While mechanisms contributing to the progression and metastasis of colorectal cancer (CRC) are well studied, cancer stage-specific mechanisms have been less comprehensively explored. This is the focus of this manuscript.Louvain algorithm and topologically and functionally compared across stages using the normalized mutual information (NMI) metric and pathway enrichment analysis, respectively. We also used Short Time-series Expression Miner (STEM) algorithm to detect potential biomarkers having a role in CRC.Using previously published data for CRC (Gene Expression Omnibus ID GSE21510), we identified differentially expressed genes (DEGs) across four stages of the disease. We then generated unweighted and weighted correlation networks for each of the stages. Communities within these networks were detected using the p-value ≤ 0.05). Comparing communities of different stages revealed that neighboring stages were more similar to each other than non-neighboring stages, at both topological and functional levels. A functional analysis of 24 cancer-related pathways indicated that several signaling pathways were enriched across all stages. However, the stage-unique networks were distinctly enriched only for a subset of these 24 pathways . We identified potential biomarkers, including HOXB8 and WNT2 with increasing, and MTUS1 and SFRP2 with decreasing trends from stages I to IV. Extracting subnetworks of 10 cancer-relevant genes and their interacting first neighbors  revealed that the connectivity patterns for these genes were different across stages. For example, BRAF and CDK4, members of the Ser/Thr kinase, up-regulated in cancer, displayed changing connectivity patterns from stages I to IV.Sixteen Thousand Sixty Two DEGs were identified between various stages (Here, we report molecular and modular networks for various stages of CRC, providing a pseudo-temporal view of the mechanistic changes associated with the disease. Our analysis highlighted similarities at both functional and topological levels, across stages. We further identified stage-specific mechanisms and biomarkers potentially contributing to the progression of CRC.The online version contains supplementary material available at 10.1186/s12885-022-09479-3. Colorectal cancer (CRC) refers to cancers affecting both colon and rectum. According to GLOBOCAN 2020 data, CRCs are the third most diagnosed and the second most deadly form of cancer worldwide, comprising 11% of all cancer diagnoses .p53, a key biomarker, is a tumor suppressor gene, mutated in 34% of the proximal colon tumors and in 45% of the distal colorectal tumors , Louvain algorithm to detect communities within each stage-specific network given its established status as the leading method for community detection [Louvain detects network communities by maximizing modularity (a measure of the density of links (edges) within communities compared to links between communities). Briefly, the search for communities using the algorithm proceeds in two phases. During the first phase, communities are detected by optimizing modularity locally. During the second phase, nodes of the same community are aggregated as pseudo-nodes to generate a new network. The combination of these two phases is iterated, until the modularity reaches a local maximum. The computational complexity of this algorithm is (O(nlogn)) which makes it extremely fast [We used the etection , 14. Loup-value. Hence, we generated 1000 random networks with the same number of nodes, edges and degree distribution as the stage I-, II-, and III-specific networks. Communities of random networks were identified using the Louvain algorithm and compared between stage I- and II- and stage II- and III-specific networks using the NMI metric. p-values for comparing the stage-specific networks were then calculated from the histogram of the 1000 NMI values.Normalized Mutual Information (NMI) metric was utilized to compare communities of different stages at a topological level . NMI is Jaccard index (JI), the ratio of the count of common genes to the count of union of genes in two groups, was used to identify pairs of communities which were similar to each other in terms of genes common between them. The most similar communities were then compared at a functional level. We used Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment available via DAVID version 6.8 , 24 for p-values for edge-based enrichment were computed using a hypergeometric test for edges (gene pairs) [d DEGs, the total number of edges, N, is calculated as d(d - 1)/2. Similarly, for a given KEGG pathway with dKEGG enriched genes (from our master list of d DEGs), mKEGG edges are calculated (mKEGG = dKEGG(dKEGG - 1)/2). Suppose a network contains n edges, of which k edges are between dKEGG genes of the given KEGG pathway, then a p-value for the edge-based enrichment of this pathway is calculated from a hypergeometric distribution as:e pairs)  which ack or more edges between pKEGG genes for the given KEGG pathway [phyper with 4 parameters was used to calculate the edge-based p-value using phyper .Equation  pathway . The R fp-value) ≤ 0.05.The STEM algorithm , 16 see was utilDetection of the first neighbors of 10 key genes from STRING-db with two criteria: score threshold ≥ 0.4 and up to 20 connections between genes.Identification of unique genes from the union of 10 key genes and their first neighbors found in STRING-db.Extraction of subnetworks of the unique genes from each stage-specific network and their visualization using Cytoscape . |PCC| bWe identified the interacting proteins of 10 key genes with known roles in CRC using STRING-db . A subneApproved drugs and their target genes for CRC were identified from National Cancer Institute (NCI)  and Drugp-value ≤ 0.05 resulting in 15,634 DEGs between stage I and normal, 528 DEGs between stages II and I, 745 DEGs between stages III and II, and 503 DEGs between stages IV and III. The union of all DEGs  was considered as the master list of DEGs for all downstream analysis.The CRC dataset used here contained 41,834 probe IDs across 128 samples after pre-processing see . Outlierp-value, we identified the corresponding threshold for PCC. Unweighted, stage-specific and stage-unique networks were subsequently constructed using the 16,062 DEGs [PCC was calculated for all pairs of DEGs to construct the networks see . For eac062 DEGs . Table 1In the following section, we compare communities detected within stage-specific networks at the topological and functional levels. The NMI metric was used to compare networks at a topological level. Highly similar networks  were further analyzed at a functional level. KEGG pathway enrichment analysis was used to assess functional similarity of the communities detected.p-value of NMI between communities of the stages I- and II-specific networks was 0.001 and between communities of the stages II- and III-specific networks was 0.05).Using the NMI metric we evaluated the similarity between networks. Table p-value ≤ 0.01 and with more than 10 genes from the third community of stage I. For some pathways such as Ras signaling pathway, the number of enriched genes and the p-values were similar between the third community of stage I and the first community of stage II but did not meet the threshold for the corresponding communities of other stages.We next used JI values for direct topological comparison of individual communities across stage-specific networks. The higher the value of JI, the more similar were the communities being compared. For example, JI values for comparing the communities of stage I-specific network with the communities of other stages is shown in Fig. Analyzing each community individually can leave out important functions due to the distribution of functionally related genes between them. Hence, we carried out an edge-based functional analysis see  at the wSome of the edges were common among two or more stage-specific networks. To identify edges unique to each stage, we constructed stage-unique networks See . We idenp-values and performed an enrichment for the 24 pathways for the stage-specific  and carried out a supervised analysis. A list of all pathways enriched for the master list of genes can be found in Additional file CCND1 and CDK6 were two genes with high connectivity (degree) in normal only. CCND1 is a proto-oncogene which is known to play a critical role in promoting the G1-to-S transition of the cell cycle in many cell types [CCNE1, also a proto-oncogene, displayed high degree of connectivity in stages II and III which was not present in normal. CCNE1 serves as a positive regulator of cell cycle and promotes G1-to-S phase transition by activating CDK2 [CDK2 also showed high degree of connectivity in stage III, although it was not present in normal.Among the 24 cancer-related pathways, we observed that central carbon metabolism pathway was enriched across stages II and III and is known to play a role in cancer progression . Cell cyll types . Likewising CDK2 , 40. CDKFAK or PTK2) is a major integrin-dependent tyrosine phosphorylated protein in this pathway and known to contribute significantly to inflammatory signaling pathways. PTK2 has been suggested to be a potential target for CRC therapies [Focal adhesion pathway was more enriched in normal, stage II and stage III than in other stages. Focal adhesion kinase . The total number of genes used for the hypergeometric test was 24,136. The log2FC of genes identified as common between the COAD, READ and our dataset are also provided in Additional file To validate our result at the gene and pathway level, we analyzed the TCGA COAD-READ data available through GEPIA2 by identifying DEGs with q-value < 0.05 for COAD and READ cohorts, resulting in 16,438 DEGs common to both. Since GEPIA2 does not allow for stage-wise identification of DEGs, we calculated DEGs across all stages (104 samples) and normal (22 samples) at q-value < 0.05 within our dataset. A total of 16,641 DEGs were identified, of which 11,389 were common with the TCGA COAD-READ cohort. A hypergeometric test on the overlap indicated that the number of DEGs as common were statistically significant  for all 110 potential biomarkers were listed in Additional file PROCR, a stage I-specific biomarker and MLXIPL (ChREBP), a stage IV-specific biomarker, respectively. The trends for these two biomarkers identified were consistent with TCGA COAD-READ cohort results obtained through GEPIA2 . EGFR also serves as a drug target for Cetuximab and Panitumumab. BRAF, another key player in CRC was up-regulated across all cancer stages compared to normal, yet had distinct connectivity patterns across different stages. BRAF was connected to TGFB3, TP53BP2, and SOS1 in the subnetwork of stage I. Although the stage II-specific network had more edges compared to stage I-specific network, BRAF was connected to only one gene, YWHAG, in the subnetwork of stage II. The chemotherapy drug for CRC, Regorafenib, targets BRAF and modulates the activity of its protein.These subnetworks all showed differences in connectivity patterns for key genes. For example,  therapy . We idenFinally, we sought to understand the functional mechanisms for some of the current drugs used in CRC treatment in the context of our current analysis and identify if any temporal variation in gene-expression of the drug-targets may indicate stage-specificity of the drugs.We identified 14 FDA-approved drugs for CRC from the NCI website and 32 target genes (included in the master list of DEGs) for these 14 drugs from the DrugBank website . There wVEGFR1/2/3, FGFR1/2), proliferation , tumor microenvironment and metastasis [CSF-1R, important for macrophage differentiation and survival [MAPK11 and RET were both down-regulated and had greater weights in early stages. MAPK11 is a member of protein kinases family involved in several cellular processes, including cell proliferation or differentiation. It was also enriched for MAPK and VEGF signaling pathways. RET, as a member of the tyrosine protein kinases family, has been identified as a novel tumor suppressor gene in the colon which can reduce apoptosis and is considered as a target for CRC treatment [FLT1 and DDR2. FLT1, a member of the vascular endothelial growth factor receptor (VEGFR) family, was up-regulated in CRC and strongly connected (PPI edge) to three ligands, namely, VEGFA, VEGFB and PGF [DDR2, down-regulated in CRC, is considered a critical regulator of cancer invasion and an attractive therapeutic target in metastatic CRC (mCRC) [Several targets of Regorafenib, a popular CRC drug, were found to be differentially regulated within our networks Fig. . Studiestastasis , 63. It survival . Out of reatment , 66. The and PGF . DDR2, dC (mCRC) .VEGFA and VEGFB are targets of the drug Ziv-Aflibercept, and participated in Ras and PI3K-Akt signaling pathways with known roles in CRC progression. VEGFA had larger weights in stage I whereas VEGFB had larger weights in stage II. TYMS (part of the Folate‐mediated one‐carbon metabolism pathway) is a crucial player of DNA methylation and repair and a critical target for Fluorouracil Injection (5-FU) drug, used in CRC treatment [TYMS is highly expressed in patients with CRC and might be used as a predictor for efficacy of chemotherapy [TOP1 and TOP1MT, both up-regulated in CRC, had also greater weight in stage III and were targets for Irinotecan Hydrochloride which is one of the key drugs for the treatment of mCRC [Two up-regulated and highly connected genes in this network, reatment . Studiesotherapy . Its wei of mCRC .C1QB, a target for both of those drugs, was up-regulated with greater weights in stage III. Cetuximab blocks ligand-induced receptor signaling and modulates tumor-cell growth by binding to the extracellular domain of EGFR. Studies have also shown that Cetuximab improves overall survival and progression-free survival and preserves quality-of-life measures in CRC patients in whom other treatments have failed [VEGF, also has demonstrated improved overall survival for patients with mCRC [Besides Regorafenib, two other drugs, Cetuximab and Bevacizumab, commonly used in treating CRC also showed several targets enriched within our networks. e failed . Bevacizith mCRC .in-silico, using a large cohort database (TCGA COAD-READ). However, further experimental validation is required to decipher their pathology-associated mechanisms. Additionally, we were limited by the unequal number of patient samples across stages and lacked sufficient clinical metadata to support downstream survival analysis. Nevertheless, the modular-network-based approach presented in this work will be useful for understanding mechanisms for disease progression and may contribute to identifying potential targets for disease intervention. In addition, while digital sequencing data are more robust, this microarray analog gene expression data set has been used extensively and our quest was to explore topological network analyses to demonstrate the ability to obtain stage-specific biomarkers and mechanisms. We demonstrate the validity of our conclusions through extant results and additional analyses.The pathogenesis of CRC is yet to be fully understood. In this study we detected a few potential biomarkers which were further validated Louvain algorithm. Comparing communities of different networks at the topological and functional levels revealed that neighboring stages were more similar to each other than non-neighboring stages. We also carried out the functional analysis at the whole network level for the stage-specific and stage-unique networks by analyzing the enrichment of 24 cancer-related pathways across different stages. For the stage-specific networks, most of the pathways related to CRC such as PI3K-Akt and MAPK signaling pathways were enriched at all stages. However, stage-unique networks revealed functional differences across the stages. For example, MAPK signaling pathway was enriched across stages I-III and Notch signaling pathway (important for metastasis and tumor angiogenesis) was enriched in stages III and IV. We then identified key biomarkers to differentiate between CRC (any stage) and normal using STEM analysis. WNT2 and SFRP2 were two biomarkers validated by others in stool DNA and were over-expressed and under-expressed in CRC tissues, respectively. To incorporate legacy knowledge in our analysis, we performed a supervised analysis with 10 key genes related to CRC and their first neighbors based on STRING-db, across different stages. The subnetworks were analyzed to study the progression of cancer across stages. In particular, we identified that BRAF, a Ser/Thr kinase that activates MAP kinases, appeared in all subnetworks and was upregulated in stages I-IV as compared to normal. Its connectivity pattern changed across the subnetworks for normal and different stages of CRC. Finally, we constructed a Drug-Target-PPI network enabling us, in the light of present data, to understand the functional mechanisms for some of the current drugs for CRC treatment. We saw that the target gene weights changed across the four stages extensively. For example, TYMS, associated with folate-mediated one carbon metabolism and a target for some drugs such as Fluorouracil Injection (5-FU) and Capecitabine, was found to be upregulated in cancer stages with larger weights in stage III than in other stages.In this study, we utilized a published transcriptomic data from 128 patients at various stages of CRC to find modular mechanisms potentially causal for progression of CRC from normal to stages I-IV and to find stage-specific biomarkers. We constructed stage-specific networks and identified their communities using the Additional file 1: Supplementary tables.  An .xlsx file containing all supplementary tables listed in manuscript as individual sheets.Additional file 2: Supplementary figures. A .pdf file containing all supplementary figures referenced in manuscript.Additional file 3. Supplementary Methods [ Methods .


This study aimed to investigate the molecular mechanism of Tongfengding capsule (TFDC) in treating immune-inflammatory diseases of gouty arthritis (GA) and interleukin-1-beta , Online Mendelian Inheritance in Man (OMIM), and GeneCards databases. The TFDC-GA-potential targets interaction network was accomplished by the STRING database. The TFDC-active compound-potential target-GA network was constructed using Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were used to further explore the GA mechanism and therapeutic effects of TFDC. Quantitative real-time PCR (qPCR) was used to verify whether the TFDC inhibited IL-1β mRNA expression in GA by qPCR. Molecular docking results suggested that rutaecarpine was the most appropriate natural IL-1β inhibitor.  133 active compounds and 242 targets were screened from the TFDC, and 25 of the targets intersected with GA inflammatory targets, which were considered as potential therapeutic targets. Network pharmacological analysis showed that the TFDC active compounds such as quercetin, stigmasterol, betavulgarin, rutaecarpine, naringenin, dihydrochelerythrine, and dihydrosanguinarine had better correlation with GA inflammatory targets such as PTGS2, PTGS1, NOS2, SLC6A3, HTR3A, PPARG, MAPK14, RELA, MMP9, and MMP2. The immune-inflammatory signaling pathways of the active compounds for treating GA are IL-17 signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway, NF-kappa B signaling pathway, Toll-like receptor signaling pathway, HIF-1 signaling pathway, etc. The TFDC reduced IL-1β inhibitors development in GA. Our findings provide an essential role and bases for further immune-inflammatory studies on the molecular mechanisms of TFDC and IL-1 Repeavoidable . TherefoGentiana Macrophylla Pall (Qinjia), Phellodendri Chinrnsis Cortex (Huangbo), Corydalis Rhizoma (Yanhuosuo), Radix Paeoniae Rubra (Chishao), Cyathulae Radix (Chuanniuxi), Smilacis Glabrae Rhixoma (Tufuling), Plantaginis Semen (Cheqianzi), and Alisma Orientale (Sam.) Juz. (Zexie), is one of the Chinese patent medicines approved by the National Medical Products Administration (NMPA) of the People's Republic of China and listed in the Chinese Pharmacopoeia (2020) [α (TNF-α), and interleukin-6 (IL-6) in this pathway [Tongfengding capsule (TFDC), which contains 8 kinds of traditional Chinese medicine, including  pathway . Clinica pathway .β, a key bone marrow-derived proinflammatory cytokine, involves in a variety of autoimmune inflammatory responses and cellular activities including cell proliferation, differentiation, and apoptosis [β is excessively produced, it can cause atherosclerosis, rheumatoid arthritis, gout, Parkinson's disease, Alzheimer's disease, tumors, and other diseases [β plays a key role in the occurrence and development of GA [β, which in turn promotes the influx of neutrophils into the joint to trigger joint inflammation [β [β also has been implicated in bone destruction and bone erosion in GA [β signaling pathway, and that curcumin can suppress the expression of Nod-like receptor 3 (NLRP3) inflammasome to downregulate the level of IL-1β via inhibiting the activation of nuclear factor-kappa B (NF-κB) signaling pathway in monosodium urate (MSU)-induced GA [β in GA with better efficacy and fewer side effects.IL-1poptosis . When ILdiseases , 13. In nt of GA . Urate dammation –18. Moreation [β . IL-1β aon in GA . In viewduced GA , 20. Anyβ, an important target in the pathological mechanism of GA, was verified by the experiment. Finally, the optimal active compounds of TFDC docked with IL-1β were analyzed by molecular docking technology in order to explore and develop novel optimum compounds that could inhibit IL-1β in GA. Taken together, this study aimed to provide theoretical basis and more research ideas for the molecular mechanism of TFDC against GA and discover more natural compounds as IL-1β inhibitors that can be used to better treat GA with fewer side effects. The workflow is shown in Network pharmacology, originally proposed by Andrew L. Hopkins in 2007, mainly studies the complex and diverse relationships among drugs, targets, diseases, and pathways, providing new methods for the development of new drugs and new ideas for the treatment of some diseases –23. Molehttps://tcmspw.com/index.php) [https://www.uniprot.org/) [Homo sapiens”.The Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform   was usedot.org/)  was usedhttps://www.genecards.org/) [https://omim.org/search/advanced/geneMap) [https://bioinfogp.cnb.csic.es/tools/venny/) intersection.The GA-related targets were collected from GeneCards (ds.org/)  and OMIMgeneMap)  with thehttps://string-db.org/) [Homo sapiens” and a confidence score of >0.9. Then, the PPI network of TFDC-GA-potential targets was imported into Cytoscape software (version 3.8.0) [The TFDC-GA-potential targets were processed by the STRING database (db.org/)  with then 3.8.0)  for netwTo accurately elicit interactions between active compounds and its corresponding GA-related targets of TFDC, a visual network was established through Cytoscape software. Furthermore, the network topology parameters were applied to select the key compounds and targets.P < 0.05.The packages “org.Hs.eg.db”, “DOSE”, “enrichplot”, “clusterProfiler” , and “ggμg/mL streptomycin . They were maintained in an incubator at 37°C with 5% CO2 and saturated humidity, and the culture medium was replaced with complete culture medium every 2 or 3 days. The cells were treated with phorbol-12-myristate-13-acetate (PMA) (100 nM) for 3 h to induce their differentiation into resting M0 macrophage. A fresh complete culture medium was added and cultured for 24 h after washing the cells with phosphate-buffered saline (PBS). Differentiated THP-1 cells were then stimulated with MSU  with optimal concentration and treated with TFDC at final concentrations of 25, 50, 75, 100, and 125 μg/mL as well as colchicine  simultaneously at 37°C for 24 h [Human monocytic leukemia THP-1 cell was purchased from Procell Life Science & Technology Co., Ltd . Cells were cultured in RPMI-1640 medium supplemented with 10% fetal bovine serum , 100  U/mL penicillin, and 100 for 24 h .6 cells/well with a final volume of 100 μL. After treatment, 10 μL of CCK-8 was added to each well, and the plates were then incubated for additional 2 h. The absorbance at 450 nm was measured using a microplate reader. The experiments were performed in quadruplicate and repeated at least three times.THP-1 cells were harvested during the logarithmic growth phase and seeded into a 96-well plate at a density of 1 × 10β and β-actin primers are, respectively, as follow: Forward: CTCGCCAGTGAAATGATGGCT, Reverse: GTGGTGGTCGGAGATTCGTAG; and Forward: TGGCACCCAGCACAATGAA, Reverse: CTAAGTCATAGTCCGCCTAGAAGCA. The relative quantifications of genes expression were calculated using the 2−ΔΔCq method with β-actin served as a normalization control.Total RNA was extracted from cells in 12-well plates, and cDNA was prepared according to the manufacturer's protocol. qPCR was amplified and measured on the BIO-RAD CFX96TM  in the presence of SYBR Green. Then, the fluorescence values were collected, and a melting curve analysis was performed. The experiments were conducted at least three times. The sequences of IL-1β receptor. Before performing the docking progress, the 2D structures of active compounds were acquired from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) [β receptor was acquired from the RCSB PDB database (https://www.rcsb.org/) [Active compounds selected from the TFDC-active compound-potential target-GA network were molecularly docked with IL-1ih.gov/)  and optisb.org/) . Then, AP < 0.05 was considered statistically significant. All data were processed using GraphPad Prism 8.3.0 software.Data were expressed as the mean ± standard deviation (SD). Intergroup differences were determined using one-way ANOVA followed by Tukey's comparison tests. Based on the criteria of OB ≥ 30% and DL ≥ 0.18, a total of 155 (after removing duplication: 133) eligible active compounds of TFDC were screened, of which 9 were from Cheqianzi (CQZ), 29 were from Chishao (CS), 4 were from Chuanniuxi (CNX), 37 were from Huangbo (HB), 2 were from Qinjia (QJ), 15 were from Tufuling (TFL), 49 were from Yanhuosuo (YHS), and 10 were from Zexie (ZX). The characteristics of all the screened out eligible active compounds in the TFDC are shown in β), prostaglandin G/H synthase 1 (PTGS1), peroxisome proliferator activated receptor gamma (PPARG), transcription factor p65 (RELA), and matrix metalloproteinase-9 (MMP9). Among them, IL-1β is at the core because it is most associated with other targets in the PPI network, which indicates that IL-1β plays a significant role in the occurrence and development of GA.A total of 207 GA-related targets were obtained from GeneCards and OMIM databases. After removing duplicate targets, 191 GA-related targets in total were collected at last. Similarly, 242 unrepeated targets of 133 active compounds in the TFDC were obtained from TCMSP database. The Venny 2.1 platform was used to intersect the GA-related and TFDC-related targets. 25 potential therapeutic targets were obtained  Table 2Table 2. The potential targets and their corresponding eligible active compounds were entered into the Cytoscape software to obtain a TFDC-active compound-potential target-GA network . A totalκB signaling pathway, Toll-like receptor signaling pathway, etc.  terms, 95 molecular function (MF) terms, and 14 cellular component (CC) terms. The results of GO enrichment analysis mainly include cellular response to lipopolysaccharide, cellular response to molecule of bacterial origin, membrane raft, membrane microdomain, heme binding, tetrapyrrole binding, etc. . Additioay, etc. . What's μg/mL had little effect on the viabilities of THP-1 cells (P > 0.05). THP-1 cells stimulated with MSU (50 and 100 μg/mL) showed insignificant differences compared with the control group (P > 0.05), but that with 200 μg/mL MSU obviously decreased the viabilities of THP-1 cell (P < 0.01). Based on the above results, 200 μg/mL was the optimum induction dosage in further experiments. For anti-inflammatory activity, MSU-induced THP-1 cells treated with TFDC among 25, 50, 75, 100, and 125 μg/mL exhibited different degrees of protection  (β mRNA expressions in the colchicine group and the TFDC group were separately apparently decreased (P < 0.001)  . Compare< 0.001) . The resβ to varying degrees , NOS2, MAPK14, MMP9, MMP2, and activation of PPARG could improve inflammation in GA models, suggesting that these targets had potential importance in the pathogenesis of GA [In addition, in this study, the analysis results of the TFDC-active compound-potential target-GA network also found that these seven active compounds quercetin, stigmasterol, betavulgarin, rutaecarpine, naringenin, dihydrochelerythrine, and dihydrosanguinarine and these 10 targets PTGS2, PTGS1, NOS2, SLC6A3, HTR3A, PPARG, MAPK14, RELA, MMP9, and MMP2 played important roles in treating GA, which are worthy of further research in the future. Studies found that quercetin had a significant joint protective effect in gouty arthritis and, in the mouse model of gouty arthritis induced by monosodium urate crystals (MSU), exerted analgesic and anti-inflammatory effects in a naloxone-sensitive manner , 42. In is of GA –51.κB signaling pathway, Toll-like receptor signaling pathway, etc. These biological functions and signaling pathways were related to the occurrence and development of GA, which might be the mechanism of TFDC in the treatment of GA. In particular, IL-17 signaling pathway and TNF signaling pathway played an extremely vital role in exploring the therapeutic mechanisms obtained by the analysis. IL-17, an important proinflammatory cytokine associated with several autoimmune diseases, significantly increased in the serum of patients with GA in the early stage of acute exacerbations, positively correlated with disease activity, and also correlated with the IL-1β level in serum [α is a member of the TNF family and participates in systemic inflammation. It was confirmed that IL-1β, TNF-α, IL-6, interleukin-8 (IL-8), and interleukin-4 (IL-4) concentrations in the serum of GA patients increased significantly, which might be related to the pathogenesis of GA [β, TNF-α, and IL-6 [α could promote the secretion of IL-1β in MSU-induced human neutrophils [κB signaling pathway, Toll-like receptor signaling pathway, and their representative targets such as NOD-, LRR-, and pyrin domain-containing protein 3 (NLRP3) inflammasome, Toll-like receptor-4 (TLR4), Toll-like receptor-2 (TLR2), IKKα, IκBα, and NF-κB, could effectively improve the joint inflammatory response of the GA model [GO enrichment analysis of potential therapeutic targets revealed that they mainly involved biological functions such as cellular response to lipopolysaccharide, cellular response to molecule of bacterial origin, membrane raft, membrane microdomain, heme binding, and tetrapyrrole binding. Meanwhile, the main signaling pathways enriched for potential therapeutic targets were IL-17 signaling pathway, TNF signaling pathway, NOD-like receptor signaling pathway, NF-in serum . In the in serum . The GA in serum . TNF-α iis of GA . It was and IL-6 . And TNFtrophils . BesidesGA model , 58–61. β in GA. In addition, rutaecarpine, one of the active compounds in TFDC, had the best binding activity with IL-1β by the molecular docking method, which could be investigated as the most suitable natural IL-1β inhibitor. These findings have provided new clues and ideas for further research on the molecular biological mechanism of TFDC in the treatment of GA and offered a practical and reliable theoretical basis for the clinical treatment of GA by TFDC.In conclusion, the TFDC has obvious advantages and significant efficacy in the treatment of GA, which is consistent with the published results of relevant clinical and experimental studies. In this study, the network pharmacology method was used to investigate the biological functions and signaling pathways of the targets of TFDC's active compounds in the treatment of GA. Simultaneously, the experiment clearly verified that the TFDC could significantly inhibit the key target IL-1


This study used network pharmacology and molecular docking technology to elucidate the mechanism of action of Shishiwei Wendan Decoction against lung adenocarcinoma.  By using the world's largest TCM System Pharmacology Database and Analysis Technology Platform (TCMSP) system to conduct in-depth mining analysis and data collection of the main active components of the medicinal components in Shishiwei Wendan Decoction and using the human gene card database (GeneCards), Human Mendelian Inheritance Online System (OMIM), and Human Disease-Related Gene and Mutation Information Database (DisGeNET) to collect the pathogenic targets of lung adenocarcinoma and build a PPI network; for the core drug targets, use GO enrichment analysis and KEGG pathway analysis; use Cytoscape software to build relevant network maps; and use AutoDock to achieve molecular docking.  Shishiwei Wendan Decoction screened 144 active ingredients and 384 drug targets; 7680 lung adenocarcinoma disease targets were obtained, including 380 targets for Shishiwei Wendan Decoction in the treatment of lung adenocarcinoma. GO enrichment analysis demonstrated 2,299 downstream genes, and key target genes were closely related to nutrient levels, membrane rafts, and protein serine/threonine kinase activity; KEGG functional enrichment analysis yielded 179 related pathways, including tumor necrosis factor signaling pathway which is related to the target gene. Molecular docking showed that the core active ingredients and key targets could be well combined.  Through the network pharmacology analysis and molecular docking experiments of Shishiwei Wendan Decoction against lung adenocarcinoma, it is found that Shishiwei Wendan Decoction has multidimensional effects on the treatment of lung adenocarcinoma, and it is the first Shiwei Wendan Decoction to treat lung adenocarcinoma. Decoction in the treatment of lung adenocarcinoma provides biointellectual support and the oretical support. Lung cancer is now one of the malignant tumors with the highest mortality rate in the world. According to the statistical results released by the World Natural Health Fund and the Global Cancer Research Institute by 2020, the incidence rate ranks second . Accordi“Three Causes Extreme Disease Syndrome Volume Nine Deficiency Syndrome Treatment” is the name of the book. recorded Shisiwei Wendan decoction: “after the treatment of serious illness, virtual annoyance can not sleep, this gallbladder cold death, this medicine is the main.” This is inherited from Wendan decoction, which is the inheritance of Wendan decoction. Although Wendan decoction is called “warming gallbladder,” but in fact, the treatment focuses on clearing gallbladder and stomach, and its symptoms are more obvious in Zhongjiao, so the most important drugs are regulating qi and resolving phlegm and clearing gallbladder and stomach. Pinellia pinellia is the core medicine of Shiniwei Wendan Decoction; it eliminates dampness and phlegm and treats tumor diseases, etc. Bamboo Ru and Pinellia ternata match, not only resolving phlegm and stomach but also clearing gallbladder heat, making gallbladder and stomach harmonious; Chen Pi Liqi Huatan invigorates spleen dryness and dampness; Fructus Aurantii breaks qi accumulation, resolves phlegm, and dispels ruffian; Poria cocos invigorates the spleen and tranquilizes the heart; licorice Yiqi and middle reconcile various medicines. All kinds of medicines are used together to regulate qi and resolve phlegm, clear gallbladder and stomach, eliminate annoyance, and stop vomiting. Shisiwei Wendan decoction is developed by Professor Zhu Jinzhong on the basis of Wendan decoction and combined with his own clinical experience for many years. In clinical application, it can significantly reduce cough and pain in patients with lung adenocarcinoma. In view of the complex active ingredients in this compound, the current mechanism of Shishiwei Wendan Decoction in the treatment of lung adenocarcinoma is still unclear, and this study was aimed at clarifying the molecular mechanism of its treatment of lung adenocarcinoma.Shishiwei Wendan Decoction is made by Professor Zhu Jinzhong based on Wendan Decoction and combined with his many years of clinical experience. The functions of Ophiopogon japonicus, Solanum lyratum, Acorus tatarinowii Schott, Polygala Radix, Rehmannia glutinosa, and Salvia chinensis  are sootNetwork pharmacology is an emerging discipline in recent years. It is a systematic analysis method based on the interaction of diseases, drugs, active ingredients, target genes, and target proteins. The complex synergistic functions and associations also coincide with the notable features of the holistic treatment of modern Chinese medicine and the systemic synergistic treatment of drugs in the treatment of disease processes , which chttp://tcmspw.com/tcmsp.php), set the oral bioavailability threshold (OB) parameter in the database to ≥30%, the drug similarity threshold (DL) parameter to ≥0.18, and the remaining parameters set as the default value, by adjusting the ADME parameter value to achieve preliminary quantitative screening of the relevant active ingredients of the drug.Using the TCMSP database (http://www.swisstargetprediction.ch) to predict relevant anticancer targets. The species parameter was set to “Homo sapiens” to assess the reliability of the predicted results with a confidence threshold.The drug active ingredients obtained in 1.1 were imported into Swiss Target Prediction (https://www.GeneCards.org), the human Mendelian genetic code database , and human disease-related genes. The corresponding disease target sites were screened in the mutation information database . At the same time, the duplicate disease targets obtained by screening were deleted.Setting the keyword to “lung adenocarcinoma” can be obtained from the GeneCards database (https://string-db.org) in turn, and the species was set as “Homo sapiens,” and the confidence was set as >0.7, the main targets were screened, and the protein-protein interaction (PPI) network of the target proteins of Shishiwei Wendan Decoction against lung adenocarcinoma was constructed. Two-dimensional planarization was performed using Cytoscape (version 3.8.2).The fourteen Wendan Decoctions obtained in 1.3 were entered into the STRING database  enrichment analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG), to find the key to the module. For the common functions and related pathways of a large number of genes in the gene set, the screening conditions were set as Cytoscape (version 3.8.2) software was used to construct a “drug-active ingredient-target-disease network diagram” and a “key target-function regulation network diagram.”https://pubchem.ncbi.nlm.nih.gov/) database. Second, download the corresponding crystal structures in the PDB (https://www1.rcsb.org/) database. Then, use AutoDock Vina software to complete the molecular docking experiment. Finally, the interaction between Shishiwei Wendan Decoction and key target proteins was visualized using PyMOL and finally displayed in a two-dimensional plane.The top 5 core active ingredients were calculated by the degree value. First, the 3D Conformer structure of citromitin was downloaded from the PubChem  was used to intersect the drug targets obtained above and lung adenocarcinoma (LUAD) disease targets, and 284 key targets were obtained as a result, as shown in In this study, GO functional annotation was performed on the 284 key target genes described in 3.4, and the biological significance represented by each gene was mined.Bar graphs were drawn using the R language “enrichplot” (version 1.10.2) to display the GO functional enrichment results . The resThe ordinate represents the enriched GO term, the length of the bar represents the number of key targets enriched by the GO term, and the color from blue to red represents the reliability of the result from low to high.In this study, the enrichment analysis of 284 key target genes was carried out with the help of the KEGG database, and 179 related pathways were obtained, and the R language “enrichplot” (version 1.10.2) was used to draw a bubble chart to display the top 15 pathways . It can The bubble volume represents the number of pathway genes, and the color from light blue to red indicates the reliability of the result from low to high.We put forward the corresponding key targets of the GO_BP, GO_CC, and GO_MF top 10 terms  and constructed a key target-function regulatory network diagram and used Cytoscape (version 3.8.2). For visualization, We proposed the KEGG top 20 pathways and their corresponding key targets, constructed a key target-pathway regulatory network diagram, and used Cytoscape (version 3.8.2) to visualize . The nethttps://string-db.org) to construct a PPI network for 284 key targets. The confidence level is 0.7, and the discrete proteins are removed to obtain the interaction network relationship of 349 proteins, including 349 nodes and 4062 edges, as shown in To explore whether there is an interaction between key targets, this study used STRING . For visualization, as shown in https://www1.rcsb.org/) database, and the original small molecules and water molecules were removed (1.5.6). Complete protein hydrogenation and calculate charge. Active ingredient structures were downloaded from the PubChem (https://pubchem.ncbi.nlm.nih.gov/) database and charge-balanced, rotatable bond checks were performed on small molecules using AutoDock Tools. Then, the extent of the docking box is selected according to the receptor active center. Finally, use AutoDock Vina to calculate receptor and ligand docking, and select the structure with the lowest binding free energy (highest binding affinity) in the output. Finally, use PyMOL (version 2.5) software for visualization and beautification. We selected top 5, the core gene of the PPI network, and found the targeted active molecules of these genes in the TCM pharmacological network (the key targets with multiple active molecules are selected according to the comprehensive selection of OB and DL). docking.First, the protein structures of key targets were obtained from the PDB /Akt signaling pathway, the upstream pathway of HIF-1, is involved in the regulation of angiogenesis, proliferation, invasion, metastasis, and apoptosis of general tumor cells under hypoxic conditions. The prognosis of cancer patients is closely related . The stun HIF-1α . The MAPn HIF-1α . Stutvoeotherapy . The extotherapy .The research group found that the expression of PRKCZ-AS1 positively regulates the expression of MAPK1 by consulting the literature. Similarly, the expression of MAPK1 is negatively regulated by miR-766-5p expression. Moreover, the binding ability between miR-766-5p and MAPK1 was confirmed. In addition, the knockdown of MAPK1 partially saved the promoting effect of miR-766-5p inhibition on the proliferation and migration of PRKCZ-AS1-transfected lung adenocarcinoma cells. Overall, PRKCZ-AS1 promotes the development of lung adenocarcinoma by upregulating the expression of MAPK1 through spongy miR-766-5p . AKT is Then, using Cytoscape (version 3.8.2) to construct the drug-active ingredient key target regulatory network and PPI network, we further counted the degrees, and the top 5 key target genes were TP53, HSP90AA1, MAPK1, MAPK3, and AKT1. Finally, we extracted five key targets and their corresponding active molecules, downloaded protein crystal structures and small molecule 3D structures, and performed molecular docking experiments. The experimental results show that the binding energies of the core components and key targets in Shishiwei Wendan Decoction are all <-5 kcal·mol-1, indicating that there is a strong binding ability between the key target proteins and the drug-active small molecules.Based on the above analysis, we screened the key targets of all drugs in Shishiwei Wendan Decoction for the treatment of lung adenocarcinoma using an online database. The purpose of this study was to clarify the molecular mechanism of Shishiwei Wendan Decoction in the treatment of lung adenocarcinoma and to provide a theoretical basis and reference value for Shishiwei Wendan Decoction in the treatment of lung adenocarcinoma.


This is the first study to explore the active components and potential mechanisms of Rosa roxburghii Tratt fruit for treating T2DM based on UPLC-Q-Exactive Orbitrap/MS and network pharmacology.Type 2 diabetes mellitus (T2DM) is a global disease with growing prevalence that is difficult to cureRosa roxburghii Tratt fruit were obtained from UPLC-Q-Exactive Orbitrap/MS analysis and retrieval in the SciFinder, PubMed, Web of Science, and CNKI databases. The potential targets of the active components were obtained from the SwissTargetPrediction and PharmMapper databases. The disease targets for T2DM were obtained from GeneCards, OMIM, TTD, DisGENent, and GEO databases. The intersection of the two datasets was used to obtain the potential targets of Rosa roxburghii Tratt fruit against T2DM. The target protein interaction network was constructed using the String database and Cytoscape software. The R software ClusterProfiler package was used for target enrichment analysis and the Cytoscape CytoNCA plug-in was used to screen core targets. Molecular docking and result visualization were performed using PyMOL and Autodock Vina software.The active components of Rosa roxburghii Tratt fruit were active against T2DM-related therapeutic targets. Rosa roxburghii Tratt fruit may play a therapeutic role in T2DM by regulating the PI3K/AKT, RAS, AGE-RAGE, and other signaling pathways.We obtained 20 bioactive ingredients, including alphitolic acid, quercetin, and ellagic acid, as well as 13 core targets, such as AKT1, TNF, SRC, and VEGFA. All bioactive ingredients in Rosa roxburghii Tratt fruit in the treatment of T2DM, laying the foundation for a further experimental study based on pharmacodynamic substances and their mechanisms of action.This study explored the active components and potential mechanisms of The online version contains supplementary material available at 10.1186/s13020-023-00713-z. Type 2 diabetes mellitus(T2DM), a critical risk factor for cerebral infarction, cardiovascular disease, blindness, and kidney failure, is caused by the joint influence of lifestyle and genetic components . At presRosa roxburghii Tratt (Family: Rosaceae) is a shrub indigenous to Guizhou province in southwest China. It is an edible and medicinal plant named “Cili” in Chinese. The initial record of this plant can be traced back to the ancient book “Gui Zhou Tong Zhi”  −). Compound 58 was tentatively identified as alphitolic acid, and the mass fragmentation pathway corresponding to those fragment ions is proposed in Additional file Seven compound families were identified: triterpenoids, flavonoids, organic acids, amino acids, tannins, phenolic acids, and coumarins. Among them, triterpenoids and flavonoids were the primary chemical clusters for structural annotation. For triterpenoids, the frequently observed mass fragmentation pathways included cleavage of the glucosyl residue 162.05 Da), neutral losses of HCOOH (46.00 Da), H62.05 Da,1,2A−, 1,2B−, 1,3A−, 1,3B−, 1,4A−, 1,4B−, 0,4A−, and 0,4B− that were termed by following the ion nomenclature rule were observed frequently. There were also other neutral losses of H2O (18.01 Da), CO (27.99 Da), and CO2 (43.98 Da) according to the relative literature [m/z 289.07 [M − H]−, C15H14O6) yielded fragment ions at m/z 245.08 ([M − H − CO2]−), 203.05 ([M − H − CO2 − C2H2O]−), 271.06 ([M − H − H2O]−), 179.03 ([M − H − C6H2O2]−), 163.04 ([M − H − H2O − C6H2O2]−), 151.04 ([M − H − C7H5O3]−), 137.02 ([M − H − C8H8O3]−), 123.04 ([M − H − C7H5O3 − CO]−), 109.03 ([M − H − C9H8O4]−) [2, and H2O served as the main fragmentation channels. Therefore, compound 23 was tentatively identified as catechin by matching chromatographic and MS/MS information with an authentic compound. Similarly, the other flavonoids  were structurally annotated by applying the proposed mass cracking rules to their MS/MS spectra, and the plausible structures are summarized in Additional file Regarding flavonoids, those featured fragment ions such as terature . For insFor organic acids, amino acids, tannins, phenolic acids, and coumarin, a similar program was performed on tentative identification with relative literature , 36, 38.Rosa roxburghii Tratt fruit for further screening.We analyzed the results obtained from the database and UPLC-Q-Exactive Orbitrap/MS. Through UPLC-Q-Exactive Orbitrap/MS component identification, we obtained 30 components identical to those reported in the literature Fig. . To ensuRosa roxburghii Tratt fruit.Twenty candidates Fig.  were selP-value < 0.05 and log2 (fold change) > 1 or log2 (fold change) <  − 1, we identified 661 differentially expressed genes associated with T2DM. Among them, there were 299 up-regulated genes and 362 down-regulated genes that were used to construct volcano maps , which identified, the PI3K (phosphatidylinositol 3-kinase)/AKT (serine/threonine kinase) signaling pathway, cancers, viral infection, and AGE-RAGE signaling pathway in diabetic complications.There were 2643 enrichment GO terms with a We selected the first six target proteins  as receptor proteins for molecular docking with corresponding compounds and Glyburide as the control drug. The results of molecular docking are shown on the heat map Fig. . The topRosa roxburghii Tratt has potential antidiabetic effects, but its material basis and mechanism of action remain to be identified. Therefore, using bioinformatics analysis including network pharmacology and molecular docking are needed to explore the molecular mechanism of Rosa roxburghii Tratt fruit in the treatment of T2DM. It is beneficial to explain the modern scientific basis of Rosa roxburghii Tratt fruit in the treatment of T2DM.T2DM is one of the most prevalent and serious metabolic diseases in the world. Insulin resistance in T2DM is associated with both hyperglycemia and hyperlipidemia, which are associated with an imbalance between endocrine pancreatic function and hepatic and extrahepatic insulin sensitivity. In recent years, traditional Chinese medicines and extracts have had unique advantages in the treatment of T2DM. Modern pharmacological studies have also confirmed that Rosa roxburghii Tratt fruit has a strong pertinence in treating T2DM. In addition, many of these compounds had common targets, suggesting that different compounds may provide synergistic effects. The identified compounds were mainly triterpenoids and flavonoids. The flavonoids of Rosa roxburghii Tratt can significantly reduce serum glucose and triglyceride and increase serum insulin in experimental hyperglycemic mice induced by alloxan. The extract increased superoxide dismutase (SOD) and catalase (CAT) activities and decreased malondialdehyde (MDA) [Rosa roxburghii Tratt had favorable inhibitory effects against α-glucosidase activities [Rosa roxburghii Tratt exhibit good hypoglycemic effects by activating the P13K/AKT signaling pathway and regulating the expression of the Forkhead box protein O1 (FOXO1), and glycogen synthase kinase-3 beta (GSK-3β) proteins, controlling liver gluconeogenesis and improving insulin resistance to hepatic glycogen storage, to alleviate symptoms of T2DM [By constructing the compound-target network, we found that all 20 active compounds had interactive effects with multiple targets, indicating that de (MDA) . Furthertivities , 41. Pol of T2DM .Rosa roxburghii Tratt fruit for T2DM treatment. Among the top six core genes, the first AKT1 is one of the three members of the AKT family, AKT1/PKBα, AKT2/PKBβ, and AKT3/PKBγ [. AKT1 is involved in many processes, including metabolism, proliferation, cell activity, growth, and angiogenesis, and is responsible for regulating the body's glucose uptake by mediating insulin-induced transport of the SLC2A4/GLUT4 glucose transporter to the cell surface and plays an important role in maintaining blood sugar stability [Thirteen targets, including AKT1, TNF, SRC, VEGFA, EGFR, CASP3, MAPK3, STAT3, PPARG, ESR1, MTOR, PTGS2, and MAPK1, were proposed as the possible key targets of KT3/PKBγ . AKT1 istability . Tumor ntability .Steroid receptor coactivator (SRC) is a nonreceptor tyrosine kinase that is associated with the cell membrane and plays a key role in various signal transduction pathways. Inhibition of SRC (c-Src) activation was found to decrease endogenous ROS production and increase ATP production in diabetic mice with hyperlipidemia . Vasculaβ-cell apoptosis, and apoptosis of β-cells can lead to decreased insulin secretion, which in turn leads to the appearance of T2DM [The last two of the first six key genes are EGFR and CASP3. EGFR is a transmembrane tyrosine kinase receptor belonging to the ErbB family . The epi of T2DM .Rosa roxburghii Tratt fruit mainly regulates signaling pathways including the PI3K/AKT signaling pathway, the RAS signaling pathway and the AGE-RAGE signaling pathway for the treatment of T2DM. The PI3K/AKT signaling pathway is one of the key pathways for insulin to regulate blood glucose levels. The phosphorylation of insulin receptors leads to the activation of PI3K, subsequently recruiting signaling proteins that include AKT and its downstream mediators. All are critical steps for stimulating insulin-induced glucose transport [In addition, the enrichment analysis of the KEGG pathway, the results showed that ransport . In the ransport .Molecular docking was further applied to verify the interaction between compounds and targets. The combination with the lower binding energy scores is more stable, and the binding energy ≤  − 5.0 kcal/mol was defined as the standard of well binding between ligands and receptors in some studies. As shown in Fig. Rosa roxburghii Tratt fruit, so we use mass spectrometry to make up for the total amount of selected and evaluated compounds. Because the components of Rosa roxburghii Tratt are not clear and commercial, it is difficult to carry out experimental verification temporarily. At present, there are many varieties and origins of Rosa roxburghii Tratt with unclear quality markers and uneven quality, so we will try our best to establish quality standards for Rosa roxburghii Tratt in the future.However, there are some limitations in this work. Less references and databases were collected to predict the active components and potential mechanisms of Rosa roxburghii Tratt fruit against T2DM by UPLC-Q-Exactive Orbitrap/MS and network pharmacology. We finally obtained 20 key compounds and identified 214 potential targets active against T2DM, among which AKT1, TNF, SRC, VEGFA, EGFR, CASP3, MAPK3, STAT3, PPARG, ESR1, MTOR, PTGS2, and MAPK1 were the key targets. The mechanism of Rosa roxburghii Tratt fruit treatment of T2DM involves a complex network with multiple targets and pathways. Rosa roxburghii Tratt fruit can play a therapeutic role in T2DM by regulating pathways including the PI3K/AKT signaling pathway, the RAS signaling pathway, the AGE-RAGE signaling pathway and resistance to the EGFR tyrosine kinase inhibitor. Although the present results are found to be consistent with some reports, indicating the rationality and accuracy of our method, further experiments in vivo and in vitro are still needed to validate these predictions. At present, most research are performed to explore the function of fruit from Rosa roxburghii Tratt, as well as the product development of Rosa roxburghii Tratt mainly is focused on fruit juice, but there are few novel products to be developed in other product forms as functional food and dietary supplements. There are less academic research and R&D development on the bioactive ingredients of Rosa roxburghii Tratt, so the work are still needed to perform in-depth and systematic research and promote the further development and utilization of these edible and medicinal resources in future.This study explored the active components and potential mechanisms of Additional file 1: Table S1. Chromatographic and MS/MS spectral information for the constituents detected in the QC sample of Rosa roxburghii Tratt fruit using UPLC-Q-Exactive Orbitrap/MS. Figure S1. The MS2 spectrum of compound 58. Figure S2. Proposed mass fragmentation pathways in response to the primary fragment ion species of a representative triterpenoid Alphitolic acid. Figure S3. The MS2 spectrum of compound 23. Figure S4. Proposed mass fragmentation pathways in response to the primary fragment ion species of a representative flavonoid namely Catechin.


To explore the active compounds of the Chinese medicine prescriptions of Bushen Hugu Decoction (BHD) and demonstrate its mechanisms against malignant tumor bone metastasis (BM) through network pharmacology and molecular docking analysis. The main components and targets of BHD were retrieved from the TCMSP database, and the targets were normalized by UniProt. The Herbs-Components-Targets network of BHD was established by Cytoscape. The main BM targets were obtained from GeneCards, TTD, DrugBank, and OMIM. STRING and Cytoscape were used to construct a PPI network and obtain hub genes. DAVID and Metascape were used for GO and KEGG enrichment analyses. According to the network topology parameters, the top 4 components were selected for molecular docking verification with the core targets. p < 0.05), and 164 signaling pathways (p < 0.05) were found in KEGG, including PI3K-Akt signaling pathway, proteoglycans in cancer, prostate cancer, MAPK signaling pathway, and IL-17 signaling pathway. Molecular docking analysis showed that the active components of BHD, quercetin, luteolin, kaempferol, and aureusidin have good binding activity to the core targets.  Compound–target network of BHD mainly contained 51 compounds and 259 corresponding targets including 107 BHD-BM targets. PPI interaction network and subnetworks identified ten hub genes. GO enrichment analysis found 1970 terms ( The potential molecular target and signaling pathways were found for BHD major active components. It provides guidance for the future mechanism research of the BHD in malignant tumor bone metastasis. This study also established the foundation for the new strategy for the pharmacology study of Chinese medicine. Cancer incidence increased worldwide. Cancer, especially malignant tumor, ranks as the disease with the highest fatality rate after cardiovascular disease globally . Most maΒ ligand (RANKL). The RANK/RANKL/OPG axis is critical for the regulation of bone formation and resorption. Inhibition of RANKL may serve as an ideal therapeutic strategy for excessive bone resorption [The standard cares for bone metastases in cancer patients are bisphosphonates and denosumab. Bisphosphonates, the inhibitors of osteoclast activity, can inhibit tumor growth by blocking prenylation. However, clinical studies showed minimal efficiency on tumors due to limited uptake at tumoral sites . Denosumsorption . Howeversorption . Standarsorption , 15.Chinese medicine is a traditional treatment method original to China. A variety of Chinese medicine prescriptions, herbs, and extracts have been confirmed with the potential to treat malignant tumors by experimental studies and clinical trials . Combiniα signaling pathway [Cistanches Herba extract phenylethanol glycosides increase the sensitivity of liver cancer patients to oxaliplatin by inhibiting HIF-1 pathway .Cuscutae Semen, Dipsaci Radix, Drynariae Rhizoma, and Eucommiae Cortex can inhibit bone destruction through the immune system and inflammation-related pathways –24. In thttps://old.tcmsp-e.com/index.php) was used to authenticate chemical composition of various herbs in BHD. TCMSP consists of 499 Chinese herbs registered in the Chinese Pharmacopoeia, with 29,384 ingredients, 3,311 targets, and 837 associated diseases [https://www.uniprot.org). “Herbs-components-targets” network of BHD was constructed based on network topology parameters (NTP) through Cytoscape 3.7.2. The larger the node, the more targets associated with it, and the more likely it will be a key ingredient in BHD.Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform , OMIM database (http://www.omim.org), TTD database (http://bidd.nus.edu.sg/group/cjttd), and DrugBank database (https://www.Drugbank.ca) using “Bone Metastasis”, “Bone Metastases”, “Osseous Metastasis”, “Bone Metastasis of Malignant Tumor” and “Metastatic Carcinoma of Bone” as keywords. The targets were supplemented through literature search. Use Venn script in R language to get the intersection of the targets of BHD components and BM. BHD-BM genes were uploaded to STRING11.0 (https://string-db.org) to build a PPI interaction network. Initial screening of the BHD-BM gene by setting the degree score. The data were corrected by setting the biological species to 'Homo sapiens'. The PPI interaction network of BHD-BM genes was visualized with Cytoscape using 0.9 as the highest confidence level. Topological analysis of BHD-BM genes by Cytohubba in Cytoscape to obtain the hub genes [The bone metastases-related targets were identified in the GeneCards database (ub genes .https://david.ncifcrf.gov/) online analysis tool and Metascape platform (http://metascape.org/gp/index.html). A p < 0.05 was used to screen BHD-BM-related biological process (BP), molecular function (MF), and cellular component (CC). Then, the top ten results were chosen for visualization. Similarly, a p < 0.05 was used to screen KEGG pathway analysis. BHD compounds, BHD-BM genes, and top 20 model pathways were used in KEGG enrichment analysis. The NTP  was analyzed by the built-in tool of Cytoscape to build the BHD compound, BM target, and pathway network.GO and KEGG enrichment analyses of BHD-BM genes were performed using DAVID (https://pubchem.ncbi.nlm.nih.gov/) [http://www.RCSB.org/) [Molecular docking is a well-established computer-based structural method for structurally docking small molecules to target proteins and assessing their binding affinity . It is wih.gov/) . And savSB.org/) . Use AutSB.org/) .As shown in Database searching found 8,691 nonduplicated BM-related genes. By setting the degree score greater than the median three times, 1086 targets were finally obtained. Combined with the target screened for BHD active compounds in TCMSP dataset, 107 genes associated with both BHD and BM were considered to build the BHD-BM PPI interaction network . PPI netGO enrichment analysis found 1,970 terms. Multiple targets of BHD were closely related to BM. The major biological process enriched for BHD-BM has a response to an inorganic substance, positive regulation of cell death, response to cytokine, positive regulation of cell migration, and regulation of apoptotic signaling pathway. The molecular function related to BHD-BM has kinase binding, protein kinase binding, ubiquitin-like protein ligase binding, DNA-binding transcription factor binding, and cytokine receptor binding. BHD-BM-related cellular components have membrane raft, transcription regulator complex, vesicle lumen, serine/threonine protein kinase complex, and extracellular matrix . KEGG paAccording to the NTP analysis, the core compounds  A3 quer PTGS2, HBone is one of the common sites of cancer metastasis. The unique structure and the dynamic metabolism pattern in bone make bone metastasis a different mode of malignant tumor metastasis. The mechanism of BM is still not very clear yet.β1, PDGFs, etc. [The “seed and soil” theory for tumor metastasis proposed by Paget in 1889 believed that the spread of tumor cells is determined by the interaction between cancer cells (seed) and host organs (soil) . After mFs, etc. . TME incFs, etc. . Bone maFs, etc. . The croThe interaction between TME and BME involves multiple pathways as a comprehensive network. However, during the drug development process, it is very common to identify or design pharmacologically effective agents specifically targeting a single molecule or site. Drugs that act on a single molecular target often show unsatisfactory therapeutic effects or have large side effects when used to treat complex diseases in clinical. Therefore, drug development has gradually shifted from a “single target and single drug” model to a “network target multicomponent therapy” model , 41. MulIn this study, a total of 51 active components and 259 targets for BHD and 1086 BM targets were obtained. Combination of these two dataset, 107 BHD-BM intersection targets, was obtained finally. PPI interaction network analysis obtained the hub targets. GO and KEGG enrichment analyses showed that multiple targets of active components of BHD were closely related to BM. Membrane raft, transcription regulator complex, vesicle lumen, serine/threonine protein kinase complex, and extracellular matrix are the top cellular components found in GO and KEGG enrichment analyses. These cellular components are closely related to the formation of extracellular vesicles (EVs). EVs include exosomes, activation- or apoptosis-induced macrovesicles, and apoptotic bodies, which are key players in intercellular communication . EVs are+ helper T cells [KEGG enrichment analysis of BHD-BM showed that BHD compounds can affect BM through various pathways including regulation of apoptosis, immunity, inflammatory response, and treatment of primary tumors. A3 (quercetin), A2 (kaempferol), DR13 (aureusidin), DR9 (luteolin), A1 (beta-sitosterol), EC13 (syringetin), DR10 , and B2 (stigmasterol), the top 8 active components of BHD, were obtained after the bioinformatic analysis. Their combination to core targets was verified by molecular docking, which confirmed the multitarget and multichannel therapeutic effect of BHD on BM. PI3K-Akt signaling pathway was identified as one of the top signaling pathways in BHD-BM network. PI3K-Akt could participate in multiple cellular events to promote tumor initiation, progression, and metastasis. PI3K/Akt significantly increases bone metastases and osteolytic bone lesions in prostate cancer by mediating the stabilization of histone methyltransferase WHSC1 . TLRs ar T cells . Th17 ce T cells . By bloc T cells . All thiIn this study, we built the network between BHD compounds and BM target and screened and verified the in vivo combination of BHD components and their target employing network pharmacology and molecular docking analysis. Network pharmacology showed that quercetin and kaempferol were the top 2 core compounds of BHD.Quercetin and kaempferol, as natural bioflavonoids, are widely found in a large number of natural herbs and have broad-spectrum antitumor effects , 53.Animal and cellular experiments have shown that quercetin and kaempferol could downregulate AKT1, PTGS2, and VEGFA expressions –77 and uThis study established the foundation of the new strategy for the pharmacology study of Chinese medicine. This study shows that the main active components of BHD can participate in the regulation of tumor and bone microenvironment through extracellular vesicles and affect the occurrence and development of malignant tumor bone metastases through various mechanisms such as immunity, apoptosis, and inflammation. It provides a basis for the study of the molecular mechanism of BHD and provides a new option for the treatment of bone metastases.


PCBs and their metabolites may act on the key targets of MAPK3, MAPK1, RXRA, PIK3R1, and TP53. The toxic effect of sulfated and methyl sulfone PCBs is greater. The method of screening targets based on the simultaneous action of multiple PCBs can provide a reference for other research. The targets were not found in previous metabolite toxicity studies. It also provides a bridge for the toxic effects and experimental research of PCBs and their metabolites in the future.Polychlorinated biphenyls (PCBs) are persistent and highly toxic pollutants, which can accumulate in organisms and produce toxic effects, especially damaging the function of thyroid hormones. So far, the molecular mechanism of PCBs mixture and their metabolites interfering with thyroid hormones has not been studied thoroughly except for individual compounds. In this study, PubMed, Web of Science, and STITCH databases were used to search PCBs and their corresponding target proteins. The intersection of PCBs and thyroid hormone dysfunction target proteins was obtained from GeneCards. The “compounds-targets-pathways” network was constructed by Cytoscape software. And KEGG and Go analyses were performed for key targets. Finally, molecular docking was used to verify the binding effect. Four major active components, five key targets, and 10 kernel pathways were successfully screened by constructing the network. Functional enrichment analysis showed that the interference was mediated by cancer, proteoglycans, PI3K-Akt, thyroid hormone, and FoxO signaling pathways. The molecular docking results showed that the binding energies were less than -5 kcal·mol With the rapid development of the electronic products market in the world, the growth of electronic waste has become a global environmental health issue . The remAlthough they have been banned, they exist in soil, sediment, and biota as persistent organic pollutants (POP) . PCB expIt is important to note that the metabolites of PCBs in humans, including hydroxylated, sulfated, and methyl sulfone PCBs, are more toxic than their parent PCBs. These metabolites have harmful effects on reproductive processes, the endocrine system, and brain function in humans . ElectroPCBs prototypes and their metabolites exist in the form of mixtures in the environment. At present, the research on PCB toxicity mainly starts from monomers, but in reality, PCB exists in the form of a mixture, so it is necessary to use a holistic idea and method to explore the mechanism of PCBs homologs and metabolites interfering with thyroid hormone disorder, to evaluate the potential health risk.Network toxicology is a new discipline based on network theory and the principles of systems biology. It is considered to be an effective tool for systematically revealing complex network relationships. It can reveal the complex overall biological network relationships between “toxicity-targets-components-drug” and provide a new perspective for analyzing and predicting the functional mechanisms of components . PCBs hoMolecular docking is an important approach in structural molecular biology and computer-aided design of new drugs, which can further predict the recognition and interaction patterns between components and their predicted targets –19.To fully understand the toxicity of phototype and metabolites  of PCBs on human thyroid dysfunction, the study explores their mechanism of action based on network pharmacology and molecular docking systematically, which was shown in http://www.lilab-ecust.cn/pharmmapper/),UniProtdatabase (https://www.uniprot.org/), PubChem (https://pubchem.ncbi.nlm.nih.gov/), STRING database (https://string-db.org/), David database 6.8 (https://david.ncifcrf.gov/), STITCH database (http://stitch.embl.de/), Swiss Target Prediction (http://www.swisstargetprediction.ch/), SEA database (http://sea.edbc.org/), GeneCards database (https://www.GeneCards.org/), PDB database (https://www.rcsb.Org/), KEGG (https://www.kegg.jp/), and Venny 2.1 (http://bioinfogp.cnb.csic.es/tools/venny/index.html) online tool. The softwares used are as follows: Cytoscape 3.6.1, MGLTools-1.5.6, PyMOL software (http://pymol.org/2/), PLIP database: (https://projects.biotec.tu-dresden.de/plip-web/plip/index), and AutoDock Vina software (http://vina.scripps.edu/).The databases used are as follows: PubMed, Web of Science and PharmMapper database .Fourteen compounds in 2.2 were saved as 3D structures in SDF format and uploaded to the PharmMapper database. In the select targets set option, select “Homo sapiens” for target prediction, and use the UniProt database to correct disease targets and PCBs targets. Input the smiles structural formula of the compounds into STITCH, respectively, Swiss target prediction, and SEA databases. In the database, the species “Homo sapiens” was selected to retrieve, and the target was obtained and saved in the corresponding format. Finally, the target was summarized, and the duplicate items were deleted (date: 2021.03.15-03.24).GeneCards and PubMed databases were used to input “thyroid dysfunction” as the keyword, and the species “Homo sapiens” were selected for retrieval. Then, intersect and delete duplicate targets to obtain disease targets. UniProt database was used to correct disease targets (date: 2021.03.25-3.29).Venny 2.1 online tool was used to obtain the common targets of PCBs and diseases by entering the above The intersection targets of “PCBs thyroid dysfunction” obtained in The key targets screened in To explore the main active components of PCBs, the selected active components, targets, and signaling pathways of PCBs were used to construct the “components-targets-pathways” network visualization by using the software Cytoscape 3.6.1 (date: 2021.04.26-04.29).http://www.rcsb.org/) [x-dimension, y-dimension, and z-dimension. By adjusting the size of the box, the network wraps the whole protein for the next docking. PLIP database was used to check the interaction force, and PyMOL software was used for molecular docking visualization [When the conformation of ligand and receptor is stable, the lower the energy, the greater the binding effect. Select PCBs  with the highest degree value in the “components-targets-pathways” network and the target genes  with the highest degree value in the PPI network for molecular docking. The 3D structures of the PCBs were obtained by Chem3D 19.0 software and optimized by the MM2 function to obtain the best structure. Protein crystals holding core targets were downloaded using the RCSB PDB database (sb.org/) . Water asb.org/) , and eaclization  (date: 2p < 0.001) decrease in serum thyroxine (T4) levels. Morphological analysis of the thyroid showed that the colloidal area of rats treated with PCB-95 decreased and the proliferation of thyroid epithelial cells increased. The results suggest that the hypothalamus-pituitary-thyroid (HPT) axis appears to be a target of ortho-substituted PCBs [Experimental evidence shows that exposure to halogenated compounds such as PCBs may interfere with the endocrine system, especially thyroid function , 24. Accted PCBs . PCB-126ted PCBs . The phated PCBs . Bivariated PCBs . PCB-99 ted PCBs . It has ted PCBs . Based oThe smile structural formulas of the 14 PCBs were input into the Swiss target prediction database, SEA database, and STITCH database, respectively. With “Homo sapiens” as the screening condition, 1, 22, and 29 predicted targets were obtained. Then, input the 3D structures of the above compounds into the PharmMapper database one by one, select “Homo sapiens” in the select targets set option, and summarize 183 related targets of all compounds. Two hundred and nineteen predicted target genes were obtained by combining the above targets and deleting repeats.7,505 disease targets were screened by using “thyroid hormone dysfunction” as the keyword in the GeneCards database. The targets with a score value greater than the double median were selected as targets related to thyroid hormone dysfunction. The maximum score value of thyroid hormone dysfunction targets was 135.17, the minimum score value was 0.41, and the double median was 5.52. Therefore, 1,849 targets with scores bigger than 5.52 were selected as targets of thyroid hormone dysfunction. On the other hand, 1,306 targets of thyroid hormone dysfunction were obtained by using the PubMed database. 2,438 targets of thyroid hormone dysfunction were predicted by deleting duplicate items in total.One hundred and forty intersection genes  were anap value <0.05, FDR<0.05, and Benjamin<0.01. The items of biological process, cell composition, and molecular function are 101, 51, and 16, respectively. The top 10 of each are selected for visualization. As shown in Using David 6.8 database to perform GO function enrichment analysis on 140 predicted target genes. Three GO items were selected, and a functional annotation chart was selected. A total of 614 items were selected. The dataset was established. One hundred and sixty-eight items were selected according to the conditions of p value <0.05, FDR<0.05, and Benjamin <0.01, a total of 77 pathways were screened out. The top 10 related signaling pathways are shown in David 6.8 database was used to analyze the KEGG pathway of 140 predicted target genes. According to The selected 14 PCBs, 115 targets, 10 signaling pathways, and diseases are visualized by using Cytoscape 3.6.1 software to construct a “components-targets-pathways” network, as shown in Molecular docking is to place small-molecule ligands in the binding region of macromolecular receptors by computer simulation and then calculate the physical and chemical parameters to predict the binding affinity between them. The docking energy is determined by the ligand-receptor affinity score function. In this study, the target proteins with the highest screening degree of the PPI network were used for molecular docking verification with the active PCBs with the highest screening degree of the “components-targets-pathways” network. Meanwhile, the hydroxylated, sulfated, and methyl sulfone metabolites of the main active PCBs were selected for molecular docking with the kernel target proteins. PCB-126 had no reference metabolite data. Metabolite conversion is shown in Based on the degree ranking, the top four active components from the “components-targets -pathways” network and the top five core targets from the PPI network were verified by molecular docking. The RCSB PDB database, powered by the protein database, contains archived information about the three-dimensional shape of proteins, nucleic acids, and complex assemblies, helping researchers understand all aspects of biomedicine, from protein synthesis to health and disease. AutoDock Vina is an open-source molecular docking program, which can produce improved scoring functions. Therefore, we searched the PDB database for the top five potential targets, which were MAPK3 (PDB ID:6GES) , MAPK1 with PCBs and metabolites was higher than that of its ligand. From low to high, the top three binding energies are RXRA (PDB ID: 6GNO) and 3-sulfate-PCB-118, PIK3R1 (PDB ID: 7JIS), and 3-MeSO2-PCB-118, MAPK3 (PDB ID: 6GES) and 3'-sulfate-PCB-180, which suggested that sulfated and methyl sulfonated PCBs had a strong effect on thyroid hormone signaling. The detailed information on target compound interaction obtained by docking simulation is shown in Figures As shown in The results show that the low binding energy between PCBs and target proteins means good affinity, which results in strong interference. It has been found that the binding energies of the sulfated and methyl sulfone PCBs metabolites are relatively low, suggesting that they bind better to proteins and may have greater toxicity. This may be because of the hydrogen bonds. According to the cluster analysis , in the Π-stacking with TRP-305 , and high affinity with thyroid hormone-binding protein and metabolic enzymes . After eβ-Catenin and RXRA enhancing human cholangiocarcinoma growth through simultaneous activation of Wnt/β-Catenin and nuclear factor-kappa B pathways (NF-kappa B) to promote cancer proliferation [Five key targets were obtained through PPI network screening, namely, MAPK3, MAPK1, RXRA, PIK3R1, and TP53. Some studies have shown that these targets are related to thyroid diseases. As for important targets, protein mitogen-activated protein kinase 1 (MAPK1) is a protein kinase, which plays a crucial role in cell growth, differentiation, proliferation, survival, and inflammatory response . Studiesferation .Through KEGG pathway analysis, we screened out kernel pathways such as a tumor, prostate cancer, PI3K-Akt signaling pathway, Ras signaling pathway, thyroid hormone signaling pathway, prolactin signaling pathway, pancreatic cancer, and FoxO signaling pathway, indicating that the underlying mechanisms of PCB interference in thyroid hormone dysfunction are mainly concentrated in the tumor, inflammation, and hormone levels. A PI3K-Akt signaling pathway is involved in cell proliferation, apoptosis, and cycle regulation. Studies have shown that hypothyroidism can inhibit the PI3K-Akt signaling pathway by inhibiting the expression of estrogen receptors and ultimately induce testicular cell apoptosis . Thyroid−1, which verified the binding ability of targets screened by network toxicology. The results of molecular docking showed that the binding energy of sulfated and methyl sulfone metabolites of PCBs was relatively low compared with polychlorinated biphenyls and hydroxy polychlorinated biphenyls. The prototype cannot form hydrogen bonds, while some metabolites can indicate that the sulfated and methyl sulfone metabolites of PCBs bind well to protein and have greater toxicity. Studies have shown that PCB sulfate is a high-affinity ligand for human TTR, which is stronger than the binding ability of hydroxylated PCB metabolites to TTR [To further clarify the research results of network toxicology, four prototype and nine metabolites of PCBs were calculated by molecular docking technology. The results showed that the docking binding energy was less than -5 kcal • mols to TTR , 61. It Through data mining predicted by bioinformatics, we combine the exposure of organic chemical pollutants with molecular action pathways to better understand the toxic effects of exposure to organic pollutants on health. Use it to generate more innovative hypotheses, to design new experiments to establish the causal relationship between chemical environment and health, and to provide strategies for basic toxicity testing and verification . The resThe relationship between the screened targets and PCB metabolites is first reported in the study, in which the targets were not found in the previous PCB metabolite toxicity studies. It means the network toxicology screening method, which is the target result of multiple PCB mixtures, is a useful tool and can provide new ideas for further research.The study combined network toxicology with the molecular docking method. Through literature review, target prediction, network construction, functional enrichment analysis, and molecular docking analysis, the potential mechanism of PCBs interfering with thyroid hormones were predicted, clarified, and confirmed. In conclusion, PCBs can interfere with thyroid hormone dysfunction by regulating MAPK3, MAPK1, RXRA, PIK3R1, and other potential targets, as well as the cancer pathway, PI3K-Akt signaling pathway, thyroid hormone signaling pathway, prolactin signaling pathway, and FoxO signaling pathway. And the signal pathway shows that the screened core targets MAPK3, MAPK1, RXRA, PIK3R1, and TP53 are mainly enriched in the thyroid hormone pathway. The results of molecular docking show that the sulfated and methylsulfonated metabolites of PCBs have lower binding energy with target proteins, indicating that they are more toxic and thus have stronger endocrine disrupting effects on thyroid hormones. It is speculated that the structural formula contains oxygen, which makes it easier to form hydrogen bonds and a more stable binding force. This study preliminarily analyzed the molecular mechanism of PCBs and their metabolites interfering with thyroid hormone dysfunction, but further experimental verification is still needed. The findings provide a theoretical basis for further understanding the interference of PCBs and their metabolites on thyroid hormone dysfunction. Follow-up experiments will further study the impact of exposure to PCBs and their metabolites on thyroid hormone disorders.


The purpose of the present study was to explore the biomarkers related to lung cancer based on the bioinformatics method, which might be new targets for lung cancer treatment.  GSE17681 and GSE18842 were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed miRNAs (DEMs) and genes (DEGs) in lung cancer samples were screened via the GEO2R online tool. DEMs were submitted to the mirDIP website to predict target genes. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted via uploading DEGs to the DAVID database. The protein-protein interaction network (PPI) of the DEGs was analyzed by STRING's online tool. Then, the PPI network was visualized using Cytoscape 3.8.0.  46 DEMs were identified in GSE17681, and the website predicted that there were 873 target genes of these DEMs. 1029 DEGs were identified in the GSE18842 chip. GO analysis suggested that the co-DEGs participated in the canonical Wnt signaling pathway, regulation of the Wnt signaling pathway, a serine/threonine kinase signaling pathway, the Wnt signaling pathway, and cell-cell signaling by Wnt. KEGG analysis results showed the co-DEGs of GSE17681 and GSE18842 were related to the Hippo signaling pathway and adhesion molecules. In addition, six hub genes that were related to lung cancer were identified as hub genes, including mTOR, NF1, CHD7, ETS1, IL-6, and COL1A1.  The present study identified six hub genes that were related to lung cancer, including mTOR, NF1, CHD7, ETS1, IL-6, and COL1A1, which might be a potential target for lung cancer. At present, the incidence rate and mortality of lung cancer rank first in China , while nMiRNA is a small noncoding RNA that can regulate gene expression by targeting mRNA. It is closely related to cell proliferation, differentiation, migration, and invasion and is involved in tumor development. The development of high-throughput technology provides abundant gene expression profile data for the study of the pathogenesis of NSCLC . Many scTherefore, this study screened two NSCLC chip datasets (GSE17681 and GSE18842) to find common DEGs, aiming to provide data support for the precise treatment and prognosis of NSCLC.The microarray data from Gene Expression Omnibus (GEO) databases, GSE17681 and GSE18842 were utilized in this study. The dataset GSE17681, which was the miRNA profiling and based on the GPL570 platform, collected 17 NSCLC specimens and 19 normal tissues. The dataset GSE18842 collected 46 NSCLC specimens and 45 normal tissues, which were based on the GPL570 platform.P < 0.05 and |logFC| > 2 were set as the criteria for significant differences. DEMs were submitted to the mirDIP website (https://ophid.utoronto.ca/mirDIP/index.jsp#r) to predict target genes. The intersection of the DEGs in GSE17681 and GSE18842 was conducted to screen out the common DEGs. The heatmap based on top 10 and bottom 10 DEGs were created by the “pheatmap” package. A volcano map was generated using the “ggplot2” package.Firstly, the GEO2R online tool was utilized to screen the differentially expressed miRNAs (DEMs) and genes between lung cancer samples and healthy samples. The “GEOquery” and “limma” packages in R software were utilized to analyze data tables. adj. https://david.ncifcrf.gov/) was used for Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, including cell components, molecular functions, biological processes, and signal pathways. The “pheatmap” package of R software was used to visualize the enrichment analysis results.The biological information annotation database David (https://cn.string-db.org/). Then, the visualization of the PPI network was performed by Cytoscape 3.8.0, and the genes with a high level of connectivity with surrounding genes were selected as hub genes.The PPI network of the DEGs was analyzed using STRING  were screened on the GSE17681 chip. The website predicted that there were 873 target genes for these DEMs. 1029 DEGs (419 upregulated and 610 downregulated) were screened on the GSE18842 chip. GO analysis results showed that the co-DEGs participated in the canonical Wnt signaling pathway, regulation of the canonical Wnt signaling pathway, the transmembrane receptor protein serine/threonine kinase signaling pathway, the Wnt signaling pathway, and cell-cell signaling by Wnt. KEGG analysis results showed the co-DEGs of GSE17681 and GSE18842 were related to the Hippo signaling pathway and adhesion molecules. Further verification found that six hub genes were screened in relation to lung cancer, including mTOR, NF1, CHD7, ETS1, IL-6, and COL1A1.The activation of the mTOR signaling pathway is involved in the occurrence and development of human tumors and promotes the occurrence of tumors through a variety of mechanisms. The activation of various components in the mTOR signaling pathway is also a poor prognostic factor for many tumors. Inhibition of the mTOR signaling pathway can reverse drug resistance and improve the effect of chemotherapy and radiotherapy in vivo and in vitro . MTOR waCHD7 is a member of the chromatin helicase DNA binding protein family. Its biological functions are mostly related to human congenital malformations , and it β-Mrtf-a silencing in the catenin pathway can reduce the binding of acetylated histone H3K9 and RNA polymerase on the COL1A1 promoter and reduce the expression of COL1A1, while the transcription factor Osterix can directly bind to the COL1A1 promoter and upregulate the expression of COL1A1 [IL-6 is also a downstream effector of the oncogene RAS, which affects the immune regulation and hematopoietic regulation of the body  and promf COL1A1 . COL1A1 f COL1A1 . Liu et f COL1A1 .In conclusion, the present study screened 46 DEMs in GSE17681 and 1029 DEGs in GSE18842 and identified six hub genes related to lung cancer, including mTOR, NF1, CHD7, ETS1, IL-6, and COL1A1, which might be potential targets for lung cancer. This study used bioinformatics to find the key to lung cancer, provided important potential targets for early diagnosis and prognosis of lung cancer, and formulated new diagnosis and treatment strategies for patients. However, the limitation of this study lies in the lack of experimental evidence for the key genes screened. The next line of research can continue to explore the impact of these key genes on lung cancer in vivo and in vitro experiments.


After that only common DEGs in all three datasets were collected to apply further analysis. Gene ontology (GO) and pathway analysis were implemented to explore the functional behaviors of DEGs. Then protein–protein interaction (PPI) networks were built to identify the most active genes, and a clustering algorithm was also implemented to identify complex parts of PPI. TF-miRNA networks were also constructed to study OC-associated DEGs in-depth. Finally, top gene performers from PPI networks were used to apply drug signature analysis. After applying filtration and cut-off criteria, 2508, 3377, and 670 DEGs were found for GSE74530, GSE23558, and GSE3524 respectively, and 166 common DEGs were found in every dataset. The GO annotation remarks that most of the DEGs were associated with the terms of type I interferon signaling pathway. The pathways of KEGG reported that the common DEGs are related to the cell cycle and influenza A. The PPI network holds 88 nodes and 492 edges, and CDC6 had the highest number of connections. Four clusters were identified from the PPI. Drug signatures doxorubicin and resveratrol showed high significance according to the hub genes. We anticipate that our bioinformatics research will aid in the definition of OC pathophysiology and the development of new therapies for OC.Oral cancer (OC) is a serious health concern that has a high fatality rate. The oral cavity has seven kinds of OC, including the lip, tongue, and floor of the mouth, as well as the buccal, hard palate, alveolar, retromolar trigone, and soft palate. The goal of this study is to look into new biomarkers and important pathways that might be used as diagnostic biomarkers and therapeutic candidates in OC. The publicly available repository the Gene Expression Omnibus (GEO) was to the source for the collection of OC-related datasets. GSE74530, GSE23558, and GSE3524 microarray datasets were collected for analysis. Minimum cut-off criteria of |log fold-change (FC)| > 1 and adjusted  OC, often known as mouth cancer, is the world’s sixth most prevalent kind of cancer. OC refers to a tumor that develops in a part of the mouth; it may be found on the surface of the tongue, the inside of the cheeks, the roof of the mouth , the lips, or the gums. Among all the types of OC, oral squamous cell carcinoma (OSCC) is the most common type (more than 90 percent of OC are OSCC) and also the most lethal . OC deveBioinformatics is the application of computer programming, big data, and biology to assist scientists in understanding and identifying patterns in biological data. It is especially valuable in genome research and DNA sequencing, since it helps scientists to arrange vast volumes of data. An extensive network-based bioinformatics methodology was constructed in this study to reveal the influence of OC on gene expression patterns and how these could contribute to promoting other illnesses. In the beginning, we perused various gene expression patterns of three datasets, and after filtration of minimum criteria, all common genes were taken for further analysis. The protein interaction network was the major outcome of this study, based on which hub genes, GO terms, disease pathways, and cluster analysis were employed. All the steps of the study are demonstrated in https://www.ncbi.nlm.nih.gov/geo/, Access Date 1 August 2022) database was used to extract the microarray datasets. GEO is a publicly accessible gene expression collection with over 94 000 datasets and over 2 million samples [p-value < 0.05 and logFc ± 1, and a comparison method was also devised to identify the common genes between the three datasets using online Venny toll  [GEO   .The biological activities of the common DEGs were evaluated by functional analysis. To complete the functional analysis, the GO and pathway analysis were performed through clueGO. ClueGO is a Cytoscape App that pulls typical functional biological information from long lists of genes or proteins. The functional enrichment study was based on the most recent publicly accessible data from several annotations and ontology resources, which ClueGO can automatically retrieve. To make the analysis easier, predefined options for term selection are supplied. The results are shown as networks, in which GO terms and pathways are classified according to their biological function . GO termThe PPI network is a visual representation of the protein–protein edge-interaction, which helps to identify the critical genes and potential biomarkers . The proHub genes are highly connected proteins in the PPI network that can play a significant role in identifying the potential therapeutic biomarker . The thrp-value 0.01 and an overlap gene count of >= 9 were used as cutoff criteria for finding pharmaceutical targets.Therapeutic targets for the chosen hub DEGs were identified using the Drug Signatures Database (DSigDB) . The DSihttp://www.regnetworkweb.org/, Access Date 1 August 2022), and may play a significant role in identifying novel information about the OC. MicroRNA (miRNA) is a form of non-coding RNA molecule that regulates gene expression after it has been transcribed. MiRNAs have a crucial role in tumor growth, differentiation, and apoptosis, according to recent research [The TF-miRNA co-regulatory network was extracted from the RegNetwork repository  and transcription factors (TFs) are essential regulators of gene expression . MiRNAs p-values to gain insight into the etiology of OC.Oral cancer is a major health issue that has a high morbidity and fatality rate. Early identification and prevention are critical in reducing the global incidence of oral cancer . We examThe GO is a gene regulatory framework based on a general conceptual paradigm that makes genes and their interactions easier to grasp. Accumulated biological knowledge about gene activities and regulation in a range of ontological areas has evolved over time to achieve this . The GO The most effective approach for reflecting an organism’s behavior through internal alterations is pathway analysis. KEGG, Reactome, and WikiPathways were used to compile the pathways of the most common DEGs. The groups of pathway terms Cell cycle, Influenza A, ECM-receptor interaction, Leishmaniasis, Epithelial cell signaling in Helicobacter pylori infection, etc. are associated with the pathway of KEGG. On the other hand, the pathway group of Reactome reported that the Formation of Cyclin B: Cdc2 complexes, CDK1 phosphorylates CDCA5 (Sororin) at centromeres, Association of Nek2A with MCC: APC/C, DNA polymerase alpha: primase binds at the origin, Kinesins move along microtubules consuming ATP, etc. pathways are connected with the common DEGs. In addition, Type I interferon induction and signaling during SARS-CoV-2 infection, Type II interferon signaling (IFNG), DNA Replication, Non-genomic actions of 1,25 dihydroxy vitamin D3, etc. are groups of pathways that are related to the WikiPathways.Using common DEGs, a PPI network was created to understand the biological characteristics in-depth and to explore disease biomarkers . Using tThe hsa-mir-590-3p is an important target in the TF-miRNA co-regulatory network. Previous studies have reported that the hsa-mir-590-3p may play a significant role in pancreatic cancer , colorecStreptomyces paucities spp. anthracycline antibiotic. Doxorubicin inhibits topoisomerase II, intercalates DNA, and produces free radicals, resulting in cell death or growth inhibition [Doxorubicin, often called Adriamycin, is a hibition . Doxorubhibition ,69 due thibition . Doxorubhibition ,72,73, whibition , thyroidhibition , and orahibition . One stuhibition . Resverahibition . Cell cyhibition . This fiThe biologic areas and regulatory components discovered were quickly addressed in this work, which may hasten clinical activity against other OC-related illnesses. Our study’s strength is that it is the largest transcriptome investigation into OC. This study’s transcriptomic analysis yielded a shared ontological entity, pathways, illness connections, and transferrin genes. In the OC investigation, the related genes between datasets were found, resulting in additional molecular results and demonstrating the interaction of DEGs. This research could aid in the development of therapeutic targets and therapies in the future.