Du Zhong (DZ), or Eucommiae Cortex, a traditional Chinese herbal medicine, has been used to treat osteoporosis. Although it has been reported that DZ can improve bone mass in ovariectomized rats, its pharmacological mechanisms in treating osteoporotic fractures (OPF) remain unclear. R language was utilized to conduct the enrichment of Gene Ontology biological process and KEGG pathways.  In this study, we used a network pharmacological manner to explore its potential complicated mechanism in treating OPF. We obtained DZ compounds from TCMSP and BATMAN-TCM databases and collected potential targets of these compounds through target fishing based on TCMSP and BATMAN-TCM databases. Next, we collected the OPF targets by using CTD, GeneCards, OMIM, HPO, and GenCLiP 3 databases. And then the overlapping genes between DZ potential targets and OPF targets were used to build up the protein-protein interaction (PPI) network and to analyze their interactions and find out the big hub genes in this network. Subsequently, clusterProfiler package in P value and found that the DZ anti-OPF effect was mainly associated with the regulation of ROS and inflammatory response. Furthermore, 64 hub genes in PPI network, such as MAPK1 (degree = 41), SRC (degree = 39), PIK3R1 (degree = 36), VEGFA (degree = 31), TP53 (degree = 29), EGFR (degree = 29), JUN (degree = 29), AGT (degree = 29), MAPK1, SRC, PIK3R1, VEGFA, and TP53, were considered as potential therapeutic targets, implying the underlying mechanisms of DZ acting on OPF.  There were totally 93 active compounds and 916 related targets in DZ. After the enrichment analysis, we collected top 25 cellular biological processes and top 25 pathways based on the adjusted  We investigated the possible therapeutic mechanisms of DZ from a systemic perspective. These key targets and pathways provided promising directions for the future research to reveal the exact regulating mechanisms of DZ in treating OPF. Osteoporosis is a systemic bone disorder characterized by low bone density, poor bone quality, reduced bone strength, and an accompanying increased incidence of fractures . The latRecently, traditional Chinese medicine (TCM) has attracted worldwide attention and has served as a main alternative treatment in East Asia, North America, and Europe due to its satisfactory curative effect, relatively low toxicity, and low cost –11. TCM Du Zhong (DZ), or Eucommiae Cortex, is one of the most commonly used TCM herbs in the treatment of osteoporosis. DZ has been found to facilitate osteogenesis through activating osteoblasts and to inhibit osteolysis by suppressing osteoclast activity . An animR to conduct biological process and KEGG pathway enrichment analyses. The protocol of our experimental procedures is shown in In this network pharmacology work, we aim to comprehensively dissect the mechanisms of DZ in treating OPF. We collected related compounds of DZ from multiple databases and obtained the compounds' potential targets via target fishing. Then, we matched these targets with OPF-related targets that were collected from a multisource database. Next, using overlapping targets obtained from the previous process, we built a protein-protein network to analyze their internal interactions and then screened out the hub genes. Furthermore, we used the clusterProfiler package in http://lsp.nwu.edu.cn/tcmsp.php) [http://bionet.ncpsb.org/batman-tcm/) [To collect the active compounds of DZ, we utilized the Traditional Chinese Medicine System Pharmacology Database  , which ian-tcm/) , one of Oral bioavailability (OB) refers to the percentage of an orally administered drug that reaches systemic circulation, and it is one of the most important pharmacokinetic profiles for drug screening. The TCMSP platform has adopted the OBioavail1.1 system, which integrates P450, 3A4, and P-glycoprotein information to obtain the OB value . To deteDrug-likeness (DL) is a qualitative index that represents the degree to which the target compound is “drug-like” and is used to remove chemically unsuitable compounds. TCMSP uses the Tanimoto similarity method to calculate the DL index by comparing the target compound to all 6511 molecules in the DrugBank database . In thisP-value cutoff <0.05.To collect candidate compounds, we set the potential TAR score cutoff ≥20 and The compounds have effects on the targets that induce them to exert their biological functions. Thus, we used the TCMSP and BATMAN-TCM platforms to predict the targets of active compounds.http://ctdbase.org/) [https://www.genecards.org/) [https://www.omim.org/) [https://hpo.jax.org/app/) [http://ci.smu.edu.cn/genclip3/analysis.php) [https://string-db.org/) [We used multiple databases to collect OPF-related targets, and the terms “Osteoporotic” and “Fracture” were used as the key words for the search. The databases in this step included the Comparative Toxicogenomics Database  , GeneCards.org/) , OMIM (him.org/) , HPO (htrg/app/) , and Gensis.php) . In totadb.org/) , in whichttp://www.cytoscape.org/) was used for all the network visualization work. Cytoscape 3.6.2 is one of most powerful open-source software programs for constructing different networks visually [Two main networks were built in this process, including the compound-hub gene network and the hub gene-pathway network. The target information was obtained from the KEGG pathway enrichment results. Cytoscape 3.6.2  “Degree” means the number of edges shared with other nodes. Examining the node's degree is the most straightforward method of quantifying its centrality . (2) “BeR (R 3.6.2 for Windows) to perform Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis to identify biological processes and systemic involvement of pathways.We used the clusterProfiler package in A total of 147 related compounds were found in DZ collected from the TCMSP database, and of these, 27 candidate compounds remained after the screening of ADME thresholds . Using the BATMAN-TCM database, we obtained 73 active compounds that matched the filter criteria. In total, we collected 93 unique compounds.For these unique compounds, we obtained 961 unique related targets, 104 from TCMSP and 857 from BATMAN-TCM. We integrated the OPF genes that were obtained from multisource databases, including the CTD, GeneCards, OMIM, HPO, GenCLiP 3 databases, and a total of 3,834 related genes were collected. After construction of the Venn diagram, three hundred twenty-five overlapping targets between the related targets of DZ and OPF were selected as the key targets on which DZ exerts its anti-OPF effects .The data obtained from the String database were used to establish the PPI network for the 325 overlapping targets. In this network, there were 249 nodes and 1006 edges in total. Then, three main parameters, namely, “degree”, “betweenness”, and “closeness”, were used as filters to select key genes and to build the large hub nodes to determine the anti-OPF effect of DZ. The first screening threshold was degree ≥ 6, closeness ≥ 0.312, and betweenness ≥ 0.000, which resulted in 122 nodes and 745 edges. Then, these 72 key nodes were further screened with a second threshold consisting of degree ≥ 11, closeness ≥ 0.340, and betweenness ≥ 0.016, and 64 nodes and 414 edges remained after this screening . These nThen, we further built the big hub nodes-compound network  based onP value, we found that the top 25 biological processes  terms in ascending order of adjusted rocesses  mainly cP value  mice, which may result from the interaction between its active compounds and related targets. Thus, we investigated the anti-OPF mechanism of DZ through a network pharmacology method. Based on the big hub genes-compounds network , we founα and IL-6) and promoting macrophage polarization in delayed osteoporotic fracture healing [α and IL-6 [β-estradiol, which is used in the clinic to prevent and treat postmenopausal women with osteoporotic fracture, can significantly downregulate ROS production and the expression of TNF-α and IL-1β, as well as the phosphorylation of ERK2 (MAPK1) [In the present study, 325 common gene targets of DZ and OPF were identified and 64 hub genes were identified that may play critical roles in this treatment. Some anti-OPF effects of these genes have been confirmed by clinical trials or animal experiments. Chow et al. found that low-magnitude high-frequency vibration stimulation is able to promote fracture healing by positively regulating the impaired innate immune response  . Wang et (MAPK1) . De-Ugar (MAPK1) . Swanber (MAPK1) . Based o (MAPK1) . These g (MAPK1) .κB [κB and calcineurin-NFAT pathways [κB signaling can be attenuated by strengthening the nuclear factor-erythroid 2-related factor 2 [We conducted GO biological process and KEGG enrichment analyses of the overlapping genes to identify their functions. We found 44 (13.6%) genes involved in reactive oxygen species metabolic processes. Reactive oxygen species play key roles in regulating cell proliferation, apoptosis, migration, and differentiation , as wellκB . Srinivapathways . The actfactor 2 . Therefoα are involved in the pathogenesis of osteoporosis and influence bone metabolism [α are the overlapping genes between the regulation of inflammatory response  and the TNF signaling pathway  based on the results of the BP and KEGG pathway enrichment analyses. Ali Aydin et al. found higher expression levels of TNF- α and IL-6 in OVX model rats, and these cytokines reached their apex when a fracture occurred [Forty-five (13.9%) genes were involved in the regulation of the inflammatory response. In vitro and in vivo studies have shown that the proinflammatory cytokines IL-6 and TNF-tabolism , 61. Sevtabolism –64 and ttabolism . IL-6 anoccurred . Moreoveoccurred , 68. TheBy utilizing network pharmacology, we investigated the potential targets of DZ and the underlying mechanism of its anti-OPF effects, which may be based on the regulation of ROS and the inflammatory response. According to the KEGG pathway enrichment results, we found that the PI3K-Akt signaling pathway, MAPK signaling pathway, and TNF signaling pathway may be the main pathways in treating OPF. Thus, we believe that the anti-OPF effect of DZ is mainly based on its direct or indirect regulation of the abovementioned potential targets and pathways and that DZ provides promising directions for future research, which is essential to reveal its exact regulatory mechanisms.


For nearly a decade, the difficulties associated with both the determination and reproducibility of Ras-dependency indexes (RDIs) have limited their application and further delineation of the biology underlying Ras dependency. In this report, we describe the application of a computational single sample gene set enrichment analysis (ssGSEA) method to derive RDIs with gene expression data. The computationally derived RDIs across the Cancer Cell Line Encyclopedia (CCLE) cell lines show excellent agreement with the experimentally derived values and high correlation with a previous in-house siRNA effector node (siREN) study and external studies. Using EMT signature-derived RDIs and data from cell lines representing the extremes in RAS dependency, we identified enriched pathways distinguishing these classes, including the Fas signaling pathway and a putative Ras-independent pathway first identified in NK cells. Importantly, extension of the method to patient samples from The Cancer Genome Atlas (TCGA) showed the same consensus differential expression patterns for these two pathways across multiple tissue types. Last, the computational RDIs displayed a significant association with TCGA cancer patients’ survival outcomes. Together, these lines of evidence confirm that our computationally derived RDIs faithfully represent a measure of Ras dependency in both cancer cell lines and patient samples. The application of such computational RDIs can provide insights into Ras biology and potential clinical applications. Although KRAS mutations mainly occur in pancreatic, lung, and colon cancers, Ras gene mutations and amplifications are also found in many other cancer types. Given the significant role of these mutations, full-scale efforts to advance our understanding of Ras biology are underway with the hope of providing insights into oncogenesis mechanisms that will provide benefits for cancer diagnosis and treatment5.The Ras genes  are the most frequently mutated oncogenes in human cancers, with KRAS showing the highest overall mutation frequencies6. These efforts led to the classification of cancer cell lines into two categories: Ras dependent and Ras independent7. To assess variable KRAS dependency across a panel of KRAS mutant human cancer cell lines, Ras dependency indexes (RDIs) were proposed and measured experimentally to examine cancer cell addiction to oncogenic KRAS in a quantitative manner7. Many molecular features that distinguish KRAS-dependent and KRAS-independent cancer cell lines have been uncovered. For example, PI-3 kinase activation was found to contribute to the loss of KRAS dependency in a context-specific manner7. Similarly, KRAS dependency was found to be strongly linked to epithelial differentiation status7. In other words, the EMT  or MET  status is associated with Ras dependency. As a result, an EMT signature that was linked to KRAS addiction was identified, and this signature has value for the prediction of KRAS dependency by classification using the PAM  method applied to gene expression data7.Nearly a decade ago, efforts were made to investigate the oncogene “addiction” phenomenon, whereby tumors require the sustained expression and activity of a single aberrantly activated oncogene8. Inspired by these studies, recent efforts have revealed that all KRAS-deficient cells exhibit phosphoinositide 3-kinase (PI3K)-dependent mitogen-activated protein kinase (MAPK) signalingInterestingly, a subsequent effort was made with a colon-specific study that derived a colon-specific gene signature9. This topic was further discussed in the Ras Central Dialogue10.and induced sensitivity to PI3K inhibitorsThe concept of RDIs has provided new insights into oncogene addition phenomena. However, the experimental procedure used to determine RDIs is both time and labor consuming and is not easily reproduced. This likely stems from the diversity in the culture conditions of a variety of cancer cell lines (in-house observations) and, potentially, the biological readout assessed . Thus, there are only a handful of cancer lines for which RDIs exist that are available for researchers to utilize. This obviously represents a hurdle to the application of RDIs to wider libraries of existing cancer lines and consequently has slowed progress in understanding the mechanisms of Ras dependency and their relevance across different cancer samples.Derivation of RDIs for the full panel of available cancer cell lines from CCLE collections (and other sources) would greatly facilitate the study of Ras dependency and stimulate research for understanding the underlying biological mechanisms. We postulate that although the current experimentally derived list of RDIs is relatively small, using the experimental values that have been determined as a metric to validate computationally derived estimates would permit extension across the full breadth of the CCLE collection of cell lines. Such a computational process would avoid many of the experimental obstacles outlined above. Further, this would allow an extension to those cell lines derived from cancers presumed to be unrelated to RAS dependency and possibly even application to patient samples.11. The ssGSEA method is an extension of the GSEA method13, working at the level of a single sample rather than a sample population as in the original GSEA application. The score derived from ssGSEA reflects the degree to which the input gene signature is coordinately up- or downregulated within a sample11.Based on this rationale and given the expression data and existing gene signatures, an existing method, namely, the ssGSEA method, seems a perfect fit for our need7, show excellent correlation with experimentally measured RDIs independent of the technology platform (RNAseq and microarray). Interestingly, these ssGSEA RDI scores also showed a close relationship with our previous in-house siREN study that examined oncogene dependency14 and high-throughput RNAi (RNA interference) and CRISPR  screening studies from the Broad Institute that defined a cancer dependency map and identified cancer-type-specific vulnerabilities on genes in the entire genome, respectively16.In this report, we used previously reported Ras-dependency-related gene signatures and expression data to computationally derive ssGSEA score-based RDIs for the full panel of cell lines from CCLE. Surprisingly, the derived ssGSEA RDI scores, especially those derived using the EMT signature17 and gene-level pathway analysis to the expression profiles of cell lines with high and low derived ssGSEA scores. This showed that the main differences between the most highly Ras-dependent and Ras-independent cell lines were related to differences in the Fas signaling pathway and an NK cell activation pathway that leads to MAPK activation independent of Ras. The latter pathway, originally identified in NK cells, is functionally relevant and operates in a RAS-independent manner in a generic cancer context, thus extending its importance beyond the immune cell functional domain. Additionally, using expression data to derive EMT signature-based ssGSEA scores for individual TCGA patient tumor samples, the same two pathways were identified, extending the applicability of RDIs to patient samples. Finally, the ssGSEA scores of RAS dependency-related signature and Ras pathway genes showed a significant association with cancer patients’ survival outcome by our in-house survival analysis method18. Together, these observations indicate that computationally derived ssGSEA scores faithfully represent the levels of Ras dependency of cancer cell lines and cancer patient samples, directly providing insights for cancer research and potential clinical applications. To our knowledge, this is the first report of a computational method that uses genome-wide gene expression profiling to represent RDIs, and we assert that these findings constitute an opportunity to revitalize the RAS dependency discussion.Using these computed RDIs, we applied our comprehensive pathway pattern extraction pipeline (PPEP) analysishttps://portals.broadinstitute.org/ccle). RNAseq data, either processed by CCLE or raw data, were downloaded and processed in-house with a customized procedure for the purpose of comparison. The RNAseq data from TCGA for selected tumor types were downloaded from the TCGA data portal . Gene signatures were obtained from various sources. In summary, a list of many gene signatures was used to derive ssGSEA scores, including EMT_RasDep_Sig: Ras-dependent EMT signatures7; EMT_RasDep_Sig_Valid: Ras-dependent EMT signatures of only validated genes7; Colon_RasDep_Sig: colon signatures8; BMC_RasDep_Sig: Ras pathway dependency signatures19; RAS_pathway_v3: Ras pathway gene signature updated and derived from the Ras pathway from Ras Central . siREN_DEGs_KRAS_vs_RSK_Full and siREN_KRAS_vs_RSK_sig are differentially expressed genes (DEGs) between KRAS- and RSK-type lines and EN-derived gene signatures, respectively, from a previous siREN study . “Up” and “Dn” in the colon signature indicate upregulated or downregulated signature subsets, respectively, as defined in the original publication8. The Ras pathway dependency signature was obtained from the original publication19. The KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway signatures used KEGG pathway gene lists (http://www.genome.jp/kegg/pathway.html). The Biocarta pathway signature used the Biocarta pathway gene lists from MSigDB of the Broad Institute, and all Biocarta pathway maps used in the manuscript with data overlay were manually drawn based on the pathway information from MSigDB of UC San Diego and the Broad Institute.Gene expression data, including microarray and RNAseq data for CCLE, were downloaded from the CCLE portal (http://software.broadinstitute.org/gsea/msigdb/genesets.jsp?collection=CP:BIOCARTA) , which is licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) as described (https://www.gsea-msigdb.org/gsea/msigdb_license_terms.jsp). and used as controls for the purpose of comparison of their behaviors with the derived ssGSEA scores, e.g., correlations with experimentally measured RDIs from the original study7 as well as correlation with the EMT signature. DEMETER scores and CERES scores were obtained from the Cancer Dependency Portal  based on initial publications16.MEK or RAF inhibitor IC50 data were downloaded from the GDSC databasewww.r-project.org). The R code for the ssGSEA analysis method was obtained through email communications with the original authors of ssGSEA11 and customized to fit the needs for computing ssGSEA scores for each CCLE cell line or TCGA patient sample for the selected EMT signature7, colon signature8, BMC Ras dependency signature19, MSigDB gene sets or genes from individual Biocarta or KEGG pathways using microarray or RNAseq data. The two original experimentally measured RDI datasets were taken from supplementary figure 7. RDI_SF7 predicted Ras dependencies based on the PAM algorithm but with subsequent experimental validation7. For comparison with ssGSEA scores, the RDI_SF7 data were first transformed by base 10 logarithm and then multiplied by (−1) for RAS-dependent lines or by (1) for RAS-independent lines to reflect the direction of Ras dependency (positive for Ras dependency and negative for Ras independency). The data were then standardized to z-scores across these cell lines for comparison. ssGSEA scores or the RDI data were also converted to z-scores for the purpose of comparison in heatmaps and the creation of correlation matrix heatmaps and correlation statistics.Data manipulation and analysis were mostly performed using customized R scripts or existing R packages  to evaluate robustness/consistency. The top 15 vs. bottom 15 samples or top 30 vs. bottom 30 samples were also used for evaluating robustness/consistency. The corresponding DEG lists were collected and applied to customized R scripts that implemented the in-house developed pathway pattern analysis method called PPEP, which was previously described17. Briefly, the gene lists were subjected to Fisher’s exact test-based pathway or gene set enrichment analysis using annotated databases including Gene Ontology (www.geneontology.org/), KEGG pathways (www.genome.jp/kegg/pathway.html) and MSigDB (http://software.broadinstitute.org/gsea/msigdb/index.jsp). The derived p-values were transformed by using the formula (-1)*log10 with all p-values less than 0.05, and p-values with the number of “hit” genes from the gene list for the corresponding pathway/gene set less than 2 were all converted to 0. The transformed p-value data matrix was used to derive pathway-level heatmaps using either customized R scripts that used the pheatmap or gplots R package or the TM4 MeV tool from TIGR (mev.tm4.org/). The “hit” genes for specific pathways or gene sets from selected DEG lists were retrieved using PPEP-based customized R scripts, and gene-level heatmaps were generated similarly to the pathway-level heatmaps. PCA  plots were generated using the Partek genomic suite (www.partek.com). The significance of the overlaps between the gene lists and gene signatures was assessed using Fisher’s exact test with customized R scripts. Survival analysis for association of ssGSEA scores of Ras-dependency related signatures or Ras pathway genes with cancer patients’ survival outcome from TCGA was performed using an in-house analysis method described previously18. GeneGO database query and network analysis were performed using the GeneGO portal .DEGs for microarray data were derived using the limmaTo expand the cell line coverage of experimentally derived Ras dependency indexes, we developed a robust computationally derived metric that could be calculated from gene expression data that was able to faithfully reproduce the experimentally derived values. As a proof of concept, we used this method to compute the ssGSEA scores for each CCLE cell line using gene expression data.7, each of which covers different cell lines. We named these sets RDI_SF1B and RDI_SF7, since they are from supplementary figure 7. We first used both of these sets of data for comparison with ssGSEA-computed scores from the corresponding cell lines using the CCLE microarray data. Both sets of data showed a very consistent pattern with the computationally derived data in heatmaps of actual data  were used for comparison. The consistent pattern and high correlation levels are very similar between the ssGSEA scores from the EMT signature and both sets of RDI data regardless of the data source and technology platform  , as well as the BioCarta and KEGG Ras pathway gene lists.We wanted to further investigate whether the EMT signature-derived ssGSEA scores represented the best metric of Ras dependency. For this, we leveraged several additional sources of Ras pathway involvement-associated gene lists. One was obtained through data mining for coherent expression of RAS pathway-related genes across multiple datasets for the specific purpose of prediction of RAS pathway dependencyWe computed the ssGSEA scores with these gene lists/signatures using the cell line data and then compared the scores with the EMT signature and colon-derived scores described above. The ssGSEA scores derived from the EMT signature showed the best correlation with the original RDI datasets regardless of the data source or technology platform  cell lines along the dependency continuum. Interestingly, high-level PCA of the gene expression data between the top 15 vs. the bottom 15 cell lines (highest or lowest EMT signature-derived ssGSEA scores) in the lung, pancreas, and colon showed very different expression profiles between these two groups of cell lines in all three tissue types  between the cell lines with the top and bottom ssGSEA scores were derived from expression data, including microarray and RNAseq data . In contrast to the more universal NK pathway, the “FAS signaling pathway (CD95)” was specifically enriched in all of the DEGs from the lung in the microarray data and enriched in all of the limma-voom method-derived DEGs from the lung in the RNAseq data  thus behaved differently from TCGA SKCM (7 DEGs) in terms of the number of DEGs in this pathway, which may suggest that differences between patient tumors and derived cell lines may be dependent upon the source tissue and perhaps may reflect context-dependent adaptation regimens for tumors from different tissues. However, overall, cell lines and cancer patients’ tumors showed relatively consistent expression profile behavior in this pathway, highlighting the potential connection between this pathway and Ras dependency.To further test the robustness of these results, instead of the top 15 vs. the bottom 15 patient samples, a similar DEG analysis was performed using the top 30 vs. bottom 30 patient samples with the highest or lowest EMT signature-based ssGSEA scores from the three types of cancer. Regardless of the analysis method selected, similar observations were made  for RAC GTPases  and works as an AML-specific activator of MAPK signaling. Notably, RAC1 is the key component for one of the signaling branches that converges at the PAK1 gene in the Ras-independent pathway in NK cell-mediated cytotoxicity from Biocarta , were consistently downregulated in Ras-independent LUAD patient samples .As expected, the ssGSEA scores derived from the Ras pathway genes showed a significant association with pancreas cancer patient survival outcomes in TCGA PAAD data Fig. , which iome Fig. , postulaome Fig.  and 22. ome Fig. . In cont11. Surprisingly, the derived ssGSEA scores, specifically from the EMT signature, showed excellent correlation with experimentally measured RDIs compared to other signatures that we evaluated. We also found that the ssGSEA scores derived using microarray and RNAseq expression data were highly correlated with the experimentally measured RDIs, making our observations independent of the data source and platform applied. The correlation levels and significance that we observed rule out all but the remotest possibility that these observations occurred by chance, which is consistent with the notion that the computationally derived dependency indexes indeed recapitulate those that are experimentally derived. In addition, the indexes were very specific to the EMT signature-based ssGSEA scores, which are superior to any control KEGG and Biocarta pathway gene list or other RAS-dependent or RAS-related gene signatures that we evaluated. This finding further confirms the hypothesis that Ras dependency may be determined by the EMT  or MET  status, which is consistent with the original viewpoint about RAS dependency7.The very high frequency of observation of perturbations of genes in the RAS pathway through mutation and copy number alterations confirms the involvement of the pathway in many cancers. Therefore, further understanding of the biology that underlies the concept of Ras dependency is an important objective. In this report, we sought to derive and apply Ras-dependency-related gene signatures and expression data to derive ssGSEA score-based RDIs for the full panel of cell lines from CCLE in a computational manner by utilizing the single-sample GSEA (ssGSEA) method14, and modest-to-low levels of correlation with an external high-throughput RNAi screening study and CRISPR studies from the Broad Institute16. The large overlap of the genes and high correlation of the derived ssGSEA scores between the EMT signature and the siREN study-derived gene signatures indicate a strong biological connection, given that these data were derived from two independent studies with completely different technologies. The high concordance between the EMT and colon signature-derived ssGSEA scores with the RSK/KRAS classification in the siREN study also indicates the strong connection between Ras dependency and the identified RSK/KRAS subtypes. The cancer dependency map-based data from the Broad Institute showed modest levels of correlation with the EMT signature-derived ssGSEA scores but with high statistical significance, providing another line of evidence showing that the EMT-derived ssGSEA scores indeed reflect the Ras dependency level metric of each cancer cell line.Furthermore, the computationally derived RDIs indeed showed high correlations and connections with previous relevant studies, including a siREN in-house studyPerhaps more surprisingly, the computationally derived RDIs showed applicability beyond cell lines to include human cancer patient samples. The extension to both cell lines perhaps not conventionally thought to involve Ras dependency as well as patient samples significantly extends the range for which this concept can be experimentally assessed and further investigated. Thus, this study has shown that the ssGSEA method can be very useful for ranking certain behaviors of cell lines or samples using any related signature and gene expression data.By selecting cell lines representing the extremes in Ras dependency and then characterizing DEGs between them, we probed the biological themes underlying Ras dependence. Expression profiling showed significant involvement of two Biocarta pathways, namely, the Ras-independent pathway in NK cell-mediated cytotoxicity and the Fas signaling pathway, although other themes were observed to a lesser extent. The other identified pathways are potentially also interesting and warrant further investigation.9. Consistent with this initial report, the gene expression patterns of this pathway in our data revealed that PI3K is located upstream in this putative generic Ras-independent pathway and plays a role in Ras-independent MAPK signaling. Furthermore, our observation that this Ras-independent signaling pathway includes SYK, PAK1 and PI3K as the critical genes in the context of Ras independency provides additional insights into the role of PI3K in the Ras independency postulated by the initial report9. Lastly, the uncovered fact that two critical kinase genes, namely, SYK and PAK1, are the only shared genes between the EMT signature and Ras-independent pathway in NK cells also highlights the underlying biological connections between the computational RDIs and Ras-independent contexts.The first pathway is annotated specifically for the Ras-independent MAP kinase activation that occurs in the NK cell context. However, we found that the genes in this pathway are not NK-cell specific; we observed consistent expression patterns for these genes in CCLE cell lines from multiple tissue types and in TCGA patient samples regardless of the data source, technology platform or differential expression analysis method applied to RNAseq data. Thus, it seems likely that a generic Ras-independent activation pathway exists for various tissue types or cell types that may comprise the same components as the one annotated for NK cells. We also identified an aspect of tissue specificity for Ras dependency, where we observed consistent gene expression profile alterations in KRAS-associated tissue types, including lung, pancreas, and colon, but not in other tissue types, in which KRAS may be less involved, such as breast. We propose that this pathway may constitute a global mechanism underlying Ras-independent signaling for MAP kinase activation and that its “wake-up” status may depend upon the cellular context, tumor status, and tumor stage as well as the environmental context. The fact that we see the involvement of this pathway even in tumor-derived cell line data suggests that the observed expression is derived from tumor cells and not immune infiltrating cells. Again, this is indicative of the more global and generic nature of this pathway across a wide range of tissue or cell types beyond NK cells. Interestingly, it was initially reported that KRAS knockout in cells revealed an alternative bypass mechanism supported by noncanonical MAPK signaling, and nearly all KRAS-deficient cells exhibited PI3K-dependent MAPK signaling and induced sensitivity to PI3K inhibitors28, which is also depicted as one of the schemes for Ras-independent MAPK signaling in this Biocarta pathway. This provides additional support for MAPK signaling in Ras-independent contexts that were defined by our ssGSEA score-based computational Ras dependency index. In addition, the previously observed feedback mechanism at the phosphorylation level between PREX and PAK29 is also consistent with our gene expression analysis in that these genes may have a tight regulation mechanism in Ras-independent contexts.Interestingly, we also observed complementary differential expression patterns of the RAC1 and PREX1 genes, which are among the critical upstream driving components in the pathway and its GEF, respectively, in TCGA PAAD patient samples and CCLE lung cell lines and various potential make-up expression schemes from other RAC1-related genes in our data. This is highly consistent with the uncovered synthetic lethality scheme of RAC1/PREX1 and Ras genes as well as the postulated PI3K/VAV-triggered and RAC1/PREX1-mediated MAPK signaling in a Ras-independent context from a previous essentiality profiling study8. Finally, the involvement of mediators of this pathway may be consistent with tumor cells transmitting a signal that does impact immune cell activity in the context of a tumor environment with EMT involvement, which is consistent with a recent report30.It should be noted that many of the previous studies did uncover the biological connections of these genes piece by piece. However, these previous studies did not indicate the role of PAK in the activation of downstream MAPK signaling in a RAS-independent context, as uncovered by our study. In addition, the Ras-independent pathway in NK cell-mediated cytotoxicity from Biocarta that depicts PAK as the convergence point for downstream MAPK signaling was originally found and described in NK cells. The integrative and comprehensive data mining and analysis in our study revealed such a scenario to be highly generic in Ras-independent contexts that can be defined by our computational RDIs. This is our novel notion for the field, that is, one can use computational RDIs to define the Ras dependency of a biological system with great application potential in both research and clinical settings. Furthermore, our report emphasizes the whole picture of these connections of these critical gene players in Ras-dependent or Ras-independent contexts to help understand and investigate their interplay, which we believe may help reveal the potential mechanisms of drug resistance for Ras-related treatment, given that Ras dependency and drug resistance are believed to be interconnected27. Taken together, these two pathways may play essential roles in Ras dependency and represent or be associated with the underlying mechanisms that seem worthy of further investigation and experimental validation.Interestingly, although more specific for lung tissue in our statistically assessed pathway enrichment analysis, the second pathway, namely, “Fas signaling pathway”, did show highly generic behavior at the gene level, especially for the upstream signaling genes, including FAS, FLICE and many caspases, in pancreas and colon samples in addition to lung samples. This may also suggest a highly generic role of FAS and FAS-mediated apoptosis in Ras dependency, which is consistent with a previous reportIt is intriguing that the dynamic range of the derived ssGSEA scores is very broad and not limited to tissues/samples in which RAS dependency would be expected to play a role, thus permitting easy delineation of the high and low ends of the spectrum characteristic of the Ras-dependent and Ras-independent natures, respectively. This allows for assessment of the relative strength of the Ras dependency or Ras independency values as a continuum rather than as discrete categories. We also observed that the KRAS mutation status showed no connection with Ras dependency, suggesting that Ras dependency may be a unique feature that might allow us to assess a patient’s prognosis for drug resistance in the clinical setting beyond the conventional scenarios in which Ras gene involvement might be considered a priority . It will be interesting to evaluate the copy number and other genomic changes beyond Ras mutation status in this new light. It should be noted that some of our observations indicate that such Ras dependency may be tissue specific, and this tissue dependency may be linked to the levels of KRAS involvement in the tumors of such tissue types, although larger sample sizes and independent datasets may be needed to confirm these observations.4. This is also supported by our initial analysis that assessed the impact of mutations, copy number alternations and elevated gene expression of RAS pathway genes on individual patients across all TCGA tumor types and found that the vast majority, if not all, of the TCGA cancer patients were “hit” one way or another by RAS pathway genes (unpublished observations). Ras dependency may be an intrinsic nature of all cell types, and it is the “wake-up” status of the intended cells that determines whether Ras dependency becomes an influential factor for the behavior of these cells dependent upon their cellular and environmental contexts.In addition, the large overlap between the dynamic ranges of the EMT signature-based ssGSEA scores across a variety of tissue types suggests that Ras dependency may not just operate in tissue types in which KRAS was conventionally believed to play a major role, such as pancreas, lung, and colon. In fact, our recent report showed that KRAS or RAS pathway genes not only play a critical role in these major “RAS-engaged” tissue types but also very likely play important roles in many other tissue typesThe association between the EMT signature or colon signature-based ssGSEA scores with TCGA cancer patients’ survival outcomes in various tumor types shed further light on the importance of Ras dependency in cancer patient prognosis and drug resistance during treatment. This is indeed the first line of direct evidence showing a strong connection between Ras dependency and cancer patient survival outcome and its clinical application potential, although additional independent datasets with larger sample numbers may be needed to further validate the connections established here.This is the first computational application that uses genome-wide gene expression profiling to represent RDIs. Conceivably, such a gene signature-based RDI scoring system can be essentially applied to cancer patients’ tumors using gene expression data. Consequently, we may be able to evaluate the ability to use EMT signature-based RDIs as a biomarker to predict the sensitivity of individual patient tumors to KRAS inhibition, a currently unmet need for effective therapeutic translation. In the context of the target of delivering precision medicine, it seems likely that a Ras dependency index can represent one dimension in a complex interplay of “usual” suspects that, when fully integrated, can help to correctly type a patient’s optimal treatment regimen. We encourage others in the community to investigate the relationships of these two pathways with Ras dependency and validate our findings that may help expedite our understanding of drug resistance in terms of the underlying mechanisms for Ras dependency and potential interaction with the immune system as well as the clinical values of such computational RDIs.Supplementary Information File.


YuPingFeng (YPF) granules are a classic herbal formula extensively used in clinical practice in China for the treatment of COPD. However, the pathological mechanisms of YPF in COPD remain undefined. In the present research, a network pharmacology-based strategy was implemented to elucidate the underlying multicomponent, multitarget, and multipathway modes of action of YPF against COPD. First, we identified putative YPF targets based on TCMSP databases and constructed a network containing interactions between putative YPF targets and known therapeutic targets of COPD. Next, two topological parameters, “degree” and “closeness,” were calculated to identify target genes in the network. The major hubs were imported to the MetaCore database for pathway enrichment analysis. In total, 23 YPF active ingredients and 83 target genes associated with COPD were identified. Through protein interaction network analysis, 26 genes were identified as major hubs due to their topological importance. GO and KEGG enrichment analysis results revealed YPF to be mainly associated with the response to glucocorticoids and steroid hormones, with apoptotic and HIF-1 signalling pathways being dominant and correlative pathways. The promising utility of YPF in the treatment of COPD has been demonstrated by a network pharmacology approach. Chronic obstructive pulmonary disease (COPD) is currently the fourth leading cause of death in the world , which wβ2 receptor agonists, and M receptor blockers [Due to the different pathogenic mechanisms of COPD, the drugs commonly used in clinical practice are inhaled agents, including corticosteroids, blockers . HoweverWith the advancement of traditional Chinese medicine (TCM) research, many have realised the value of TCM, and a large number of clinical reports have shown the advantages of TCM for the treatment of COPD in recent years. The obvious superiority of TCM for COPD treatment is based on overall and comprehensive therapeutic approaches through multiple targets and pathways. The TCM “YuPingFeng” (YPF), which has been used in China for a thousand years, has good clinical efficacy and safety. Recently, several basic studies have confirmed that YPF granules  can significantly improve specific and nonspecific immune functions –9. CliniThe aim of this research was to explore the mechanism of YPF granules in patients with COPD using network pharmacology, which can predict potential molecules for further analysis. The flowchart of the experimental procedures of our research is shown in http://tcmspw.com/tcmsp.php, updated on May 31, 2014). TCMSP is a unique systems pharmacology platform of Chinese herbal medicines that captures relationships between drugs, targets, and diseases. The database includes pharmacokinetic properties for natural compounds involving oral bioavailability (OB), drug-likeness (DL), intestinal epithelial permeability, blood-brain barrier, and aqueous solubility, among others. This breakthrough has sparked a new interest in the search for candidate drugs in various types of traditional Chinese herbs [To determine the chemical ingredients of the three herbs contained in YPF, we performed a search in the Traditional Chinese Medicine Systems Pharmacology database  (TCMSP dse herbs . The herhttps://www.genecards.org/), a searchable, integrative database that provides comprehensive, user-friendly information on all annotated and predicted human genes. GeneCards automatically integrates gene-centric data from 150 web sources, including genomic, transcriptomic, proteomic, genetic, clinical, and functional information. The keyword “chronic obstructive pulmonary disease” was used to choose a protein-encoded function with a GeneCards Inferred Functionality Score (GIFtS) ≥ 30 as the gene symbol [http://www.omim.org/, updated August 9, 2019), which catalogues all known diseases with a genetic component, links these diseases to the relevant genes in the human genome, and provides references for further research and tools for genomic analysis of a catalogued gene [We collected COPD targets from two sources. One was GeneCards (e symbol . The GIFhttp://www.UniProt.org) was used to obtain the official gene symbols of all the targets, and this information was used for subsequent analysis of network pharmacology data.Targets of the active ingredients of all herbs in YPF granules were identified in the TCMSP database. UniProt  language, which is an interpreted scripting language. We ran the common drug-disease target, target symbol, and Perl script prepareCyto.pl together to obtain the three drug networks and MoLLISTs, respectively. Then, we used the merge function in Cytoscape software  (versionhttps://string-db.org/) and employed to construct a PPI network model with the species set to “Homo sapiens”; the lowest mutual action threshold was set to medium “medium confidence” (>0.4), and other parameters were the default settings.Protein-protein interaction (PPI) data were obtained from STRING , which c“degree” > 20 and “closeness” > 48.8 to be major hubs. Both the hub gene and network were retained, the calculation data were downloaded, the above indicators were sorted, and 26 genes that met the requirements were selected.After establishing the PPI network, the Cytoscape plug-in cytoHubba was used to filter out hub genes. An important hub gene can be selected through calculation and analysis of the network structure and the weighted reconnection between nodes. The network visualization software Cytoscape  (versionhttp://metascape.org/) is a web-based portal designed to provide a comprehensive gene list annotation and analysis resources for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledge bases within one integrated portal [Metascape  database to obtain drug targets, which include 94 targets for Huangqi, 26 for Fangfeng, and 10 for Baizhu. In the end, we got a total of 96 targets to remove duplicate values , as shown in Gene symbols with a protein coding function and a GeneCards Inferred Functionality Score ≥ 30 were selected in GeneCards, which filtered insignificant genes in the database. In total, 3616 known therapeutic targets for COPD were collected from the GeneCards database. In addition, 470 known therapeutic targets for the treatment of COPD were acquired from the OMIM database. After eliminating redundancy, 4037 known therapeutic targets in the treatment of COPD were collected in this study , three drugs , 23 active ingredients, and 83 target genes, as illustrated in the network shown in k-means clustering (the network was clustered into a specified number of clusters) to obtain 3 clusters.As depicted in “degree” > 20 and “closeness” > 48.3 to be major hubs. Eventually, 26 major hubs were selected for further study, as shown in the network in We employed the Cytoscape plug-in cytoHubba to filter the hub genes based on the PPI. We considered proteins with Metascape was used for GO and KEGG analyses of the 26 hub genes to obtain enriched ontology clusters, which are expressed in the following two forms: a bar graph, as shown in In this study, using the TCMSP analysis platform, 41 active ingredients and 96 drug targets were identified in YPF granules by screening oral bioavailability (OB) and drug-likeness (DL). Next, we constructed a quaternary structure network that contained disease-drug-ingredient targets. According to the results, the most active ingredients and drug targets in YPF granules are Radix Astragali (Huangqi), followed by Radix Saposhnikoviae (Fangfeng) and Atractylodis Macrocephalae Rhizoma (Baizhu). Therefore, the significance of Radix Astragali (Huangqi) in the whole prescription may be the focus of future compatibility studies. After identifying the intersection of the targets of the drugs and disease, 83 common targets were obtained. To narrow the scope, a topology analysis method was adopted, and the degree and closeness were used as the main references based on the established PPI network. Twenty-six hub genes were analysed with the Metascape database to identify enriched GO and KEGG signalling pathways. The main functions of these genes are involved in the response to glucocorticoids and steroid hormones, the apoptotic signalling pathway, and the HIF-1 signalling pathway.COPD is a global health problem characterized by incomplete reversible airflow limitations and aggravation. Airflow limitation is closely related to structural damage, which causes airway remodelling characterized by chronic inflammation and structural destruction of the airway wall and lung parenchyma. Changes in the composition of the bronchial wall cause the airway space to become constricted and distorted, resulting in the development of emphysema, which is enhanced in airflow resistance, and chronic airflow obstruction. Cytokines and inflammatory mediators play important roles in the pathophysiological processes of COPD , and morWe identified hub genes on the basis of PPI network construction, and the top four targets with the highest degrees of freedom were interleukin-6, caspase-3, vascular endothelial growth factor A, and mitogen-activated protein kinase 8. These target genes play important roles in the development of COPD. IL-6 is an important marker of the inflammatory response. Ghobadi et al.  found a α subunit and a constitutively expressed cytoplasmic HIF-1β subunit. HIF-1α is a major regulator of oxygen homeostasis and regulates the pathophysiology of hypoxia [α) and promotes the binding of HIF-1 to HRE, thereby inducing activation of hypoxia-sensitive target genes and further promoting the cellular response to hypoxia. This study found that YPF granules play a role in COPD treatment through the HIF-1 signalling pathway. Nonetheless, the specific mechanism of action of the pathway has not yet been elucidated, which should be addressed in subsequent studies.Imbalanced oxidation/antioxidant function is also an important factor in COPD pathogenesis . Cells a hypoxia . The maiIn summary, the potential biological mechanism of YPF granules in the treatment of chronic obstructive pulmonary disease involves multiple components, targets, and channels, and the therapeutic effects occur through pathways involving glucocorticoids and steroid hormones. The pharmacodynamic mechanism of YPF granules in COPD, including the inflammatory mechanism, apoptosis, and HIF-1 signalling pathway, was elucidated from a holistic and systematic perspective. This study provides a scientific basis for the subsequent development and utilization of YPF granules. Of course, because only network pharmacology techniques were applied to examine the active components and targets of YPF granules, there may be experimental limitations such as inconsistent original information, limited information on drug targets and small molecule compounds, and biased information in the database due to the popularity of some topics. Therefore, experimental research is needed to verify the results of this study, especially the mechanism through which YPF granules and its components regulate apoptosis and the HIF-1 signalling pathway.The mechanism of action of YPF in COPD involves multiple compounds, targets, and pathways. The therapeutic effects of YPF in COPD may be dependent on the response to glucocorticoid and steroid hormones and pathways related to HIF-1 signalling and apoptotic signalling. The systems pharmacology approaches developed in our study provide an alternative strategy for comprehensively understanding the mechanisms of YPF in COPD.


Rodgersia sambucifolia Hemsley (known as Yantuo and abbreviated as YT) and Bentonite (BN). Now, it is also a Chinese patent medicine used in the clinic to treat infantile diarrhea. Besides evaluating the antidiarrheal effect of SCZX on diarrhea mice induced by Folium Sennae, the purpose of this study is to outline the characteristics of the antidiarrheal effect and reveal the potential mechanisms of SCZX through the analysis of the mechanism and active components of YT via network pharmacology and molecular docking, combined with the research progress of BN obtained from the literature. SCZX (3.12 and 12.48 g/kg) effectively inhibited diarrhea in mice, significantly lowering the loose stool rate (LSR), loose stool level (LSL), and loose stool index (LSI). Using network pharmacology, the “herb-compound-target-pathway-pharmacological action” network was mapped to indicate the antidiarrheal mechanism of YT. And the docking results revealed that 4 components of YT including quercetin, geranyl-1-O-α-L-arabinopyranosyl-(1 ⟶ 6)-β-D-glucopyranoside, 3α-O-(E)-p-hydroxy-cinnamoyl-olean-12-en-27-oic acid, and daucosterol showed significant docking activities with STAT3, EGFR, and SLC10A2, involving 11 pathways such as Th17 cell differentiation, Jak-STAT signaling pathway, ErbB signaling pathway, and HIF-1 signaling pathway. According to our research results and literature reports, the antidiarrheal could be summarized into five aspects: inhibiting intestinal inflammation, acting as a barrier to the intestinal mucosal, regulating water and ion transport, involving the purification of intestinal microorganisms, and intestinal transmission, which might be dependent on multiple proteins and intervention in multiple pathways.Sechang-Zhixie-San (SCZX) is an ancient prescription used for pediatric diarrhea by the Yi people in China, which consists of  Diarrhea is defined by the World Health Organization (WHO) as having three or more loose or liquid stools per day or having more stools than that is normal for that person . It is uRodgersia sambucifolia Hemsley . Bentonite, a clay mineral whose main component is montmorillonite, can be used to cleanse the digestive tract of pathogens and to protect the intestinal mucosa [Sechang-Zhixie-San (SCZX) originates from a secret prescription handed down by family members of Liu Guozhong, a famous doctor of Yi nationality in Yunnan Province, which has been used in China for hundreds of years . Presentl mucosa . SCZX isl mucosa . SCZX haThe molecular targets of most traditional Chinese medicine (TCM) prescriptions and ingredients are elusive, which remain one of the biggest hurdles in the application of TCM prescription and TCM-based drug discovery . NetworkSennae Folium was purchased from the Niu En Tang pharmacy  and was identified by Prof. Ya Tu from the China Academy of Chinese Medical Sciences. The water extract of Sennae Folium was obtained according to the following protocol . Sennae Male SPF ICR mice (body weight 20 ± 2 g) were obtained from Beijing Vital River Laboratory Animal Technology Co., Ltd. (Certificate No. SCXK(Beijing) 2016-0002; Beijing, China) and were free-fed standard chow and water under a 12/12 h dark/light cycle using adaptive feeding for 3 days. The animal protocols were approved by the Animal Care and Use Committee of the Jingjinji Lianchuang Institute of Pharmaceutical Research, China .SCZX  and loperamide hydrochloride  were prepared with distilled water. A total of 50 mice were randomized into five different treatment groups as follows: control group, model group, SCZX (3.12 g/kg and 12.48 g/kg) group, and loperamide (2.08 mg/kg) group, with 10 mice in each group. Mice in the SCZX and loperamide groups were, respectively, given SCZX and loperamide for 7 days, whereas mice in the control and model groups were given an equal amount of distilled water, 0.2 mL/10 g body weight. On the 6th and 7th day, after administration for 30 min, the mice in the model, SCZX, and loperamide groups continued to be given the Sennae Folium water extract (0.2 mL/10 g body weight) orally to construct acute diarrhea, while mice in the control group were given distilled water.After administration for 7 days, the mice were restricted from food and reared individually in separate cages. The filter paper was placed underneath each cage to count loose stools, and the paper was changed once per hour. The defecation of mice was observed for 1–5 h, and the loose stools were classified into four levels based on the diameters of stain formed by loose stools on the filter paper . Loose sAfter fasting (water was not restricted) for 14 h, each mouse was given orally a nutritious semisolid paste (0.5 mL each mouse) prepared with water and comprised of sodium carboxymethyl cellulose (5 g), soluble starch (4 g), whole milk powder (8 g), oral glucose (4 g), and activated carbon (2 g). After absorbing it for 20 min, all mice were sacrificed and exposed by laparotomy, and the small intestine was carefully removed to observe the leading edge of the semisolid paste. The length of the small intestine from the pylorus to ileocecal and the length of the semisolid paste promoting were measured. The formula for calculating intestinal propulsion is as follows: intestinal propulsion rate = length of semisolid paste/entire length of the small intestine.P value of <0.05 was considered to have a significant difference, and P value <0.01 indicated an extremely significant difference.All results are expressed as means ± SD and evaluated for significance using one-way analysis of variance using SPSS17.0 . https://www.ncbi.nlm.nih.gov/pccompound/), the Chemistry Database developed by Shanghai Institute of Organic Chemistry, Chinese Academy of Science (http://202.127.145.134/scdb/default.asp), and the Traditional Chinese Medicine Systems Pharmacology Database [http://lsp.nwu.edu.cn/tcmsp.php). Furthermore, we also searched the literature to supplement compounds. Chemical structures were obtained from ChemDraw software  and stored in SMILES format.Components of YT were collected from the PubChem Database (Database  (http://http://www.swisstargetprediction.ch/) in SMILES format to predict the potential targets, and the attributes were set as “Homo sapiens.” Then, these targets were uploaded into the Enrichr web server (https://amp.pharm.mssm.edu/Enrichr/) to obtain gene-disease associations from DisGeNET [https://string-db.org/) to obtain protein–protein interactions (PPI); then, a network diagram (YT-target network) was constructed using Cytoscape 3.6.0.The components of YT were submitted to the Swiss Target Prediction platform (DisGeNET . The tarhttps://www.drugbank.ca/). The UniProt database was used to standardize the targets and convert them into Homo sapiens gene names. The targets only related to diarrhea would be selected and processed by String to obtain PPI; then, a network diagram (diarrhea-target network) was constructed using Cytoscape 3.6.0.The disease targets of diarrhea were searched in Online Mendelian Inheritance in Man  and DrugBank (YT-target network was performed using MCODE to find clusters (highly interconnected regions). The common potential targets of YT and targets of diarrhea were collected, and the network topological features of these common targets in the YT-target network and diarrhea-target network were defined. These common targets were considered as the potential hub proteins for YT in the treatment of diarrhea, and the target clusters containing hub proteins were selected.Metascape database (https://david.ncifcrf.gov) for further investigation including GO and pathway enrichment [Homo sapiens” was selected as the analyzed species. The threshold value was set as P < 0.01, minimum count 3, and enrichment factor >1.5. GO annotated and KEGG pathway analyses were also conducted.The targets of diarrhea and target clusters from the YT-target network were submitted to the richment . “Homo sPathways that contained the core proteins were selected from the results of the KEGG pathway enrichment analysis and considered as the potential mechanism for YT-treating diarrhea. We collected the targets in the MCODE clusters, which can map the above pathways, and the components related to these targets were searched and determined. Thus, we conducted a “herb-compound-target-pathway” network of YT using Cytoscape 3.6.0, and the unclustered targets and their pathways would also be fitted into the visual network. The hub proteins related to YT and their covered signaling pathways would command attention and may be selected for follow-up studies.in vitro screening. The potential components of YT were submitted to AdmetSAR (http://lmmd.ecust.edu.cn/admetsar2), which was developed as a comprehensive source and open-source tool for the prediction of chemical ADME properties and drug-likeness (DL) [The properties of absorption, distribution, metabolism, and excretion are important indicators of the effectiveness of herbs and play key roles in drug discovery . Computaess (DL) . Consideess (DL) , 22, we ess (DL) .http://www.rcsb.org) and saved in pdb format. PyMOL (https://pymol.org) was used to isolate the macromolecules and their protoligands, and the structures of the macromolecules were then optimized using AutoDock Tools 1.5.6. It removed water molecules, added hydrogen atoms, repaired the charges by adding a Gasteiger charge, and was saved in pdbqt format. The structure information of small molecule compounds of YT was obtained from PubChem (https://pubchem.ncbi.nlm.nih.gov/) and then kept in mol2 format. AutoDockTools 1.5.6 was used to count the number of rotatable chemical bonds, and the compounds were saved in pdbqt format. The macromolecule grid box was defined according to the protoligand site using AutoDockTools 1.5.6. Furthermore, an accurate docking with the components of YT and target proteins was performed using AutoDock vina 1.1.2, setting the energy range = 5 and exhaustiveness = 100, and the best docking conformation was analyzed with PyMOL and LigPlot+ v.2.2.Molecular docking was performed between the core proteins and their associated components of YT to further explain the potential mechanism and material basis of YT in the treatment of diarrhea. The 3D structures of target proteins were downloaded from the RCSB Protein Data Bank , while only LSR was significantly different at the 4th h (P < 0.01). Compared to the model group, the DR of mice treated with SCZX (3.12 g/kg and 12.48 g/kg) and loperamide decreased at all time points. At the second and third hours after administration, the DR, LSR, LSL, and LSI of mice in SCZX groups significantly decreased (P < 0.01). The results are shown in Compared to the control group, Sennae Folium could increase the rate of diarrhea in mice at the second and third hours after administration; the LSR, LSL, and LSI of mice significantly increased , cellular component (CC), and molecular function (MF), and the BP, CC, and MF of CNA and CNB from the YT-target network are shown in Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource, which combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledge bases within an integrated portal. Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era . The resKEGG pathway enrichment analysis of CNA and CNB was conducted using Metascape, and the top 20 pathways are shown in The pathways in Homo sapiens could not be found in PDB, and bile acid metabolism is associated with diarrhea [Homo sapiens [α-L-arabinopyranosyl-(1 ⟶ 6)-β-D-glucopyranoside, geranyl-1-O-β-D-xylopyranosyl-(1 ⟶ 6)-β-D-glucopyranoside, 3α-O-(E)-p-hydroxy-cinnamoyl-olean-12-en-27-oic acid, 3-methoxy-4-O-D-glucopyranosyl- phenylpropane-7, 8, 9-triol, and arbutin. Daucosterol also constituted three hydrogen bonds with EGFR residue including two oxygen atoms of carboxyl group on the γ-carbon atom of Glu . Furthermore, the affinity value on an optimal docking conformation between daucosterol and EGFR was −9.1 kcal/mol. Daucosterol formed three hydrogen bonds with STAT3 residue, which included Ser , Gly , and Thr , and the affinity value on an optimal docking conformation between daucosterol and STAT3 was −7.5 kcal/mol. The affinity of the optimal docking result is shown in Three proteins were involved in molecular docking including signal transducer and activator of transcription 3, STAT3 (6NJS), epidermal growth factor receptor, EGFR (5UG9), and ileal sodium/bile acid cotransporter, ASBT (3ZUY). Although the 3D structure of the three macromolecules including sodium/glucose cotransporter 1 (gene name SLC5A1), M-OR-1 (gene name OPRM1), and ileal sodium/bile acid cotransporter (gene name SLC10A2) from diarrhea , we sele sapiens . The griα -L-arabinopyranosyl-(1 ⟶ 6)- β -D-glucopyranoside, 3-methoxy-4-O-D-glucopyranosyl- phenylpropane-7, 8, 9-triol, 3α-O-(E)-p-hydroxy-cinnamoyl-olean-12-en-27-oic acid, and daucosterol. As one of the components of SCZX, BN is a clay comprising smectite minerals, mainly montmorillonite, which is a member of the smectite family with a general composition of 0.332(Si4O10) (OH)2nH2O [According to the results of network pharmacology, SLC10A2, SLC5A1, EGFR, and STAT3 were defined as the core target proteins of YT-treating diarrhea, which were derived from the cluster analysis, involving two antidiarrheal modules. These core proteins could be mapped to 11 pathways, including cytokine–cytokine receptor interaction (hsa04060), Ras signaling pathway (hsa04014), Jak-STAT signaling pathway(hsa04630), MAPK signaling pathway (hsa04010), phospholipase D signaling pathway (hsa04072), HIF-1 signaling pathway (hsa04066), chemokine signaling pathway (hsa04062), bile secretion (hsa04976), ErbB signaling pathway (hsa04012), Th17 cell differentiation (hsa04659), and regulation of actin cytoskeleton (hsa04810). Most of them had been confirmed to be related to diarrhea, their functions and roles were identified, and their signaling pathways were closely related to intestinal inflammation, intestinal mucosal barrier, and water and ion transport. The details would be discussed later. Furthermore, molecular docking was provided to excavate the potential effective components of YT, and that included quercetin, geranyl-1-O- OH)2nH2O . It was Thus, we constructed a visual “herbal medicine-compound-target-pathway” network to explain the antidiarrheal mechanism of SCZX, which includes the potential active ingredients, core target proteins, and their pathways. And the pharmacological characteristics of SCZX were also linked to the network, which were summarized as the intestinal inflammatory, water and ion transport, intestinal motility, intestinal mucosal barrier, and purification of intestinal microorganisms. They are shown in Yuanyang Yi Medicine Book, written by ancient Yi authors in 957 AD [Chinese Herbal Medicine of Yunnan , the efficacy of BN is in its astringency to stop diarrhea, and the efficacy of YT is in its capacity for expelling wind-dampness and stopping diarrhea. YT has been used to treat diarrhea, abdominal distension, and dyspepsia among the Yi people, and the Yantuo Zhixie decoction is a folk prescription in TYM that only contains YT [The Yi people are mainly present in the Yunnan, Sichuan, Guizhou, and Guangxi Provinces in China, and traditional Yi medicine (TYM) has a long history of more than 1000 years and is a type of traditional Chinese medicine. The written history of TYM can be traced back to the n 957 AD  and was tains YT  and can Sennae Folium is a laxative and a traditional Chinese Materia Medica (CMM) that can cause diarrhea as well, commonly used for the treatment of diet, stress, or medication-related constipation, and it is very often used to construct animal models to support modern research on diarrhea-related diseases. The anthranoids are considered as the major contributor to diarrhea caused by Sennae Folium . In our Network pharmacology is a novel approach for investigating the system-level mechanisms of drugs, which was put forward by Hopkins in 2007 , 32. NetIn our study, considering the diversity of antidiarrheal approaches, the DL and ADME properties of YT's components were not screened before constructing the compound-target relationship. Primarily, all of the components were included for target prediction, and the core targets of YT in the treatment of diarrhea originated from the common targets of the gene-diarrhea association data of YT and diarrhea disease targets. The gene-diarrhea association data of YT were enriched through DisGeNET, which is integrated in the Enrichr tools. DisGeNET integrates data from CTD (Comparative Toxicogenomics Database), UniProt, OMIM, ClinVar, Orphanet, GWAS Catalog, GAD (Genetic Association Database), and LHGDN (The Literature Human Gene Derived Network) data resources, and it can also provide the Disease Specificity Index (DSI) and Disease Pleiotropy Index (DPI) to assist the prioritization of genotype-phenotype relationships. DisGeNET can be used to research the molecular underpinnings of specific human diseases and their comorbidities, analyze the properties of disease genes, and predict drug therapeutic action and drug adverse effects . Relyingγ may contribute to diarrhea associated with intestinal inflammation in part through the regulation of epithelial aquaporin-1 water channel via a nonclassical Jak-STAT signaling pathway [+ T cells stimulated by antigen and maintain their mucosal barrier function in the normal intestine, while Th17 cells can secret inflammatory factors such as IL-17A, IL-17B, IL-17C, IL-17D, IL-17E, and IL-17F, which mediate tissue inflammatory response by inducing the expression of proinflammatory cytokines, inflammatory chemokines, and matrix metalloproteinases. They also participate in the proliferation, maturation, and chemotaxis of neutrophils [As determined by network pharmacology research, the mechanism of YT for treating diarrhea may involve the targets of STAT3, EGFR, SLC5A1, and SLC10A2. STAT3 is one of the major transcriptional regulators of inflammatory signaling. The inflammatory response triggered by the activation of STAT3 can cause dysregulation of the intestinal epithelial barrier  and inte pathway . Th17 cetrophils , 37, thutrophils .κB, PI3K/AKT/mTOR, and Jak/STAT [EGF is an endogenous substance for tissue repair and cell protection secreted by the salivary glands, liver, pancreas, kidney, and intestines. EGF exhibits a wide range of physiological effects including the stimulation of electrolyte and nutrient absorption and the protective effects of gastrointestinal mucosa . EGF medJak/STAT . ErbB reJak/STAT . DiarrheJak/STAT . FurtherJak/STAT , MAPK anJak/STAT , 45, regJak/STAT , and intJak/STAT  are relaα-L-arabinopyranosyl-(1 ⟶ 6)-β-D-glucopyranoside, 3α-O-(E)-p-hydroxy-cinnamoyl-olean-12-en-27-oic acid, and daucosterol showed fine docking activities with the macromolecules of STAT3, EGFR, and SLC10A2.Molecular docking was used to explore the effective ingredients of YT for diarrhea, and these compounds which conformed to IA, OB, and DL were selected out to perform docking. If the affinity is less than -7.0 kcal/mol, it means that small molecule has better binding activity with protein . The resHerbal approaches are being preferred for clinically treating diarrhea in children due to the adverse effects of chemical drugs, and the vast availability of antidiarrheal traditional medicine has shown efficacy. In this study, SCZX as an ancient prescription of Yi medicine was further verified to treat acute diarrhea. Depending on the focus study of YT via network pharmacology and molecule docking, we outlined the characteristics of antidiarrheal effect and “multicompounds, multitargets” mechanisms of SCZX completely, which include five aspects: inhibiting intestinal inflammation, acting as a barrier to the intestinal mucosal, regulating water and ion transport, involving the purification of intestinal microorganisms, and intestinal transmission. The results of this study might provide a clear direction for subsequent studies, but the extensive related experiments will be the focus of our next work.


Ischemia-reperfusion injury and drug-induced nephrotoxicity are the two most common reasons for acute kidney injury (AKI). However, little attention has been paid to early activation of fibroblasts in the progression of AKI to chronic kidney disease (CKD). The present study aimed to identify related genes and pathways on fibroblast activation in two mouse models of AKI: ischemia-reperfusion injury (IRI) model and folic acid (FA)-induced injury model.GSE62732 and GSE121190 were downloaded from the GEO database, and the differentially expressed genes (DEGs) was analyzed using the Limma package of R software. Principal component analysis (PCA) was also performed using R. The functional information of gene products was annotated by Gene Ontology (GO) and DAVID online database, and the pathway analysis was carried out by using the Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) database. Protein-protein interactions (PPI) network was constructed by STRING and Cytoscape. Furthermore, in the Hypoxia/Reoxygenation (H/R) model, the morphological changes of cells were observed under microscope and the expression of the hub genes in NRK-49F cells were validated by qRT-PCR assays.The microarray expression profiles of A total of 457 DEGs were identified. Among these, 215 DEGs were upregulated and 242 DEGs were downregulated in the acute injured samples compared with uninjured samples. The GO enrichment analysis indicated that these DEGs were mainly involved in transport, the oxidation-reduction process, the metabolic process, metal ion binding, hydrolase activity, and oxidoreductase activity. The KEGG analysis revealed that these DEGs were significantly enriched in the PI3K-Akt signaling pathway, protein digestion and absorption pathway, and focal adhesion pathway. The hub genes including Hnf4α, Pck1 and Timp1 were validated by the qRT-PCR assay in NRK-49F cells in the H/R model.Hnf4α, Pck1 and Timp-1 may play a pivotal role in the early activation of fibroblasts, providing novel therapeutic strategies for early prediction and treatment of renal fibrosis. Acute kidney injury (AKI) is a renal complication that occurs in up to 20% of hospital admissions , and parRecently, some scholars have found that the activation of Sirtuin 2(SIRT2) promotes fibroblast activation and aggravates renal fibrogenesis . As expeThe present study identified differentially expressed genes (DEGs) in kidney ischemia-reperfusion injury (IRI) and folic acid (FA)-induced injury models of C57BL/6 mice using the bioinformatics analysis of Gene Expression Omnibus (GEO) database. Then, functional enrichment analysis including Gene Ontology (GO) and KEGG, as well as protein-protein interaction (PPI) network analysis were carried out to explore the potential pathways and hub genes for further research.http://www.ncbi.nlm.nih.gov/geo/) was searched using ‘(Acute Kidney Injury) AND Fibroblasts’, and two datasets were selected for the present analysis. Dataset GSE62732 was based on the GPL1261 platform (Affymetrix Mouse Genome 430 2.0 Array), which included five ischemic samples and three normal samples (GPL11180 platform (Affymetrix HT MG-430 PM Array Plate), which included three FA-induced samples and three uninjured kidney samples (The GEO database ( samples . Dataset samples . Subsequ2 of the fold change (logFC) was calculated. The —logFC—>1 and corrected P-value of <0.05 were set as the cut-off criteria. The venn diagram and the heatmap were generated by using the online tool imageGP (http://www.ehbio.com/ImageGP/index.php/Home/Index/index.html). The volcano plot was generated using the “ggplot2” R package (https://ggplot2-book.org/).Principal component analysis (PCA) was performed to assess the similarity of gene expression patterns in the two different AKI models by using the “gmodels” R package (https://cran.r-project.org/web/packages/gmodels/index.html).The original data was normalized using the robust multi-array average (RMA) method. The average of multiple probes for a single gene was calculated. The linear model analysis of microarray data package (LIMMA) in R was used to identify DEGs in AKI samples compared with the non-injured renal samples. The loghttp://david.abcc.ncifcrf.gov) was used to perform functional analyses namely biological process (BP), molecular function (MF), and cellular component (CC), as well as KEGG pathway analyses in May, 2020  online database , which is a database of known and predicted protein interactions (https://cytoscape.org). Subsequently, Molecular Complex Detection (MCODE) and Cytohubba plugin of Cytoscape sofeware were used to identify potential modules and key genes in the PPI network  and 95% air at 37 °C. In order to establish the hypoxia/reoxygenation (H/R) injury model, the renal fibroblasts were deprived of serum and exposed to hypoxia  for 12 h to induce hypoxic injury. Subsequently, the culture medium was refreshed, and the culture plate was reoxygenated in a regular incubator (5% CO2 and 95% air) for 6 h, while the control group was cultured in an ordinary cell incubator.NRK-49F cells were purchased from the American Type Culture Collection, and incubated in Dulbecco’s Modified Eagle’s Medium (DMEM) , supplemented with 10% fetal bovine serum (GIBCO) and 1% penicillin /streptomycin, under the conditions of 5% carbon dioxide  after being washed three times with PBS. In addition, the ImageJ software (Version 1.52) was used to count cells in the captured images.NRK-49F cells were equally seeded in 60 mm culture dishes  at a density of 5 × 10β-actin was used as an internal reference. Three replicate holes were performed for the experiment, and the primer sequences are shown in Ct−ΔΔ method, with normalization to the β-actin gene.In the present study, qRT-PCR assay was used to validate the expression levels of 10 hub genes with the highest degree of interaction and significance in the PPI network. TRIzol reagent  was used to isolate total RNA from the H/R model and control group. Then, the RNA was reverse transcribed into complementary DNA according manufacturer’s instructions of the Promega kit . Using the ABI PRISM 7500  to measure the mRNA expression levels of hub genes by qRT-PCR reactions,and t-test. P < 0.05 was considered statistically significant.GraphPad Prism 8.0  was used for the statistical analysis of all data. R software version 3.5.0. was used for analysis. The qRT-PCR results were presented as mean ± standard deviation. The differences of cell count in captured pictures and the qRT-PCR results were analyzed by Student’s GSE62732 and GSE121190) between acute injured kidney and normal samples, 457 DEGs  were screened, which included 215 upregulated genes and 242 downregulated genes. Venn diagram and volcano plots , cellular component (CC) and molecular function (MF) domains in the co-expressed DEGs were ranked according to enriched gene count and P-value . In the P-value. Overall, the top seven pathways were selected from the 269 pathways correlated to fibroblast activation in the IRI and FA-induced kidney injury models , we observed the cellular morphology of NRK-49F cells exposed to normoxia and H/R conditions . The celEvery DEG analyzed with Cytoscape has an interactional degree with others. The 10 hub genes , which had the highest interactional degree among all DEGs, were selected as the most prominent fibroblast activation-associated DEGs. The qRT-PCR analysis was performed to verify the expression levels of these hub genes. α-SMA is a marker of activation and differentiation of fibroblasts into myofibroblasts. As identified by the above analyses in the IRI and FA-induced injury model, Timp1 was an upregulated DEG, and the expression level was confirmed to be elevated in the H/R model, while Hnf4α and Pck1 were the downregulated DEGs, and the expression levels decreased, when compared to normal cultured fibroblasts. The difference in expression level of the other seven genes was not statistically significant between the H/R model and control group .Tubulointerstitial fibrosis is the common endpoint of virtually all progressive renal diseases, regardless of the primary cause . The train vivo and in vitro evidences have suggested that DHA and Nrf2 may attenuate renal fibrosis through the regulation of fibroblast proliferation and differentiation by inhibiting the PI3K/AKT pathway, and both of these have been used as therapeutic antifibrotic targets for the treatment of renal fibrosis , which are endogenous protein regulators of the family of matrix metalloproteinases (MMPs) . Except fibrosis . In addifibrosis . The expfibrosis . Neverthfibrosis . DiffereIn summary, a comprehensive bioinformatics analysis of the possible DEGs and pathways involved in two different AKI models was conducted. DEGs, such as Hnf4α, Pck1 and Timp-1, as well as the PI3K-Akt signaling pathway, protein digestion and absorption pathway, focal adhesion pathway and gluconeogenesis/glycolysis pathway, may play a pivotal role in the early activation of fibroblasts. The findings of the present study may facilitate our understanding the different roles of fibroblast activation in AKI.However, the present study also has some limitations. First, the sample size of the microarray analysis was small, which may cause a potential false positive rate. Second, in vivo experimental verifications were absent from the present study. Therefore, in-depth experiments with a large sample size should be considered to corroborate these discoveries.10.7717/peerj.10926/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.10926/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.10926/supp-3Supplemental Information 3Click here for additional data file.10.7717/peerj.10926/supp-4Supplemental Information 4Click here for additional data file.10.7717/peerj.10926/supp-5Supplemental Information 5Click here for additional data file.10.7717/peerj.10926/supp-6Supplemental Information 6Click here for additional data file.10.7717/peerj.10926/supp-7Supplemental Information 7Click here for additional data file.10.7717/peerj.10926/supp-8Supplemental Information 8Click here for additional data file.10.7717/peerj.10926/supp-9Supplemental Information 9Click here for additional data file.10.7717/peerj.10926/supp-10Supplemental Information 10Click here for additional data file.10.7717/peerj.10926/supp-11Supplemental Information 11Click here for additional data file.


Signaling pathway analysis methods are commonly used to explain biological behaviors of disease cells. Effector genes typically decide functional attributes  by abnormal signals they received. The signals that the effector genes receive can be quite different in normal vs. disease conditions. However, most of current signaling pathway analysis methods do not take these signal variations into consideration.In this study, we developed a novel signaling pathway analysis method called signaling pathway functional attributes analysis (SPFA) method. This method analyzes the signal variations that effector genes received between two conditions  in different signaling pathways.p-value of target pathways, and the percentages of significant pathways. The results confirmed that SPFA was the top-ranking method in terms of median rank of target pathways and the fourth best method in terms of median p-value of target pathways. SPFA’s percentage of significant pathways was modest, indicating a good false positive rate and false negative rate. Overall, SPFA was comparable to the other methods. Our results also suggested that the signal variations calculated by SPFA could help identify abnormal functional attributes and parts of pathways. The SPFA R code and functions can be accessed at https://github.com/ZhenshenBao/SPFA.We compared the SPFA method to seven other methods across 33 Gene Expression Omnibus datasets using three measurements: the median rank of target pathways, the median  Recently developed high-throughput functional genomics technologies have generated large amounts of experimental disease data and detected new biological information. Challenge for biologists is understanding the biological behaviors of disease cells using both newly generated disease data and existing biological knowledge. Signaling pathway analysis methods are used to better understand the biological behaviors of disease cells. The understanding of biological behaviors of disease cells benefits to understand the pathological scenery and treatment. Over-representation analysis (ORA) based methods were initially presented as signaling pathway analysis methods to help biologists identify over-represented pathways from a list of relevant genes produced from experimental data. ORA-based methods merely count the number of differentially expressed genes in specific functional category gene sets such as the Gene Ontology (GO) , the KyoSignaling pathway impact analysis (SPIA), one of the most widely-used pathway-topology-based methods, considers both the number of differentially expressed genes (DEGs) in a given pathway and the topology information of that pathway . Many imThese methods still have their disadvantages. Pathway-topology-based methods do not consider the importance of genes in pathways. Gene-weight-based methods have been proposed to overcome this limitation. Pathway analysis with down-weighting of overlapping genes (PADOG) uses the frequency of a present gene in the analyzed pathways to improve gene set analysis . FunctioA signaling pathway is a cascade of molecular reactions that bring out the functional attributes  associated with the biological behaviors of disease cells using effector genes. Effector genes receive signals without outputting signals to other genes in an individual signaling pathway . DiseaseAdditionally, the functional attributes in the same signaling pathway may be very different from one another, and can sometimes be opposites. For example, there are two opposite functional attributes on the axon guidance pathway: axon repulsion and axon attraction (see the hsa04360 pathway in the KEGG dataset). We cannot determine which functional attributes contribute more to the disease using most current pathway analysis methods. Furthermore, some pathways consist of several parts, each with very different contributions. For example, the Wnt signaling pathway is significant across different diseases and can be divided into three parts. Most existing pathway analysis methods cannot determine which part of the Wnt signaling pathway most significantly contributes to a specific disease.We propose a new method that considers the signal variations between normal and disease conditions that effector genes received in pathways: the signaling pathway functional attributes analysis (SPFA) method. SPFA calculates the gene expression changes in a given pathway using an ORA method and then combines the ORA method results with the signal variation results under two conditions . The signal variations can help identify functional attributes and abnormal pathways. We tested the capabilities of the proposed signaling pathway analysis method on a series of real datasets using three parameters. We also showed that the two types of probabilities considered in this method were indeed independent. Ultimately, we verified the usefulness of the signal variations the effector genes received under two different conditions using the SPFA method.Signaling pathway analysis methods require two types of input: a collection of pathways and a list of genes or gene products with accompanying expression values across different samples between the compared phenotypes. We used the KEGG signaling pathway as it is the most common manually-curated signaling pathway used for pathway analysis. We downloaded 213 signaling pathways from the KEGG PATHWAY dataset.The dataset’s DEGs were available. If no DEGs were selected, other comparable methods would return null results.p-values of 1 could not be analyzed.The results of these datasets could be analyzed. Pathway analysis result The target pathways of these datasets were KEGG pathways since we used KEGG pathways as examples.We acquired 33 disease gene expression datasets from the KEGGdzPathwaysGEO R-package and KEGGandMetacoreDzPathwaysGEO R-packages  Tarca, . Each dip-values < 0.05. Otherwise, we selected more than 200 genes with original p-values < 0.05 and absolute log (fold change) > 1.5. If DEGs still less than 200 genes, we selected the top 1% of genes ranked by p-values as DEGs.DEGs were selected if they contained more than 200 genes with FDR adjusted eg and an upstream gene sg, the signal variation from the gene sg to the effector gene eg can be defined as:To assess the signal variations between two conditions  that the effector genes received from upstream genes, we calculated the sum of signal variations from all upstream genes to effector genes. Given an effector gene nditions . If the s genes in the upstream of the gene For each effector gene The accumulated signal variation k is the total number of effector genes in the given pathway.For a given signaling pathway, the total accumulated signal variation ASV can be defined as:per-th time. The probability I is a function that returns 1 when the argument is true and 0 when the argument is false.Ultimately, the probability m genes, n genes are the number of DEGs in the m genes, and the given pathway contains t genes and r DEGs.The probability c is a product of The probability ment}Pde . The forp-values are calculated using a hypergeometric test.The SPFA procedure identifies significantly enriched pathways in two steps . The firFinding all effector genes in each signaling pathway.Ascertaining all paths from the upstream genes to the effector genes in each signaling pathway. If a path exists between the upstream genes and effector genes, an interaction must exist between them. The path’s network distances are used to weight the corresponding interactions.Using the Pearson correlation coefficient absolute difference values between the disease and normal samples to calculate the signal variations of the corresponding interactions.Using the network distance of each interaction to decrease their signal variations.Calculating the accumulation of the signal variations between the effector genes and upstream genes for each effector gene.Calculating the sum of the accumulations of all effector genes in each signaling pathway.Evaluating the statistical significance of each pathway based on their score.The second step is to measure the signal variations between the two conditions  that effector genes received from upstream genes in the signaling pathways. This is completed by:p-value. We used the FDR adjust method on the combined p-value to determine the significance of each signaling pathway. The pathways with the adjusted combined p-values smaller than a threshold value were considered as significant pathways.Ultimately, the results of the two steps are combined into one Studying the signal variations between two conditions  that the effector genes received leads to a deeper understanding of the biological behaviors of disease cells. Effector genes are widely scattered throughout the signaling pathways. If a gene has no signal inputs in an individual signaling pathway, the gene is not considered an effector gene. The distribution of effector genes in each signaling pathway can be seen in We compared seven methods to SPFA, including Fisher , GSA Ef, GSEA S, MRGSE , SPIA U, ROnoTooThere is no universally accepted technique for the validation of the results of pathway analysis methods. Current pathway analysis methods use the results of a very small number of datasets based on searching corresponding published life literature. This approach has its limitations. First, the number of datasets used is small. Second, authors often search their own, leading to biased results. Third, complex biological phenomena always directly or indirectly correspond to multiple signaling pathways.p-value of the 33 target pathways of the 33 disease datasets. Smaller median p-values meant higher sensitivity. The second measurement was the median rank of the 33 disease target pathways. The higher ranked methods were more accurate. To further validate the different pathway analysis method results, we used a third measurement: the ratio of significant pathways  in the 33 datasets. This measured the method’s ability to control false positive and false negative rates.The two probabilities datasets . Our resp-value of the 33 target pathways, the median rank of the 33 target pathways, and the ratio of significant pathways. The signaling pathways with adjusted p-values ≤ 0.05 were significant.We compared SPFA with the other seven methods using three measurements: the median p-value of the 33 target pathways, SPFA ranked fourth  and an Alzheimer’s disease dataset (GSE16759). The colorectal cancer microarray GSE4183 (Affymetrix array HG-U133 Plus2.0) included 15 colorectal cancer samples and 8 normal samples , we analyzed a colorectal cancer dataset  are the responses that effector genes respond to the signal they received. Disease cells always have abnormal functional attributes. Thus, the signal that the effector genes received can be very different. However, no current pathway analysis method takes this factor into consideration. Most pathway analysis methods only include the activation and significance of pathways. Their results give us inadequate information on functional attributes that can help explain the biological behaviors of disease cells. Here, we proposed SPFA, a novel signaling pathway analysis method that takes into account signal variations that effector genes receive under disease and normal conditions. Our results showed that SPFA was comparable to seven other signaling pathway analysis methods. We also found that the signal variations that effector genes receive can reflect the contribution of different functional attributes in the signaling pathway, deepening our understanding of disease cells’ biological behaviors. Additionally, SPFA used the effector genes with high signal variations to find the abnormal part of the disease-related pathway.p-values of target pathways. We assume this is due to the statistical models used. The probability p-values may increase when paired genes with high correlation differences are randomly selected. Future studies should use a better statistical model to resolve this problem. Additionally, the 33 gene expression datasets used in this work were still limited. More experiments need to be conducted to further validate SPFA’s performance. A large number of normal and disease samples are also needed to locate the effector genes with high signal variations in disease-related pathways. These genes could then serve as effective module biomarkers for accurately detecting or diagnosing complex diseases, or as drug discovery targets. SPFA depends on manually-curated signaling pathways which play a small role in complex cellular progression. More signaling pathways need to be discovered for SPFA’s optimal performance.However, SPFA was weaker than MRGSE, Fisher, and PADOG when comparing the median In this study, we developed a new signaling pathway analysis method called SPFA. We compared this method’s ability to identify altered signaling pathways against the other seven methods. SPFA showed better results than the seven other methods. Our results also showed that the SPFA method could help identify abnormal functional attributes under normal and disease conditions and the abnormal parts of a pathway during the disease biological process.