Idiopathic Pulmonary Fibrosis, an interstitial lung disease, is one of the most common and studied fibrotic diseases and still remains an active research target. In this study we highlight unique and common (i) genes, (ii) biological pathways and (iii) candidate repurposed drugs among 9 fibrotic diseases. We identify 7 biological pathways involved in all 9 fibrotic diseases as well as pathways unique to some of these diseases. Based on our Drug Repurposing results, we suggest captopril and ibuprofen that both appear to slow the progression of fibrotic diseases according to existing bibliography. We also recommend nafcillin and memantine, which haven’t been studied against fibrosis yet, for further wet-lab experimentation. We also observe a group of cardiomyopathy-related pathways that are exclusively highlighted for Oral Submucous Fibrosis. We suggest digoxin to be tested against Oral Submucous Fibrosis, since we observe cardiomyopathy-related pathways implicated in Oral Submucous Fibrosis and there is bibliographic evidence that digoxin may potentially clear myocardial fibrosis. Finally, we establish that Idiopathic Pulmonary Fibrosis shares several involved genes, biological pathways and candidate inhibiting-drugs with Dupuytren’s Disease, IgG4-related Disease, Systemic Sclerosis and Cystic Fibrosis. We propose that treatments for these fibrotic diseases should be jointly pursued.Fibrotic diseases cover a spectrum of systemic and organ-specific maladies that affect a large portion of the population, currently without cure. The shared characteristic these diseases feature is their uncontrollable fibrogenesis deemed responsible for the accumulated damage in the susceptible tissues.  Fibrotic diseases constitute a group of incurable maladies that are recognized by a fibrotic phenotype affecting various organs and tissues. Pertinent mechanisms escape the homeostatic signals and due to over-repairing, cause tissue scarring. The plethora and complexity of the underlying perturbations are responsible for the existing lack of treatments. Even though fibrotic diseases target various biological structures, they do share several underlying mechanisms ,2.One of the most important fibrotic mechanisms is the extracellular matrix (ECM) deposition which is known to drive both the initiation as well as the progression of fibrogenesis . ECM is Idiopathic Pulmonary Fibrosis (IPF) is one of the most common and better studied fibrotic diseases, it still remains a very active research target. IPF is an interstitial lung disease (ILD), primarily involving the tissue and space around the air sacs of the lungs. IPF is of unknown etiology and due to rapid fibrotic progression, leads to death in about 3–5 years. According to [IPF diagnosis. In [IPF diagnosis by combining evidence from high-resolution computed tomography (HRCT) and histopathological patterns of ‘usual interstitial pneumonia (UIP)’, ‘possible UIP’ and ‘indeterminate for UIP’. The recommendations strongly advise against serum biomarker  measurements as an approach to distinguish IPF from other ILDs because of the high false-positive and false-negative result rates.Although rding to , data frosis. In , a multiIPF. The diseases that we observed being more similar to IPF were Silicosis, Cystic Fibrosis (CF), Systemic Sclerosis (SSc) and IgG4-related Disease. Nevertheless, drawing conclusions regarding the similarities of the underlying mechanisms and pathogenesis among diseases still remains an open research challenge.In a previous work , we analIn this paper, we study transcriptomics datasets regarding 9 of the 14 fibrotic diseases from  and presSeries Matrix Files of the data, provided by each of these 14 experiments. We apply the normalizeQuantiles function, from the Limma package [2 transformation to non-log2 datasets. If a dataset is already log2 transformed but not normalized, we remove the log2 transformation, then normalize and finally re-apply the log2 transformation. After executing the Limma statistical analysis, we use a significance cutoff-threshold  on the results. We remove any genes that have dual sign probes (over- and under-expressed at the same time). In the case of duplicate gene entries, we keep the result with the highest significance . Finally, we extract lists of the top-150 over- and top-150 under-expressed genes, based on fold change, per experiment which we then use as input in pathway analysis and DR tools.In the first step of the described pipeline, we identify unique and common genes among fibrotic diseases by analyzing transcriptomics datasets. We then use these gene lists as input in pathway analysis and DR tools, to highlight biological pathways and candidate drugs regarding fibrosis. We initially utilize 14 microarray datasets regarding the 9 fibrotic diseases of  package  of the RMyelofibrosis dataset GSE124281, we discard disease sample GSM3526859 since it was pooled from 2 patients and also exclude disease samples from patients with Essential Thrombocythemia. For the Oral Submucous Fibrosis (OSF) dataset GSE64216, we exclude the 2 Squamous Cell Carcinoma samples. We exclude 5 Sjogren’s Syndrome samples from the IgG4-related Disease dataset GSE40568. We also exclude 7 Hepatitis B samples from the Schistosomiasis dataset GSE61376. Finally, we exclude the Non-Specific Interstitial Pneumonia (NSIP) and IPF-NSIP samples from the IPF dataset GSE110147. We also note that in the CF dataset GSE15568 patients carry the CF-specific D508 mutated CFTR-allele while in the CF dataset GSE40445 patients have a confirmed F508del homozygosity for the CFTR gene. All analyzed samples come from human patients and healthy subjects, without any treatment. We exclude any samples from the microarray datasets that do not correspond to the fibrotic diseases of this study. We provide some clarifications regarding these discarded samples: for the IPF, CF and SSc. We analyze the pre-processed Raw Count Matrix Files of the data provided by each experiment using R’s EdgeR package [trimmed mean of M-values method between each pair of samples. We keep the genes with a minimum requirement of 1 count per million (CPM) across 2 or more libraries for each group (affected and control). Once we normalize the data, we then test for differential expression between patient and healthy control samples using the quasi-likelihood F-test (QLF); this is a statistical method based on negative binomial distribution that calculates the natural variation between biological replicates [p-value = .05). In agreement with the microarray dataset analysis procedure, we extract lists of the top-150 over- and top-150 under-expressed genes per experiment, based on fold change.Complementary to the microarray dataset analysis, we use RNA-Seq datasets from GEO that are available for  package . We perfplicates . We thenCF dataset, we exclude the post-drug patient samples and compare pre-drug patient and healthy control samples. Similarly, for the IPF dataset GSE99621 we exclude the macroscopically unaffected  patient samples and test for differentially expressed genes between macroscopically affected and healthy controls. Again, all analyzed samples come from human patients and healthy subjects, without any treatment and we exclude samples that do not correspond to the fibrotic diseases of this study. Regarding the GSE124548 We aggregate the key differentially expressed genes (over- and under-expressed) among experiments per disease. We utilize these gene lists in conjunction with our PathWalks methodology  to detec0 resultant genes and attributes , (ii) weight assigned based on query gene and (iii) network types based on the default selections for gene co-expression, physical interaction and pathways. We export the “normalized max weight” column of GeneMANIA’s results that contains the relation weights between gene pairs. We then aggregate these weight-values among the different types of networks for the same gene pair to emphasize the relevance of the connection between the gene pair, and finally, we translate the gene symbols to their respective identifier from the KEGG database [We construct 9 gene-to-gene networks (1 per fibrotic disease) based on the extracted gene lists from the microarray and RNA-Seq datasets analysis procedure. We achieve this by using these gene lists as input in Cytoscape’s  GeneMANIdatabase  to match100% and reflect the relevance of each data source for predicting membership in the query list [normalized max weight” column describes the normalized value for the maximum raw edge weight of a gene pair, among its various edge-entries for the same network .In more detail, the GeneMANIA results present a weight for each participating type of network and a separate weight for each edge. GeneMANIA’s execution algorithm consists of two parts; a label propagation function which assigns weights on the edges of the composite network and a ridge regression (regularized linear regression) function which assigns weights to the various types of networks . The finery list . Edges aery list . We noteApart from the gene-to-gene networks that we implement for the PathWalks execution, a pathway-to-pathway network is also required by the algorithm. We create a network of biological pathways based on their functional relations according to the KEGG database. We assign edge scores equal to the number of common genes between each pair of pathways. We exploit the pathway-to-pathway network in all 9 executions (1 per disease) and finally, compare the pathway results of each disease associated to the respective genetic information networks.The pathway nodes and their functional communities that are highlighted by PathWalks tend to favor hubs with high betweenness centrality and strength scores, as our algorithm is based on shortest paths while traversing the network of pathways. The betweenness centrality metric represents a node’s participation in the total shortest paths of a network while the strength metric represents a node’s sum of edge weights. We are particularly attentive to the most traversed pathways that are not necessarily highlighted due to topology but mainly through the guidance of the genetic information map. We further test the statistical significance of the PathWalks results by performing an Odds Ratio analysis. Specifically, we calculate the frequencies of walked pathways in each case:pathwayMapOdds is the odds of a pathway being walked at a guided PathWalks execution, and pathwayRandomOdds is the odds of a pathway being walked at an unguided PathWalks execution.Then we measure the Odds Ratio as:We then perform a Chi-Square statistical test  to calcu2 [150 over- and top-150 under-expressed genes from each experiment, as CMap suggests each input gene list is between 10 and 150 genes for optimal results. We need the resultant substances to inverse the input genes’ (disease) signature. To achieve this in CMap, we choose the detailed list results, then select the “Compound” choice of the “Perturbagen type” subset and sort the results by ascending connectivity score. In L1000CDS2 we select the “reverse” option in the “Configuration” settings on the query page since, again, we require drugs that reverse the input disease signature. We keep substances with summary-inhibition score less than -50 (-100 denotes maximum inhibition) from each CMap experiment and all 50 returned entries from L1000CDS2.We use the differentially expressed gene lists from each of the 14 microarray and 4 RNA-Seq experiments as input in 2 signature-based DR tools: (1) Connectivity Map (CMap)  and (2) 2 . We utilWe then use our drug re-ranking tool CoDReS  to screesubstance similarity = 0.7. With this approach, we avoid prioritizing candidates with a lower chance of success for further experimentation, since drugs with similar chemical structures tend to have similar mode of action. We construct the structural similarity network through the ChemBioServer 2.0 web tool [To further screen the results, we calculate the structural similarities among the short drug lists from CoDReS and substances that have previously failed in clinical trials against fibrotic diseases. We prioritize substances dissimilar to already-failed drugs, since drugs with similar structures tend to have similar mode of action . To achiweb tool  and visuweb tool .IPF, by identifying the gene targets of the re-ranked drugs that participate in key implicated biological pathways. We utilize drug-target information from the DrugBank [in vitro and in vivo testing.Subsequently, we designate the most promising anti-fibrotic candidates while focusing on DrugBank , DrugCenDrugBank  and DGIdDrugBank  databaseDrugBank . We finaIPF has the most common over-expressed genes with Dupuytren’s Disease (35) and the most common under-expressed genes with Myelofibrosis (28). Schistosomiasis has the least number of common gene entries with IPF (3 over- and 3 under-expressed). We identify the LCN2 gene being over-expressed in IPF, CF, Schistosomiasis and SSc and the FBLN1 gene being under-expressed in CF, Myelofibrosis, Polycystic Kidney Disease and SSc and over-expressed in IgG4-related Disease.In this section, we present key fibrotic genes we identify through our microarray and RNA-Seq analyses on pertinent datasets. We aggregate the top-150 over- and top-150 under-expressed gene lists among same disease experiments and observe that LCN2 encodes for the lipocalin-2 protein which is associated with neutrophil gelatinase [et al. study the LCN2 expression in (i) the skin of patients with SSc, (ii) bleomycin-treated mice, and (iii) Fli1‐deficient endothelial cells. Their experiments show that LCN2 is associated with dermal fibrosis in early Diffuse Cutaneous SSc cases  but not with ILD markers such as diffusion lung capacity for carbon monoxide or vital capacity [LCN2 and progressive skin sclerosis as well as pulmonary vascular involvement that leads to pulmonary arterial hypertension in SSc. Furthermore, [LCN2, correlated with tubulointerstitial fibrosis and tubular cell injury in patients with Chronic Kidney Disease. In another study, Kim et al. show increased LCN2 levels in urine samples of patients with Chronic Hepatitis C accompanied by hepatic fibrosis [latinase  and is klatinase . Takahascapacity . Their rhermore,  demonstrfibrosis .LCN2 and hepatic fibrosis. Regarding Nonalcoholic Fatty Liver Disease (NAFLD), Milner et al. observe no association between LCN2 and steatosis, lobular inflammation, ballooning or fibrosis even though the expression of LCN2 is significantly elevated in NAFLD samples compared to controls [et al. study the expression levels of LCN2 in rat models with acute and chronic liver injury. Their results show correlation of LCN2 to liver damage and resulting inflammatory responses but not to the degree of liver fibrosis [Other studies show no direct correlation between controls . Furtherfibrosis .LCN2 is over-expressed in various fibrotic diseases and their respective targeted tissues, such as SSc and the affected skin tissue. However, the correlation between the over-expression of LCN2 and fibrosis is not yet clear and its role might differ among the various fibrotic diseases and affected tissues.Through our analysis, we observe that FBLN1 gene encodes for the fibulin-1 protein which is an ECM and plasma glycoprotein [et al. study the effects of TGF-β1 on the regulation of FBLN1 on primary human airway smooth muscle cells from volunteers with and without Chronic Obstructive Pulmonary Disease (COPD) where small airway fibrosis occurs. They show that TGF-β1 causes a decrease in FBLN1 mRNA and soluble FBLN1 while it increases the FBLN1 in ECM due to the sequestration of soluble FBLN1 [FBLN1 in 4 diseases of our transcriptomics analyses (even though it was over-expressed in IgG4-related Disease), since gene expression is measured by the level of the corresponding mRNA present in a cell.The oprotein . Chen etle FBLN1 . This faFBLN1 may be involved in the pathogenesis of not only COPD but also of asthma and IPF. Specifically, [FBLN1’s variant FBLN1C in mouse models inhibits airway and lung remodeling in chronic asthma and lung fibrosis while also protects against COPD. The FBLN1C variant is known to increase the proliferation of lung fibroblasts in COPD and IPF patients [IPF, [FBLN1’s deposition in the airway tissue. Finally, Hansen et al. study FBLN1 with regard to myocardial fibrosis. In [Aortic Valve Stenosis (AVS) causes cardiac fibrosis and since they observe elevated expression of the FBLN1 gene in AVS samples against controls, the FBLN1 must be expressed as part of the fibrotic process. Even though FBLN1 is undeniably implicated in various fibrotic diseases, its expression levels in different tissues and its actual role regarding different fibrotic diseases should be further tested. suggest fically,  show thapatients . Regardits [IPF,  identifyosis. In , they suWe show the 2 common-gene participation networks (over/under-expressed) in  accompan5% of pathways with the highest betweenness centrality. Pink nodes belong to the top-5% of pathways with the highest degree. Red nodes belong to both groups and yellow nodes depict pathways that do not belong to any of the above 2 groups. Yellow nodes still reach the top-5% of the PathWalks results with random pathway selection, due to their high functional connectivity edge values. Since we construct the pathway-to-pathway network based on functional connections, all colored pathways may be sensitive to biological perturbations in most use cases.We use the differentially expressed gene lists per disease, to drive our pathway analysis methodology. The PathWalks algorithm favors pathway-nodes with high betweenness centrality and strength scores. PathWalks, which is based on random walks and utilizes shortest paths, highlights such nodes. In 5% of the PathWalks results for at least 1 disease and do not belong in the topology-favored groups of 5% of pathways with highest betweenness centrality and degree scores as well as entries at the top-5% results of a random PathWalks execution (without the use of a gene map). Pathway entries that didn’t have a significant Odds Ratio  for any disease, are highlighted in red colors, namely; “Apoptosis”, “Metabolic pathways”, “Non-alcoholic fatty liver disease”, “Oxidative phosphorylation”, “Pathways in cancer” and “PI3K-Akt signaling pathway”.We identify disease-specific as well as common implicated pathways among the 9 fibrotic maladies through the PathWalks executions. In web multi-layer visualization tool [7 common pathways across the 9 diseases, all of which are favored by the topology. The pathways in question are “Metabolic”, “Cancer”, “MAPK signaling”, “PI3K-Akt signaling”, “Non-alcoholic fatty liver disease”, “Oxidative phosphorylation” and “Calcium signaling” pathways. Furthermore, 5 out of these 7 pathways  did not have significant Odds Ratio  for any of the 9 studied diseases.We then focus on the PathWalks-highlighted pathways of all 9 fibrotic diseases and visualize these connections (disease-pathway) in ion tool . We obseMyelofibrosis, the “Antigen processing and presentation” is exclusive for SSc and the “IL-17 signaling” is unique for CF. “Fat digestion and absorption”, “PPAR signaling” and “Glycerolipid metabolism” pathways are unique for Dupuytren’s Disease. “Influenza A”, “RNA transport”, “Ribosome biogenesis in eukaryotes” and “Herpes simplex virus 1 infection” pathways are unique for Schistosomiasis. Finally, the “Dilated cardiomyopathy”, “Cardiac muscle contraction”, “Hypertrophic cardiomyopathy”, “Adrenergic signaling in cardiomyocytes” and “ECM-receptor interaction” pathways are unique for OSF.We also observe unique PathWalks-highlighted pathways in some of the fibrotic diseases. The “Cell adhesion molecules” pathway is exclusive for OSF are related to cardiomyopathies. A potential connection between OSF and cardiomyopathies is missing from the bibliography. However, in their review, Jiang et al. highlight drugs from various categories such as steroids, enzymes, cardiovascular drugs, antioxidants, vitamins, and microelements that seem to ameliorate (but not cure) the fibrotic effects of OSF [OSF including (i) pentoxifylline [OSF disease should be further pursued.We observe that 4 out of the 5 unique pathways of s of OSF . In partifylline , (ii) buifylline  and  and the fewest with OSF (8/15). More information regarding the highlighted unique and in-common pathways among the 9 fibrotic diseases is shown in In 2 DR tools. We then use our CoDReS re-ranking tool to extract the highest-scoring, structural-representative and inhibiting substances among the repurposed drugs of each disease. For IgG4-related Disease, we arbitrarily choose the top-5 ranked drugs from each DR tool since there is no entry for this disease in CoDReS. In Based on the procedure outlined in the Drug Repurposing segment of our Materials and Methods section, we collect repurposed drug candidates for the 9 respective fibrotic diseases through the CMap and L1000CDSIPF, CF and SSc. Similarly, memantine is highlighted in the use cases of IPF, Dupuytren’s Disease and Schistosomiasis. Hydrocortisone is a corticosteroid and its mode of action against fibrotic diseases has been studied for more than half a century now. It is mostly used for the temporal treatment of OSF. More specifically, Desa et al. report relief of symptoms in OSF patients by injecting the submucosal fibrotic areas with specific dosages of hydrocortisone depending on the stage of the fibrosis [et al. describe how they have been clinically using optimal doses of a combination of hydrocortisone acetate and hyaluronidase for the medical treatment of OSF during a period of 20 years [There are 2 drugs suggested by CoDReS that are common among 3 of the 9 fibrotic diseases. Hydrocortisone is highlighted as an anti-fibrotic drug candidate from the re-ranking procedure for fibrosis . In a mo20 years .Pulmonary Fibrosis (PF). Xu et al. show that hydrocortisone decreases overexpressed fibrotic factors, such as TGF-β1, pulmonary type I collagen and inducible nitric oxide synthetase to normal levels in a rat model [CF, the study of Tepper et al. suggests that the application of intravenous hydrocortisone to the standard treatment of hospitalized infants with CF, produces a greater or a more sustained improvement in their lung function [Dupuytren’s Disease, they observe that the institution of hydrocortisone acetate at a mild stage keeps the tissue contracture stationary. Regarding IgG4-related Disease, Tanabe et al. propose a combination of hydrocortisone and thyroxine which result in clinical and laboratorial improvements against the disease [et al. study the effects of hydrocortisone in a UV-irradiated mouse model. The UV irradiation causes the appearance of fibroblasts and the accumulation of collagen in the model’s lower dermis. In [Hydrocortisone’s effect has also been studied against at model . Regardifunction . In addifunction  demonstr disease . Finallyrmis. In , they esAlzheimer’s Disease. A direct connection of memantine and fibrotic diseases does not exist in bibliography. However, the histopathological analysis by Abbaszadeh et al. show a successful pre-treatment of a heart failure rat model with memantine, that attenuates myocyte necrosis and fibrosis [et al. demonstrate that memantine attenuates lung inflammation in a bleomycin-induced acute lung injury mouse model [Memantine is a low-affinity voltage-dependent uncompetitive antagonist of the N-methyl-D-aspartate receptors found in nerve cells and is used for the treatment of patients with moderate to severe fibrosis . Similarse model . They prIPF. In this regard, we use R’s hypergeometric test function dhyper where (i) x is the number of shared CoDReS re-ranked drug candidates between IPF and another fibrotic disease, (ii) m is the cardinality of the intersection between the IPF CoDReS drugs (28) and the total, unfiltered drug entries that are produced by CMap and L1000CDS2 across all the experiments of a disease, (iii) n is the number of all the unfiltered repurposed drugs for the disease, excluding the shared ones with IPF, and (iv) k is the number of the CoDReS re-ranked drugs for the disease. For example, CF shares 3 (x) CoDReS re-ranked drug candidates with IPF, where 26 (m) out of the 28 re-ranked IPF drugs exist in the unfiltered outputs from the two DR tools for CF, 2448 (n) exist in the CF unfiltered DR outputs and were not highlighted for IPF through CoDReS and 25 (k) is the number of CoDReS-highlighted CF drug candidates. Therefore, the fact that CF shares 3, potentially anti-fibrotic, drug candidates  with IPF is statistically significant . Based on this test, sharing 2 or 3 re-ranked drug candidates with IPF is a statistically significant  similarity while sharing only 1 drug may happen due to randomness. IPF shares memantine, scriptaid and sunitinib with Schistosomiasis, bi-2536 and memantine with Dupuytren’s Disease, hydrocortisone with SSc and pd-0325901 with Myelofibrosis. In Based on the common re-ranked drug candidates from our analysis we draw a disease similarity network in SSc) does not have a chemical structure file in PubChem [19-rest “failed” substances.In an effort to further screen the repurposed and re-ranked candidate drugs, we explore structural similarities among them and identify drugs that have previously failed in clinical trials against fibrotic diseases. The repoDB database contains entries with substances and their clinical trial status against the respective diseases. The 4 different drug status types of annotation in the database are: “Approved”, “Suspended”, “Terminated” and “Withdrawn”. We focus on drugs that have previously failed in clinical trials against any of the 9 fibrotic diseases of our study. We consider that drugs with “Suspended”, “Terminated” or “Withdrawn” indications have failed in the respective clinical trial. We place detailed information regarding drug names, respective fibrotic disease and clinical trial indication combinations, as found in repoDB, in  PubChem , so we f19 failed drugs and compile an aggregated SDF file. We then compile another SDF file containing the structures of all unique 121 CoDReS re-ranked drug candidates from this study. We use these 2 SDF files as input in the ChemBioServer “Attach similar-only nodes to Network” function. We use the SDF file of re-ranked drugs as a base network with an edge similarity threshold of 0.5 and the SDF file of failed drugs as a secondary list to infer structural similarity links towards the base network with an edge threshold of 0.7. We choose the Tanimoto similarity metric since it is known to yield accurate results regarding cheminformatic similarity calculations [We use PubChem-available 2-dimensional structural data files (SDF) for the aforementioned ulations ,69. We tIPF highlighted pathways that are directly associated with the gene map of IPF. There exist 3 such pathways: “Pancreatic secretion”, “Protein digestion and absorption” and “Complement and coagulation cascades”, colored green in 5% PathWalks-highlighted pathways for IPF and their functional connections. The edge weights are proportional to the number of times they were traversed by our PathWalks algorithm. The “Pancreatic secretion” and “Protein digestion and absorption” pathways are highlighted for IPF, Dupuytren’s Disease, Polycystic Kidney Disease and IgG4-related Disease. The “Pancreatic Secretion” pathway is also highlighted for SSc. The “Complement and coagulation cascades” pathway is highlighted in the cases of IPF, CF, IgG4-related Disease, Polycystic Kidney Disease and Schistosomiasis.Through our analysis pipeline, we settle for 121 total repurposed and re-ranked drug candidates with potential anti-fibrotic action. From those 121, 89 target at least 1 gene participating in at least 1 of the PathWalks highlighted pathways for the 9 fibrotic diseases. We focus on substances targeting IPF in There are 34 out of the 121 total re-ranked drugs that target at least 1 gene participating in the 3 “green” pathways of IPF. Specifically, celecoxib is an anti-inflammatory drug that we identify in the CoDReS results for IPF. Celecoxib targets the CA2 and COL18A1 genes in “Pancreatic secretion” and “Protein digestion and absorption” pathways respectively and PLAU in “Complement and coagulation cascades”. We identify PLAU being over-expressed in the Dupuytren’s Disease experiments. The anti-fibrotic effects of celecoxib have been widely studied, especially in rat models and mainly regarding liver fibrosis. A number of such studies [et al. show that celecoxib protects against liver inflammation in early stages of fibrosis even though it has no effect on the actual fibrosis [et al. observe that celecoxib treatment has no effect in the liver fibrosis of mice [CTGF expression in rats [CTGF are established pro-fibrotic mechanisms. Nevertheless, and mostly due to contradicting studies, we suggest that other potential drug choices from our study should be prioritized instead of celecoxib for further experimentation against fibrosis.Celecoxib is the only candidate targeting all 3 “green” pathways of  studies –74 sugge studies ,76 demonfibrosis . Similar of mice . In a ca in rats . CollageOSF. As we mention in the pathway community analysis paragraph, there are 4 unique cardiomyopathy-related pathways involved in OSF, a finding that needs further experimental validation. Digoxin targets the “Pancreatic secretion” and “Protein digestion and absorption” through 8 genes: ATP1A1, ATP1A2, ATP1A3, ATP1A4, ATP1B1, ATP1B2, ATP1B3 and FXYD2. ATP1A2 is over-expressed in OSF and under-expressed in Dupuytren’s Disease while ATP1B1 is over-expressed in Dupuytren’s and FXYD2 over-expressed in Polycystic Kidney Disease. Peckham and colleagues study the effects of digoxin on nasal potential difference in CF subjects with perturbed airway epithelium. Their results indicate that digoxin is unlikely to treat CF [et al. study the effects of digoxin in CF patients with heart failure complications. Their results show promise in cardiac process reversion even though it is not certain which treatment among respiratory care, vigorous antibiotic therapy or digoxin, attributed most to the survival [et al. concludes that digoxin neither increases exercise capacity nor improves exercising cardiac function in CF patients with moderate to severe airway obstruction [Digoxin has been adopted for the treatment of atrial fibrillation and congestive heart failure . We identreat CF . On the survival . Contrartruction .CF, Haller et al. observe remarkable clearing of myocardial fibrosis by an affinity-purified digoxin antibody in rats with Chronic Renal Failure [OSF due to the results of our study hinting towards a link between OSF and heart diseases.Disregarding  Failure . We obseSchistosomiasis. Captopril targets the F2 and SERPINE1 genes in the “Complement and coagulation cascades” pathway and the DPP4 gene in the “Protein digestion and absorption” pathway. We observe SERPINE1 being over-expressed in Myelofibrosis and SSc but under-expressed in IPF. There are various studies highlighting captopril’s anti-fibrotic mode of action in experimental rat models. Regarding lung fibrosis, Ghazi-Khansari’s histopathological findings show that captopril helps in a paraquat‐induced lung fibrosis rat model [IPF due to a number of existing reports for its capacity to improve lung fibrosis.Captopril is used for the treatment of hypertension and some types of congestive heart failure  and is hat model  while Baat model . Jalil aat model . Notableat model , liver [at model  and coloat model . We shouat model  while Okat model . HoweverDupuytren’s Disease and Schistosomiasis. Ibuprofen targets the PLAT and THBD genes in the “Complement and coagulation cascades” pathway and CFTR in the “Pancreatic secretion” pathway. PLAT is over-expressed in SSc. Carlile et al. demonstrate that ibuprofen is a CF transmembrane conductance regulator (CFTR) protein corrector and suggest that ibuprofen may be suitable in a CF combination therapy [et al. suggest that high doses of ibuprofen administered based on weight can treat polyposis in children with CF [CF without serious adverse effects. Therefore, ibuprofen should be further experimentally pursued in drug combinations against fibrosis.Ibuprofen is an anti-inflammatory agent that is highlighted in the use cases of  therapy . Additio with CF . There a with CF –98 in agPLA2G1B gene in the “Pancreatic secretion” pathway as well as COL1A2 and SLC1A1 genes in the “Protein digestion and absorption” pathway. In our analysis, we identify SLC1A1 as being over-expressed in Polycystic Kidney Disease. COL1A2 is under-expressed in Polycystic Kidney Disease but over-expressed in IPF, IgG4-related and Dupuytren’s Disease. We note that the PLA2G1B gene is under-expressed in 3 out of the 6 total IPF experiments and over-expressed in 1 of them. Staurosporine is returned as a repurposed drug for CF. However, due to its non-specific selectivity on kinase-binding, staurosporine has a high chance of undesirable side effects [et al. shows that staurosporine upregulates PDGFR-α gene expression and its protein levels in pulmonary myofibroblasts of rats [PDGFR-α is known to induce myofibroblast hyperplasia during pulmonary fibrosis. Therefore, we consider that staurosporine is not a viable anti-fibrotic candidate.Staurosporine targets the  effects ,100. Mor of rats . The oveCF. Nafcillin targets the F2RL3 gene in the “Complement and coagulation cascades” pathway and the PGA5 gene in “Protein digestion and absorption”. F2RL3 is under-expressed in IPF experiments. We suggest that nafcillin’s mode of action against fibrosis should be explored via cell line and model experiments since there are currently no relevant studies.We designate nafcillin, an antibiotic drug, as a repurposed and re-ranked candidate in CF and Polycystic Kidney Disease. Wortmannin targets the F2R and SLC1A1 genes in the “Complement and coagulation cascades” and “Protein digestion and absorption” pathways respectively. There are a few studies regarding SSc that hint at wortmannin’s potential anti-fibrotic mode of action. Shi-Wen et al. report that wortmannin greatly reduces SSc patients’ fibroblast ability to contract a collagen gel matrix [SMA and CCN2 in dermal fibroblasts from PTEN knockout mice [et al. describes that wortmannin significantly decreases the expression of collagen type I in adult rat cardiac fibroblasts [et al. show that wortmannin cancels the effects of cannabinoid receptor type 2 agonist including improving cardiac function, decreasing fibrosis and reducing inflammatory cytokine secretion and oxidative stress in a mouse model with infarcted heart [Wortmannin is a phosphoinositide 3-kinase inhibitor  and is hl matrix . In a lal matrix . Parapurout mice . The sturoblasts . On the ed heart . Lastly,ed heart . In Fig Furthermore, we present how our 7 suggested drug candidates compare to the rest of the 121 repurposed drugs. To achieve this, we utilize both structural and functional properties of the drugs. We perform a structural hierarchical clustering on all 121 drugs through ChemBioServer, with selected parameters: Soergel distance, Ward linkage and cluster threshold = 1. We provide the respective clusters in web tool to visualize an overall 4-layer network consisting of the studied fibrotic diseases, their key implicated pathways, the associated participating genes and the repurposed drugs that target these genes, in IPF in this article.Finally, we utilize the Arena3DIPF but each of the Myelofibrosis, CF, SSc, Dupuytren’s Disease, Schistosomiasis and OSF diseases have unique pathway entries that should be further explored as to their contribution in the pathogenesis of these diseases. We observe that the PathWalks results hint evidence for a potential common mechanism between OSF and cardiomyopathies. Therefore, we suggest that combinatorial regimens against OSF including cardiovascular agents should be looked at.In this study, we identify genes, biological pathways and potential inhibiting drugs that are associated with fibrotic diseases. Through our PathWalks methodology we identify 7 common biological pathways that are associated with all 9 fibrotic diseases investigated in this paper. These include “Metabolic”, “Cancer”, “MAPK signaling”, “PI3K-Akt signaling”, “Non-alcoholic fatty liver disease”, “Oxidative phosphorylation” and “Calcium signaling” pathways. We do not identify any exclusive fibrotic mechanisms for 2) and the drug re-ranking tool (CoDReS) to identify and propose drug candidates with potential anti-fibrotic mode of action. We observe that hydrocortisone and memantine are returned in 3 out of the 9 fibrotic diseases of this study. Memantine’s action has not been studied against fibrotic diseases but, as mentioned in our respective Results section, there are hints for its potentially beneficial anti-fibrotic action [We use 2 DR tools Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.


These components act on key target genes such as AKT1, IL6, MAPK3, TP53, EGFR, SRC, and TNF and regulate the PI3K/Akt-signaling and MAPK-signaling pathways. These molecules participate in a series of processes such as cellular differentiation, apoptosis, metabolism, and inflammation and can ultimately be used to treat PMOP; they reflect the overall regulation, network regulation, and protein interactions.Deer velvet antlers are the young horns of male deer that are not ossified and densely overgrown. Velvet antler and its preparations have been widely used in the treatment of postmenopausal osteoporosis (PMOP) in recent years, although its mechanism of action in the human body remains unclear. To screen the effective ingredients and targets of velvet antler in the treatment of PMOP using network pharmacology and to explore the potential mechanisms of velvet antler action in such treatments, we screened the active ingredients and targets of velvet antler in the BATMAN-TCM database. We also screened the relevant targets of PMOP in the GeneCards and OMIM databases and then compared the targets at the intersection of both velvet antler and PMOP. We used Cytoscape 3.7.2 software to construct a network diagram of “disease-drug-components-targets” and a protein-protein interaction (PPI) network through the STRING database and screened out the core targets; the R language was then used to analyze the shared targets between antler and PMOP for GO-enrichment analysis and KEGG pathway-annotation analysis. Furthermore, we used the professional software Maestro 11.1 to verify the predictive analysis based on network pharmacology. Hematoxylin-eosin (H&E) staining and micro-CT were used to observe the changes in trabecular bone tissue, further confirming the results of network pharmacological analysis. The potentially effective components of velvet antler principally include 17 Postmenopausal osteoporosis (PMOP) refers to a metabolic disease in which the endocrine function of women's ovaries declines after menopause and the levels of estrogen diminish, which in turn causes bone resorption by osteoclasts to exceed bone formation by osteoblasts –3. PMOP Deer antler was included in the 2020 Chinese Pharmacopoeia , and itsNetwork pharmacology involves biology, biological network construction and analysis, connectivity, redundancy, and pleiotropy. It is used to reveal the effectiveness, toxicity, and metabolic properties of drugs. Network pharmacology transcends the traditional drug research and development model dominated by “one drug, one target, and one disease” and opens a new research model of the complex network relationship among multiple targets and multiple diseases. Velvet antler and its preparations have been widely used in the treatment of PMOP in recent years, but its underlying mechanism of action in the human body is still unclear. Therefore, in the present study, we used network pharmacology to screen the primary active ingredients and key targets of velvet antler treatment for PMOP and explored the mechanism of action of velvet antler in a “multicomponent, multitarget, and multipathway” manner.We herein screened the active ingredients and targets of velvet antler in the BATMAN-TCM database. We also screened the relevant targets of PMOP in the GeneCards and OMIM databases and then compared the targets between velvet antler and PMOP. The target points of osteoporosis were then taken as an intersection. We used Cytoscape 3.7.2 software to draw the network diagram of “disease-drug-component-target.” We constructed a protein-protein interaction network (PPI) through the STRING database and screened out core targets. The R programming language was adopted to perform GO-enrichment analysis and KEGG pathway-annotation analysis on common targets of velvet antler and PMOP. Eleven core targets were obtained from the PPI network for molecular docking analysis. We then utilized the experimental results to develop our conclusions.p < 0.05. Thus, the active ingredients contained in deer antler and their corresponding potential targets were screened.In the BATMAN-TCM, a bioinformatics analysis tool platform for elucidating the molecular mechanisms underlying traditional Chinese medicine, “deer antler” was selected as the keyword and “drug-target” as the screening condition, with the similarity model threshold score cut-off ≥20 and In the GeneCards and OMIM databases, “postmenopausal osteoporosis” was used as the search term for all gene targets related to PMOP and then the intersection of the selected PMOP targets and the potential targets of deer antler were assessed, that is, the potential targets of velvet antler in the treatment of PMOP.The intersection target data for PMOP and velvet antler were input into Cytoscape 3.7.2 software to construct the regulatory network “disease-drug-component-target.” The different network nodes, respectively, represented PMOP, the traditional Chinese medicine velvet antler, the effective components of velvet antler, and the common targets of PMOP and velvet antler; the connecting line between the nodes represented the mutual relationships and effects.http://string-db.org) and the screening condition was set—species “Homo sapiens”—with the remaining conditions set as defaults; thus, the PPI network for velvet antler in the treatment of PMOP was constructed. The Cytoscape 3.7.2 software plug-in network analyzer was used to analyze the topological parameters of the average and maximal degrees of freedom in the PPI network. The core targets were screened out based on the metric value. Those with more chance to be the core ones of the treatment had larger value. The upper limit of the screening range was the maximum degree value in topological data, and the lower limit was twice the average degree of freedom [The common targets of PMOP and velvet antler were input in the STRING database . The filtrate was freeze-dried by a Heto PowerDry LL3000 Freeze Dryer  and stored at −80°C. When the mice need to be administered by gavage, the velvet antler was dissolved in pure water. FA1004N electronic balance , Leica RM2255 paraffin microtome , Leica EG1140 paraffin-embedding machine , Olympus BX51 optical microscope , and NIS-ELEMNT BR image analysis system (Japan NIKON Company) were used.The deer antler used in the following experiments was the same as the ones prepared as previously described . Antler Eight-week-old C57 female mice were purchased from Liaoning Changsheng Biotechnology Co., Ltd.  2020-0001). After 1 week of adaptive feeding, a dorsal approach was used to remove both ovaries. Models were created and randomly allocated to the control group and the administration group. All mice were intraperitoneally injected with penicillin sodium  for three consecutive days after the operation and were bred adaptively for one week. The control group was administered pure water intragastrically (i.g.), and the administration group was administered 4 mg/10 g/d deer antler i.g. for three consecutive weeks. After the experiment, the mouse femurs were removed under anesthesia. All experimental procedures were carried out in accordance with national and international guidelines and regulations, and all experiments were approved by the Animal Ethics Committee of the Changchun University of Traditional Chinese Medicine (no. ccucm-2017-0015).μA, and a 14 min scanning time [The femur from each animal was sampled and the soft tissue was cleaned off. The microarchitecture of the femur was analyzed using a Quantum GX Micro-CT imaging system . Each sample was carefully positioned such that the orientation of the femoral shaft was as vertical as possible. The distal femur was scanned with the following settings: 90 kV, 80 ing time . Bone moing time .μm thick slices that were stained with hematoxylin and eosin (H&E). Section images were acquired with an Olympus BX51 optical microscope . The distribution of trabecular bone was observed. The static parameters, namely, osteoblast surface/bone surface (ObS/BS), osteoclast surface/bone surface (OcS/BS), eroded surface/bone surface (ES/BS), and osteoid volume/bone volume (OV/BV), were analyzed with a quantitative stereological method for histology known as the Weibel technique [The femoral samples were fixed in 4% paraformaldehyde for 48 h, followed by decalcification in ethylenediaminetetraacetic acid (EDTA), dehydration in alcohol, clearing in xylene, and finally embedding in paraffin for further tissue sections. A paraffin microtome  was used to section the sample into 4 echnique . The staechnique .t-test  and expressed as the mean ± SD.The Shapiro–Wilk normality test was used to determine the distribution of the data as the sample size was less than 100. All of the groups were found to be normally distributed. Thus, the data were analyzed by SPSS 25.0 software using parametric Student's p < 0.05 with strict screening, a total of 14 potential velvet antler active molecules were retrieved. The effective chemical components, as presented in β-estradiol, glucosamine, adenosine triphosphate, and oestrone.In BATMAN-TCM, with “antler” as the keyword, the screening condition set to “drug-target,” the similarity model threshold score cut-off set to ≥20, and As shown in β-D-glucosyl gentiopicroside), reflecting the multicomponent and multitarget effects of velvet antler on PMOP. As shown in β-estradiol, oestrone, and adenosine triphosphate exhibited the highest degree values, which are presumably the main active ingredients of velvet antler.The regulatory network comprising velvet antler for PMOP treatment is shown in We entered 233 common targets in the STRING database to obtain protein interaction relationships and drew PPI network diagrams. The PPI network ultimately contained 230 nodes and 4434 edges. The network topology analysis results were as follows: network density (0.157), network heterogeneity (0.799), and shortest path . According to the previous studies, the key core the lower limit should be twice the average degree of the nodes , 21. SinWe obtained the GO functional enrichment analysis of the 233 potential targets, and the results are shown in The 233 potential targets of velvet antler treatment of PMOP were enriched in 178 signaling pathways shown in . It can β-estradiol, oestrone, and adenosine triphosphate, which bind to key targets and exhibit scores <−5. The visual results of the docking of other targets are shown in We selected eleven core target genes with a support degree higher than 100 for molecular docking and analyzed the docking results, with Gscore used in the scoring of molecular docking; the lower the score, the stronger the binding force. A negative value indicated that the ligand molecule could spontaneously bind to the receptor protein, and a score <−5 indicated stronger binding activity. A total of three compounds were selected to be docked on eleven target proteins, and the molecular docking scores of the compounds are summarized in To estimate the effect of deer antler treatment on trabecular bone structure, trabecular architectural parameters were measured in the distal femoral bone by using micro-CT analysis . The BV/In the model group, the femoral shaft trabeculae were loosely distributed, the number of bone trabeculae was sparse, and the bone trabeculae showed low interconnectedness. The trabecular bone was thinner and thick locally. The distance between trabecular bones was augmented, and the proportion of the trabecular bone distribution area in the observation field was small. Compared with the model group, the number of trabecular bones in the femoral shaft of the deer antler group increased, and they were interconnected in a larger grid. The trabecular bones were locally thickened between the trabecular bones with the distance diminished. The trabecular bone distribution area accounted for the increase in the proportion of the observation field see .According to the quantification analysis from the H&E staining, supplementation of antler significantly increased the ratio of ObS/BS and significantly decreased the ratios OcS/BS and ES/BS, compared to those in the OVX group. There was no significant difference in the ratio change of OV/BV after three weeks of antler treatment . These rκB in response to RANKL. DAE also inhibited bone resorption and survival of osteoclasts [Deer antler has been reported to possess bone-strengthening activity and has been used in treating arthritis, osteoporosis, and avascular necrosis of the femoral head in animal models, and it plays pivotal roles in facilitation of proliferation and mineralization of osteoblasts. Wang et al. reported that deer antler type I collagen could induce BMSC differentiation into osteoblasts through the two signal transduction pathways, ERK1/2 and p38-MAPK, and can regulate the gene expression of Runx2 and Osx, which were specific transcription factors in this case . Yun et eoclasts . In addieoclasts . Thus, tβ-E2, adenosine triphosphate, and oestrone were important components in velvet antler for use in the treatment of PMOP, the primary component of which is cytoporosis. Lack of estrogen is the principal cause of PMOP, and estrogen replacement therapy can effectively improve the bone density of postmenopausal women with osteoporosis. Ge et al. [α activity [In the present study, we initially collected the effective ingredients of velvet antler and screened them. We then obtained genes that are differentially expressed in PMOP from the GEO database and OMIM database, constructed a drug-component-target-disease network, and further concluded that 17e et al.  demonstractivity  and can activity  and promactivity . ATP is activity . Miyazakactivity .We constructed KEGG and PPI networks based on target information and determined twenty-nine core targets based on their degree values. AKT1, IL6, MAPK3, TP53, EGFR, ESR1, SRC, TNF, STAT3, MAPK1, and JUN were the key target genes with higher-node degrees; these genes can be considered to be the core targets in the PPI network and may thus be the key target genes for velvet antler treatment of PMOP. AKT1 belongs to the protein kinase B gene family and is a key factor in the PI3K/AKT-signaling pathway; it can phosphorylate elements downstream to regulate cellular metabolism, proliferation, survival, growth, and many other processes. Researchers have demonstrated that after knocking out the rat AKT1 gene, rats showed limited bone development, indicating that AKTI may promote bone growth in bone development . Akt sigβ/Smad-signaling pathways [Achyranthes bidentata and Eclipta prostrata can regulate the ERK MAPK- and JNK MAPK-signaling pathways, respectively, to improve the former as major biomarkers of osteoblasts, and their augmented expression levels in turn promote the differentiation of BMSCs into osteoblasts.In this study, we used GO-enrichment and KEGG pathway-enrichment analyses to explore the multidimensional pharmacological mechanisms underlying the effects of velvet antler on PMOP. We also analyzed pathways containing other annotated genes, for example, viral infections with human cytomegalovirus, human papillomavirus, hepatitis B, human T-cell leukemia virus 1, and Kaposi sarcoma-associated herpesvirus. Based on this, we infer that velvet antler may have a broad-spectrum antiviral effect; and proteoglycans in cancer, prostate cancer, microRNAs in cancer, gastric cancer, breast cancer, and so on are mainly related to cancer. These approaches are not closely related to the treatment of postmenopausal osteoporosis with velvet antler. The PI3K/Akt- and MAPK-signaling pathways are two pathways that must be given more attention. The PI3K/AKT-signaling pathway is a key pathway for cellular proliferation and migration, and it is also a critical element in the development of osteoporosis. Overexpression of MIR-29b enhanced the proliferation and migration of bone marrow mesenchymal stem cells in ovariectomized osteoporotic rats, which may be due to the activation of PI3K/Akt- and TGF-pathways . The MAPpathways , and thepathways , 54 AchyWe then used molecular docking technology to verify the combination of eleven core targets and three primary components of velvet antler; our results show that the molecular docking score Gscore of the three main components and the core target is negative, indicating that the ligand molecule can spontaneously bind to the receptor protein, and the score <−5 indicates that the binding activity is stronger.Finally, in order to further confirm the results of the network pharmacology analysis, experimental verification was carried out using a postmenopausal osteoporotic mouse model. The structural and static histomorphometry parameters measure the microstructure and cellular components of the trabecular bone, respectively . Micro-Cβ-E2, adenosine triphosphate, and oestrone. These components act on key target genes, such as AKT1, IL6, MAPK3, TP53, EGFR, ESR1, SRC, TNF, STAT3, MAPK1, and JUN, and regulate the PI3K/Akt- and MAPK-signaling pathways. These molecules and pathways then participate in a series of processes, such as cellular differentiation, apoptosis, metabolism, and inflammation, and can ultimately be used to treat PMOP, reflecting the overall regulation, network regulation, and protein interactions. This study can provide a certain theoretical basis for the research and development of related drugs for the clinical treatment of PMOP. However, the regulation mechanism of its action still needs to be further verified through experiments.In this study, we used a combination of network pharmacology and molecular docking to explore the mechanism of action of velvet antler in the treatment of PMOP. The potentially effective components of velvet antler primarily included 17


However, its underlying mechanisms of anti-colon cancer (CC) remain unknown. Therefore, a systematic approach is proposed in the present study to elucidate the anticancer mechanisms of CNC based on network pharmacology and experimental validation. Initially, the potential active ingredients of CNC were verified via the TCMSP database based on the oral bioavailability (OB) and drug-likeness (DL) terms. Hub targets of CNC were acquired from SwissTarget prediction and TCMSP databases, and target genes related to CC were gathered from GeneCards and OMIM databases. Cytoscape was used to establish the compound-target networks. Next, the hub target genes collected from the CNC and CC were parsed via GO and KEGG analysis. Results of GO and KEGG analysis reveal that quercetin and luteolin in CNC, VEGFA and AKT1 targets, and PI3K-Akt pathway were associated with the suppression of CC. Besides, the result of molecular docking unveils that VEGFA demonstrates the most powerful binding affinity among the binding outcomes. This finding was successfully validated using in vitro HCT116 cell model experiment. In conclusion, this study proved the usefulness of integrating network pharmacology with in vitro experiments in the elucidation of underlying molecular mechanisms of TCM.Camellia  Colon cancer (CC) is a type of malignant tumor with high incidence and mortality worldwide . It is cCamellia nitidissima Chi (CNC) is a wild woody plant that is widely used as a traditional folk medicine to effectively treat various diseases, such as hypertension, hyperlipidemia, and infection [Traditional Chinese medicine (TCM) has gained popularity around the world due to its fewer side effects and reliable efficacy . With thnfection . Moreovenfection –10. Evenin vitro experimental validation to elucidate the mechanism of action of CNC against CC. At the same time, it is the first time systematically studying the anti-CC effect of CNC by using network pharmacology combined with an in vitro experimental model. It provides a scientific basis for elucidating the mechanism of CNC in the treatment of CC and provides a new idea for more in-depth research.With the rapid progress of bioinformatics, network pharmacology which was first proposed by Cao et al. in 2007 is proven as a powerful tool in the exploratory research of TCM –13. The Briefly, targets for active compounds of CNC and CC were collected through TCMSP and SwissTarget prediction platforms, whereas the intersection targets were identified by constructing a Venn diagram. Next, a protein-protein interaction (PPI) network diagram of intersection proteins was constructed, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Lastly, a “compound-target-pathway” network diagram was constructed by using Cytoscape software and molecular docking was performed using AutoDock. Experimental evidence shows that quercetin and luteolin in CNC, VEGFA and AKT1 targets, and PI3K-Akt pathway were involved in the suppression of CC by blocking the HCT116 cycle and inducing apoptosis. This research provides evidence to support the clinical use of CNC in CC treatment. The detailed flow chart of the current study is as follows .https://www.chemsrc.com/) and PubChem [https://pubchem.ncbi.nlm.nih.gov/). In the TCM systems pharmacology Database and Analysis Platform (TCMSP), the chemical constituents of CNC were searched. According to the standard of TCMSP [http://www.swisstargetprediction.ch/) for screening of the active components (https://www.uniprot.org/) in CNC. UniProt database [https://www.uniprot.org/) was used to normalize the target names in the predicted target library.The main chemical components of CNC were identified via literature search using CNKI and PubMed databases. The corresponding chemical structure, chemical English name, CAS number, and other information of the compounds were obtained from the chemical source network  of active chemical compounds was retrieved and exported from the PubChem to SwissTarget prediction database with the species selection of “https://www.omim.org/) and the annotation of the human genome  database to identify CC-related genes. GeneCards database is a platform that can provide the proteome, transcription, inheritance, and function of all known genes in the human genome. By combining the genes obtained from the two databases after removing the duplicates, protein targets associated with CC were identified. With the help of the Draw Venn Diagram (http://bioinformatics.psb.ugent.be/webtools/Venn/) website and the targets obtained in The search term “colon cancer” was input into the human genes and genetic phenotypes comprehensive database   [Homo sapiens,” while other parameters were set at a default value. Eventually, a target interaction network diagram was obtained and saved in .tsv format. The above data were then imported into Cytoscape3.7.2 software for visual processing. Lastly, the “Network Analyzer” tool was used to analyze the network to obtain the degree value (connectivity) and draw the PPI network diagram. The network was exported in PNG format and the top 10 hub genes were selected.The potential targets of CNC against CC identified in db.org/) , and thehttps://metascape.org/) is a bioinformatics database that integrates biological data and analytical tools for GO enrichment analysis and KEGG pathway analysis. The potential targets identified in p < 0.01 as the critical value of significant functions and pathways. The main signal pathways and biological processes involved in the pharmacological effects of CNC against CC were obtained. The Cytoscape3.7.2 software was used to construct the “compound-target-pathway” network diagram of the signal pathway obtained in the pathway enrichment analysis to realize the visualization research and analyze the multicomponent, multitarget, and multipathway action mechanisms of CNC against CC.Metascape  (https:/http://www.pdb.org/) [Molecular docking is an important method of computer-aided drug design. Intermolecular interaction recognition is carried out by simulating the geometric structure and intermolecular force of molecules to predict the structure of receptor-ligand complexes. The crystal structures of the candidate targets were downloaded from RCSB Protein Data Bank (db.org/)  and embeμm filter membrane and then frozen at −20°C. Quercetin, luteolin, kaempferide, and kaempferol with a purity of 99% were purchased from MCE.A total of 25 g dried CNC  was weighed and then immersed in 0.625 l of 75% (v/v) ethanol for 30 min. Reflux extraction was carried out for 1.5 h twice. The extracts from two stages of extraction were combined and concentrated by using a rotary evaporator. Next, the concentrated extract was freeze-dried to obtain powder (3.66 g). DMSO was used to dissolve the powder in 100 mg/kg concentration and filtered by a 0.22 2 at 37°C.Human CC HCT116 was selected in this study. HCT116 cell line was purchased from Procell Life Science and Technology Co., Ltd. . The cells were cultured and maintained on the McCoy's 5 A medium supplemented with 10% FBS, 100 U/ml penicillin, and 100 mg/ml streptomycin in a humidified chamber with 5% COμM), luteolin , kaempferide , and kaempferol  for 24, 48, and 72 h, respectively. Next, 20 μl of MTS  was added to each well, and the cells were cultured for another 2 h at 37°C. The absorbance was measured at 490 nm using the high-throughput microplate reader screening system . The cell morphology was observed and captured by using an inverted microscope .For the cell viability assay, 3,000 HCT116 cells/well were seeded in 96-well plates. After overnight incubation, the cells were treated with different concentrations of quercetin  and luteolin  for seven consecutive days. On the fourth day, the medium was replaced with the fresh medium containing quercetin or luteolin. After fixation with 4% paraformaldehyde for 20 min, the cells were stained with crystal violet solution for 10 min. Then, the plates were rinsed with PBS, and the colonies were counted and their picture was captured by using a GelCount equipped with count analysis system.To evaluate the colony forming ability of cells, 1,000 cells/well were seeded in 6-well plates. After overnight incubation, the cells were treated with or without quercetin  and luteolin  for 48 h. The cells were collected and suspended in a 100 μl binding buffer containing 5 μl FITC-conjugated Annexin V and 5 μl PI . After dark incubation for 15 min at room temperature, a 400 μl binding buffer was added. The apoptosis condition of the cell samples was analyzed using the Attune Flow cytometer  within 1 h.For apoptosis analysis, 1.5 × 10For cell cycle analysis, the cells were collected and fixed with 75% ethanol at −20°C overnight. Then, the cells were incubated with 0.5 ml propidium iodide  in the dark for 45 min. The cell samples were tested using the Attune Flow cytometer  for cell cycle analysis.6 cells/well were seeded in 6-well plates. After overnight incubation, the cells were treated with or without quercetin , luteolin , and CNC extract  for 48 h. Next, the cells were collected and rinsed twice with ice-cold PBS, lysed, incubated in RIPA buffer containing a 1% protease inhibitor cocktail  for 30 min on ice, and finally centrifuged at 12,000 ×g for 15 min. The supernatant was harvested, and the protein concentration was determined using a Pierce™BCA Protein Assay Kit . The protein samples were then separated by 12% SDS-PAGE  and then transferred to a PVDF membrane . After blocking for 1 h, the antibodies were incubated at 4°C overnight. The primary antibodies detected include EGFR, SRC, AKT, PIK3R1, and IGF1R . The protein bands were visualized using ECL detection reagents .For WB analysis, 1 × 10t-test, and p < 0.05 was considered statistically significant. All results were presented as mean ± standard deviation (SD).To compare the means between the groups, a one-way ANOVA followed by the LSD-t multiple comparison test was performed. Each experiment was carried out in triplicate. The GraphPad 8.0.2 software was used to perform a two-tailed A total of 83 compounds of CNC were retrieved from SwissTarget prediction and TCMSP databases. Among these compounds, there were eight compounds that met the OB threshold at ≥30%, and DL index at ≥0.18 . These cThrough the SwissTarget prediction database and TCMSP analysis platform, the corresponding targets of the eight active compounds of CNC were obtained. A total of 185 targets were identified after the duplicates were removed. Besides, a total of 156 CC-related targets were selected from the OMIN database and 1,271 targets were selected from the GeneCards database. After removed the duplicate targets, a total of 1,336 CC-related targets were identified. The targets were correlated, and a total of 84 targets were indicated as the potential targets. p value <0.05) that were mainly enriched in the key targets of CNC for the treatment of CC were identified. The main pathways that were identified are cancer pathway, PI3K-Akt signaling pathway, EGFR tyrosinase inhibitor resistance pathway, Rap1 signaling pathway, etc. The top 20 pathways were then selected according to the logq value to draw a bubble chart (p < 0.05) were also selected and presented in a bubble diagram, as shown in Results of GO enrichment analysis show that the biological processes (BP) were mainly related to the cellular response to hormone stimulus, phosphatidylinositol 3-kinase signal transduction, and oxidative stress. The cell composition (CCP) is mainly included in the extracellular matrix, lysosome, and cysts. The molecular functions (MF) were mainly the nuclear receptor activity, transcription factor activity, and protein kinase activity. le chart . In addiTo demonstrate a better understanding on the molecular mechanism of CNC against CC, the targets of CNC and CC and the related pathways were taken as the nodes to construct the “compound-target-pathway” network diagram, as shown in π-bond and amino acids CYS60 and CYS26 via hydrogen bond  to investigate the possible interactions between the compounds  and the active site of the targets. The binding affinity of each ligand-target was retrieved and luteolin and VEGFA were found to show the strongest binding affinity among the all binding results . Besidesgen bond . More cogen bond .50 of quercetin and luteolin was 168.3, 153.1, 160.7 μM and 30.8, 20.6, 18.2 μM, respectively (Quercetin and luteolin significantly inhibited cell proliferation of HCT116, whereas kaempferol and kaempferide had a lesser effect. After intervention with quercetin and luteolin for 24, 48, and 72 h, the ICectively . Accordiin vitro experiments. HCT116 was intervened with quercetin and luteolin, or the extracts of CNC. After protein collection, the expression of proteins was measured by WB. All of them had downregulation effects on EGFR, SRC, AKT, IGF1R, P-GSK-3β, and P-PTEN. Interestingly, PIK3R1 was upregulated in the CNC group (The effect of quercetin and luteolin on the apoptosis of HCT116 was determined based on the ability of Annexin V-FITC/PI to stain the phosphatidylserine on the outer membrane of cells and DNA fragmentation of the apoptotic cells , 30.  FiNC group . The resIn recent years, the rise and development of TCM has been rapid. In clinical application, the advantage of the multicomponent, multipathway, and multitarget synergy of TCM has gradually become prominent over the single-target chemical drugs with high toxicity, side effects, and poor efficacy . HoweverCC is the second deadliest malignant tumor globally, causing at least 600,000 deaths each year . In thisAKT is a serine/threonine protein kinase, also known as protein kinase B. The AKT family mainly includes Akt1, Akt2, and Akt3, among which Akt1 plays a significant role in promoting tumor cell proliferation and inhibiting tumor cell metastasis . VEGFA iin vitro HCT116 cell model was established and the anti-CC effects of quercetin, luteolin, kaempferide, and kaempferol compounds were verified. The results show that quercetin and luteolin had a prominent inhibitory effect on the proliferation of HCT116, whereas kaempferide and kaempferol had no obvious inhibitory effect. After 48 h of drug treatment, the cell morphology, such as size, shape, and brightness, changed significantly. In addition, the cell clone formation was reduced gradually with the increase of concentration when the drug was applied to HCT116 for seven consecutive days. Results of Annexin V/PI double staining show that apoptosis rate was increased with the increasing dose, particularly at the early-stage apoptosis. Cell cycle experiments proved that quercetin could block the G2/M phase of the cells in a dose-dependent manner. In contrast, the inhibitory effect of luteolin in G2/M phase of cells was dose-dependent. In addition, the results of WB on some core protein pathways such as EGFR, SRC, AKT, IGF1R, and PI3K-Akt suggest that quercetin, luteolin, and CNC extracts had downregulated the protein expression of EGFR, SRC, AKT, IGF1R, P-GSK-3β, and P-PTEN. This finding was in accordance with many published studies [The hallmarks of cancer comprise six biological abilities acquired during the development of human tumors . TCM wit studies . It shouin vitro research method. It provides a scientific basis for elucidating the anticancer mechanisms of CNC. Due to the different cultural environments, the cells cultured in vitro cannot be entirely equal to those grown in vivo, and their biological behaviors are also different. Therefore, single cell culture is not enough to draw a general conclusion but should be combined with in vivo experiments to draw a more reliable conclusion.The present study preliminarily elucidated the chemical components and mechanisms of the anti-CC effect of CNC. Compared with other anti-CC TCM, CNC has the characteristics of high efficiency and low toxicity. It has been applied as tea in folk to treat various diseases and has a long history. Modern pharmacological experiments also show that CNC has a wide range of safety and has no obvious toxic and side effects on mice. This research provides evidence to support the clinical use of CNC for the treatment of CC. Meanwhile, it is the first time systematically studying the mechanisms of CNC against CC using network pharmacology combined with an in vitro cell experiments proved that luteolin exhibited a stronger anti-CC effect than quercetin by inhibiting cell proliferation and colony formation, and inducing cell apoptosis and cell cycle arrest. The mechanism of anti-CC of luteolin may be related to its ability to inhibit the downstream signal transmission by targeting on the VEGFA and inhibiting the PI3K-Akt signaling pathway. The present research proved that CNC which is multicomponent, multitarget and multipathway could be effectively used to treat CC.In this study, an approach to combine network pharmacology and experimental verification was used to predict and verify the anti-CC mechanisms of CNC. Results of network pharmacology analysis show that quercetin, luteolin, kaempferide, and kaempferol were the main active constituents that suppress the activity of CC by regulating the VEGFA, AKT1, EGFR, and SRC proteins. GO and KEGG analysis found that PI3K-Akt and kinase signaling pathways were the main downstream mechanism pathways of the key targets. In addition, the results of molecular docking show that luteolin had the best binding ability with VEGFA, followed by quercetin. Furthermore, in vivo experiments, which will be further verified in the follow-up research. In addition, the total extract of CNC should also be studied, rather than a few single chemical constituents.However, there are still some shortcomings in this study. The current work lacks


Natural language processing has long been applied in various applications for biomedical knowledge inference and discovery. Enrichment analysis based on named entity recognition is a classic application for inferring enriched associations in terms of specific biomedical entities such as gene, chemical, and mutation.The aim of this study was to investigate the effect of pathway enrichment evaluation with respect to biomedical text-mining results and to develop a novel metric to quantify the effect.P value metrics were compared in simulation experiments to test the robustness of the proposed metrics.Four biomedical text mining methods were selected to represent natural language processing methods on drug-related gene mining. Subsequently, a pathway enrichment experiment was performed by using the mined genes, and a series of inverse pathway frequency (IPF) metrics was proposed accordingly to evaluate the effect of pathway enrichment. Thereafter, 7 IPF metrics and traditional IPF metrics were evaluated in a case study of rapamycin-related gene set. By applying the best IPF metrics in a pathway enrichment simulation test, a novel discovery of drug efficacy of rapamycin for breast cancer was replicated from the data chosen prior to the year 2000. Our findings show the effectiveness of the best IPF metric in support of knowledge discovery in new drug use. Further, the mechanism underlying the drug-disease association was visualized by Cytoscape.The results of this study suggest the effectiveness of the proposed IPF metrics in pathway enrichment evaluation as well as its application in drug use discovery. Homo sapiens genes, and the abundance of data makes the correlation of disease-related genes or drug-related genes possible. In addition, there are multiple ways to identify a relevant gene set for a given disease [The rising health issues worldwide and outbreaks of drug resistance have drawn a great amount of attention to new drug development . However disease . While g disease  or mRNA  disease  is the t disease . However disease . The tex disease , the div disease  in a co- disease  in a morP values. Finally, we performed a case study to show the effectiveness of the IPF metrics in pathway enrichment as well as the promising application of the text mining pipeline for new drug use discovery.The framework of this study was as follows. First, we used various biomedical text mining strategies to investigate the drug-related gene sets. Second, we designed novel metrics for pathway enrichment of text-mined genes. Here, 7 novel inverse pathway frequency (IPF) metrics were proposed and they were compared with the traditional In this paradigm, a drug-centric text resource was obtained to extract the related genes. We set the drug as rapamycin, also known as sirolimus, as the target drug, which is used for the treatment of renal cell carcinoma and malignant lymphoma. Relevant texts and pathway data were collected targeting rapamycin as follows:Text resources: 31,118 abstracts reporting rapamycin were downloaded from PubMed.P value. In total, there are 166 pathways that are related to rapamycin.Rapamycin-related pathway data set: The drug pathway was retrieved from the comparative toxicogenomic database (CTD) , in whicABSTRACT (co-occurrence in abstract) [SENTENCE (co-occurrence in sentence), (3) Method 3: DEPENDENCY (under consideration of dependency tree structure) [TEES (Turku Event Extraction System) [P values in KEGG enrichment are regarded as the ground truth of pathway enrichment without using the text-mined knowledge. Furthermore, the feasibility of the text mining system for drug mechanism prediction was investigated.As shown in bstract) , (2) Metructure) , and (4) System) . By takiTo extract gene pairs from the abstracts of papers, PubTator  and TEESABSTRACT. Only abstracts containing the specific drug name were collected. If more than 2 genes showed up in one collected sentence, these genes were extracted and regarded as drug-related genes.Method 1: SENTENCE. Similar to the abstract-level extraction rule, gene pairs were extracted based on a sentence co-occurrence rule.Method 2: DEPENDENCY. Being stricter than sentence-level gene-pair extraction, the syntactic rule was introduced to restrict the co-occurrence filtering rule. Here, the Stanford parser was used to identity the gene subject or the gene object in a sentence. The gene pair is maintained only when the 2 genes act as sub or obj in the syntactic tree.Method 3: TEES. TEES [Method 4: ES. TEES  is a sopP value setting for the significance test of the relevant pathway for a given gene set.Based on the drug-related abstract text file, 1 text mining tool extracts 1 group of genes. This group of genes is considered to be associated with the drug. For the sake of new drug use discovery, a group of drug-related genes is obtained using a text mining tool. Meanwhile, in the KEGG database, 1 pathway contains a group of genes, which are related to the disease the pathway correlates with. Thus, the matching degree between the drug-related gene group and the disease-related pathway represents the potential of the matching degree between the drug and the disease. ClusterProfile  is a knoN background genes related to a specific pathway and there is a given gene set with k genes, pathway enrichment is performed to evaluate the significance for the given gene set to be relevant to the specific pathway. The significance value is obtained via chance computation for the given gene set in comparison to a randomly sampled gene set. In random sampling, k genes are sampled and x out of k genes are related to the pathway. Then, the probability for this instance is as follows:Assuming in total that there are P value used to address the significance of the pathway for the gene set is as follows:The P value as a traditional enrichment metric reflects solid statistical concern in terms of chance computation. It relies on the hypothesis that the chance for each gene belonging to a given gene set is equal. However, this prerequisite is in some cases not met, for example, housekeeping genes have higher chances to appear in any given pathway, while on the contrary, certain specific genes only appear in a specific pathway.The The 4 text mining methods extracted 4 different sets of drug-related genes. Through these gene-drug relations, a bridge between the genes and the drug was established. The aim of this study was to investigate how a drug is associated with its indication through the gene. The next part was to establish the bridge between these genes and the indication. Mature gene-disease relations were easily accessed through KEGG in the form of the KEGG pathway. The KEGG pathway is a collection of manually drawn pathway maps representing the knowledge on the molecular interaction, reactions, and relation network. Thus, a bridge between the genes and the drug was established via KEGG. The whole path in that mechanism was addressed by finding a gene bridge between the drug and its indication. The next step was to evaluate this strategy. We paid attention to which text mining method is more suitable in this strategy. We focused on the drug-related gene set extracted by the text mining method in terms of the quantity and importance. Thus, we needed to define the importance standard of the gene to the indication. The standard of the gene to the indication in this case is based on the KEGG pathway information. One gene specifically shows up in a specific pathway, which means that this pathway can be identified with this gene. In other words, the less pathways a gene appears in, the more important it is to its related pathway. To calculate this situation, we give a value IPF.P={p1,p2…..pM} refers to all KEGG pathways, where M=#{P} is the number of pathways in the KEGG database.Where m|genei ∈ pm} refers to a pathway that contains the i-th gene, denoted as igene. Thus, every gene in the KEGG database receives a basic score. Simply adding all the gene scores together is unfair. Because all pathways show up in KEGG in the form of a map, each map consists of a set of node boxes and severe edges instead of genes and edges. Therefore, we need to figure out how to calculate that score that one text mining method receives from all node boxes in a specific pathway.{ptT is a gene set that contains all of the genes mined by the t-th text mining method. In order to evaluate how tT genes are enriched in a specific pathway, mP, we defineAssume t,PmTIPF_gene considers the number of genes that exist in a pathway as well as the weight of each gene. The sum of the IPFs can be used to evaluate the association of the group of genes to a pathway. By doing this, cumulative associations along with gene weights are represented.Where jnode is a single node,In KEGG, a node box in some cases represents 1 set of homologous genes, instead of 1 separate gene. Generally, although there exists more than 1 gene, these genes play the same role. Therefore, even the text mining method digs more than one gene belonging to this pathway but they play the same role in the same node box. We only applied the max gene score to represent the score that this text mining method receives in this node box in this pathway. If wherejnode has E subnodes,If i ∈ {Njnode ∩ Tt}, gi = gmaxgWhere igene, which belongs to gene set jnode as well as tT, the maximum IPFigene is assigned, which means igene belongs to gene set jnodeN.For each It is noted that a node box sometimes represents 1 set of protein complex genes that need to work together to play a role in the pathway. Therefore, we applied the sum of all the gene scores that the text mining method received in this node and multiplied it with a coefficient to represent the score that this text mining method receives in this node box in this pathway.jnode∣∣N means the gene number of gene set jnodeN, while ∣ig ∈ {jnode ∩ tT}∣ means the gene number of the union of gene set jnodeN and gene set tT.where Besides the inclusion of genes in 1 node, the graph theory of the node in the pathway should be taken into consideration. In graph theory, the degree of a vertex is the number of edges associated with the vertex. In a pathway graph, one node holding a high degree indicates that this node connects with more vertices. In term of gene, this gene is associated with many genes. Mutations and regulation of the gene affect more genes. In 1 pathway, the more a node shows up in the shortest path between the 2 genes, the more important this gene is in this pathway.r,nodesnodeSP refers to the shortest path between 2 arbitrary nodes, that is, rnode and snode in pathway mP, then, we count the occurrence of jnode in r,nodesnodeSP with respect to mP.First, assume r,nodej,nodeknodeNShortPath is a binary value, which denotes whether or not jnode appears in the shortest path between rnode and knode.In addition, r,nodej,nodeknodeNShortPath. Here, the softmax function is the gradient logarithmic normalization of the discrete probability distribution of finite terms. The result of softmax is suitable for describing the importance of 1 node in 1 pathway.Thus, each node in the pathway holds a “count” score. To compare the importance of a node among all the nodes in one pathway, softmax function is applied to jnodeIPF to represent the total score that the text mining method receives in this pathway,Then, we added all whereIPF_gene (equation 4), IPF_node (equation 8), and IPF_shortpath (equation 11), we formulate a generalized formula for t,PmTIPF_node.Based on the above discussion on IPF_gene in equation (4) holds if 1 is assigned to j,PmnodeWeight. Equation (12) is assigned to t,nodejTScore Score and equation (3) to igeneWeight. The full list of generalized IPF metrics is shown in Here, equation (13) summarizes all the above metric considerations and proposes a generalized form of IPF metrics. For instance, P values were used to map to obtain vital pathways and their pathway ranks. We then evaluated the pathway ranks by counting the occurrences of the key CTD pathways depicted in the Methods section. As shown in We evaluated the effectiveness of IPF metrics by observing the rank counts of topic-related genes obtained from the 4 text mining methods. First, the 4 baseline text mining methods, that is, ABSTRACT, SENTENCE, DEPENDENCY, and TEES, were used to filter the vital genes in rapamycin-related texts. Afterwards, for each gene set obtained by the various text mining methods, 7 IPF metrics and traditional P value and 7 IPF metrics in P values map to less vital pathways than genes from IPF metrics. In detail, the cumulative percentage curves of P values are given in the left 4 plots, and it is straightforward to observe that the y obtained by the P value grades the lowest in all the text mining cases. If computing the area under the cumulative percentage curve, the areas are 0.634, 0.638, 0.625, and 0.529 for P values for each case, which are as well the least in all cases. In all, the consistency of the poor performance of the P value positively shows the effectiveness of the IPF metric in support of the key pathway enrichment. Furthermore, in all the 7 IPF metrics, the black bar, which represents IPF_node, performs the best with the highest value of area under the cumulative percentage curve. It achieves 0.68, 0.679, 0.688, and 0.635 in ABSTRACT, SENTENCE, DEPENDENCY, and TEES, respectively.The bars from 0 to 8 in the bar plot represent the IPF_node prevails over the rest of all in a consistent manner. The results show that the IPF_node represents the best semantic feature from the view of the natural language processing method and it is the most supportive for vital pathway enrichment.Although the area values among IPF metrics do not differ substantially from each other, the IPF_node, 1640 abstracts of rapamycin prior to the year 2000 were obtained and 243 genes were obtained. Afterwards, a standard pathway enrichment was obtained, and the top 0.5% of the pathways under each enrichment path index was statistically analyzed. As expected, the breast cancer pathway was listed in the enrichment results, and the results indicated that the potential activity of rapamycin can be obtained by enriching the gene pathway by text mining interaction genes.The discovery of the efficacy of rapamycin was replicated via a pathway enrichment experiment. PubReminer was used to retrieve the research trend of rapamycin and breast cancer drugs. A total of 1502 abstracts were obtained, and the starting time was the year 2000. The experiment was designed to test if the gene interaction of rapamycin could be excavated by the text mining method from literature without reporting the relevance of breast cancer and rapamycin. All the gene pairs in the literature related to rapamycin from the years 1978 to 2000 were excavated, the active genes of rapamycin were obtained, and the enrichment analysis of the strategic gene pathway in this study was carried out. After applying the The text mining system was investigated to bridge the drug, protein, and disease pathway in order to explore the pharmaceutical mechanism of rapamycin. Starting from the Literature Network application, the disease-related gene network was constructed, and 480 genes obtained by rapamycin-centric text mining were used to highlight the overlapping parts in the breast cancer gene network. All the breast cancer–related genes were collected from the STRING database. According to all the existing databases and text information, each gene was sorted for rapamycin correlation, and in this verification section, 100 breast cancer–related genes from STRING were selected. The breast cancer gene network was constructed according to the gene interaction mentioned in more than 40,000 papers, and the network was constructed using the literature network application program. After gene pathway enrichment analysis, the drug was associated with the pathway and Cytoscape was used for network visualization. In view of the relation between the pathway information and the disease, the drug was further associated with the disease. In order to further analyze the relationship between drugs and diseases, the distribution of the drug-active genes excavated in the disease gene network was analyzed.STAT3, TP53, CDK4, CTLA4, AR, MYC, NOTCH1, IL6, ERBB2, CXCL12, BECN1, IGF1R, CDK2, EGF, ERBB4, MMP9, PIK3CA, CXCL8, ABCB1, EZH2, CDK6, SOX2, AKT1, CDH1, SRC, MTOR, ABCG2, KDR, CCND1, VEGFA, EGFR, ZEB1, ATM, PTEN, CXCR4, ERBB3, MDM2, and GATA3. These 38 genes are based on the intersection of the breast cancer text network and the drug rapamycin-active gene obtained in this strategy. The size of the point in the graph represents the degree of the point, the greater the degree, the larger the point, and the degree in this network is the number of proteins that interact with the protein. The edge thickness in the figure represents the number of sentences that support the protein-protein relationship. The edge color in the figure also represents the number of sentences that support the protein-protein relationship. It can be seen from the figure that the yellow bright spot covers the vast majority of breast cancer gene networks with moderately large spots. The 38 genes were enriched by the P value pathway, and 16 of them, that is, EGFR, IL6, TP53, CDK6, CDK4, PTEN, CDK2, KDR, AKT1, IGF1R, CCND1, VEGFA, PIK3CA, MDM2, MTOR, and the MYC signaling pathway belong to one of the MTOR signaling pathways. Among them, MTOR is an important gene targeted by rapamycin. The MTOR pathway plays an important role in multiple activities of rapamycin and is therefore linked to breast cancer. The reason that literature network is used to construct breast cancer–related network is that the protein interaction involved in constructing the network is obtained from the literature related to breast cancer, and it is the programmed realization of protein interaction based on sentence coexpression in this study. It is convenient for users to quickly construct interactive protein interaction networks based on text relationships. In this study, the breast cancer–related genes obtained from the STRING database were rearranged according to the text information, and the protein interaction information excavated from the text was reflected in the size of the protein gene points. Thus, breast cancer genes were given different weights. It is more convenient to give priority to the location of the active genes under the active conditions defined by the interaction. The overlap of disease and drug-active genes was observed and the possible mechanism of action was speculated.In order to construct a disease-specific gene network, the genetic relationship of this network in nature was obtained from disease-related abstracts. Since Cytoscape is a high-quality visualization platform for network analysis, a literature network application program based on Cytoscape was applied to address the drug disease associations obtained after pathway enrichment. MTOR, the target of rapamycin, is often upregulated in a variety of cancers, while rapamycin is extremely selective in blocking MTOR. Interestingly, our case study pinpointed MTOR correctly and made our pathway enrichment method conceivable in the study of breast cancer. Hopefully, the investigation of rapamycin action in the treatment of breast cancer will be propelled by further extensive and abundant text mining results in the future.In this study, all text resources were obtained from a rapamycin-centric literature data set prior to the year 2000, and all predicted drug efficacies for rapamycin were based on knowledge ahead of this timeline. Therefore, it was interesting to “replicate” and evaluate a novel pathway-discovery method in our case study and to investigate the research paradigm based on pathway enrichment. Several studies after the year 2000 provide evidences to show that the mined rapamycin-centric pathway make sense. For example, after Liu et al  reportedIn conclusion, this research proposed a group of new pathway enrichment metrics by combining protein-interaction mechanisms, graph theories, information retrieval, and data mining weighting technology and by providing a new idea on pathway enrichment analysis. Moreover, the effectiveness of the best new enrichment metric for rapamycin was analyzed and the new activity of the drug shown by our method is supported by evidence from the literature. This research strategy sheds light on the investigation of the mechanism of action of drugs on diseases by using text-mined genes that are enriched in signaling pathways.


The term RASopathy was originally created to describe a phenotypically similar group of medical genetic syndromes caused by germline pathogenic variants in components of the RAS/mitogen-activated protein kinase (RAS/MAPK) pathway. In defining a RASopathy syndrome, one needs to consider the complex nature of the RAS/MAPK pathway, the numerous genes and regulatory components involved, its crosstalk with other signaling pathways and the phenotypic spectrum among these syndromes. Three main guiding principles to the definition should be considered. First, a RASopathy is a clinical syndrome with overlapping phenotypic features caused by germline pathogenic variants associated with the RAS/MAPK pathway. Second, a RASopathy is caused by multiple pathogenetic mechanisms, all of which lead to a similar outcome of RAS/MAPK pathway activation/dysregulation. Finally, because a RASopathy has dysfunctional germline RAS/MAPK pathway activation/dysregulation, it may, therefore, be amenable to treatment with pathway modulators. Summary: RASopathy is a term used to unify a phenotypically similar group of medical genetic syndromes, yet its definition is challenging and should consider the perspectives of clinicians, scientists and the patients. PTPN11, SOS1, RAF1/CRAF, KRAS, NRAS, SHOC2, CBL, RRAS, RIT1, RASA2, SOS2, MAP3K8, SPRY1, MYST4/KAT6B, LZTR1, A2ML1, PPP1CB, MRAS, RALA, RRAS2 and ERK2/MAPK1. Cardio-facio-cutaneous syndrome (CFC) is caused by activating mutations in multiple genes, including BRAF, MAP2K1/MEK1, MAP2K2/MEK2, KRAS and possibly YWHAZ. Other RASopathies, including neurofibromatosis type 1 (NF1), Noonan syndrome with multiple lentigines (NSML), Costello syndrome (CS), Legius syndrome, SYNGAP1 and central conducting lymphatic anomalies (CCLA), are caused by alterations in one or two genes in the RAS pathway  pathway . Collect pathway  , RAS guanine nucleotide exchange factors (RAS-GEFs), kinases, scaffolding/adaptor proteins, ubiquitin ligases, phosphatases and pathway inhibitors – with all identified mutations culminating in the activation of ERK1/2 . AlthougFrom an oncologist's perspective, the RASopathies are cancer syndromes due to the presence of potentially oncogenic mutations in the germline . RAS/MAPThe RASopathies provide a unique perspective in the study of cancer genes because individuals harbor a single germline mutation, unlike a somatic cancer, which typically acquires many oncogenic mutations. Understanding the function of RASopathy-associated genes in the RAS/MAPK pathway, and their role in development, provides clues as to its function in cancer and vice versa. NF1 is the best-studied cancer syndrome caused by germline haploinsufficiency of the tumor suppressor gene neurofibromin 1. NF1 individuals have more than twice the risk of cancer of the general population . MalignaHras mouse model  within a gene(s) that encodes a component or regulator of the RAS/MAPK pathway, which causes the activation of ERK1 and/or ERK2. The pathogenic variant may be