Oral cancer (OC) is one of the most pernicious cancers with increasing incidence and mortality worldwide. Surgery is the primary approach for the treatment of early-stage OC, which reduces the quality of life of the patients. Therefore, there is an urgent need to discover novel treatments for OC. Targeting ferroptosis to induce cell death through the modulation of lipid oxidation has been used as a new approach to treat many cancers. Glutathione (GSH) is a coenzyme factor of GSH peroxidase 4, and it carries potential applicability in treating OC. By using network pharmacology and molecular docking followed by systematic bioinformatic analysis, we aimed to study GSH-targeting ferroptosis to treat OC. We identified 14 core molecular targets, namely, EGFR, PTGS2, HIF1A, VEGFA, TFRC, SLC2A1, CAV1, CDKN2A, SLC3A2, IFNG, NOX4, DDIT4, CA9, and DUSP1, involved in ferroptosis that were targeted by GSH for OC treatment. Functional characterization of these molecular targets showed their importance in the control of cell apoptosis, cell proliferation, and immune responses through various kinase activities such as the mitogen-activated protein kinase activity  and modulation of TOR signaling . Molecular docking further revealed the direct binding of GSH with EGFR, PTGS2, and HIF1A proteins. These findings provide a novel insight into the targets of GSH in ferroptosis as well as possible molecular mechanisms involved, suggesting the possible use of GSH as a combined therapy for treating OC. OC is oll death . Althougll death , has beell death . Using all death . In thishttps://portal.gdc.cancer.gov/) was used to determine OC-associated genes. Using the limma package of R in the Bioconductor software, genes with FDR <0.05 and |log2 fold change| > 1 were considered as OC-associated genes (The Cancer Genome Atlas (TCGA) database  network analysis by using the STRING (Version 11.0) database . The netThe common targets were subjected to Bioconductor packages in the R-language software for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Then, the Cytoscape software was used to visualize the biological processes and signaling pathways involved in the GSH action against OC through ferroptosis .The binding of GSH to its core targets including the epidermal growth factor receptor (EGFR), prostaglandin-endoperoxide synthase 2 (PTGS2), and hypoxia inducible factor 1 subunit alpha (HIF1A) was studied by molecular docking. The protein structures of EGFR, PTGS2, and HIF1A were obtained from the PDB database. Then, MGLTools 1.5.6 of AutoDock Vina and the ChemBio3D Draw tool were used to conduct the docking analysis . The docBy searching the FerrDb and GeneCards databases, 259 ferroptosis-associated targets were identified . AdditioGO enrichment analysis of the 14 core targets highlighted the biological processes related to stress responses such as oxidative stress and hypoxic stress . BesidesMolecular docking analysis was conducted to investigate the possible direct binding of GSH to its target proteins. The results showed that GSH had a high binding affinity to EGFR, PTGS2, and HIF1A . The proIn the present study, we investigated the possible use of GSH in treating OC by targeting ferroptosis. Ferroptosis, a newly discovered type of cell death mediated by iron-dependent lipid peroxidation , is repovia the response gene FOS as a subunit of the AP-1 complex (By using network pharmacology, we identified 14 core targets of GSH action against OC by targeting ferroptosis. Bioinformatic analysis of these 14 targets suggested the role of GSH in cell apoptosis, cell proliferation, and immune responses through the regulation of MAPK, ERK, and TOR signaling pathways. MAPK was found to be involved in ferroptosis regulation in many human body functions . Activat complex . Also, i complex . Besides complex , endomet complex , hepatoc complex , and pan complex . Specifi complex . For TOR complex . Another complex . TherefoIn addition to network pharmacology, we applied molecular docking to investigate the possible direct binding of GSH to its target proteins involved in ferroptosis. Our results showed a strong binding affinity of GSH to EGFR, PTGS2, and HIFIA.EGFR, a receptor tyrosine kinase, is a major regulator of cancer development . It coorIn conclusion, we identified the targets of GSH in ferroptosis associated with OC, providing new mechanistic insights that may be clinically relevant for combination therapies of OC. However, the findings of the present study are mainly based on network pharmacology and bioinformatics analysis; therefore, further preclinical investigation is needed to verify the results.


Osteoporosis (OP) is a systemic disease susceptible to fracture due to the decline of bone mineral density and bone mass, the destruction of bone tissue microstructure, and increased bone fragility. At present, the treatments of OP mainly include bisphosphonates, hormone therapy, and RANKL antibody therapy. However, these treatments have observable side effects and cannot fundamentally improve bone metabolism. Currently, the prescription of herbal medicine and their derived proprietary Chinese medicines are playing increasingly important roles in the treatment of OP due to their significant curative effects and few side effects. Among these prescriptions, Gushukang Granules (GSK), Xianling Gubao Capsules (XLGB), and Er-xian Decoction (EXD) are widely employed at the clinic on therapy of OP, which also is in line with the compatibility principle of “different treatments for the same disease” in herbal medicine. However, at present, the functional interpretation of “different treatments for the same disease” in herbal medicine still lacks systematic quantitative research, especially on the detection of key component groups and mechanisms. To solve this problem, we designed a new bioinformatics model based on random walk, optimized programming, and information gain to analyze the components and targets to figure out the Functional Response Motifs (FRMs) of different prescriptions for the therapy of OP. The distribution of high relevance score, the number of reported evidence, and coverage of enriched pathways were performed to verify the precision and reliability of FRMs. At the same time, the information gain and target influence of each component was calculated, and the key component groups in all FRMs of each prescription were screened to speculate the potential action mode of different prescriptions on the same disease. Results show that the relevance score and the number of reported evidence of high reliable genes in FRMs were higher than those of the pathogenic genes of OP. Furthermore, the gene enrichment pathways in FRMs could cover 79.6, 81, and 79.5% of the gene enrichment pathways in the component-target (C-T) network. Functional pathway enrichment analysis showed that GSK, XLGB, and EXD all treat OP through osteoclast differentiation (hsa04380), calcium signaling pathway (hsa04020), MAPK signaling pathway (hsa04010), and PI3K-Akt signaling pathway (hsa04151). Combined with experiments, the key component groups and the mechanism of “different treatments for the same disease” in the three prescriptions and proprietary Chinese medicines were verified. This study provides methodological references for the optimization and mechanism speculation of Chinese medicine prescriptions and proprietary Chinese medicines. Osteoporosis (OP) is the most common bone disease characterized by decreased bone mass and degradation of bone microstructure. The main clinical manifestations are decreased bone density, chronic pain, decreased mobility, and so on . The comThe current treatment drugs for OP mainly include estrogen , bisphosHerbal medicine has been diffusely used at the clinic on OP therapy due to its fewer side effects and irritation . It is kEpimedium brevicornum Maxim., Radix Rehmanniae Preparata, Auricularia auricular (L.) Underw., Astragalus mongholicus Bunge, Cucumis sativus L., Davallia mariesii Moore ex Bak., and Salvia miltiorrhiza Bge. XLGB consists of six herbs: Epimedium brevicornum Maxim., Rehmannia glutinosa (Gaetn.) Libosch. ex Fisch. et Mey., Salvia miltiorrhiza Bge., Dipsacales, Anemarrhena asphodeloides Bunge, and Psoralea corylifolia Linn. EXD has six kinds of herbs: Curculigo orchioides Gaertn., Epimedium brevicornum Maxim., Angelica sinensis (Oliv.) Diels, Morinda officinalis How., Phellodendron chinense Schneid., and Anemarrhena asphodeloides Bunge. Clinical studies have shown that GSK can increase sex hormones (estrogen and androgen), inhibit bone absorption, effectively improve bone mineral density (BMD), reduce bone loss, increase osteocalcin (OC) and blood alkaline phosphatase (ALP) levels, and enhance osteoblast activity, which can effectively prevent and treat OP  of different prescriptions in the therapy of OP. In order to further decode the key components group of different prescriptions on OP, a genetic algorithm-based optimization model was designed to figure out the FRMs from SDFMs. The distribution of high relevance score, the number of reported evidence, and the coverage of the enrichment pathway of target genes in FRMs were used to evaluate the accuracy and reliability of the FRMs detection model. Then, the effective proteins in the FRMs of each prescription were employed to screen the key components group of each prescription. The importance scores of the components in the key components group of each prescription were obtained based on information gain and target influence, and the effects of these key components were verified by cell experiments. Finally, the potential action mode of different prescriptions in treating the same disease was speculated based on the key components group of each prescription.In conclusion, according to the herbal informatics strategy proposed in this study, the main mechanisms and relevant pharmacological effects of different treatments of OP can be detected through FRMs, which provide a new network-based method for herbal medicine in the clinic therapy of complex diseases.in vitro experiments to confirm the precision of our proposed key components group selection model. 6) Finally, the potential action mode of different prescriptions treating the same disease mechanism was inferred by function analysis of the key components group in each prescription.The schematic diagram of the whole process is shown in The original components in the three prescriptions were extracted by searching the Traditional Chinese Medicine Integrated Database (TCMID 2.0), TCM Database@Taiwan, and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) with the herbs in GSK, XLGB, and EXD as keywords. Open Babel (version 2.4.1) was used to convert the structures of all chemical components into canonical SMILES. The Similarity Ensemble Approach (SEA), Hit Identification and Target Prediction (HitPick), and SwissTargetPrediction were used to predict the drug targets of GSK, XLGB, and EXD.in vivo, so they tend more to become oral drugs. In drug research and development, the Lipinski Rule of Five is used in the preliminary screening of the compound library to exclude those molecules that are unfit for drugs, narrow the range of selecting, and economize the cost of drug research and development. The detailed description of Lipinski’s Rule of Five is that the molecular weight of the compound is less than 500 Da. The number of hydrogen bond donors (including hydroxyl groups and amino groups) in the structure of the compound does not exceed 5. The number of hydrogen bond receptors in the compound does not exceed 10. The logarithmic value (logP) of the lipid-water partition coefficient of the compound is between −2 and 5. The quantity of rotatable bonds in the compound does not exceed 10  was employed to build a C-T network, and its plug-in NetworkAnalyzer was used to analyze network topology parameters.p-value. All statistical analysis was performed using the R language (version 4.0.5).Function enrichment is conducted based on KEGG (Kyoto Encyclopedia of Genes and Genomes). The hypergeometric distribution model is employed to calculate the significance of the biological pathway containing the target gene. The Benjamini–Hochberg method was used to correct the In order to find SDFMs in the three prescriptions for treating OP, we designed the below function motif discovery model.The function motif discovery model is based on the combination of random walk and information compression. It uses the double-layer Huffman coding method to associate community discovery with information coding, records the paths generated by a random walk in the graph, and finds the community division with the shortest length. The average coding length of each step described in the random walk can be measured by the following prescription:L(M) of each step is the weighted sum of two parts. One part is the average byte length required by the coding community name, and the other part is the average byte length required by the coding node in each community. The prescription can be expressed asIn order to further screen the SDFM, we designed a novel genetic-based optimization model, which is described in detail as follows:1) Random generation of the initial population: the chromosome coding method is represented by a binary column code of length 2) Individual evaluation: under the premise of not exceeding 3) Individual selection: the roulette model is used to convert individual fitness into the area of the roulette wheel in proportion and rotate the roulette wheel. Finally, the individual corresponding to the landing position is selected.4) Two points cross: two points are randomly set in the individual code, and some genes are exchanged in the middle of the two intersection points.5) Basic mutation, a number is randomly generated for each chromosome, indicating whether the chromosome needs to be mutated. If a mutation is needed, a random variable is generated, indicating which bit of the chromosome to modify.6) A new population is formed, and the iteration continues until the termination condition is met: The genetic algorithm (GA) is a random walk method that simulates the evolution of the genetic mechanism of the evolutionary laws of nature. It takes all individuals in a population as the object and efficiently searches a coded parameter space through genetic operations of selection, crossover, and mutation. As a new global optimization search algorithm, the genetic algorithm has the obvious characteristics of simplicity and generality, strong robustness, high efficiency, and practicality. It can be applied in all kinds of fields, has achieved good results, and has become one of the critical, intelligent algorithms by degrees. The calculation process is as follows:Cytoscape (version 3.7.2) was used to combine osteoclast differentiation, calcium signaling pathway, MAPK signaling pathway, and PI3K-Akt signaling pathway into an integrated pathway.In order to screen the key component groups in different prescriptions, we designed a novel component importance calculation method that combined the information gain and target influence. We sort all the components in descending order of the corresponding target number and calculate the information gain 2. Cells were seeded into 96-well plates (1×103 cells per/well) for 24 h and then treated with 5 μM quercetin, isoliquiritigenin, rutaecarpine, isofraxidin, and secoisolariciresinol for 24 and 48 h. Quercetin, isoliquiritigenin, rutaecarpine, isofraxidin, and secoisolariciresinol (≥98% purity by HPLC) were dissolved in DMSO.Mouse preosteoblastic MC3T3-E1 cells were purchased from the American Type Culture Collection (ATCC) and stored in Minimal Essential Medium, Alpha (α-MEM), supplemented with 10% fetal bovine serum (FBS), 100 units/mL of penicillin G, and 100 μg/ml of streptomycin at 37°C under 5% COTECAN, infiniteM200).Cell Count Kit-8 (CCK-8) assay was utilized to measure cell viability. After cell culture, add 10 μl CCK8 to the culture medium and incubate at 37°C for 2 h. The absorbance was measured at 450 nm with a microplate reader , TCM@TAIWAN, and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Generally speaking, each Chinese medicine compound contains multiple herbal medicines, and each herbal medicine contains a series of chemical components. The pharmacological properties of these components are closely related to their therapeutic effects. Ingredients with better pharmacological properties may have positive therapeutic effects. Therefore, before analyzing the pharmacological effects, we first screen for pharmacological properties. SwissADME was used to screen the components in accordance with Lipinski’s Rule of Five, gastrointestinal absorption, and bioavailability. After SwissADME screening, 271, 242, and 344 active components were obtained in GSK, XLGB, and EXD, respectively . Our furCytoscape was used to construct a C-T network to analyze the relationships between the active components and targets of the three prescriptions . The resIn the C-T network of XLGB, the average degree of the components was 30.48. Among them, phenylalanine had the highest node degree, acting on 202 drug targets (degree: 202), and tyrosine acted on 124 targets in descending order (degree: 124). Quercetin acted on 106 targets (degree: 106). The average degree of the target was 6.05. Among them, the highest degree of the node was Microtubule Associated Protein Tau (MAPT), which targeted 105 compounds (degree: 105), followed by Tyrosyl-DNA phosphodiesterase 1 (TDP1), which targeted 74 compounds (degree: 74). Carbonic anhydrase 7 (CA7) targeted 63 compounds (degree: 63).In the C-T network of EXD, the average degree of the components was 30.58. Among them, asperglaucide had the highest node degree, acting on 267 drug targets (degree: 267), and phenylalanine acted on 197 targets in descending order (degree: 197). n-cis-Feruloyltyramine acted on 177 targets (degree: 177). The average degree of the target was 7.16. Among them, the highest degree of the node was cytochrome P450 family 1 subfamily B member 1 (CYP1B1), which targeted 94 compounds (degree: 94), followed by carbonic anhydrase 3 (CA3), which targeted 90 compounds (degree: 90). Carbonic anhydrase 7 (CA7) targeted 86 compounds (degree: 86). Through the comparative analysis of the number of targeted genes, it can be seen that GSK had a stronger control rate on the C-T network.The results showed that, in the three prescriptions, there were a relationship between one component corresponding to multiple targets and a phenomenon of different components acting on the same target, consistent with the function of “multi-components-multi-targets-multi-pathways” in herbal medicine, reflecting the complicacy of the underlying mechanism of herbal medicine.p < 0.05). In order to figure out the FRMs and remove the noise in the SDFM in each prescription. We designed another genetic-based optimization model to optimize the SDFM and finally obtained 11, 13, and 15 FRMs in GSK, XLGB, and EXD, respectively  is a member of the protein kinases family associated with cell cycle regulation . In addiAmong the specific genes of XLGB, CD38 has been shown to play a role in bone remodeling. It is expressed in osteoclasts, and when activated, it increases IL-6 release and inhibits bone resorption . MeanwhiAmong the specific genes of EXD, BCL2 is a gene that inhibits apoptosis. Studies have shown that upregulation of BCL2 may inhibit bone loss, while upregulation of BCL2 in osteoblasts can restrain osteoblast differentiation and cause osteocyte apoptosis . Colony The above results indicate that the FRMs of different prescriptions have different and co-functional effects in treating OP. They provide a possible mechanism reference for us to reveal multiple treatments for the same disease.in vitro to prove the model’s reliability  on the MC3T3-E1 cells at different time points . The results showed that the cell viabilities of MC3T3-E1 cells were 115.93, 119.74, 111.27, 115.11, and 117.02% after exposure to 5 μM quercetin, isoliquiritigenin, rutaecarpine, isofraxidin, and secoisolariciresinol on Day 1, respectively . On Day OP causes fractures and other complications, which increase the physical pain of patients and bring great pressure and burden to the society and family. For the treatments of OP, drugs that promote bone mineralization, inhibit bone resorption, and promote bone formation are widely used . HoweverHerbal informatics is an interdisciplinary subject that integrates Chinese medicine, computer science, biology, mathematics, multi-directional pharmacology, and other disciplines. It researches complex herbal medicine systems by systematically observing the intervention and influence of drugs on disease networks . Herbal In this study, after ADME screening, 517 active components were obtained. Among the three prescriptions, there were 88 common components; there were 65, 37, and 232 specific components in GSK, XLGB, and EXD, respectively. This suggests that the three prescriptions impact OP treatment through common and specific components.p < 0.05). The FRMs were used to detect the latent action mode of GSK, XLGB, and EXD in the clinical therapeutics of OP.To quickly extract vital information from the complex C-T networks, we designed a novel function motif discovery model and a genetic-based optimization model and obtained 11, 13, and 15 FRMs  can retain good individuals and maintain the group’s diversity. The information gain inspects the feature’s contribution to the whole system and considers both the occurrence and nonoccurrence of the feature. Meanwhile, it considers comprehensively and uses the statistical attributes of all samples to reduce the sensitivity to noise.The accuracy and reliability of FRMs were evaluated using the relevance score, the number of reported evidence, and the coverage of functional pathways of FRMs. Results showed that the predicted FRMs overlapped with the C-T network in pathogenic genes and functional pathways at high coverage.Next, we conducted pathway enrichment analysis for each FRMs and found that 16 enriched pathways were shared by three prescriptions. Through a literature search, we found that osteoclast differentiation, calcium signaling pathway, MAPK signaling pathway, and PI3K-Akt signaling pathway were most reported. Recent research has shown that RANKL-induced ROS signaling regulates the MAPK and NF-κB activity, and the loss of NF-κB signaling in mouse models causes bone defect formation, leading to osteoporosis . RANKL cFurthermore, we found that some enriched genes were shared by four pathways, but others were specific to one pathway, and these genes all played different roles in the influence of OP, indicating that the FRMs of different prescriptions have different and co-functional effects in the treatment of OP. It provides a possible mechanism reference for us to reveal multiple treatments for the same disease.in vitro experiments to prove the model’s reliability.Then, we devised a novel component importance calculation method that combined the information gain and target influence to screen the key component groups in different prescriptions. The components with a In vitro cell experiments also showed that these components can increase the viability of MC3T3-E1 cells, speculating that these components have a certain role in treating OP. Additionally, to better evaluate the dependability of our proposed network pharmacological model, we will conduct in vivo studies in future studies.However, there are still some limitations in this study. First, more ingredients from key component groups should be selected for validating the reliability of our method and model. Second, the action mode of GSK, XLGB, and EXD in the clinic therapy of OP is not verified in this study. Meanwhile, the issue of drug dose should be considered in future studies.In conclusion, we propose a new network pharmacology strategy that reveals the hidden mechanisms of different prescriptions for OP through new bioinformatics models and experimental validation, providing a new web-based approach for herbal medicine treatment of complex diseases.


M. cordata for the treatment of bovine relevant active compounds in M. cordata were searched for through previous research bases and literature databases, and then screened to identify candidate compounds based on physicochemical properties, pharmacokinetic parameters, bioavailability, and drug-like criteria. Target genes associated with hoof disease were obtained from the GeneCards database. Compound−target, compound−target−pathway−disease visualization networks, and protein−protein interaction (PPI) networks were constructed by Cytoscape. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in R language. Molecular docking analysis was done using AutoDockTools. The visual network analysis showed that four active compounds, sanguinarine, chelerythrine, allocryptopine and protopine, were associated with the 10 target genes/proteins  obtained from the screen. The enrichment analysis indicated that the cAMP, PI3K-Akt, and ErbB signaling pathways may be key signaling pathways in network pharmacology. The molecular docking results showed that sanguinarine, chelerythrine, allocryptopine, and protopine bound well to MAPK3 and JAK2. A comprehensive bioinformatics-based network topology strategy and molecular docking study has elucidated the multi-component synergistic mechanism of action of M. cordata in the treatment of bovine hoof disease, offering the possibility of developing M. cordata as a new source of drugs for hoof disease treatment.Based on network pharmacological analysis and molecular docking techniques, the main components of  Hoof disease is a common surgical disease in equine animals and cattle, with a higher incidence in dairy cattle . In the Macleaya cordata (Willd.) R. Br. (M. cordata), also called Boluohui, is a plant of the opium poppy family native to China, and is widely used in animal husbandry in Asia, Europe, and the Americas. Numerous studies have proven that M. cordata extract can eliminate intestinal inflammation, regulate intestinal flora, and promote growth in livestock [M. cordata extract also showed good antibacterial effects and facilitated the elimination of bacterial resistance [M. cordata is used externally to relieve pain and swelling and to treat bruises and arthritis [M. cordata has promising applications for the treatment of hoof disease in cattle. However, herbal treatments are multi-component, multi-pathway, and multi-target, and the mechanism of action of M. cordata in the treatment of arthritis has not been elucidated and needs further research.Chinese medicine, as the primary means of treating disease in Chinese veterinary medicine, has definite therapeutic effects and fewer side effects, and can be used as a complementary and alternative therapy to modern medicine. ivestock ,8,9,10. sistance . The Chirthritis . With thM. cordata based on network pharmacology, to analyze the combined pharmacological effects of M. cordata at the network level for the treatment of bovine hoof disease, and to validate the interactions between the predicted compounds and the targets using molecular docking techniques, with a view to fully exploit M. cordata for wider use in veterinary practice.Network pharmacology is based on an extension of pharmacology under systems biology and the application of technical tools such as databases and computational chemistry to study the targets, pathways, and efficacy of drugs. It can also be combined with molecular biology or a range of emerging histological tools, such as metabolomics and transcriptomics, to provide a macroscopic and systematic understanding of the interactions between drugs, diseases, and the organism from a holistic perspective . In the M. cordata were constructed through the following steps: (1) data collection and collation, including identification of the chemical composition of M. cordata, screening of candidate compounds, identification of hoof disease-related disease targets, and intersection of the predicted targets of candidate compounds with the disease targets; (2) network topology analysis and protein interaction network construction; (3) enrichment analysis; and (4) molecular docking validation.Network pharmacology-based predictions for the treatment of hoof disease with M. cordata are alkaloids, based on literature search engines such as China National Knowledge Infrastructure (CNKI) (https://www.cnki.net/) (accessed on 26 October 2021), PubMed (https://pubmed.ncbi.nlm.nih.gov/) (accessed on 28 October 2021), and Chemistry Database (http://www.organchem.csdb.cn/) (accessed on 27 October 2021) [According to a large amount of literature, it is known that the main active substances of er 2021)  for relahttp://www.swissadme.ch/) (accessed on 30 October 2021) [p); the smaller the value, the less molecules are permeated by the skin. There are five indicators under the class of drug properties, two of which are yet to be included in the selection; with a bioavailability F > 10%, the higher the value, the better the bioavailability.Traditional Chinese medicine is usually administered orally or externally, and a large number of chemicals enter the body and are involved in physiological processes such as absorption, distribution, metabolism, and excretion (ADME). The new drug development process, where approximately 90% of compounds are eliminated due to drug properties, has shown, in a large number of cases, that ADME properties are more critical in the screening process of new drugs than the pharmacological activity of the compounds themselves . ADME teer 2021)  and evalhttps://pubchem.ncbi.nlm.nih.gov/) (accessed on 30 October 2021) [http://www.swisstargetprediction.ch/) (accessed on 1 November 2021) [https://www.uniprot.org/) (accessed on 1 November 2021) [The Simplified molecular input line entry system (SMILES) of the selected compounds were queried using the PubChem database (er 2021) .®: The Human Gene Database version 5.5 database (https://www.genecards.org/) (accessed on 2 November 2021) [Based on the presentation and site of hoof disease, the GeneCardser 2021)  was searFor all of the target genes related with the chosen chemicals and hoof disease, a Venn diagram was constructed using R 4.0.3 , demonstrating the intersection between compound predicted targets and disease known targets.https://github.com/cytoscape/cytoscape/releases/3.7.1/, accessed on 20 January 2019) and through systematic exploration of the active compounds and the possible molecular mechanisms for the treatment of hoof-like diseases by M. cordata.Visualization of compound−target networks and compound−target−pathway networks by Cytoscape 3.7.1 (Bos taurus), the minimum interaction score was set to “medium confidence (0.40), and the unconnected protein nodes were excluded. The constructed PPI network data were mapped using Cytoscape 3.7.1.The target genes obtained from the Venn analysis of intersections were imported into the Multiple Proteins text box in String database v11.5  to consthttps://david.ncifcrf.gov/) (accessed on 4 November 2021) [Bos taurus). A false discovery rate (FDR) < 0.05 was considered statistically significant in the results of the enrichment analysis.Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed on the target genes using the DAVID Bioinformatics Resources 6.8 database (er 2021) ,26. The https://www.rcsb.org/) (accessed on 5 November 2021) [The TOP5 proteins were screened for molecular docking validation based on the results of the PPI network. High resolution standard protein 3D structure pdb files were obtained from the RCSB Protein Data Bank database (er 2021) . Compouner 2021) . The scrM. cordata and an extensive pre-laboratory base [Four candidate compounds were screened based on the alkaloid content in ory base ,30, nameo/w) of 0–3, with moderate water solubility and skin permeability of −5 to −7 cm/s. All of the drug-like parameters were met and the bioavailability was 0.55. All four candidate compounds were eligible by evaluation screening; 161 compound-related genes were obtained in the UniProt database through the SwissTargetPrediction tool.The information related to the four compounds evaluated by ADME is shown in A total of 24,465 target genes associated with six diseases, including arthritis and tenosynovitis, were retrieved from the GeneCards database, including 12,456 for pain, 358 for tenosynovitis, 77 for bursitis, 7146 for arthritis, 3487 for laminitis, and 941 for thrombus. After eliminating 20,411 genes by correlation scoring and repeatability screening, 4054 disease target genes were retained.A total of 91 genes were obtained by intersecting 161 compound predicted target genes with 4054 disease target genes by R 4.0.6 .M. cordata, the light blue nodes represent the compounds, and the lavender nodes represent the interactions. M. cordata. The higher the degree value of a node, the higher its degree centrality, meaning the node is more important in the network, and the betweenness describes the node importance in terms of the number of shortest paths through the node [The constructed network of compounds and their potential target interactions is shown in the node . ALL hasM. cordata, with the six diseases associated with hoof disease. Of these, ALL had 32, 26, and 26 targets associated with pain, arthritis, and thrombosis, respectively; CHE had 18 targets associated with pain, 18 with thrombus, and 16 with arthritis; the number of intersecting targets for pain, arthritis, and thrombus with PRO was 13, 12, and 9, respectively; and SAN had fewer target genes associated with all six diseases, with the most being associated with thrombus at 5.The compound−target interaction network was mapped to the disease network to construct a compound−target−disease interaction network graph . The netThe nodes in the PPI network represent proteins and the edges represent the interactions between proteins. The constructed network  has 106 The GO enrichment analysis included three biological aspects, namely biological processes (BP), cellular components (CC), and molecular functions (MF). As shown in KEGG pathway analysis was used to identify the target functions and signaling pathways, and the bubble diagram  demonstrThe four active compounds were semi-flexibly docked with the top 10 protein targets in the PPI interaction network using AutoDockTools, with the Nice Level set to 20, and four results were obtained that met the screening criteria, namely SAN-MAPK3, CHE-MAPK3, ALL-MAPK3 and PRO-JAK2. The docking parameters are shown in M. cordata extract has good anti-inflammatory and growth-promoting effects and is now widely used in livestock production. In traditional Chinese medicine, M. cordata is used as an external medicine for bruises and arthritis, but the mechanism of action has not been elucidated. In relation to veterinary clinical and animal production, hoof disease is a common surgical disease in cattle, horses, and other animals. Based on the efficacy of empirical medicine and the rational use of medicinal plant resources, M. cordata has potential therapeutic effects and is expected to be developed as a new source of drugs for the treatment of hoof disease.Traditional drug development follows a reductionist approach based on molecular biology, but it is difficult to reveal the systemic characteristics of traditional Chinese medicine (TCM). Network pharmacology can resolve the complex system of multi-component, multi-target, and multi-pathway of TCM at the systemic level, which is of guidance for both active ingredient screening and target discovery of TCM . For exap) also determines whether a drug can pass through the skin into the body; at Log Kp < −10, the substance is essentially unable to pass through the skin [M. cordata. Toxicity and/or adverse effects are always a concern during the development of a drug. Although the results of the cytotoxicity tests showed a high cytotoxicity of SAN and CHE [Chelidonium majus L, which has a similar active ingredient to M. cordata, has been used by EMA in the treatment of skin conditions such as tissue healing [In this study, four alkaloid active compounds, SAN, CHE, ALL, and PRO, were screened based on previous studies, which have shown the compounds to have good antibacterial and anti-inflammatory pharmacological effects ,35,36. Wthe skin ,38. Accothe skin ,40. In s and CHE ,42. Howe and CHE . In addi and CHE . Chelido healing ,46. Howe healing . TherefoA total of 161 potential targets associated with compounds and 4054 gene targets associated with hoof disease were identified, and 91 target genes were obtained from targets where the compounds interacted with hoof disease. The proteins that play a key role were obtained through the PPI network, and the GO and KEGG pathway enrichment analyses showed evidence of bioregulatory processes such as cell proliferation and differentiation, apoptosis, and hormone secretion associated with hoof disease. The good binding ability of ALL-MAPK3 and PRO-JAK2 was illustrated by rigorous molecular docking.The top 10 proteins in the PPI network in terms of connectivity all play an important role in the course of hoof disease. Of these, SRC was the most highly associated and was shown to bind to PTK2B/PYK2 to provide energy for anti-osteoporosis drugs and to prevent the inhibitory effect of calcitonin by activating mitochondrial cytochrome C oxidase . SRC canKey proteins in the cAMP, PI3K-Akt, Prolactin, and Ras pathways and PPI networks obtained from KEGG pathway enrichment analysis interact with each other to form a complex target−pathway network. For example, the downstream proteins of the Prolactin pathway are JAK2, MAPK, and SRC and regulate apoptosis and inflammation through the JAK/STAT pathway ,62,63. MM. cordata, in particular, the MAPK pathway may play a very relevant role.The semi-flexible molecular docking results showed good binding of SAN to MAPK3, CHE to MAPK3, ALL to MAPK3, and PRO to JAK2. The results of molecular docking are consistent with the reported results. These targets with a high affinity to the compounds are likely to be the key to the treatment of hoof disease with M. cordata has the potential to treat hoof diseases, where the therapeutic effects of the four main active substances, SAN, CHE, ALL, and PRO, are related to targets such as SRC, MAPK3, JAK2, and MTOR, and act through signaling pathways such as cAMP, JAK/STAT, PI3K-Akt, and Ras/MAPK. This shows that the treatment of hoof disease-related diseases by M. cordata is the result of the interaction of multiple components, multiple targets, and multiple pathways.In summary,


Endogenous metabolite levels describe the molecular phenotype that is most downstream from chemical exposure. Consequently, quantitative changes in metabolite levels have the potential to predict mode-of-action and adversity, with regulatory toxicology predicated on the latter. However, toxicity-related metabolic biomarker resources remain highly fragmented and incomplete. Although development of the S1500+ gene biomarker panel has accelerated the application of transcriptomics to toxicology, a similar initiative for metabolic biomarkers is lacking. Our aim was to define a publicly available metabolic biomarker panel, equivalent to S1500+, capable of predicting pathway perturbations and/or adverse outcomes. We conducted a systematic review of multiple toxicological resources, yielding 189 proposed metabolic biomarkers from existing assays , 342 biomarkers from databases , and 435 biomarkers from the literature. Evidence mapping across all 8 resources generated a panel of 722 metabolic biomarkers for toxicology (MTox700+), of which 462 (64%) are associated with molecular pathways and 575 (80%) with adverse outcomes. Comparing MTox700+ and S1500+ revealed that 418 (58%) metabolic biomarkers associate with pathways shared across both panels, with further metabolites mapping to unique pathways. Metabolite reference standards are commercially available for 646 (90%) of the panel metabolites, and assays exist for 578 (80%) of these biomarkers. This study has generated a publicly available metabolic biomarker panel for toxicology, which through its future laboratory deployment, is intended to help build foundational knowledge to support the generation of molecular mechanistic data for chemical hazard assessment. The measurement of endogenous metabolite levels to predict toxicity, whether by untargeted metabolomics or targeted metabolite assays, has received increasing attention over the last decade . Other mIn 2013, the U.S. National Toxicology Program (NTP) launched an initiative that utilized data- and knowledge-driven approaches to create a human transcriptomics biomarker panel, the S1500+ targeted gene set, to enable cost-effective, high-throughput measurements that are predictive of pathway perturbations .The overall aim of this work was to define for the first time a metabolic biomarker panel for toxicology, similar to the definition of the S1500+ gene biomarker panel. This was achieved by mining multiple toxicological resources—including existing multiplexed molecular assays, databases, and the literature—to identify a panel of human health-relevant metabolites associated with disease, toxicity, and other AOs in humans. The first objective was to create a universal list of detectable human metabolites (“metabolite master list [MML]”), derived from the Human Metabolome Database (HMDB), for facile and rigorous filtering of metabolites through all subsequent phases in the project. Next, multiple toxicological resources containing information on metabolic biomarkers were identified, and data were extracted from each of these, including 3 multiplexed assays, 4 databases, and the published literature. To maximize confidence in the predictivity of these biomarkers and hence their utility in regulatory decision making, further information was gathered to assign one or more disease and/or AOs to each metabolite. To help provide guidance on the context of use of the metabolic biomarkers, the type(s) of samples in which the biomarkers have previously been measured was collected. Furthermore, pathway sources were interrogated to link the biomarkers to molecular mechanisms, allowing pathway complementarity to the S1500+ gene biomarker panel to be assessed. The availability of analytical assays and reference standards for each metabolic biomarker was investigated to assess the community’s capability to measure the biomarkers routinely. This first version of the proposed metabolic biomarker panel for predictive toxicology is termed MTox700+, which is made available at An “MML” of detectable human-relevant metabolites was created based on the HMDB ” is a pharmacological biomarker panel originally developed by a consortium of pharmaceutical companies (“Toxicology in the 21st Century (Tox21)” program is a joint initiative between 3 U.S. agencies, the National Center for Advancing Translational Sciences, NTP, and the Food and Drug Administration (“Adverse Outcome Pathway (AOP) Wiki” is an open-source toxicological database, managed by the OECD, that structures data around a framework connecting a molecular initiating event to an AO (“Comparative Toxicogenomics Database (CTD)” is an open-source database, funded by the U.S. National Institute of Environmental Health Sciences, that focuses on the environmental exposures affecting human health ” is a commercial knowledgebase that was created by compiling a vast amount of information on molecular mechanisms associated with disease (primarily) and toxicology, in human, mouse, and rat ” is an open resource developed by The Metabolomics Innovation Center, Canada, that focuses on providing mechanisms of toxicity and target molecules for each toxin  contained metabolite-disease associations that were derived from the Online Mendelian Inheritance in Man (OMIM) database  analysis” was conducted to extract pathway associations for each metabolite.KEGG and Reactome were selected due to their rich pathway content and S1500+ panel compatibility. “KEGG”  is an open-source pathway database for understanding high-level functions and utilities of biological systems  from molecular-level information. To extract pathway associations from KEGG, identifiers from the MTox700+ panel were first converted from HMDB to KEGG using CTS  is an open source, manually curated, and peer-reviewed pathway database. It provides bioinformatics tools for the visualization, interpretation, and analysis of pathway knowledge to support basic and clinical research using omics data. Using Reactome, all of the pathway-metabolite associations were extracted, and identifiers were converted from ChEBI to HMDB using CTS. The metabolites with associations were then filtered against the MTox700+ panel metabolites yielding panel-specific pathway-metabolite associations.“PathBank”  is an open-source interactive pathway database with more than 100 000 pathways. Its primary focus is metabolomics, and it therefore contains a set of unique pathways not found in other databases. Using PathBank, all the pathway-metabolite associations were imported, and the metabolites with associations were filtered against the MTox700+ panel yielding panel-specific pathway-metabolite associations.For reliability of the interrogated pathways and consistency with the Tox21 S1500+ methodologies, after obtaining pathway association data from all 4 pathway sources, each pathway list was reviewed and only pathways with 3 or more participating metabolites (termed “reliable” molecular pathways) were retained. The list of metabolites associated with these reliable pathways was collated and described in the results.Furthermore, the reliable molecular pathways (associated with metabolic biomarkers from the KEGG and Reactome databases) were compared with the pathways associated with the S1500+ gene biomarker panel , 1 database resource (CTD), and publications. Specifically, analytical assay types for measuring metabolites of interest (such as Liquid Chromatography–Mass Spectrometry (LC-MS), Gas Chromatography–Mass Spectrometry (GC-MS), Nuclear Magnetic Resonance (NMR), etc.) were obtained from The availability of reference standards was first assessed by checking against 2 large metabolomics libraries—IROA Technologies Mass Spectrometry Metabolite Library of Standards and MetaSci COMPLETE Metabolite Library, followed by a manual search for the remaining metabolites from other vendors including ABI Chem, Avanti, CaymanChem, Enzo Life Sciences, MedChemExpress, MolPort, Sigma Aldrich (Merck), TargetMol, and Thermo Fisher.To aid the ranking of metabolites in the MTox700+ panel based on their perceived importance in toxicological responses, several criteria were developed. Three separate scores were assigned to each metabolite in the panel based upon each of 3 properties: (1) total coverage in toxicological resources, AO sources, and pathway sources, (2) pathway consistency with the S1500+ gene panel, and (3) measurement feasibility. = /3. Finally, the total coverage score was categorized into 3 levels—low (score below 33%), medium (score 33–66%), and high (score above 66%).The first ranking score for each metabolite was based on its “total coverage in toxicological resources, AO sources, and pathway sources”, considering whether the metabolite featured in the original 8 resources , whether the metabolite was present in the sources of AO information , and whether the metabolite was present in the pathway sources . The total coverage score was calculated as: ScoreThe second ranking score for each metabolite described the “pathway consistency with the S1500+ gene panel,” either as consistent (score of 1) or not (0), with consistency defined as the metabolite being present in a reliable molecular pathway (see definition in Associating Molecular Pathways With Each Metabolite in the Proposed MTox700+ Panel section), where that pathway is also measured by genes in the S1500+ panel.Finally, the third ranking score was based on “measurement feasibility,” based on the availability of an analytical assay  and a reference standard ; total score of 0–2.Multiple international data resources utilize their own identifiers for individual metabolites. Since the principal objective of this work was to integrate data from multiple toxicological resources, we first established a core list of consistently named metabolites against which we could map each of the metabolite resource lists of proposed biomarkers. The HMDB was selected as the primary source of metabolites and their identifiers for the master list as it is the most extensive human-relevant metabolite resource internationally. This was complemented by metabolites reported in the European Bioinformatics Institute’s MetaboLights database, the most extensive repository of experimental metabolomics data in Europe.The HMDB unfiltered metabolite list contained 9052 detectable (quantified or nonquantified) metabolites. In addition, the MetaboLights database was imported to ensure that any (newly) detected metabolites missing from the HMDB were included, yielding 822 metabolites that could be assigned HMDB IDs (converted from ChEBI IDs using CTS). Of these, 121 were not present in the 9052 HMDB metabolite list, hence were added to form a master list with 9173 metabolites. After ontology filtering  were retained. Curation of the retained publications yielded 511 metabolite entries, of which 439 were metabolites with HMDB IDs . The metabolite resource lists shared common metabolites, although the extent of overlap was relatively low  with 525To maximize the confidence in the predictivity of the biomarkers, it was important to attempt to assign 1 or more disease and/or AOs to each of the proposed 722 metabolites. According to information extracted from HMDB, 492 of the proposed metabolic biomarkers are associated with at least 1 disease, based on the disorder classification from OMIM  of the proposed metabolic biomarkers are associated with at least 1 disease or AO, with 8 of the proposed biomarkers having a recognized adverse phenotype in all 4 AO sources . Of the To increase the informative value of the biomarkers, it was attempted to assign 1 or more reliable molecular pathways to each of the proposed 722 metabolites see .Next, we sought to address the question whether the measurement of the proposed MTox700+ panel would add value to an experiment that is already applying the S1500+ gene panel. The reliable molecular pathways associated with both panels (MTox700+ and S1500+) were compared, first based on KEGG pathways and then those in Reactome see . Of the Considering the findings from the KEGG and Reactome databases together, the 80 KEGG pathways represented on both molecular panels include 375 MTox700+ panel metabolites, and the 215 common Reactome pathways encompass 277 MTox700+ panel metabolites. In total, 58% (420 unique metabolites out of 722) of the proposed metabolic biomarkers are associated with 295 molecular pathways included in the S1500+ panel. Of particular note is that measurement of the proposed MTox700+ panel would also provide information on many reliable molecular pathways that are not measured by the S1500+ panel, specifically 93 additional pathways in KEGG and 540 pathways in Reactome , generally being present in just 1 toxicological resource  and containing scarce information from AO sources and/or pathway sources. Higher coverage of resources, AO, and pathway sources was observed for 255 metabolites (medium score of 33–66%), and 61 metabolites scored highly with > 66% coverage in the resources, AO, and pathway sources.The first ranking took into account each metabolite’s total coverage of 8 toxicological resources, 4 AO sources, and 4 pathway sourcesThe second ranking considered pathway consistency with S1500+ gene panel, resulting in 420 proposed metabolic biomarkers meeting the criteria.The third ranking was derived based on the measurement feasibility for each metabolite, including assay and reference standard availability. Both of these were available for the majority of metabolites (515 of 722), with a further 197 metabolites associated with either an assay or a reference standard.Although the publicly available S1500+ human biomarker panel has helped to drive the application of transcriptomics to predict pathway perturbations . AnotherSeveral challenges were encountered while developing MTox700+, particularly the lack of consistency in classifying and naming both metabolites and metabolic pathways. First, metabolite repositories rarely delineate between drugs, dietary-derived metabolites, other metabolites arising from environmental exposure , and endogenous metabolites, all of which can be part of the detectable human metabolome; some resources refer to these simply as “chemicals,” lacking important subclassifications. Even where attempts have been made to define an appropriate ontology , 346 proposed biomarkers from 4 database resources , and 435 proposed biomarkers from the literature. Merging all 8 resources generated a list of 722 metabolites, representing a metabolic biomarker panel for toxicology—MTox700+, of which 578 (80%) of the markers are associated with a disease (or toxicity or AO) and 465 (64%) are associated with reliable molecular pathways. Assessing the pathway compatibility between the MTox700+ and S1500+ panels showed that 420 (58%) of the metabolic biomarkers are associated with shared reliable molecular pathways. Through the future use of this panel, it is possible that some metabolites may be removed (if they do not demonstrate sufficient predictivity) while further metabolic biomarkers will be added (discovered via untargeted or hybrid targeted-untargeted metabolomics), hence the MTox700+ panel is predicted to evolve over time. Here, we have launched this metabolic biomarker panel, with the intention to help build foundational knowledge to support the generation of molecular mechanistic data for chemical hazard assessments. Toxicological Sciences online.Professors Mark Viant and John Colbourne are employees of the University of Birmingham. They are also Founders and Directors of Michabo Health Science (MHS) Ltd., a spin-out company of the University of Birmingham. MHS also operates as a trading division of University of Birmingham Enterprise Ltd., a wholly owned subsidiary of the University of Birmingham. MHS provides scientific consultancy services in New Approach Methodologies (NAMs) specialising in ‘omics technologies and computational toxicology.kfac007_Supplementary_DataClick here for additional data file.


Diminished ovarian reserve (DOR) significantly increases the risk of female infertility and contributes to reproductive technology failure. Recently, the role of melatonin in improving ovarian reserve (OR) has attracted widespread attention. However, details on the pharmacological targets and mechanisms of melatonin-improved OR remain unclear.A systems pharmacology strategy was proposed to elucidate the potential therapeutic mechanism of melatonin on DOR at the molecular, pathway, and network levels.The systems pharmacological approach consisted of target identification, data integration, network construction, bioinformatics analysis, and molecular docking.From the molecular perspective, 26 potential therapeutic targets were identified. They participate in biological processes related to DOR development, such as reproductive structure development, epithelial cell proliferation, extrinsic apoptotic signaling pathway, PI3K signaling, among others. Eight hub targets  were identified. From the pathway level, 17 significant pathways, including the PI3K-Akt signaling pathway and the estrogen signaling pathway, were identified. In addition, the 17 signaling pathways interacted with the 26 potential therapeutic targets to form 4 functional modules. From the network point of view, by regulating five target subnetworks , melatonin could exhibit anti-aging, anti-apoptosis, endocrine, and immune system regulation effects. The molecular docking results showed that melatonin bound well to all hub targets.This study systematically and intuitively illustrated the possible pharmacological mechanisms of OR improvement by melatonin through anti-aging, anti-apoptosis, endocrine, and immune system regulation effects. Infertility affects a significant proportion of humanity and is regarded as a global public health issue by the World Health Organization , 2. Dimiin vitro fertilization (IVF), might increase the number of retrieved oocytes (The decline of ovarian reserve (OR) is a continuous, gradual process starting from the oocyte death of embryos at 20 weeks of gestation until menopause . The pre oocytes ; however oocytes .Melatonin (5-methoxy-N-acetyl tryptamine), a pineal gland hormone, plays a significant role in regulating the circadian sleep-wake cycle, reproductive physiology, and immune functions . As a diAlthough anti-DOR activities exerted by melatonin have been reported, in-depth mechanistic preclinical studies are currently limited. Moreover, details of biomarkers and the biological pathways through which melatonin exerts its effects in improving OR are yet to be completely elucidated. In a previous study, a network pharmacology-based approach was successfully used to uncover the target proteins and potential therapeutic mechanisms of drugs –38. Accohttps://pubchem.ncbi.nlm.nih.gov/) was used to obtain simplified molecular-input line-entry specification (SMILES) information and the 3D structure of melatonin (http://www.lilab-ecust.cn/pharmmapper/), and the SMILES for melatonin was uploaded to the SwissTargetPrediction database (http://www.swisstargetprediction.ch/) to predict the potential melatonin targets (https://go.drugbank.com/), SuperTarget (http://insilico.charite.de/supertarget/index.php), and TCMSP (https://tcmspw.com/tcmsp.php) databases were used to identify known melatonin targets (https://www.uniprot.org/).The PubChem database , GeneCards (https://www.genecards.org/), and NCBI Gene databases (https://www.ncbi.nlm.nih.gov/gene/) were utilized to identify targets related to DOR (DisGeNET (d to DOR . The keyhttps://string-db.org/)  platform (db.org/) . The spehttps://www.kegg.jp/kegg/pathway.html) for further analysis.To clarify the role of potential therapeutic targets in gene function and signaling pathways, the ClusterProfiler package of R 4.0.2 was used to perform GO and KEGG pathway enrichment analysis of the common genes for melatonin and DOR . The patFive networks were constructed: (1) the melatonin-putative target network was built by connecting melatonin and its targets; (2) the PPI network of DOR targets; (3) another PPI network was constructed using the intersected melatonin and DOR genes; (4) the melatonin-targets-pathways network was established by linking melatonin, its targets, and key pathways with literature support for DOR treatment. For further analysis, the network was divided into functional modules using the Community Cluster algorithm (Glay) of clustermaker2  and (5) http://www.rcsb.org/). The downloaded protein structures were pretreated with PyMol 2.4.0 to remove the original ligand, solvent molecules, redundant protein chains and add polar hydrogen. Then, AutoDock Tools 1.5.6 was used to compute the Gasteiger and determine the docking box's center and size , betweenness centrality (BC), and closeness centrality (CC), which were 47.14553991, 0.004491643, and 0.527211414, respectively  . Then, the PPI network of these 26 common targets was constructed . To find the hub targets in this complex biological network, the topological parameters were analyzed. As a result, there are eight hub targets in this PPI network according to DC, BC, and CC mean values, including MAPK1, AKT1, EGFR, HRAS, SRC, ESR1, AR, and ALB . Meanwhile, as shown in Based on the above results, 26 common melatonin and DOR targets  were obtained using the Venn Diagram tool (s/Venn/) . Then, ts/Venn/) . Then, ts/Venn/) . Then, tp-value. The results were visualized using the R package's ggplot2 and are shown in The 26 potential therapeutic targets were analyzed using the ClusterProfiler package of R 4.0.2. The top 10 terms of each part of the GO enrichment results were selected based on the counts of hit genes and the p-value <0.05. After data screening, 17 significant pathways were identified . Then, the genes enriched in each pathway were sorted, and a melatonin-target-pathway network was constructed . In the network, the PI3K-Akt signaling pathway (hsa04151) and estrogen signaling pathway (hsa04915) were significantly enriched . To fully understand the mechanism of melatonin in treating DOR, the target-pathway network was decomposed into functional modules using the community cluster (Glay) algorithm of clustermaker2. As illustrated in We carried out KEGG pathway enrichment analysis of the 26 therapeutic targets using the ClusterProfiler package of R 4.0.2 and obtained 123 pathways with a entified . Then, tentified . Then, tentified . Then, tentified . Then, tEight hub genes were selected for molecular docking analysis with melatonin. The active site parameters of each target were calculated and are listed in Ovarian reserve plays a crucial role in reproductive potential and endocrine stability. Driven by societal trends, many young women choose to postpone marriage and childbirth. However, their OR sharply declines after the age of 35 years , 50. Besin vitro maturation step by enhancing oocyte maturation, cumulus cell expansion, and protection from DNA fragmentation , is a downstream effector of the EGFR pathway. Activated ERK regulates the expression of LHβ and FSHβ, which are gonadotropin synthesis genes, and induces follicle growth and ovulation , 68. In As the primary female sex hormones, estrogens are responsible for maturing and maintaining the female reproductive system and are also involved in gonadotropin secretion and ovarian follicle maturation. Estrogens exert their functions by binding to ERα and ERβ, encoded by ESR1 and ESR2, respectively. SRC and HRAS, the downstream proteins of ESR1 in the estrogen signaling pathway, participate in various cellular processes, including proliferation, differentiation, and adhesion . Many stThe pathophysiological mechanism of DOR is especially complicated, and various biological processes and pathways are involved in the DOR process. The 26 therapeutic targets screened in this study mainly participate in reproductive structure development, epithelial cell proliferation, the extrinsic apoptotic signaling pathway, PI3K signaling, and response to steroid hormones. Furthermore, the KEGG pathway analysis indicated that the PI3K-Akt signaling pathway (hsa04151) and the estrogen signaling pathway (hsa04915) were the two most enriched signaling pathways –65. The To further understand melatonin mechanisms in improving OR, the target pathway network was divided into four densely linked functional modules, as shown in Importantly, in this study, to explain the multi-mechanism of melatonin on DOR, five sub-networks were constructed. The aging sub-network , 83. ConApoptosis is a critical biological process that plays a vital role in germ cell depletion in mammalian ovaries . FollicuThe sub-network of development and regeneration .In addition to maintaining homeostasis, the immune system is associated with modulation at every level of the hypothalamic-pituitary-ovarian axis, as well as the regulation of proliferation and differentiation of ovarian germline stem cells . AKT1 anIn summary, melatonin may improve OR by intervening in a series of targets , biological processes , and signaling pathways . Melatonin could exhibit anti-aging, anti-apoptosis, endocrine, and immune system regulation.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.QZ conceptualized the manuscript. LY, HX, YC, YX, CM, and YZ collected the literature, wrote the manuscript, and made the figures. QZ edited and made significant revisions to the manuscript. All authors contributed to the article and approved the submitted version.This work was financially supported through grants from the National Natural Science Foundation of China (81904241), the Program of Zhejiang Provincial TCM Sci-tech Plan (2020ZA078), the Medical and Health Science and Technology Plan of Zhejiang Province (2021KY920), and the Zhejiang Zhangqin Famous Traditional Chinese Medicine Expert Inheritance Studio Project (GZS2012014).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Human T-cell Leukemia Virus type-1 (HTLV-1) is a retrovirus that causes two diseases including Adult T-cell Leukemia/Lymphoma (ATLL cancer) and HTLV-1 Associated Myelopathy/Tropical Spastic Paraparesis  after a long latency period as an asymptomatic carrier (AC). There are no obvious explanations about how each of the mentioned diseases develops in the AC carriers. Finding the discriminative molecular factors and pathways may clarify the destiny of the infection.To shed light on the involved molecular players and activated pathways in each state, differentially co-expressed modules (DiffCoEx) algorithm was employed to identify the highly correlated genes which were co-expressed differently between normal and ACs, ACs and ATLL, as well as ACs and HAM/TSP samples. Through differential pathway analysis, the dysregulated pathways and the specific disease-genes-pathways were figured out. Moreover, the common genes between the member of DiffCoEx and differentially expressed genes were found and the specific genes in ATLL and HAM/TSP were introduced as possible biomarkers.KIRREL2, RAB36, and KANK1 in HAM/TSP as well as LTB4R2, HCN4, FZD9, GRIK5, CREB3L4, TACR2, FRMD1, LHB, FGF3, TEAD3, GRIN2D, GNRH2, PRLH, GPR156, and CRHR2 in ATLL.The dysregulated genes in the ATLL were mostly enriched in immune and cancer-related pathways while the ones in the HAM/TSP were enriched in immune, inflammation, and neurological pathways. The differential pathway analysis clarified the differences between the gene players in the common activated pathways. Eventually, the final analysis revealed the involvement of specific dysregulated genes including The identified potential prognostic biomarkers and therapeutic targets are proposed as the most important platers in developing ATLL or HAM/TSP. Moreover, the proposed signaling network clarifies the differences between the functional players in the activated pathways in ACs, ATLL, and HAM/TSP.The online version contains supplementary material available at 10.1186/s12985-021-01643-8. Human T-cell Leukemia Virus type-1 (HTLV-1) belongs to the family Retroviridae and subfamily Orthoretrovirinae . ApproxiThere are three main receptors on the cells for HTLV-1 including neuropilin, glucose transporter 1 (GLUT-1), and heperan sulfate proteoglycan . The HTLOne of the beneficial approaches to find the patterns of co-regulated genes is gene co-expression analysis . Two comSince the pathogenesis mechanism of HTLV-1 as a virus-caused cancer and virus-caused neurologic disease have not been yet fully ascertained, the identification of differential co-regulated genes can determine new functional players in ACs, ATLL, and HAM/TSP. To this end, we integrated various gene expression datasets and then found the differential co-expressed genes between every two conditions employing DiffCoEx approach. The outcomes specified the gene players and activated pathways implicated in the development of each disease after infection.To find the relevant datasets correspond to our study purpose, Gene Expression Omnibus (GEO) repository database was searched. A total of four microarray datasets including GSE29312, GSE29332, GSE33615, and GSE55851 were selected to be involved in our analysis. In total, these datasets contain 58 normal samples, 43 ACs samples, 62 ATLL samples, and 20 HAM/TSP samples. Since datasets belong to different platforms, removeBatchEffect function in the R limma package was utilized to remove the batch effect across different datasets . The expij) is determined by calculating the signed squared correlation coefficients. The higher dij values are considered as significant co-expression changes between two genes. The soft threshold parameter β is determined so that the network follows a scale-free topology [ij values reach the power of β. (3) The topological overlap dissimilarity matrix (dissTOM) is constructed to detect the shared genes with common neighbors in the graph. It is constituted based on the adjacency matrix and contains the differential correlation network. (4) The hierarchical clustering is performed with flashClust [To find the differential co-expressed modules among two conditions, the DiffCoEx algorithm was employed . DiffCoEtopology . Then, dashClust . Then, dThe most connected genes in each module were enriched in the KEGG utilizing g:Profiler webtools (version: 1185_e69_eg16)  and EnriThe DiffCoEx algorithm was employed to find the differential co-expressed gene modules between different sample groups. Through this algorithm, the co-regulated gene clusters that differentially co-expressed between two groups were determined. After computing Pearson correlation and then adjacency differences matrices according to Methods, the topological overlap matrices (TOMs) were constructed with β power of 2, 3, and 2 for Normal-ACs, ACs-ATLL, and ACs-HAM/TSP, respectively. These values are the lowest power for which the scale-free topology fit index reaches 0.80. Afterward, the differential co-expressed modules were identified in each comparative group utilizing hierarchical clustering and then merging adjacent clusters Fig. b as wellsummary and medianRank through a permutation test (200 times) [summary score is half the sum of the mean of Zdensity and Zconnectivity. MedianRank is a rank-based scale that depends on an observed preservation statistic and is not dependent on module size [Zsummary > 4 and medianRank < 8 were regarded as moderate-high preserved modules [Zsummary < 4 were excluded.In order to evaluate the reliability of the identified differential co-expressed modules, their preservations in an independent dataset were surveyed. For this purpose, the function of “modulePreservation” was used to measure Z0 times) . Zsummarule size . A lowerion test 0 times [ modules . The resion test 00 times The further pathway enrichment analysis clarified that modules related to ACs were generally entered in pathways related to viral infection, Immune system, cancer, and inflammation like Human T-cell leukemia virus 1 infection, Toll-like receptor signaling pathway, Pathways in cancer, PI3K-Akt signaling pathway, Chemokine signaling pathway, Cell adhesion molecules (CAMs), NOD-like receptor signaling pathway, Th17 cell differentiation, Th1 and Th2 cell differentiation, JAK-STAT signaling pathway, and MAPK signaling pathway  were found. To this end, DEGs between ACs and ATLL (DEGs_AA) as well as ACs and HAM/TSP (DEGs_HA) considering adj.p.val < 0.05 and |log FC|> 1 were found. Therefore, the shared genes between module pink in ATLL and DEGs_AA, as well as common genes between blue, pink, and turquoise modules in HAM/TSP and DEGs_HA, were identified. Eventually, the specific genes that were uncommon between ATLL and HAM/TSP were determined. As a result, KIRREL2, RAB36, and KANK1 were identified as specific genes and possible candidate biomarkers for HAM/TSP. Moreover, 77 specific genes were found for ATLL. To find the most important gene players in the progression of ATLL, pathway enrichment analysis was performed and the pathways enriched by at least 3 proteins were identified. Four pathways including Neuroactive ligand-receptor interaction, cAMP signaling pathway, Calcium signaling pathway, and Hippo signaling pathway were enriched by 15 genes including LTB4R2, HCN4, FZD9, GRIK5, CREB3L4, TACR2, FRMD1, LHB, FGF3, TEAD3, GRIN2D, GNRH2, PRLH, GPR156, and CRHR2. These genes may also be proposed as potential biomarkers for ATLL  have not been completely figured out. Herein, we tried to shed light partially on the differences between the involved genes and also activated pathways between these diseases and AC state through a differential co-expression analysis. To this end, we applied DiffCoEx algorithm to find differential co-expressed genes between normal and ACs, ACs and ATLL, ACs and HAM/TSP. The most important activated pathways in each condition are depicted as a schematic signaling network in Fig. While the majority of the infected subjects by HTLV-1 remain as asymptomatic carriers, the most challenging question is the reason for progression from an asymptomatic viral infection toward ATLL or/and HAM/TSP. The identified differential co-regulated modules in ACs versus normal group revealed the activation of pathways related to the HTLV-1 infection which are expected.ITGLA. LFA-1 naturally binds to ICAM-1 that leads to changing both infected and uninfected cells. This provides the proper VS condition to transfer the virus. Tax and p12 implicate in the activation of LFA-1 and ICAM-1 expression [HTLV-1 transfers between the infected and uninfected T cells through several mechanisms. One of the main mechanism is the formation of virological synapse (VS) . This typression , 24.IKBKG and PLCG1. HTLV-1 can activate both Th1 and Th2 through interference in the NF-kappa signaling pathways based on the infection step. The downregulation of LCK was also observed in ACs which may be due to the expression of the viral regulatory tax gene [NF-kappa signaling is an important pathway in cell proliferation. It maintains cellular balance as well as Th1 and Th2 cell differentiation pathway through the expression alteration of the involved genes such as tax gene . MoreoveHAM/TSP is known as an inflammatory disease in which several immune cells together have critical roles in the disease progression. The pathogenesis of HAM-TSP is explained in three levels: peripheral, transmit to the central nervous system, and damage to the nervous tissues. It appears that multiple genes and pathways are activated at each level which has been shown in this work.MAPK8, PPP2R5E, GNAI3, S1PR1, MAPK1, RAF1, BID, NSMAF, TNFRSF1A, MAPK3 in the progression of disease.Sphingolipid is a regulator of the cell signals through two secondary lipid messengers including ceramide (Cer) and sphingosine-1-phosphate (S1P). Cer stimulates apoptosis in the oligodendrocytes and neurons, and S1P speeds up the exit of lymphocytes from lymphoid tissues to blood . SphingoCer activates PP2A (PPP2R5E belongs to this protein family) and BID to stimulate apoptosis . It alsoDNA replication is another pathway that should be considered in the development of HAM/TSP. DNA replication is increased in the Tax-expressing cells causing progression of cell cycle, even in the presence of DNA damaging agents. Therefore, DNA damage can result in the accumulation of mutations in the Tax-expressing cell and also apoptosis . In our Neurotrophin signaling pathway is mostly activated in HAM/TSP. This pathway is associated with the level of neurotrophin and downstream signaling cascades. The inhibitor factors in neurotrophin signaling pathway are strongly regulated by degradation and dephosphorylation. Neurotrophin signaling pathway implicates in several neurodegenerative disorders, such as Huntington's disease, Alzheimer's disease, and psychiatric disorders like depression and substance abuse , 37. In KIRREL2 (NEPH3) belongs to the immunoglobulin superfamily of cell adhesion molecules. NEPH3 is regulated by the cooperation of WT1 and NF-κB in podocytes. NEPH3 is a downstream target of Ptf1a in the progressing central nervous system. It is also expressed in early postmitotic neurons of the developing spinal cord. NEPH3 intercedes the adhesion between the Ptf1a-progenitors, which is essential for maturation, neuronal migration, and differentiation [Three genes comprising KIRREL2, RAB36, and KANK1 were found as biomarkers for HAM/TSP. ntiation . The dysRAB36 is a member of the RAB family which is located on chromosome 22q11.2. It was reported that the depletion of RAB36 leads to G1 cell cycle arrest, disruption of mesenchymal-epithelial transition, and simplification of the malignant rhabdoid tumors dissemination [mination , 41. TheKANK1 belongs to the Kank family which is mainly involved in the cytoskeleton formation by regulating actin polymerization. The mutated KANK1 may develop the central nervous system disorder . It also inhibits the cell migration and formation of actin fiber. It can inhibit malignant peripheral nerve sheath tumors through the regulationof CXC5 as an apoptosis-related gene , 43.ATLL is another disease caused by HTLV-1 infection. The overall identified DiffCoEx_AA and the activated pathways reveal that the viral infection provides a primary condition in the infected cells to malignancy in association with genetic susceptibility and other risk factors . The difFifteen genes including LTB4R2, HCN4, FZD9, GRIK5, CREB3L4, TACR2, FRMD1, LHB, FGF3, TEAD3, GRIN2D, GNRH2, PRLH, GPR156, and CRHR2 were also determined as potential biomarkers for ATLL.2+ and as a result the regulation of CALM1 in the calcium signaling pathway [The importance of cAMP in developing hematological cancer has been reported previously . The cAM pathway . On the  pathway .TEAD3 is a transcription factor and FRMD1 is an activating transcription factor binding which have important functions in the Hippo signaling pathway, a pathway involved in tumor suppression by limiting proliferation and boosting apoptosis.FGF3 belongs to the basic fibroblast growth factor (FGF) gene family which plays a major role in cell differentiation and proliferation and is involved in the calcium signaling pathway . FurtherLTB4R2 is one of the LTB4 receptors that is associated with invasion, survival, and metastasis. TACR2 is also a receptor for tachykinins that is mainly expressed in the periphery including inflammatory cells. Overexpression of TACR2 can develop the proliferation and migration of cancer cells by regulating the Wnt signaling pathway . It seemOur results reveal that more dysregulated genes and pathways are involved in the HAM/TSP pathogenesis in comparison to ATLL, which probably explains more prevalence of ATLL in the HTLV-1 infected patients . InteresThe pathogenesis mechanism and development of diseases caused by HTLV-1 infection is obscure. Herein, through the identification of differential co-expressed module analysis and pathway enrichment, the major genes implicated the pathogenesis process of ACs toward ATLL and HAM/TSP were clarified. It seems that different dysregulated genes cause the activation of similar pathways in ATLL and HAM/TSP. They may be proposed as potential therapeutic targets. However, further studies should be designed and performed to evaluate the identified genes in each condition.Additional file 1. List of the identified differential co-expressed modules in ACs, ATLL, and HAM/TSP.Additional file 2. The preservation of modules in the external datasets .Additional file 3. The enriched pathways by the connected proteins in each identified DiffCoEx_NA, DiffCoEx_AA, and DiffCoEx_AH.Additional file 4. The list of specific pathways-genes in ACs, ATLL, and HAM/TSP.Additional file 5. The list of DEGs, common between DEGs and DiffCoEx_AA, and specific genes for ATLL.


Metabolic pathways provide key information for achieving a better understanding of life and all its processes; this is useful information for the improvement of medicine, agronomy, pharmacy, and other similar areas. The main analysis tool used to study these pathways is based on pathway comparison, using graph data structures. Metabolic pathway comparison has been defined as a computationally complex task. In a previous work, two new algorithms were introduced to treat the problem of metabolic pathway pairwise comparison. Here we provide an extended analysis with more data and a deeper analysis of metabolic pathway comparison as listed in the discussion and results section. This alignment algorithm has been key to arranging sequences of DNA, RNA, or proteins to identify regions of similarity that may be related to functional, structural, or evolutionary relationships between the entry sequences. Software tools like In the last 50 years, at least, tools for data comparison on bioinformatics have been proposed, such as the Needleman–Wunsch algorithm in 1970 ,3,4 as: There has been much work conducted on the analysis of network-based structures. In 1988, Bruce L. Clarke presented its stoichiometric network analysis, developed to study instability in inorganic chemical reaction networks, which relied on mass-action theory and used concepts of convex analysis . Alex SeAn ordered sequence of biochemical reacting metabolites, or a metabolic pathway, are chemical compounds that act as substrates to be transformed into other compounds  through a path of chemical reactions catalyzed by enzymes ,10.www.genome.jp/kegg/)  = 0.03. See the ANOVA table above (Listing 4) after applying a Bonferroni adjustment [There is a statistically significant interaction between size ratio and common families on Global score, F = 3.21, justment . The ANOp < 0.05) and similar  = 24.4, p< 0.05) size ratio to either none–several or few–several common families levels.Consequently, an analysis of simple main effects for size ratio was performed with statistical significance receiving a Bonferroni adjustment. There is a statistically significant difference in mean “Global” scores for both medium  = 7,72, As can be expected from the biological point of view, when there are no common families between the compared metabolic pathways, the scores are not influenced and are statistically demonstrated. It is the same situation when the size ratio between pathways differs for any family category.p < 0.05).All pairwise comparisons were analyzed between the different families’ groups organized by size ratio with Bonferroni correction. There was a significant difference in Global scores between the relation of groups for none–several and few–several relations  because the service required from KEGG to handle this information is not currently available as a free service. The data obtained from MetaCyc does not fulfill the same criteria. Furthermore, it is worth noting that, for some pairwise comparisons, the tool TMPAlign raised errors during the execution, with no clear explanation. All these errors were excluded from the subsequent analysis for all algorithms.It is important to remark that the main goal of using a reference tool is not to generate the same values but instead prove that, if two pathways are significantly different, both tools can denote it, and the contrary for similar pathways.As shown in previous sections, the TMPAlign tool was used in all the executed pairwise comparisons made. The proposed algorithms and TMPAlign tools show similar and comparable behaviors. The main aspect to consider here is that when a pair of metabolic pathways are compared, we want a value of valid comparison. When pathways have a similar structure and similar inside elements, we expect to have higher values from that interception. When there are a few or non-common nodes at all, we expect to have lower values to zero. It can be observed from all shown graphs that Algorithms 1 and 2, compared to TMPAlign, have similar behaviors, and they are comparable. The next section will demonstrate that the proposed algorithms are up to 10 times faster than TMPAlign.As regards the cost related to the execution time of the algorithms, this is one of the most important gains obtained with the proposed algorithms. Data from a real, updated, and important database was used;The proposed algorithms execute at least 10 times faster than the external tool used for comparison. One of the main goals for this work;Equivalent nodes ratio equals 0, meaning no common elements between the pathways being compared, will always produce a 0 score;The more similar the sizes of the metabolic pathways and the more common families between them, the better the scores obtained;This is an observation that has biological meaning, with few exceptions;As with everything in Biology, there is no rule for 100% of the cases;The data does not follow a normal distribution;After a log10 transformation, a normality test shows positively skewed data;Homogeneity of variance was not perfectly adjusted;The analysis of variance was performed Welch’s ANOVA and Bonferroni correction to the models;Thousands of comparisons executed indicated independence in the input data and the resulting scores, but there is significant statistical evidence of the influence of the size ratio and common families;Data may have some extreme outliers. It was observed that pairwise compared metabolic pathways with several common families might have a few or none similar elements for comparison, producing lower scores than its counterparts, contradicting some of the general behaviors observed for most of the comparisons;Several scientific publications have been produced as a result of this work;The algorithms have been applied and extended to real scenarios with data from labs, confirming the biological significance of the proposed algorithms.By way of summary, we propose the following points as results of interest:The pairwise comparison of metabolic pathways is an enormous yet interesting problem. In this work, we have attempted to provide some insight about it, implementing alternative methods to simplify how the data are used and, by doing so, sacrificing precision on the information representation but not on the results.The comparisons that report 0% equivalent nodes unanimously report a 0% similarity under any of the evaluated algorithms, meaning that, when there are no equivalent nodes between the graphs, it can be safely reported that both pathways are completely different, without the need for executing the comparison algorithms.It was shown that structural characteristics of the graphs, such as several nodes (size) or some edges (complexity), do not bias the comparison results when using the algorithms, as should be for a reliable tool. The only influence these factors have over the results is that, when comparing graphs with different sizes or complexities, the relative global score or numerical DbP score, respectively, will be penalized (decreased) because of the structural difference, whether the nodes or edges are similar or not. This is naturally to be expected, as these considerations are part of the design for the scoring system. In other words, the scores obtained from every pairwise comparison are dependent on the inner data nodes; two comparisons of graphs with equal size might produce different scores.On the other hand, some characteristics obtained from the pairwise comparisons slightly affect the comparison results, such as the equivalent nodes ratio between the compared graphs, how many metabolic categories share the graphs, and whether the graphs have an equivalent origin and destiny nodes. When the first two have their numerical value increased, the comparison scores tend to increase as well; similarly, when the latter characteristic checks to be true, the comparison scores tend to be higher. It is also to be expected, as said characteristics hint at the pathways related. Likewise, it is important to denote that this tendency is not always the case. There can be comparisons that do not follow this pattern, which is also perfectly normal and shows how diverse the metabolic pathways and their comparisons can be.Transformation of the 2D pathway graph to a 1D or linear structure for later alignment and evaluation and Algorithm 2: Differentiation by pairs were also shown to be extremely resource-efficient, surpassing the speed of execution and in a more predictable manner than the tool of reference. It means that the goal of the algorithms  was achieved. The algorithm’s faster, more reliable, and more predictable behavior also mean that the tool can be successfully employed for batch comparisons, using large datasets of metabolic information, even though this was not the original intended use for the algorithms.The algorithms Having verified that the proposed algorithms can provide relevant information for the analysis and comparison of metabolic pathways, it would be useful to implement a complete software tool capable of directly accessing metabolic databases, extracting information from metabolic routes of interest, and finally applying the proposed algorithms to the benefit of experts.Other techniques for sequence comparison based on valuation matrices have been proposed so that not only the elements that are the same or different are evaluated using the same alignment values for all elements but rather that the affinity between elements is also considered. One example is seen in the case of protein sequence alignments, where if the amino acids are similar: hydrophobic, polar (positive or negative), and so forth, as well as if their energy and likelihood to react are similar, the substitution penalty will be lower than that when the nature of the amino acid is changed. It would be interesting to consider the comparison of pathways aspects such as those mentioned before and, therefore, provide even more information to the researcher.Next, the interactions between routes should then be considered. Metabolites may be the final product of a route or an intermediate product which may be a precursor for other metabolic pathways. The analysis should be extended to combine these routes treated in their context as metabolic networks. As another level of comparison, enzymatic reactions that occur in each step of the metabolic process could be added into the pathways as extra information to be considered.Finally, just as there are multiple alignment algorithms for several genetic sequences, it is important to continue working on the problem of comparing multiple pathways to find, for example, similar factors among different species.


Cancer dependencies provide potential drug targets. Unfortunately, dependencies differ among cancers and even individuals. To this end, visible neural networks (VNNs) are promising due to robust performance and the interpretability required for the biomedical field.We design Biological visible neural network (BioVNN) using pathway knowledge to predict cancer dependencies. Despite having fewer parameters, BioVNN marginally outperforms traditional neural networks (NNs) and converges faster. BioVNN also outperforms an NN based on randomized pathways. More importantly, dependency predictions can be explained by correlating with the neuron output states of relevant pathways, which suggest dependency mechanisms. In feature importance analysis, BioVNN recapitulates known reaction partners and proposes new ones. Such robust and interpretable VNNs may facilitate the understanding of cancer dependency and the development of targeted therapies.https://github.com/LichtargeLab/BioVNNCode and data are available at Bioinformatics online. Precision medicine aims to improve therapy based on individual patient and disease variations. In cancer, a promising approach is to target treatment on specific genetic vulnerabilities, which encode mechanisms essential to the survival and proliferation of cancer cells. Genetic dependencies differ among cancers and individuals, unfortunately, requiring resource-intensive experimental approaches . As a r10(P) value] is significantly larger in related pathways than unrelated pathways (i.e. which do not contain the target variable gene) (P < 6.6E−06). We further expanded the analysis to other genes, and found that in total 74.3% (396 out of 533 target variable genes involved in at least six pathways) have such significant class separation in related pathways [MWW test one-sided adjusted P < 0.1 using BH method . We found that such class separation [as measured by the −logH method ]. That iTo test the third hypothesis and further explain the dependency prediction, we examined the neuron states from lower-level to higher-level pathways . Taking Finally, we investigated which features are important for the dependency prediction in BioVNN. We utilized the reaction information from Reactome, which was not used in designing models nor training, to validate whether BioVNN found important features that fit biological knowledge. Reaction information groups genes that are involved in a common process, e.g. binding, activation, translocation, degradation and biochemical events. One gene can be involved in multiple reactions, which are typically smaller gene groups than pathways. A pathway thus often consists of multiple reactions to achieve its function. Two genes involved in the same reaction are in the same pathway but not necessarily vice versa. Such reaction relationships between two genes are stronger than pathway relationships. Should one gene be deleted, its reaction partners are more likely to be affected than pathway partners. Therefore, we hypothesized that even in the same pathway, genes involved in the same reaction as the target variable gene would have higher importance than others.P < 2.9E−06; P < 5.0E−04; P < 0.1 using BH method ’ (R-HSA-2559582), consisting of 28 NFKB1 reaction partners. We found that surprisingly, those 28 genes and NFKB1 have significantly higher importance  training based on bulk RNA-seq data from only hundreds of human cell lines and (2) dependency prediction of hundreds of genes in single model. Given these innovations, a future direction would be to apply VNNs to other cell line data to predict and explain important questions like synthetic lethality and drug responses. The This study could be expanded in a few ways. First, other biological knowledge could also be embedded in the VNNs, due to the fact that Reactome contains around 10 000 genes, which is only about half of the human protein-coding genes and might limit performance. Many other pathway databases [e.g. Pathway Commons , MSigDB Second, the model could incorporate more features of cell lines. One possibility is to integrate other types of genomics data besides RNA expression, such as DNA mutation (btab137_Supplementary_DataClick here for additional data file.