15 g, and Rhizoma Atractylodis 10 g . To them, 10 times the volume of water was added, followed by soaking for 30 min. The solutions were boiled twice, 1 h each time. Further, concentrations of 4.2 g/mL (high dose solubility) were obtained using a rotary evaporator. The samples were stored in a refrigerator at −20°C, brought to room temperature before use, and diluted as required.A total of 30 male Sprague-Dawley (SD) rats were purchased from the Hangzhou Medical College (Certificate No.: SCXK (Zhe) 2019-0002, Hangzhou, China). The animals were reared in the SPF feeding room of the Fujian University of Traditional Chinese Medicine Experimental Animal Center (FJTCM IACUC-2021145). The animals were group-housed with a 12 h light/12 h dark cycle, and the temperature was set at 22–25°C. The rats were randomly divided into 6 groups: control, model, allopurinol, XHYTF low-dose (XHYTF-L), XHYTF medium-dose (XHYTF-M), and XHYTF high-dose (XHYTF-H) groups. The rats were allowed to adapt to the environment for 1 week before experiments. Weighing and recording were done before each experiment. The dosage was calculated based on the adult body weight of 70 kg. Hence, the dosage of XHYTF in rats was calculated according to body surface area [After the last administration, the rats were fasted for 12 h and deprived from water of 4 h. They were anesthetized by intraperitoneal injection with ethyl carbamate solution. Blood was collected through the abdominal aorta, and serum samples were obtained by centrifugation at 3500 rpm for 10 min, which were stored at −80°C. The bilateral kidneys of rats in each group were separated and weighed. The right kidney tissue was quickly and carefully separated on an ice plate, and a part of the right kidney tissue was cut, frozen in liquid nitrogen, and stored at −80°C for western blot analysis. Furthermore, partial kidney tissue was cut and fixed with 4% buffered paraformaldehyde for morphological analysis. All the procedures were approved by the Experimental Animal Welfare Committee of the Fujian University of Traditional Chinese Medicine.α and IL-1β were detected using ELISA according to the manufacturer's protocol.The levels of serum uric acid (SUA), serum creatinine (Scr), and blood urea nitrogen (BUN) in rats in each group were detected using an automatic biochemical analyzer. The levels of TNF-Renal tissue was dehydrated and transparent with gradient ethanol and xylene, embedded in paraffin, and stained with HE and Masson's trichrome staining. The sections were sealed with neutral gum. The renal tissue sections were carefully observed under a microscope, and images were taken. Three visual fields in each section were randomly selected, the proportion of collagen-positive area (blue) in the kidney was measured, and the target area of tissue was selected for 200-fold imaging. ImageJ analysis software was used to measure the collagen pixel area of Masson-stained sections, and the proportion of collagen deposition area was used to evaluate interstitial fibrosis.Renal tissues of each group were lysed with lysis buffer, and the supernatant was collected after centrifugation. A BCA protein concentration assay kit was used to determine the protein concentration. The same amount of protein was separated by SDS-PAGE and transferred to a PVDF membrane, and the expression levels of target proteins were detected. Protein expression levels were normalized to GAPDH expression levels.P < 0.05 was considered significant.Relevant data were analyzed using SPSS 26.0 statistical software . All results were presented as mean ± standard deviation (SD), and a univariate ANOVA was used to test the significance of differences. When variance was not uniform, the rank sum test was used. For the nonparametric tests, the Kruskal–Wallis test was used for the analysis. A1, A2, A3, B1, C1, etc. After deleting repeated targets, 439 corresponding target genes were obtained from these active ingredients.A total of 216 compounds were screened from 14 herbs of XHYTF with corresponding gene proteins, of which 6, 15, 18, 3, 23, 18, 25, 34, 17, 60, 10, 4, 7, and 4 compounds were obtained from Coicis Semen, Smilacis, Pseudobulbus Cremastrae Seu Pleiones, Spatholobus Suberectus Dunn, Hedysarum Multijugum Maxim, Phellodendri Chinrnsis Cortex, Pheretima, Codonopsis Radix, Radix Salviae, Radix Rhei Et Rhizome, Cyathulae Radix, Plantaginis Semen, and Rhizoma Atractylodis, respectively . Among tB2), luteolin (D4), beta-sitosterol (B1), and stigmasterol (A2).Overall, 358,428 and 36 UAN-related targets were respectively identified through the GeneCards, OMIM, and NCBI databases. A total of 822 disease targets were obtained after all targets were integrated and repeated values were deleted. The Venny 2.1 online tool was used to draw a Venn diagram for drug targets and disease targets , and 115A PPI network diagram was drawn according to degree and combine score values, with node area representing degree value and line thickness representing combine score value. The PPI network consisted of 115 nodes with 1823 edges representing PPIs . The aveP < 0.05 were screened. The GO item with a smaller P value was more closely related to the XHYTF treatment UAN. GO analysis results revealed that 824 items were obtained from BP analysis. The results were reclustered, and “Min Level: 0 and Max Level: 2” was selected for visualization. It mainly involved cell killing, regulation of growth, rhythmic processes, regulation of plasma lipoprotein particle levels, etc. , indicating the successful establishment of the UAN rat model. Compared with the model group, SUA, Scr, and BUN levels were significantly decreased in the allopurinol and all XHYTF groups (P < 0.05 or P < 0.01) , indicating that inflammation was activated in UAN. Compared with the model group, TNF-α and IL-1β levels in the allopurinol and all XHYTF groups were decreased (P < 0.05 or P < 0.01), and the effect was more significant in the XHYTF-M group , the renal tissue structure was normal without inflammatory infiltration and uric acid crystals, and no obvious connective tissue hyperplasia was observed in the interstitium. However, renal interstitium was widely observed in the model group, with renal tubular epithelial atrophy, small size or loss of structure, lymphocyte infiltration, increased collagen fibers in the interstitium, mild interstitial fibrosis, uric acid crystals, and multinucleated giant cell phagocytosis. Compared with the model group (P < 0.05 or P < 0.01), the allopurinol and all XHYTF groups exhibited reduced renal tubular injury, uric acid crystal deposition, inflammatory cell infiltration, and interstitial fibrosis levels to varying degrees , and it was significantly restored by XHYTF treatment (P < 0.05 or P < 0.01)  .UAN is characterized by uric acid kidney stones, chronic interstitial nephritis, and interstitial fibrosis, which may eventually lead to chronic renal failure. The potential role of uric acid in the progression of kidney disease has been demonstrated. Epidemiological studies have consistently reported that SUA is a risk factor for CKD . Elevateβ-sitosterol, stigmasterol, and luteolin as the key potential active ingredients. Furthermore, through PPI network analysis of UAN targets, we obtained 5 targets with the highest degree, including TNF, IL6, AKT1, PPARA, and IL-1β. GO functional enrichment and KEGG pathway analyses were performed to explore the biological mechanism of XHYTF. GO analysis suggested that the targets of XHYTF in treating UAN may be involved in cell killing, regulation of growth, rhythmic processes, regulation of signaling receptor activity, the GABA receptor complex, and other processes. KEGG enrichment analysis revealed that the targets were mainly enriched in the AGE-RAGE signaling pathway in diabetic complications, lipids, and atherosclerosis, the HIF-1 signaling pathway, the PI3K-Akt signaling pathway, the TNF signaling pathway, the IL-17 signaling pathway, etc. Among the predicted key compounds, quercetin was the most promising target. It has been reported that quercetin can inhibit renal inflammation and fibrosis in UUO and LPS mouse models by decreasing expressions of IL-18, IL-1β, TNF-α, and fibrosis area in Masson staining and inhibiting collagen expression [α and IL-1β) and uric acid levels, and play a role in improving renal function through the targeted regulation of the PI3k/Akt pathway, which verified the accuracy of our analysis.The results of network pharmacology and molecular docking revealed the multitarget and multipathway characteristics of XHYTF in treating UAN. Through data mining, 216 potential bioactive ingredients and 115 UAN-related targets were identified for XHYTF. The “D-C-T” network identified quercetin, pression . In addiα and IL-1β, which can cause local renal tubular injury and renal tubular interstitial fibrosis [α and IL-1β levels in rats with UAN were significantly increased and significantly decreased, respectively, after XHYTF treatment. In addition, renal pathological sections revealed that inflammatory infiltration and interstitial fibrosis were significantly reduced. Masson staining results revealed that the proportion of collagen-positive area in the kidney was significantly reduced. AKT1, whose full name is RAC-α serine/threonine protein kinase, has been reported to play a role in the regulation of multiple biological effects of activated AKT, including regulation of cell proliferation, survival, and metabolism, and is one of the key molecules activated downstream of the PI3K signaling pathway [Network pharmacology suggested that some of the anti-UAN targets of XHYTF were involved in the above pathways, and molecular docking results revealed that XHYTF exhibited a high affinity for binding with the important targets in the above pathways. Collectively, we speculated that XHYTF may play an antifibrosis and anti-inflammatory role in UAN through targeted regulation of the PI3K/AKT pathway. Among the pathological features of UAN, inflammatory reaction and fibrinosis are the fundamental pathological mechanisms, whereas the SUA-induced inflammatory mechanism pathway has been proven to be the main pathophysiological basis for the development of renal tubular injury and interstitial fibrosis and has been well-accepted as playing an important role in UAN injury . Uric acfibrosis , 24. Ind pathway . It has  pathway –28. To fThis study predicted, screened, analyzed, and verified potential active ingredients, core genes, and signaling pathways for XHYTF that may play a key role in biological processes through network pharmacology, molecular docking, and in vivo experiments. Our results indicated that XHYTF could alleviate UAN by targeting the PI3K/AKT signaling pathway, downregulating the expression of inflammatory cytokines, and reducing collagen fiber deposition. The mechanism of action of XHYTF in treating UAN might involve the synergistic effect of “multicomponents, multitargets, and multipathways.” Although these preliminary findings could not fully explain the potential mechanism of XHYTF in UAN, this study provided novel insights for further research. More studies are needed to explore the material basis and specific molecular mechanism of XHYTF in treating UAN, and more clinical studies are needed to verify our conclusions.


Pathway inference methods are important for annotating the genome, for providing insights into the mechanisms of biochemical processes and allow the discovery of signalling members and potential new drug targets. Here, we tested the hypothesis that genes with similar impact on cell viability across multiple cell lines belong to a common pathway, thus providing a conceptual basis for a pathway inference method based on correlated anti-proliferative gene properties.To test this concept, we used recently available large-scale RNAi screens to develop a method, termed functional pathway inference analysis (FPIA), to systemically identify correlated gene dependencies.AKT1, MTOR and PDPK1 were among the most correlated with those for PIK3CA (encoding PI3Kα), as returned by FPIA, whereas negative regulators of PI3K/AKT/MTOR signalling, such as PTEN were anti-correlated. Following FPIA, MTOR, PIK3CA and PIK3CB produced significantly greater correlations for genes in the PI3K-Akt pathway versus other pathways. Application of FPIA to two additional pathways (p53 and MAPK) returned expected associations (e.g. MDM2 and TP53BP1 for p53 and MAPK1 and BRAF for MEK1). Over-representation analysis of FPIA-returned genes enriched the respective pathway, and FPIA restricted to specific tumour lineages uncovered cell type-specific networks. Overall, our study demonstrates the ability of FPIA to identify members of pro-survival biochemical pathways in cancer cells.To assess FPIA, we initially focused on PI3K/AKT/MTOR signalling, a prototypic oncogenic pathway for which we have a good sense of ground truth. Dependencies for cordial’ freely available from https://github.com/CutillasLab/cordial.FPIA is implemented in a new R package named ‘Bioinformatics online. Signal transduction pathways decode extracellular signals into appropriate biological responses, thereby regulating most cell processes. Components of signalling pathways form complex networks of biochemical reactions and are frequently dysregulated in diseases such as cancer, neurodegeneration and diabetes. Therefore, discovering new signalling pathway members is important to understand the mechanisms of cellular biological processes and to uncover new anti-cancer drug targets. Pathway inference methods used for this purpose are based on literature mining techniques  and catalytic  subunits. When activated by upstream signals, such as those emanating from receptor tyrosine kinases (examples of which include EGFR and the insulin receptor), PI3K phosphorylates phosphatidylinositol 4,5 bisphosphate (PIP2) to produce phosphatidylinositol 3,4,5 trisphosphate (PIP3). This lipid second messenger nucleates protein complexes leading to the activation of an intracellular protein kinase cascade involving PDK1 (gene PDPK1), AKT1 and MTOR. The lipid phosphatase PTEN dephosphorylates PIP3, thus opposing and terminating PI3K signalling. The PI3K system therefore offers a system for which we have a good sense of ground truth and thus allows assessing the ability of FPIA to produce biologically meaningful results. FPIA was then further tested for its ability to return meaningful associations to p53 and MEK1.To test this concept, we developed a method named functional pathway inference analysis (FPIA), which uses gene dependency data (cell viability data as a function of genome-wide gene targeting) to reveal genes whose inhibition produces similar cell survival phenotypes across cell lines. This is distinct from previously published methods where the aim was to identify genetic or other perturbations correlated to gene dependencies  in RStudio (v1.4.1717).FPIA utilized as input cancer dependency datasets from the Cancer Dependency Map (DepMap) project—a collaboration of the Broad Institute  and the Wellcome Sanger Institute . Data produced by the DepMap project are made publicly available under a Creative Commons licence and are accessible through the portal  were subjected to the Benjamini–Hochberg multiple testing adjustment method with stats::p.adjust to control the false discovery rate calculating q-values; ‘cordial’ functions have the capacity to set the adjustment method (parameter method) which is passed to stats::p.adjust  to facilitate downstream analysis. The ‘cordial’ package was designed to be flexible so as not to be dependent on the input data and as such it can be used for correlation analysis of datasets beyond the molecular/genetic screens studied in this research.FPIA implemented Pearson correlation analysis to assess the similarity in dependency data via three principal functions of the ‘adjust . The genfuture package to provide the parallel backend, and vectorized mapping functions from the furrr package that process input in parallel . Prior to function usage, a multisession plan (plan) was created that generates multiple R sessions which resolve futures asynchronously  in separate R sessions equal to the number of physical processor cores; for convenience, this process has been incorporated into the ‘cordial’ function start_parallel. The input supplied was split, distributed to the workers and returned to the main R session to be combined once all chunks have been processed. This method of spawning multiple background R sessions ensures compatibility across all operating systems since the alternative of forking processes is not supported by Windows.Correlation analysis was performed in a vectorized and parallelized manner with the dataset) containing values which may also include associated metadata, or alternatively an optional dataset can additionally be supplied that may hold the metadata instead (parameter metadata). The data structure for both inputs is a data.table as this allowed for the subsequent efficient filtering of rows and subsetting of columns. Filtering (parameter filter_rows) was programmed by taking the input and a named list, whereby names specify column names to filter on, and performing a cross-join. If the dataset required subsetting to limit the correlations to be made  then a vector of column names can be provided (parameter select_cols). The resultant correlations of cor_map are symmetrical, specifically pairwise correlations are produced between all combinations of the columns specified (parameter select_cols). Unlike cor_map, cor_targets takes an additional argument of a single target column name (parameter target). The function cor_target_map calls cor_targets internally which is mapped in parallel with future_map to the argument of a vector of column names. In contrast with cor_map, cor_target_map creates non-symmetrical correlations, that is for each specified target column (parameter target), pairwise correlations are produced with the columns specified (parameter select_cols).The function arguments consisted of an input dataset (parameter stats) were used to determine the statistical significance of results and check assumptions were met. These included: Shapiro–Wilk test for normality (shapiro.test), F-test for homogeneity in variances (var.test) and Welch two-sample unpaired t-test (t.test). Fisher z-transformation (psych::fisherz) of the Pearson correlation coefficient r was used prior to testing of statistical significance for the population correlation coefficient ρ to overcome the skewed distribution observed as the sample correlation coefficient r nears ±1.Statistical methods , which minimized within-cluster variance $weight) was assigned as absolute r-value; the edge colour attribute (E(graph)$color) was assigned to the direction of the correlation (positive/negative); the edge width parameter (edge.width) was assigned inside the call to the plotting function plot.igraph as −log10. The vertex (node) colour attribute (V(graph)$color) was assigned to being a target gene. The vertex size parameter (vertex.size) was assigned in plot.igraph as weighted vertex degree with strength. Network groups were calculated in the plot.igraph parameter (mark.groups) as connected components (groups(components(graph))). The layout of the network graph ) was the Fruchterman and Reingold force-directed layout where larger weight values  produced shorter edges (degree (the number of adjacent edges of a vertex) and betweenness (the number of shortest paths through a vertex)  .cor_target_map was studied by Term Enrichment Analysis of genes correlated to the target gene in Reactome ; the false discovery rate was controlled for by calculating q-values with the Benjamini–Hochberg method  to target genes, of which 300 were in hsa04151 from the 342 hsa04151 genes present in the RNAi dataset, producing a pathway coverage of 87.7% (INPPL1 (91) followed by INPP5E (75), and this was transposed for significant correlations of genes corresponding to other pathways (PIK3R1 (71) and PDPK1 (69). INPPL1 and INPP5E are involved in PI-associated signalling and thus can only indirectly influence AKT signalling by manipulating the pool of PI lipid substrates, whilst PIK3R1 encodes a regulatory subunit of PI3K  followed by t-test of hsa04151 genes versus genes of all other pathways. This analysis revealed six target genes where the means of the significant correlations that belong to the pathway were significantly different to those of other pathways. Of these, MTOR, PIK3CA and PIK3CB exhibited means that were significantly greater for correlations to genes in hsa04151 than other pathways (FPIA found 13 725 genes in the dataset (81.6%) that were significantly correlated  which was homogenous for all key target genes , there is support to suggest that this may be a true observation as both have similar mutation patterns in cancer  for analysis. Although this represents a restriction, it is of the input dataset rather than the method and therefore will improve as more cancer models are studied. The three tumour lineages that cumulatively covered the greatest number of significantly correlated (q < 0.05) genes in hsa04151 were lung, breast and skin (PIK3R4 (42), whereas in the breast it was PDPK1 (21), whilst in skin it was INPP4B (21). The three topmost target genes ranked by number of significantly correlated genes were PDPK1, MTOR and PIK3R4, which contrasted from the gene-specific analysis in hsa04151 across all cell lines  within individual tumour lineages. Of the 31 lineages represented in the dataset, 12 had a sufficient number of cell lines without missing values  and MEK1 . Genes that positively or negatively regulate p53, including MDM2, MDM4, TP53BP1 and CHEK2, were the most significantly associated genes with TP53 (MAP2K1 include upstream regulators of this kinase (BRAF and STK38) as well as downstream effectors  , and by unbiased over-representation analysis in which the respectively associated pathways were enriched in the new unbiasedly identified network. Consequently, the FPIA method, and the associated cor_target_map function of the ‘cordial’ package, provides insights into signalling pathways and may be used to uncover novel interactions in a global, genome-wide manner. A potential limitation of the method is that it relies on functional analysis of genes that impact cell viability and therefore it may only be applied to the discovery of members of pathways that regulate cell survival and the proliferative process. Nevertheless, since dysregulated cell proliferation is a key hallmark of cancer, and the aim of most anti-cancer drugs is to inhibit cell viability/proliferation, FPIA has the potential to advance our understanding of biochemical networks involved in cancer and to inform new therapeutic targets and approaches.This work tested the hypothesis that genes with similar impact on cell viability across multiple cell lines belong to a common pathway, thus implying that the correlation of anti-cancer genetic dependencies may be used for biological pathway inference and for the annotation of genes with functional information. To test this idea, we developed a method termed FPIA to systemically determine correlated gene dependencies from large-scale gene silencing screens. Our gene-specific analysis revealed significant gene correlations within canonical signalling cascades with the potential to identify novel interactions. This was evident from inspection of PI3K, p53 and MEK1 correlated genes which returned the canonical pathway (btac769_Supplementary_DataClick here for additional data file.