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PubMed2026

Exploring the Potential Molecular Targets of Cyanidin-3-O-glucoside for Type 2 Diabetes Mellitus Treatment.

INTRODUCTION: This study aims to elucidate the multi-target molecular mechanism of cyanidin-3-O-glucoside (C3G) in treating Type 2 Diabetes (T2DM) through network pharmacology methods. METHODS: The study was designed to predict the targets of C3G through public databases and to screen for T2DM-related targets. Protein-protein interaction (PPI) network analysis, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the common targets. Core targets were further validated through molecular docking and molecular dynamics (MD) simulations. RESULTS: This research identified a total of 57 potential targets of C3G in the treatment of T2DM. Subsequent PPI analysis identified ALB (Degree=43), AKT1 (Degree=41), and TNF (Degree=41) as the top three hub proteins. Pathway analysis indicated significant involvement in the insulin signaling pathway (P = 4.205×10-9), AMPK signaling pathway (P = 9.582×10-7), and FoxO signaling pathway (P = 1.315×10-6). Molecular docking revealed strong binding affinities between C3G and NOS3 (-9.5 kcal/mol), PPARG (-9.0 kcal/mol), TNF (-8.5 kcal/mol), and INSR (-8.4 kcal/mol). MD simulations further confirmed that the C3G-target complex has excellent binding stability. DISCUSSION: C3G may intervene in the pathological progression of T2DM by regulating key pathways such as insulin sensitivity, inflammatory responses, and oxidative stress. Further studies suggest that INSR and NOS3 may be new targets through which C3G exerts its effects, but their specific mechanisms and in vivo biological functions still need to be elucidated by subsequent experiments. CONCLUSION: C3G may intervene in the progression of T2DM in a multi-pathway synergistic manner by targeting key molecules such as INSR and NOS3.

Anthocyanins↗

PubMed2026

Integrated bioinformatics analysis reveals cross-talking hub genes and therapeutic agents between sepsis and acute myocardial infarction.

BACKGROUND: Sepsis and acute myocardial infarction (AMI) are two significant diseases that may share overlapping etiological mechanisms. This study aims to systematically identify core genes common to both conditions and to explore their potential as therapeutic targets and drug candidates through an integrative analysis of clinical data and bioinformatics. METHODS: The AMI dataset was obtained from the GEO database, and RNA sequencing data were collected from blood samples of patients with sepsis at our hospital. Common genes were identified using differential expression gene analysis (DEG) and weighted gene co-expression network analysis (WGCNA). Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were performed. A protein-protein interaction (PPI) network was constructed, and hub genes were identified using the MCC/Degree algorithm. Diagnostic value was assessed via receiver operating characteristic curve analysis. Immune infiltration patterns, single-cell sequencing data, and molecular docking simulations were employed to evaluate immune relevance and identify potential therapeutic compounds. RESULTS: A total of 417 genes were identified between sepsis and AMI, with enrichment analysis revealing significant involvement in inflammatory responses. Three hub genes-JAK2, MYD88, and TIMP1-were selected for further investigation. ROC curves confirmed their strong diagnostic performance for both diseases. Immune infiltration analysis showed that these core genes were significantly correlated with the infiltration levels of various immune cell types. Molecular docking indicated that quercetin exhibited stable binding affinity with the proteins encoded by these genes. qPCR validation further confirmed the upregulation of these three genes, supporting the anti-inflammatory effects of quercetin as a potential targeted therapy. CONCLUSION: JAK2, MYD88, and TIMP1 were identified as shared core genes in sepsis and AMI. These genes not only serve as potential diagnostic biomarkers but also offer novel targets for developing common therapeutic strategies for both conditions. Furthermore, quercetin emerges as a promising candidate for targeted treatment.

Humans↗

PubMed2025

Integrated network pharmacology, molecular docking and experimental validation to investigate the mechanism of tannic acid in nasopharyngeal cancer.

Tannic acid (TA) is the primary bioactive component in the gallnut (Galla chinensis) and has exhibited the anticancer effects. However, the mechanism of its anti-cancer activity in nasopharyngeal carcinoma (NPC) remains unclear. This research aims to explore the underlying mechanism of TA in the treatment of nasopharyngeal cancer using network pharmacology, molecular docking and experimental validation. Firstly, the targets of TA and NPC were predicted and collected through databases, and the intersection targets were identified. Subsequently, protein-protein interaction (PPI) network analysis, Gene Ontology (GO) enrichment, Kyoto Encyclopedia of Genes Genomes (KEGG) pathway enrichment analysis, molecular docking and molecular dynamics (MD) simulation were conducted to uncover the potential mechanisms of TA in treatment of NPC. Finally, in vitro experiments were utilized to verify the mechanism of TA with anticancer activity in NPC. The results of network pharmacology revealed 42 intersection targets between NPC-related targets and TA-related targets. The phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signaling was identified as the main target pathway of TA against NPC. Additionally, molecular docking and MD simulation confirmed the closely binding affinities of TA with AKT1. Furthermore, the results of in vitro experiments demonstrated that TA exerts anticancer activity against NPC by targeting the PI3K/AKT signaling pathway, leading to the suppression of cell proliferation. TA is a promising therapeutic candidate for NPC through PI3K/AKT signaling pathway. These results provide insights into the clinical application of TA, particularly when considered in combination with other therapeutic modalities.

Molecular Docking Simulation↗

PubMed2006

Differential evolutionary rates of duplicated genes in protein interaction network.

In the network of protein-protein interactions (PPIs), a loss and gain of the partnering proteins can cause drastic changes of network formation during evolution. With the aim of examining the evolutionary effects of the loss and gain of the partnering proteins on PPIs, we examined a relationship between evolutionary rates and losses and/or gains of PPIs for duplicated gene pairs encoding proteins involved in the PPI network. For duplicated pairs, which provided us with a unique opportunity of making fair comparisons of the genes with the same initial condition, we found that the evolutionary rate of the protein with more PPI partners is much slower than that of the other with fewer PPI partners. Moreover, when the ratio of evolutionary rates (faster rate/slower rate) was computed for each of the duplicated pairs, the ratio for the duplicated pair sharing any PPI partners was significantly lower than that for the pair sharing no PPI partners. These results indicate that the duplicated gene pairs differentiate through the losses and/or gains of the PPI partners, resulting in a change in their evolutionary rates. In particular, we point out that the PPI losses for the duplicated gene products that are involved in the functional classes of 'transcription' and 'protein fate' have an impact on their evolutionary rates more than the PPI losses for others.

Biological Evolution↗

PubMed2004

A protein interaction network links GIT1, an enhancer of huntingtin aggregation, to Huntington's disease.

Analysis of protein-protein interactions (PPIs) is a valuable approach for characterizing proteins of unknown function. Here, we have developed a strategy combining library and matrix yeast two-hybrid screens to generate a highly connected PPI network for Huntington's disease (HD). The network contains 186 PPIs among 35 bait and 51 prey proteins. It revealed 165 new potential interactions, 32 of which were confirmed by independent binding experiments. The network also permitted the functional annotation of 16 uncharacterized proteins and facilitated the discovery of GIT1, a G protein-coupled receptor kinase-interacting protein, which enhances huntingtin aggregation by recruitment of the protein into membranous vesicles. Coimmunoprecipitations and immunofluorescence studies revealed that GIT1 and huntingtin associate in mammalian cells under physiological conditions. Moreover, GIT1 localizes to neuronal inclusions, and is selectively cleaved in HD brains, indicating that its distribution and function is altered during disease pathogenesis.

Adaptor Proteins, Signal Transducing↗

PubMed2026

Integrated network pharmacology, molecular docking, and experimental validation to reveal the potential mechanism of Ginsenoside Rg1 on chronic obstructive pulmonary disease.

Ginsenoside Rg1 (GS Rg1), a natural flavonoid exhibiting anti-inflammatory and antioxidant properties, holds significant potential for treatment chronic obstructive pulmonary disease (COPD). Nevertheless, the precise mechanisms underlying its therapeutic effects remain to be fully elucidated. This study aimed to explore the role and potential mechanism of GS Rg1 in the treatment of COPD using network pharmacology, molecular docking, and experimental validation.Targets related to GS Rg1 and COPD were screened from public databases, and the potential common targets were then imported into the STRING database to construct a protein-protein interaction (PPI) network. Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis were performed to identify key signaling pathways. Molecular docking was employed to predict the binding interactions between GS Rg1 and core targets. A BEAS-2B cell model induced by lipopolysaccharide(LPS) and cigarette smoke extract(CSE) was used to explore the protective mechanisms of GS Rg1. Western blot analysis was conducted to validate the critical targets and pathways involved in the anti-COPD effects of GS Rg1. Network pharmacology analysis revealed 105 common targets between GS Rg1 and COPD. The EGFR/PI3K/AKT and EGFR/STAT3 signaling pathways were selected for further validation. GS Rg1 was demonstrated to effectively inhibit inflammation and mucus hypersecretion in vitro models of COPD. Western blot results showed that GS Rg1 treatment significantly downregulated the expression of proteins involved in the EGFR/PI3K/AKT and EGFR/STAT3 signaling pathway, consistent with the network pharmacology findings. CSE/LPS exposure induces inflammation and oxidative stress in COPD by disrupting the EGFR/PI3K/AKT and EGFR/STAT3 signaling pathways, and GS Rg1 significantly alleviates these effects, which may be partially through regulating the EGFR/PI3K/AKT and EGFR/STAT3 signaling pathway.

Ginsenosides↗

PubMed2005

Online predicted human interaction database.

MOTIVATION: High-throughput experiments are being performed at an ever-increasing rate to systematically elucidate protein-protein interaction (PPI) networks for model organisms, while the complexities of higher eukaryotes have prevented these experiments for humans. RESULTS: The Online Predicted Human Interaction Database (OPHID) is a web-based database of predicted interactions between human proteins. It combines the literature-derived human PPI from BIND, HPRD and MINT, with predictions made from Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster and Mus musculus. The 23,889 predicted interactions currently listed in OPHID are evaluated using protein domains, gene co-expression and Gene Ontology terms. OPHID can be queried using single or multiple IDs and results can be visualized using our custom graph visualization program. AVAILABILITY: Freely available to academic users at http://ophid.utoronto.ca, both in tab-delimited and PSI-MI formats. Commercial users, please contact I.J. CONTACT: juris@ai.utoronto.ca SUPPLEMENTARY INFORMATION: http://ophid.utoronto.ca/supplInfo.pdf.

Database Management Systems↗

PubMed2025

Network pharmacology and molecular docking to explore the active compounds and mechanisms of Jerusalem artichoke for treating diabetes.

The effective components and mechanism of Jerusalem artichokes (JAs) in lowering blood glucose were studied through network pharmacology and molecular docking. The active compounds of Jerusalem artichoke were obtained by referring to the literature, and the active compounds were screened. The targets were predicted by the SwissTargetPrediction database, and the disease targets were screened using GeneCard, Disgenet, and OMIM databases. The protein-protein interaction (PPI) network diagram was constructed using the STRING database, and the intersection target was analyzed by gene ontology (GO) biological function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses using the David database. Finally, molecular docking was verified using AutoDockTools1.5.7 software. After screening, 412 gene targets, 476 disease targets, and 64 intersection targets were identified. The results of GO biological function analysis and KEGG pathway analysis showed that the technology was involved in multiple biological processes and regulatory pathways for hypoglycemia, such as the HIF-1, PI3K-Akt, and AMPK signaling pathways. Molecular docking results showed that Jasmonate, Liquiritigenin and Inulin of JAs had strong binding effects with PPARG and STAT3. JAs exert hypoglycemic effects through multi-component, multi-target and multi-pathway. In summary, this study investigated the hypoglycemic mechanism of JAs using network pharmacology and molecular interconnection technology, and concluded that JAs exert hypoglycemic effects through multiple components, targets, and pathways, which provides a theoretical basis for the study of JAs.

Molecular Docking Simulation↗

PubMed2025

Network pharmacology-based study on the mechanism of Tangfukang formula against type 2 diabetes mellitus.

OBJECTIVE: To explore the mechanism of Tangfukang formula (, TFK) in treating type 2 diabetes mellitus (T2DM). METHODS: We employed network pharmacology combined with experimental validation to explore the potential mechanism of TFK against T2DM. Initially, we filtered bioactive compounds with the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and Symptom Mapping (SymMap), and gathered targets of TFK and T2DM. Subsequently, we constructed a protein-protein interaction (PPI) network, enriched core targets through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), and adopted molecular docking to study the binding mode of compounds and the signaling pathway. Finally, we employed a KKAy mice model to investigate the effect and mechanism of TFK against T2DM. Biochemical assay, histology assay, and Western blot (WB) were used to assess the mechanism. RESULTS: There were 492 bioactive compounds of TFK screened, and 1226 overlapping targets of TFK against T2DM identified. A compound-T2DM-related target network with 997 nodes and 4439 edges was constructed. KEGG enrichment analysis identified some core pathways related to T2DM, including adenosine 5-monophosphate-activated protein kinase (AMPK) signaling pathway. Molecular docking study revealed that compounds of TFK, including citric acid, could bind to the active pocket of AMPK crystal structure with free binding energy of －4.8, －8 and －7.9, respectively. Animal experiments indicated that TFK decreased body weight, fasting blood glucose, fasting serum insulin, homeostasis model of insulin resistance, glycosylated serum protein, total cholesterol, triglyceride, and low-density lipoprotein cholesterol, and improve oral glucose tolerance test results. TFK reduced steatosis in liver tissue, and infiltration of inflammatory cells, and protected liver cells to a certain extent. WB analysis revealed that, TFK upregulated the phosphorylation of AMPK and branched-chain α-ketoacid dehydrogenase proteins. CONCLUSION: TFK has the potential to effectively manage T2DM, possibly by regulating the AMPK signaling pathway. The present study lays a new foundation for the therapeutic application of TFK in the treatment of T2DM.

Diabetes Mellitus, Type 2↗

PubMed2026

Artificial Intelligence-Driven Multi-Omics Analysis Reveals Hydroxytyrosol Targeting of the TXNIP-NLRP3 Inflammasome Axis in Traumatic Brain Injury.

Traumatic brain injury (TBI) induces secondary neuroinflammation driven by oxidative stress, inflammasome activation, and immune remodeling, yet specific mechanism-guided pharmacological interventions remain limited. This study established an artificial intelligence (AI)-integrated network pharmacology and multi-omics framework to evaluate whether hydroxytyrosol (HT), an olive-derived natural polyphenol, may regulate TBI-related neuroinflammatory targets centered on the TXNIP/NLRP3 inflammasome axis. Starting from the SMILES structure of HT, potential targets were predicted using PharmMapper, SwissTargetPrediction, and the Similarity Ensemble Approach and were standardized to UniProt identifiers. TBI-associated genes were integrated from GeneCards, DisGeNET, OMIM, and the Therapeutic Target Database. The overlapping target set was analyzed using STRING-based protein-protein interaction (PPI) networks, MCODE, CytoHubba, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Public GEO transcriptomic datasets (GSE123831 and GSE104687) were used for cross-platform expression validation, differential expression analysis, and exploratory CIBERSORT-based immune infiltration estimation. Random forest (RF), multilayer perceptron (MLP), graph convolutional network (GCN), graph attention network (GAT), SHAP/LIME explainability analysis, LASSO inflammatory-risk scoring, and two-sample Mendelian randomization (MR) were further applied for target prioritization, immune phenotype mapping, and genetic association analysis. Seventy-three overlapping HT-TBI targets were identified. PPI and topology analyses prioritized TXNIP, NLRP3, CASP1, MAPK1, and TP53 as key hubs enriched in inflammasome activation, oxidative stress, apoptosis, and NOD-like receptor signaling. TXNIP, NLRP3, and CASP1 were consistently upregulated in both TBI transcriptomic datasets. LM22-based immune deconvolution suggested increased pro-inflammatory immune signatures and a positive TXNIP-M1 macrophage association (r = 0.63, p < 0.001), which should be interpreted as a transcriptome-derived hypothesis rather than validated murine immune-cell proportions. AI-based models consistently ranked TXNIP/NLRP3 as high-contribution features under internal validation, and removal of these targets reduced model performance. A five-gene inflammatory score achieved an internally evaluated AUC of 0.87, while two-sample MR supported positive genetic associations involving TXNIP expression, TBI risk, NLRP3 and IL-1β expression. Collectively, these findings prioritize the TXNIP/NLRP3/CASP1 module as a computationally supported candidate mechanism through which HT may influence oxidative stress-inflammasome-immune coupling in TBI. This study provides an interpretable drug-target-pathway-phenotype framework and identifies TXNIP, NLRP3, and CASP1 as priority nodes for future experimental validation.

Artificial Intelligence↗

PubMed2026

Network pharmacology-based prediction and experimental validation of the anti-hyperuricemic effects of oolong tea polyphenols.

OBJECTIVE: This study aimed to identify candidate therapeutic targets of oolong tea polyphenols (TP) against hyperuricemia (HUA) using network pharmacology and bioinformatics, and to validate the predicted molecular mechanism through in vivo experimentation. METHODS: Drug and disease targets were retrieved from public databases, and overlapping targets were identified by Venn diagram analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the shared targets, and a protein-protein interaction (PPI) network was constructed to identify hub genes. For in vivo validation, an HUA mouse model was established by 15 days of oral potassium oxonate (PO) administration. Model mice then received TP by gavage at low (0.5 g⋅kg-1⋅d-1), medium (1 g⋅kg-1⋅d-1), or high (2 g⋅kg-1⋅d-1) doses for an additional 15 days. Serum biochemical markers, histopathological changes, and pathway-related protein expression were assessed by enzyme-linked immunosorbent assay (ELISA), hematoxylin and eosin (HE) staining, and western blot analysis, respectively. RESULTS: Network pharmacology analysis identified 59 overlapping targets between TP and HUA; GO and KEGG enrichment analyses revealed that these targets were primarily associated with hormone metabolism and the PI3K-AKT signaling pathway. In the animal experiment, TP dose-dependently reduced serum uric acid (SUA) levels in hyperuricemic mice. At the molecular level, low and medium doses of TP suppressed phosphorylation of phosphatidylinositol 3-kinase (PI3K), protein kinase B (AKT), and mammalian target of rapamycin (mTOR), whereas the high dose paradoxically activated this pathway and concomitantly elevated interleukin-1β levels. These findings indicate that TP modulates uric acid metabolism through a non-monotonic, dose-dependent mechanism. CONCLUSION: By combining network pharmacology with animal experiments, this study identified the PI3K/AKT/mTOR signaling pathway as a likely mediator of the anti-hyperuricemic action of oolong tea polyphenols (TP). A medium dose of TP achieved the most balanced outcome, attenuating inflammation and preserving hepatic and renal architecture; the high dose, by contrast, paradoxically elevated interleukin-1β (IL-1β) and overactivated PI3K/AKT/mTOR signaling, underscoring the importance of dose calibration. These data suggest that a medium dose of TP may represent a feasible dietary strategy against hyperuricemia. Further work-including monomer identification, direct target validation, and clinical evaluation-is warranted to confirm and extend these preclinical findings.

PI3K/Akt/mTOR signaling pathway↗

PubMed2025

Genomic mapping of diabetic kidney disease biomarkers and identification of potential inhibitors through virtual screening.

BACKGROUND: Diabetic kidney disease (DKD) is a common and serious complication of diabetes mellitus, marked by a multifactorial pathogenesis and the absence of sensitive diagnostic biomarkers. Identifying novel molecular targets and therapeutic options is essential to improve early diagnosis and treatment outcomes. METHODS: To uncover potential biomarkers and therapeutic candidates, we performed an integrated genomic analysis using microarray and RNA-seq datasets from the Gene Expression Omnibus (GEO) and Sequence Read Archive (SRA) databases. Differentially expressed genes (DEGs) were identified and subjected to protein-protein interaction (PPI) network analysis. Key genes were further explored through virtual screening of an FDA-approved compound library using molecular docking techniques. Drug-likeness was assessed via Lipinski's rule of five. RESULTS: A total of 40 DEGs were identified, among which ISCU (downregulated; involved in iron-sulfur cluster biogenesis) and AP1S2 (upregulated; associated with vesicular trafficking) emerged as potential biomarkers. PPI analysis revealed their involvement in critical DKD-related pathways, such as extracellular matrix remodeling and oxidative stress. Virtual screening identified six FDA-approved compounds with high binding affinity (≤-7.96 kcal/mol) to ISCU, notably ZINC000001576020, all of which complied with Lipinski's rule. CONCLUSIONS: This in-silico study nominates ISCU and AP1S2 as candidate diagnostic biomarkers for DKD and identifies computationally prioritized inhibitors targeting ISCU. These findings require experimental validation but provide a molecular framework for precision diagnosis and therapeutic development. These findings offer new molecular insights that could inform precision diagnosis and personalized treatment strategies for diabetic kidney disease.

Diabetic Nephropathies↗

PubMed2026

Integrative computational analysis combining network pharmacology, regulatory network modeling, and molecular dynamics reveals the mechanisms of Quanshen compound in ITP.

UNLABELLED: Immune thrombocytopenia (ITP) is a hemorrhagic disorder caused by immune dysfunction. Quanshen Compound (QSC) is an in-house preparation developed by the Uyghur Hospital in Hotan Prefecture. This study primarily investigates and validates the potential pharmacological basis and mechanism of action of QSC in modulating immune thrombopoiesis. Based on the multi-database screening of the QSC and the related targets of ITP, the intersection was obtained to construct a protein-protein interaction (PPI) network and screen the core targets; the intersection targets were analyzed for gene ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using R packages; a component-target-pathway network was constructed to screen the key active components and their mechanisms of action. At the same time, the TF-mRNA-miRNA regulatory network of the core targets was constructed, and chromosome localization and subcellular localization analysis were performed; further, the binding stability of key components and core targets was verified through molecular docking and molecular dynamics simulation. A total of 227 potential target sites were screened out, among which TNF, IL6, AKT1, TP53 and IL1B were the core targets. The enrichment results indicated that these intersecting target sites mainly participated in inflammatory responses, immune regulation and hemostasis-related biological processes, and were significantly enriched in the PI3K-Akt signaling pathway, Toll-like receptor signaling pathway, Th17 cell differentiation and PD-1/PD-L1 signaling pathway. The core target TF-mRNA-miRNA regulatory network contained 184 nodes and 200 edges, suggesting that the core targets were subject to multi-level regulation. Molecular docking results showed that the main active components had good binding activity with the core targets, and molecular dynamics simulation further verified the stability of the complex. QSC may improve ITP through a multi-component, multi-target, and multi-pathway synergistic mechanism involving key targets such as TNF, IL6, AKT1, TP53, and IL1B, as well as the PI3K-Akt signaling pathway. These findings provide new insights into the potential therapeutic mechanisms of QSC against ITP and warrant further experimental validation. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s40203-026-00718-0.

Immune thrombocytopenia↗

PubMed2026

Integrative Network Analysis of Bioactive Compounds from Punica granatum L. Peel: Multi-Target Mechanisms in Wound Healing.

BACKGROUND: Wound-healing agents often have limited efficacy and require prolonged recovery times, prompting growing interest in developing herbal-based formulations. Among these, Punica granatum L. has attracted considerable attention because of its high polyphenolic content. Despite its widespread use, the precise pharmacological targets underlying its wound-healing effects remain poorly understood and require systematic investigation. OBJECTIVES: This study aimed to elucidate the underlying pharmacological mechanisms of the topical wound-healing properties of P. granatum L. using a network pharmacology approach. METHODS: Bioactive compounds of P. granatum L. and their potential target genes were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP), Similarity Ensemble Approach (SEA), and SwissTargetPrediction databases. Wound healing-related genes were retrieved from the GeneCards database. Genes intersecting P. granatum L. targets and wound healing-associated genes were subjected to functional enrichment analyses, including protein-protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. The PPI network was further analyzed using Cytoscape, and the phytoconstituent-target interaction network was visualized using Gephi. These findings were validated using molecular docking. RESULTS: A total of 40 intersecting genes were identified as potential P. granatum L. targets involved in wound healing. Among these, EGFR, PTPN11, HRAS, IGF1R, and ESR1 were identified as key hub genes. Functional enrichment analysis indicated that the most significantly enriched signaling pathways included the MAPK, PI3K-Akt, EGFR tyrosine kinase inhibitor resistance, focal adhesion, and FoxO signaling pathways. Molecular docking analysis confirmed favorable binding of quercetin and ellagic acid to the hub targets EGFR, IGF1R, and ESR1. CONCLUSIONS: These findings elucidate the pharmacological pathways underlying P. granatum-mediated wound healing and suggest that P. granatum L. acts as a multi-target modulator in the wound-healing process.

Focal Adhesion↗

PubMed2025

Integrated data mining and network pharmacology to explore the prescription patterns from a senior TCM oncologist's clinical practice in treating chemotherapy-induced hand-foot syndrome.

Hand-foot syndrome (HFS) is a common and refractory adverse effect of chemotherapy lacking specific therapeutic strategies currently. Traditional Chinese medicine (TCM) has shown empirical efficacy in clinical HFS management. This study integrated data mining and network pharmacology to systematically elucidate the medication principles and molecular mechanisms underlying Professor Gang Xie's prescriptions for HFS. All medical records from Professor Xie's specialist clinic (January 2020 to March 2025) were retrospectively collected and standardized in Excel. Prescriptions were analyzed through frequency statistics, association and clustering. Active ingredients of core herb pairs and their disease-related targets were identified using TCMSP, HERB, GeneCards, PharmGKB and GEO databases. Protein-protein interaction (PPI) networks, gene ontology (GO), and Kyoto encyclopedia of genes and genomes (KEGG) pathway analyses were performed. Molecular docking validated interactions between key bioactive compounds and targets. This study involved 217 prescriptions containing 150 herbs. Core herb combinations comprised Radix Astragali (Huangqi), Poria (Fuling), and Radix Pseudostellariae (Taizishen), predominantly classified as spleen-tonifying agents with warm properties, targeting lung, spleen, and stomach meridians. Network analysis identified 67 bioactive compounds and 899 disease targets. Quercetin, kaempferol, acacetin and luteolin were identified the key ingredients. The core targets (TP53, STAT3, PIK3CA, HSP90AA1, AKT1, CTNNB1, PI3KR1, MAPK1) were enriched in MAPK and PI3K-Akt signaling pathways. Molecular docking confirmed strong binding affinity between key compounds and targets. Professor Xie's therapeutic strategy for HFS emphasizes "spleen fortification, phlegm elimination, and stasis resolution." The core herb combination likely exerts anti-HFS effects via modulation of MAPK and PI3K-Akt pathways, providing a pharmacological basis for TCM-driven HFS management.

Network Pharmacology↗

PubMed2025

Integrative proteomic analysis provides novel therapeutic insights for etiological subtypes of diabetes.

AIMS: Type 2 diabetes (T2D) is a highly heterogeneous disease characterised by subtypes with variations in aetiology, disease progression, and risk of complications. However, potential drug targets for these subtypes have not been explored. This study aims to investigate potential drug targets by integrating proteomics. MATERIALS AND METHODS: Summary-level data of circulating proteins were extracted from the UK Biobank and the deCODE Health Study. Genetic associations with five diabetes subtypes were obtained from Swedish All New Diabetics in Scania and Malmö Diet and Cancer cohort, including severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD). The associations between circulating proteins and diabetes subtypes were assessed through Mendelian randomisation, followed by multiple sensitivity and colocalization analyses. Additionally, tissue-specific, pathway and functional enrichment analysis, assessment of protein druggability, and the protein-protein interaction (PPI) networks were used to further explore biological mechanisms and therapeutic potential. RESULTS: Genetically predicted levels of 2, 2, 9, 3, and 5 circulating proteins were associated with SIRD, SIDD, MARD, MOD, and SAID, respectively. Colocalization analyses further revealed links between GRN with MARD/SIRD, LILRB5 with SIDD/MARD, CR1 with MARD, TNFSF12 with MOD, and DAPK2 with SAID. Enrichment analysis suggested that these proteins were mainly enriched in blood and adipose tissues and involved in immune and inflammatory related pathways. PPI analysis revealed GRN, TNFSF12, and DAPK2 are associated with known T2D targets. CONCLUSIONS: Our study identified several potential drug targets for different subtypes of diabetes using an integrated genetic approach, yielding new insights for precision medicine of diabetes.

Humans↗

PubMed2026

Elucidating the Mechanism of Xiaoqinglong Decoction in Chronic Urticaria Treatment: An Integrated Approach of Network Pharmacology, Bioinformatics Analysis, Molecular Docking, and Molecular Dynamics Simulations.

INTRODUCTION: Xiaoqinglong Decoction (XQLD) is a traditional Chinese medicinal formula commonly used to treat chronic urticaria (CU). However, its underlying therapeutic mechanisms remain incompletely characterized. This study employed an integrated approach combining network pharmacology, bioinformatics, molecular docking, and molecular dynamics simulations to identify the active components, potential targets, and related signaling pathways involved in XQLD's therapeutic action against CU, thereby providing a mechanistic foundation for its clinical application. METHODS: The active components of XQLD and their corresponding targets were identified using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database. CU-related targets were retrieved from the OMIM and GeneCards databases. Subsequently, core components and targets were determined via protein-protein interaction (PPI) network analysis and component-target-pathway network construction. Topological analyses were performed using Cytoscape software to prioritize core nodes within these networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were conducted via the DAVID database to identify enriched biological processes and signaling pathways. Molecular docking was performed to evaluate binding interactions between key components and core targets, while molecular dynamics (MD) simulations were employed to assess the stability of the component-target complexes with the lowest binding energy. Finally, CU-related targets of XQLD were validated using datasets from the Gene Expression Omnibus (GEO) database. RESULTS: A total of 135 active components and 249 potential targets of XQLD were identified, alongside 1,711 CU-related targets. Core components, such as quercetin, kaempferol, beta-sitosterol, naringenin, stigmasterol, and luteolin, exhibited high degree values in the constructed networks. The core targets identified included AKT1, TNF, IL6, TP53, PTGS2, CASP3, BCL2, ESR1, PPARG, and MAPK3. GO and KEGG pathway enrichment analyses revealed the PI3K-Akt signaling pathway as a central regulatory mechanism. Molecular docking studies demonstrated strong binding affinities between active components and core targets, with the stigmasterol-AKT1 complex exhibiting the lowest binding energy (-11.4 kcal/mol) and high stability in MD simulations. Validation using GEO datasets identified 12 core genes shared between CU-related targets and XQLD-associated targets, including PTGS2 and IL6, which were also prioritized as core targets in the network pharmacology analyses. DISCUSSION: This study comprehensively integrates multidisciplinary approaches to clarify the potential molecular mechanisms of XQLD in treating CU, highlighting its multitarget and multipathway synergistic effects. Molecular docking and dynamics simulations confirm the stable interaction between stigmasterol and the core target AKT1. Additionally, GEO dataset analysis verifies the pathogenic relevance of targets such as PTGS2 and IL6, significantly enhancing the credibility of our findings. These results provide a modern scientific basis for the traditional therapeutic effects of XQLD on CU and have important implications for developing multitarget treatments for this condition. However, this study mainly relies on database mining and computational simulations. Further in vitro and in vivo experimental validations are needed to confirm the predicted component-target-pathway interactions. CONCLUSION: This study identifies the active components, potential targets, and pathways through which XQLD exerts therapeutic effects on CU. These findings provide a theoretical foundation for further mechanistic studies and support their clinical application in the treatment of CU.

Molecular Docking Simulation↗

PubMed2026

Multi-omics identification of therapeutic targets of compound sappan decoction in hepatocellular carcinoma.

BACKGROUND: Compound sappan decoction (CSD) is a multi-herbal traditional Chinese medicine formulation with clinical relevance in hepatocellular carcinoma (HCC). However, its therapeutic mechanisms remain unclear. METHODS: Bioactive compounds of CSD were identified and standardized using pharmacological and chemical databases. Potential targets were predicted via multiple target inference platforms. HCC-related genes were curated from comprehensive disease databases. Summary-data-based Mendelian randomization (SMR) was conducted to infer causal relationships between compound targets and HCC risk using large-scale quantitative trait loci (QTL) datasets and HCC genome-wide association study data. Colocalization analysis, protein-protein interaction (PPI) network construction, and GO/KEGG enrichment were performed on SMR-identified targets. Molecular docking evaluated binding affinities of representative compounds to prioritized targets. RESULTS: A total of 784 overlapping genes between predicted CSD targets and HCC-related genes were subjected to SMR analysis. Among these, 22 targets were significantly associated with HCC risk based on transcriptomic or proteomic QTLs and showed colocalization evidence. Notably, four targets (ADRB2, APOE, SYK, and PGF) were supported by both replication in an independent cohort and strong colocalization. These 22 targets were enriched in apoptosis, PI3K-Akt signaling, redox metabolism, and detoxification pathways. PPI analysis revealed central hubs including MMP9, BCL2, CASP1, and MCL1. Molecular docking demonstrated strong binding of APOE to quercetin, PGF to luteolin-7-olate, and SYK to kaempferol. CONCLUSIONS: CSD may exert therapeutic effects on HCC through modulation of genetically validated targets involved in tumor progression, inflammation, and metabolic reprogramming, supporting its potential clinical utility as an adjunctive treatment strategy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s12672-026-04740-8.

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