Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “PPI network”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
PubMed2024

Identification of cuproptosis-realated key genes and pathways in Parkinson's disease via bioinformatics analysis.

INTRODUCTION: Parkinson's disease (PD) is the second most common worldwide age-related neurodegenerative disorder without effective treatments. Cuproptosis is a newly proposed conception of cell death extensively studied in oncological diseases. Currently, whether cuproptosis contributes to PD remains largely unclear. METHODS: The dataset GSE22491 was studied as the training dataset, and GSE100054 was the validation dataset. According to the expression levels of cuproptosis-related genes (CRGs) and differentially expressed genes (DEGs) between PD patients and normal samples, we obtained the differentially expressed CRGs. The protein-protein interaction (PPI) network was achieved through the Search Tool for the Retrieval of Interacting Genes. Meanwhile, the disease-associated module genes were screened from the weighted gene co-expression network analysis (WGCNA). Afterward, the intersection genes of WGCNA and PPI were obtained and enriched using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, the key genes were identified from the datasets. The receiver operating characteristic curves were plotted and a PPI network was constructed, and the PD-related miRNAs and key genes-related miRNAs were intersected and enriched. Finally, the 2 hub genes were verified via qRT-PCR in the cell model of the PD and the control group. RESULTS: 525 DEGs in the dataset GSE22491 were identified, including 128 upregulated genes and 397 downregulated genes. Based on the PPI network, 41 genes were obtained. Additionally, the dataset was integrated into 34 modules by WGCNA. 36 intersection genes found from WGCNA and PPI were significantly abundant in 7 pathways. The expression levels of the genes were validated, and 2 key genes were obtained, namely peptidase inhibitor 3 (PI3) and neuroserpin family I member 1 (SERPINI1). PD-related miRNAs and key genes-related miRNAs were intersected into 29 miRNAs including hsa-miR-30c-2-3p. At last, the qRT-PCR results of 2 hub genes showed that the expressions of mRNA were up-regulated in PD. CONCLUSION: Taken together, this study demonstrates the coordination of cuproptosis in PD. The key genes and miRNAs offer novel perspectives in the pathogenesis and molecular targeting treatment for PD.

Humans↗
PubMed2025

Pooled PPIseq: Screening the SARS-CoV-2 and human interface with a scalable multiplexed protein-protein interaction assay platform.

Protein-Protein Interactions (PPIs) are a key interface between virus and host, and these interactions are important to both viral reprogramming of the host and to host restriction of viral infection. In particular, viral-host PPI networks can be used to further our understanding of the molecular mechanisms of tissue specificity, host range, and virulence. At higher scales, viral-host PPI screening could also be used to screen for small-molecule antivirals that interfere with essential viral-host interactions, or to explore how the PPI networks between interacting viral and host genomes co-evolve. Current high-throughput PPI assays have screened entire viral-host PPI networks. However, these studies are time consuming, often require specialized equipment, and are difficult to further scale. Here, we develop methods that make larger-scale viral-host PPI screening more accessible. This approach combines the mDHFR split-tag reporter with the iSeq2 interaction-barcoding system to permit massively-multiplexed PPI quantification by simple pooled engineering of barcoded constructs, integration of these constructs into budding yeast, and fitness measurements by pooled cell competitions and barcode-sequencing. We applied this method to screen for PPIs between SARS-CoV-2 proteins and human proteins, screening in triplicate >180,000 ORF-ORF combinations represented by >1,000,000 barcoded lineages. Our results complement previous screens by identifying 74 putative PPIs, including interactions between ORF7A with the taste receptors TAS2R41 and TAS2R7, and between NSP4 with the transmembrane KDELR2 and KDELR3. We show that this PPI screening method is highly scalable, enabling larger studies aimed at generating a broad understanding of how viral effector proteins converge on cellular targets to effect replication.

Humans↗
PubMed2026

RCoxNet: A Deep Learning Framework Integrating Random Walk with Restart, Mutation, and Clinical Data for Cancer Survival Prediction.

Accurate survival prediction in cancer remains challenging due to the sparsity of somatic mutation profiles and the failure of existing models to capture higher-order gene-gene dependencies. Network diffusion methods such as Random Walk with Restart (RWR) can propagate mutation signals across protein-protein interaction (PPI) networks to address sparsity, yet their integration within a deep learning Cox survival framework has not been comprehensively benchmarked across multiple cancer cohorts. We present RCoxNet, a deep learning framework that maps somatic mutation profiles onto a ConsensusPathDB-derived PPI network via RWR, selects prognostic genes by log-rank filtering, and processes network-informed mutation scores through three fully connected hidden layers feeding into a Cox proportional hazards output. RCoxNet was evaluated on The Cancer Genome Atlas (TCGA) cohorts for four cancer types (breast invasive carcinoma [BRCA], lung adenocarcinoma [LUNG], glioblastoma multiforme [GBM], and ovarian serous cystadenocarcinoma [OV]) using 20 independent random splits. The model achieved mean C-index values of 0.807 ± 0.044 (BRCA), 0.750 ± 0.039 (LUNG), 0.704 ± 0.041 (GBM), and 0.668 ± 0.036 (OV), consistently outperforming DeepSurv, Cox-nnet, SurvivalNet, Cox Elastic-Net (Cox-EN), and DeepHit, with statistically significant gains over Cox-EN, Cox-nnet, SurvivalNet, and DeepHit across the majority of cohorts. RCoxNet demonstrates that embedding sparse mutation profiles into a PPI network context substantially improves cancer survival prediction and yields biologically interpretable prognostic features relevant to precision oncology.

cancer survival prediction↗
PubMed2024

Comprehensive analysis of circRNA-miRNA-mRNA network related to angiogenesis in recurrent implantation failure.

BACKGROUND: Abnormal endometrial blood flow causes a decrease in endometrial receptivity and is considered a relatively independent risk factor for recurrent implantation failure (RIF). This study aimed to explore the potentially functional circRNA-miRNA-mRNA network in RIF, and further explore its mechanism. METHODS: Datasets were downloaded from the GEO database to identify differentially expressed circRNAs, miRNAs and mRNAs. The circRNA-miRNA-mRNA and PPI networks were constructed using Cytoscape 3.6.0 and the STRING database, the hub genes were identified with the cytoHubba plug-in, and a circRNA-miRNA-hub mRNA regulatory sub-network was constructed. Then, GO and KEGG pathway enrichment analyses of the hub genes were performed to comprehensively analyze the mechanism of hub mRNAs in RIF. Due to the results of circRNAs-miRNAs-hub mRNAs regulatory network, we verified the expression of circRNA_0001721, circRNA_0000714, miR-17-5p, miR-29b-3p, HIF1A and VEGFA in the RIF mouse model by qRT‒PCR and western blotting. RESULTS: We initially identified 175 DEmRNAs, 48 DEmiRNAs and 56 DEcircRNAs in RIF associated with angiogenesis and constructed a circRNA-miRNA‒mRNA network and PPI network. We further identified six hub genes in the acquired network. Based on these genes, functional enrichment analysis revealed that the HIF-1 signaling pathway plays a vital role in endometrial angiogenesis in RIF. In addition, the interaction networks of circRNA_0001721/miR-17-5p/HIF1A and the circRNA_0000714/miR-29b-3p/VEGFA axis were predicted. In the RIF mouse model, circRNA_0001721, circRNA_0000714, HIF1A and VEGFA were down-regulated, whereas miR-17-5p and miR-29b-3p were up-regulated according to qRT‒PCR and western blotting. CONCLUSION: This study revealed that the HIF-1 signaling pathway plays a vital role in endometrial angiogenesis in RIF. The circRNA_0001721/miR-17-5p/HIF1A and circRNA_0000714/miR-29b-3p/VEGFA axes might play a role in the pathogenesis of endometrial angiogenesis in RIF.

MicroRNAs↗
PubMed2026

Identification of mitochondrial energy metabolism-related candidate genes UQCR10 and NDUFA6 in pediatric tetralogy of fallot: an exploratory bioinformatics study.

BACKGROUND: Tetralogy of Fallot (TOF) is one of the most common cyanotic congenital heart diseases in infants and young children. Its molecular basis remains incompletely understood. This study aimed to identify mitochondrial energy metabolism-related candidate genes associated with pediatric TOF using public heart tissue transcriptomic datasets from the GEO database. METHODS: Datasets GSE146218 and GSE217772 were downloaded and merged, followed by batch-effect correction. Differential expression analysis was performed to identify differentially expressed genes (DEGs). Functional enrichment analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis were used to prioritize candidate genes. The Comparative Toxicogenomics Database (CTD) was used as an exploratory literature-based tool to summarize gene-disease associations. RESULTS: A total of 960 DEGs were identified. Functional enrichment analyses showed that these genes were mainly enriched in mitochondrial energy metabolism-related pathways, including oxidative phosphorylation and the mitochondrial respiratory chain. WGCNA and PPI network analyses further prioritized UQCR10 and NDUFA6 as candidate genes, and both genes showed increased expression in TOF heart tissue samples. CTD analysis suggested literature-based associations between these genes and cardiovascular or developmental disease-related terms. CONCLUSION: This exploratory bioinformatics study identified UQCR10 and NDUFA6 as mitochondrial energy metabolism-related candidate genes upregulated in pediatric TOF heart tissue. These findings suggest that mitochondrial respiratory chain-related transcriptional alterations may be involved in TOF-associated myocardial remodeling or stress responses. Further experimental and clinical validation is required to confirm their biological relevance.

Humans↗
PubMed2026

Semaglutide treatment in MOSH is associated with altered DNA methylation patterns of genes related to glycolipid metabolism.

Male obesity-associated secondary hypogonadism(MOSH) is a common disease among severely obese male patients. Although surgical interventions have demonstrated clinical benefits, a subset of patients continue to experience MOSH following surgery. Therefore, this study aims to investigate epigenetic changes associated with the use of the weight-loss drug Semaglutide in MOSH, focusing on DNA methylation and miRNA expression. In this exploratory study, samples were classified into three groups: a control group (n = 2), a MOSH group (n = 7), and a follow-up group (n = 4). DNA methylation analysis was performed on all samples, while miRNA sequencing was conducted on a subset of the samples: 2 from the control group, 7 from the MOSH group, and 2 from the follow-up group. Differentially expressed miRNAs (DEMs) were analyzed through the R package "limma", and the methylation level of CpG sites was analyzed based on the methylation β value, obtaining differentially methylated genes (DMGs). The functional enrichment analysis of miRNA target genes and methylation change genes was conducted using the R package "clusterProfiler". Finally, the regulatory networks of miRNA and methylation genes as well as the protein-protein interaction (PPI) network were analyzed. A total of 6 DEMs were screened out. The target genes of these DEMs were mainly enriched in pathways such as ATP binding, phosphorylation, cell adhesion, and Glycosphingolipid biosynthesis. Eighty DMGs were identified, and the largest number of DMGs were found in the X chromosome. In the regulatory network of DMGs and DEMs, hsa-miR-423-5p regulates most of these DMGs. Moreover, the PPI network shows that DPP6, DPP10, CACNA1C, and CNTNAP2 are the proteins with the strongest connectivity. Notably, differential CpG methylation changes were observed on chromosome 7, indicating a potential region of epigenetic alteration in MOSH; however, the biological and functional relevance of these changes remains unclear. Collectively, these findings suggest that Semaglutide treatment in MOSH may be associated with concurrent alterations in DNA methylation and miRNA expression, implicating genes related to energy and glycolipid metabolism, including DPP6, DPP10, CACNA1C, and CNTNAP2. These results are exploratory and hypothesis-generating, providing preliminary observations to inform future validation studies.

Semaglutide↗
PubMed2026

A Network Pharmacology and Molecular Docking Study of TongBi Formula for Osteoarthritis.

This study applied network pharmacology combined with molecular docking to predict the potential therapeutic targets and molecular mechanisms of TongBi Formula (TBF) in osteoarthritis (OA). Active components and corresponding targets of TBF were retrieved from the traditional Chinese medicine Systems Pharmacology Database and Analysis Platform, while OA-related targets were collected from Online Mendelian Inheritance in Man, GeneCards, DrugBank, and Therapeutic Target Database. A network visualization and analysis software was used to construct compound-target and protein-protein interaction (PPI) networks. Gene Ontology functional annotation and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery platform. Molecular docking analysis was conducted using a molecular docking software to evaluate the predicted binding affinity between key active compounds and core target proteins. A total of 47 overlapping targets between TBF and OA were identified. PPI network analysis highlighted JUN, RELA, IL6, MAPK1, and IL10 as potential hub targets. Enrichment analysis suggested that TBF may regulate inflammation, lipid metabolism, and multiple intracellular signaling pathways associated with OA progression. Molecular docking results demonstrated favorable predicted binding affinities between core active compounds and key OA-related protein targets. These findings provide a computational framework for understanding the potential mechanisms of TBF against OA and support further experimental validation.

Molecular Docking Simulation↗
PubMed2025

Comprehensive circRNA expression profile and hub genes screening during human liver development.

BACKGROUND: Understanding the expression of non-coding RNA in the liver during embryonic development provides important insights into liver diseases. Therefore, we investigated circular RNA (circRNA) roles in human liver development, an unexplored research domain. METHODS: Using high-throughput sequencing and bioinformatics, we analysed foetal liver samples across developmental stages (7-20 weeks post-conception). Differentially expressed (DE) genes were identified and subjected to enrichment analysis using Gene Ontology (GO), Kyoto Encyclopaedia of Genes and Genomes (KEGG), and Disease Ontology (DO). Modular analysis was performed using the Search Tool for Retrieval of Interacting Genes (STRING), followed by construction of a protein-protein interaction (PPI) network using Cytoscape software. The key genes were screened using Molecular Complex Detection (MCODE). The mRNA levels of hub genes were validated using quantitative reverse transcription polymerase chain reaction (qRT-PCR). RESULTS: There were 645 DE circRNAs and 5,145 DE mRNAs between human livers at the three growth stages (HB, EH, and LH). It was found that the activity of circRNAs was boosted remarkably in the hepatoblastic stage. Enrichment analysis found they mainly involved in nervous system regulation of liver function, embryonic organ development and digestive system development. In addition, DE circRNAs were primarily involved in the PI3K-AKT, MAPK and calcium pathways, potentially contributing to adult liver diseases. Notably, only hsa_circ_001471 and novel_circ_017382 were simultaneously identified at all stages and were persistently downregulated. A co-expression regulatory network involving these circRNAs was established. Three hub genes (LGR5, FOXL1 and RSPO3) were identified from the PPI network of 167 genes and may play key roles in human liver development. The RT-qPCR validation results were in agreement with the sequencing data. CONCLUSIONS: Our findings provide the first insights into the roles and regulatory networks of circRNAs in human liver development, laying the groundwork for further investigations of molecular and signalling networks.

Humans↗
PubMed2026

Identification of Critical Genes Related to Breast Cancer with Brain Metastasis Through Bioinformatics Analysis.

INTRODUCTION: Distant metastasis accounts for the majority of Breast Cancer (BC)-related mortality. The brain is one of the most common regions of metastasis. However, the underlying molecular mechanisms remain uncertain. METHODS: In this study, gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Datasets GSE100534 and GSE52604, containing 16 primary brain tumor samples and 38 breast cancer brain metastasis samples, were used to identify the Differentially Expressed Genes (DEGs). The Metascape database was used to analyze enriched Gene Ontology (GO) entries and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway entries in DEGs. The STRING database was then used to construct a Protein-Protein Interaction (PPI) network, and the Cytoscape platform was employed to visualize the network. Furthermore, the Kaplan-Meier curve was used to analyze the Relapse-Free Survival (RFS) among the hub genes. Finally, the iRegulon plugin was used to construct a regulatory network to find the transcription factors (TFs) that regulate the expression of the hub genes. RESULTS: A total of 344 DEGs, including 182 up-regulated and 162 down-regulated genes, were identified by using the limma package in R. A module with 18 nodes and 9 hub genes was selected from the PPI network by using the plugins MCODE and Cyto- Hubba, respectively. KEGG pathway analysis demonstrated that brain metastasis in BC was closely related to the oocyte cell cycle. The Kaplan-Meier curve showed that high expression of these 9 hub genes was associated with poor RFS in BC patients. TFs' analysis showed that E2F4, SIN3A, FOXM1, and TFDP1 interacted with these hub genes. DISCUSSION: This study revealed that Breast Cancer Brain Metastasis (BCBM) may have a promoting effect on the cell cycle of oocytes and affect the maturation and division of oocytes through the KEGG and GO analyses of 344 DEGs. The selected 9 hub genes (ASPM, BUB1, BUB1B, CCNA2, CCNB1, CDK1, NDC80, NCAPG, and TOP2A) and 4 transcription factors (E2F4, SIN3A, FOXM1, TFDP1) may play a critical role in brain metastasis of BC. CONCLUSION: The results of this study may aid in the early diagnosis and suggest potential targets for the treatment of BCBM.

Brain Neoplasms↗
PubMed2026

Diosmetin Inhibits Bladder Cancer through Suppression of the PI3K-AKT Signaling Pathway and Activation of the p53 Signal Pathway Revealed by Network Pharmacology and In Vitro Experimental Verification.

INTRODUCTION: Diosmetin, a naturally occurring flavonoid abundant in plants such as chrysanthemums, lemons, and oranges, has been reported to exhibit diverse antitumor properties. However, its potential efficacy against bladder cancer remains unexplored. This study aims to investigate the anti-bladder cancer effects of Diosmetin and elucidate the underlying mechanisms using network pharmacology combined with in vitro experiments. METHODS: Public databases were employed to identify shared targets between Diosmetin and bladder cancer. A Protein-Protein Interaction (PPI) network was constructed, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to predict core targets and signaling pathways. The predicted mechanisms were subsequently validated through in vitro assays. RESULTS: A total of 48 common targets were identified. PPI network analysis revealed 22 hub genes, including AKT1 and MDM2. GO analysis indicated enrichment in 208 biological processes, 23 cellular components, and 38 molecular functions. KEGG analysis suggested that Diosmetin exerts anti-bladder cancer effects primarily through pathways such as Pathways in cancer, PI3K-AKT signaling, and Proteoglycans in cancer. Notably, the PI3K-AKT pathway showed the highest gene enrichment, indicating its potential prominence. In vitro experiments demonstrated that Diosmetin suppresses bladder cancer cell proliferation and induces apoptosis. Additionally, Diosmetin reduced the expression of p-PI3K, p-AKT, and MDM2, while upregulating p53 expression, suggesting involvement of both the PI3K-AKT and p53 pathways. DISCUSSION: These findings align with network pharmacology predictions and highlight the potential of Diosmetin as a multi-target agent against bladder cancer, warranting further in vivo investigation. CONCLUSION: Diosmetin inhibits bladder cancer cell proliferation and promotes apoptosis by suppressing the PI3K-AKT pathway and activating the p53 pathway.

Diosmetin↗
PubMed2026

Integrated Bioinformatics Analysis Revealing that the NSDHL Gene Might Be Associated with the Progression of Western HFD/SW-Induced Hepatocellular Carcinoma.

BACKGROUND AND OBJECTIVE: Hepatocellular carcinoma (HCC) remains a significant global health concern. However, the etiology and pathogenesis of HCC have yet to be fully elucidated. Previous studies have indicated a close association between obesity and the occurrence and progression of HCC. The objective of this study was to employ bioinformatics strategies in order to explore key genes associated with the clinical diagnosis and prognosis of HCC induced by a Western high-fat diet and sugar water (HFD/SW). MATERIALS AND METHODS: We obtained the expression profile chip data GSE197884 from the Gene Expression Omnibus (GEO) database. Subsequently, “DESeq” and “Limma” R packages were employed to identify differentially expressed genes (DEGs) while constructing a co-expressed gene network using weighted gene co-expression analysis (WGCNA). Functional enrichment analyses were then carried out, followed by the construction of a protein-protein interaction (PPI) network to uncover core genes. The core genes were confirmed through data retrieved from The Cancer Genome Atlas (TCGA) database in order to determine their status as hub genes. Finally, survival and tumor immune infiltration analyses were performed to unveil the prognostic significance of these hub genes. RESULTS: In total, 126 intersection targets were retrieved through the Venn diagram. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that the DEGs were primarily related to the proliferation and apoptosis of HCC cells, the digestion and metabolism of liver cells, the HCC tumor microenvironment, and immune response. The PPI network analysis identified 11 core targets, among which seven hub genes, including NSDHL, MVK, SQLW, GCAT, ALAS2, GLDC, and AGXT, were obtained after TCGA database validation. Furthermore, it was found that NSDHL was closely associated with the clinical diagnosis and prognosis of HCC induced by HFD/SW and also affected the cellular immune infiltration in the HCC tumor microenvironment. CONCLUSION: The present study demonstrated a significantly elevated expression of NSDHL in HCC tissues, suggesting its potential as a specific biomarker for precise clinical diagnosis and prognosis assessment of HCC induced by HFD/SW.

Computational Biology↗
PubMed2023

Discussion on the mechanism of Lingguizhugan Decoction in treating hypertension based on network pharmacology and molecular simulation technology.

To explore the mechanism of Lingguizhugan Decoction in treating hypertension based on network pharmacology and molecular simulation. The active ingredients and potential targets were screened by the Systematic Pharmacological Analysis Platform of Traditional Chinese Medicine (TCMSP). Hypertension-related targets were obtained from OMIM and GeneCards databases. Common targets between drug and hypertension were screened in the Venny platform. A protein-protein interaction (PPI) network was constructed in the STRING database using intersection targets. Key targets in PPI network were analyzed by Cytoscape. R language program was used for Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, the binding abilities of the main active ingredients to critical targets were verified by molecular simulation. Naringenin, quercetin, kaempferol, and β-sitosterol in Lingguizhugan Decoction, and potential targets such as STAT3, AKT1, TNF, IL6, JUN, PTGS2, MMP9, CASP3, TP53, and MAPK3, were screened out. KEGG Enrichment analysis revealed that the common targets of Lingguizhugan Decoction and hypertension are mainly involved in the lipid and atherosclerosis signaling pathway, AGE-RAGE signaling pathway in diabetic complications, fluid shear stress and atherosclerosis, and IL17 signaling pathway. The molecular simulation results showed that naringenin-MAPK3, quercetin-MMP9, quercetin-PTGS2, and quercetin-TP53 were the top four in the docking scores. Naringenin-MAPK3 and quercetin-MMP9 were stable, with binding free energies of -27.97 ± 1.41 kcal/mol and -21.15 ± 3.17 kcal/mol, respectively. The possible mechanism of Lingguizhugan Decoction in treating hypertension is characterized of multi-component, multi-target, and multi-pathway.Communicated by Ramaswamy H. Sarma.

Network Pharmacology↗
PubMed2025

The signed two-space proximity model for learning representations in protein-protein interaction networks.

MOTIVATION: Accurately predicting complex protein-protein interactions (PPIs) is crucial for decoding biological processes, from cellular functioning to disease mechanisms. However, experimental methods for determining PPIs are computationally expensive. Thus, attention has been recently drawn to machine learning approaches. Furthermore, insufficient effort has been made toward analyzing signed PPI networks, which capture both activating (positive) and inhibitory (negative) interactions. To accurately represent biological relationships, we present the Signed Two-Space Proximity Model (S2-SPM) for signed PPI networks, which explicitly incorporates both types of interactions, reflecting the complex regulatory mechanisms within biological systems. This is achieved by leveraging two independent latent spaces to differentiate between positive and negative interactions while representing protein similarity through proximity in these spaces. Our approach also enables the identification of archetypes representing extreme protein profiles. RESULTS: S2-SPM's superior performance in predicting the presence and sign of interactions in SPPI networks is demonstrated in link prediction tasks against relevant baseline methods. Additionally, the biological prevalence of the identified archetypes is confirmed by an enrichment analysis of Gene Ontology (GO) terms, which reveals that distinct biological tasks are associated with archetypal groups formed by both interactions. This study is also validated regarding statistical significance and sensitivity analysis, providing insights into the functional roles of different interaction types. Finally, the robustness and consistency of the extracted archetype structures are confirmed using the Bayesian Normalized Mutual Information (BNMI) metric, proving the model's reliability in capturing meaningful SPPI patterns. AVAILABILITY: S2-SPM is implemented and freely available under the MIT license at https://github.com/Nicknakis/S2SPM.

Protein Interaction Mapping↗
PubMed2026

Leveraging bioinformatics approaches for drug repositioning in space radiation protection.

The health effects of space radiation, primarily Galactic Cosmic Rays (GCRs), on humans remain largely unknown, with potential cardiovascular consequences posing a significant threat to astronauts on long-duration spaceflight missions. Currently, there are no established pharmacological countermeasures for GCR exposure. Drug repositioning offers a promising strategy to accelerate pharmaceutical research in space medicine. This study leverages existing bioinformatics techniques to identify and prioritize potential drug candidates associated with proteomic perturbations following simulated GCR exposure using previously published murine cardiac proteomic data. A protein-protein interaction (PPI) network was constructed using the top differentially expressed proteins (DEPs) from murine heart tissue following exposure to 5-ion GCRs as seed nodes, focusing on experimentally supported interactions. Network topology, Markov clustering, and functional enrichment analyses were used to characterize biologically relevant proteins and pathways. Drug-protein interactions were predicted using Drugst.One and mapped to PPI clusters of interest to identify candidate drugs. Selected drug-macromolecule interactions were further explored using CB-Dock2 molecular docking and short-duration molecular dynamics simulations as hypothesis-generating structural assessments. Analysis of a key PPI network cluster consisting of several ATP synthase proteins identified 23 unique drug candidates. These analyses demonstrate a systematic approach for leveraging bioinformatics techniques to identify candidate molecular targets and generate pharmacological hypotheses in the context of space radiation countermeasures. Ultimately, this strategy introduces a hypothesis-generating framework for the prioritization of potential drug candidates for future computational characterization and experimental investigation against spaceflight stressors.

Animals↗
PubMed2024

Decoding the genetic landscape of allergic rhinitis: a comprehensive network analysis revealing key genes and potential therapeutic targets.

BACKGROUND: Allergic Rhinitis (AR), an inflammatory affliction impacting the upper respiratory tract, has been registering a substantial surge in incidence across the globe. METHODS: We embarked on examination of differentially expressed genes (DEGs) and the Weighted Gene Co-Expression Network Analysis (WGCNA). With this armory of genes identified, we engaged the tools of Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Our study continued with the establishment of a protein-protein interaction (PPI) network and the application of LASSO regression. Finally, we leveraged a docking model to elucidate potential drug-gene interactions involving these key genes. RESULTS: Through WGCNA and different express genes screening, PPI network was performed, identifying top 20 key genes, including CD44, CD69, CD274. LASSO regression identified three independent factors, STARD5, CST1, and CHAC1, that were significantly associated with AR. A predictive model was developed with an AUC value over 0.75. Also, 105 potential therapeutic agents were discovered, including Fluorouracil, Cyclophosphamide, Doxorubicin, and Hydrocortisone, offering promising therapeutic strategies for AR. CONCLUSION: By fuzing DEGs with key genes derived from WGCNA, this study has illuminated a comprehensive network of gene interactions involved in the pathogenesis of AR, paving the way for future biomarker and therapeutic target discovery in AR.

Humans↗
PubMed2026

Identifying JAK2 and ANXA5 as Key Genes Linking Obstructive Sleep Apnea and Oxidative Stress via Machine Learning and Multilayer Transcriptomic Integration With Functional Validation.

Obstructive sleep apnea (OSA) is a common and severe sleep disorder closely associated with oxidative stress (OS). This study aims to identify and validate potential OS-related genes associated with OSA through bioinformatics methods. We successfully identified OS-related differentially expressed genes (OS-DEGs) by combining the limma test, weighted correlation network analysis (WGCNA), and OS-related genes from the GeneCards database. Key genes and potential biological roles were further identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), enrichment analysis, protein-protein interaction (PPI) network analysis, Lasso regression analysis, random forest algorithm, and support vector machine recursive feature elimination (SVM-RFE) method. Evaluate and validate the accuracy of key genes through receiver operating characteristic (ROC) curve analysis. The human single-cell RNA sequencing (scRNA-seq) dataset is used for cell classification annotation, analysis of key gene single-cell expression profiles, and virtual gene knockout experiments based on the scTenifoldKnk algorithm. Integrating scRNA-seq sequencing, pseudotime trajectory inference, cell-cell communication analysis, and bulk immune infiltration deconvolution reveals monocyte subtype remodeling in OSA. Finally, the expression levels of key genes in clinical samples were validated using real-time quantitative PCR (RT-qPCR) and Western blotting. A total of 57 common DEGs, indicating significant enrichment in OS, inflammation, and tumor pathways, particularly prominent in the immunometabolism pathway. By integrating DEGs, WGCNA, PPI results, and machine learning methods, key genes Janus kinase 2 (JAK2) and ANXA5 were screened out. JAK2 was significantly upregulated under disease conditions, while ANXA5 was significantly downregulated. ROC curve exhibited high accuracy (area under the curve [AUC] > 0.85). Human scRNA-seq analysis revealed that key genes were predominantly highly expressed in monocytes. Virtual knockout experiments demonstrated that these key genes play a crucial role in regulating immune responses and inflammatory reactions. PPI networks and enrichment analysis verified that downstream genes S100P, ALOX5AP, PROK2, and PADI4 may collaboratively participate in immune response and inflammation regulation. Finally, clinical sample experiment further validated the results of bioinformatics analysis. This study provides new research insights for the diagnosis, mechanism research, and treatment development of OSA in the future by integrating multilayer transcriptomic and machine learning techniques.

Humans↗
PubMed2026

The Anti-Osteoporosis Effects of Panax japonicus via Downregulation of Inflammatory Factors: A Network Pharmacology and Ovariectomized Rat Model Study.

OBJECTIVE: Osteoporosis is a major and growing public health problem characterized by decreased bone mineral density and destroyed bone microarchitecture. Panax japonicus has been clinically used in the treatment of bone diseases, especially osteoporosis. However, there is a lack of study on the mechanism of osteoporosis treatment with Panax japonicus. MATERIALS AND METHODS: A network pharmacology approach was employed to identify the targets of osteoporosis and Panax japonicus. Cytoscape 3.7.2 and DAVID were used to visualize the pharmacological mechanism of Panax japonicus in treating osteoporosis by building up compound-target and protein-protein interaction (PPI) networks and conducting Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. An ovariectomized SD rat osteoporosis model was used to assess the potential therapeutic effect of Panax japonicus in vivo. The biomechanical properties, pathological changes, inflammatory cytokines, bone density, and bone microstructural parameters in rat bone tissue were carefully measured. The biochemical markers of bone metabolism in serum were detected by Enzyme-Linked Immunosorbent Assay (ELISA). RESULTS AND DISCUSSION: Fifty-two active components and sixty-five target genes of Panax japonicus involved in the treatment of osteoporosis were identified. The PPI network revealed IL-6, TNF, NR3C1, IL-1β, CASP3, ESR1, PGR, and AR to be involved in the treatment of osteoporosis with Panax japonicus. Chikusetsusaponin IVa and Radix ginsenoside-Ro were the main saponins found in Panax japonicus. Panax japonicus was found to exert potent preventive effects on osteoporosis by maintaining biomechanical properties, increasing bone mineral density, and protecting the trabecular microstructure in an ovariectomized rat osteoporosis model. Panax japonicus hindered the initiation of osteoporosis induced by ovariectomy by regulating bone metabolism and downregulating the expression of IL-6 and TNF-α. CONCLUSION: Panax japonicus was found to contain 52 compounds and 65 targets in the treatment of osteoporosis. The administration of Panax japonicus could mitigate osteoporosis in rats induced by ovariectomy, and one of the mechanisms was associated with downregulating the expression of inflammatory factors.

Animals↗
PubMed2026

In silico analysis based on network pharmacology and biomolecular informatics to explore the mechanism of action of Erjing Pills (from Shengji Zonglu) in the treatment of leukotrichia.

This study aimed to explore the core active ingredients and potential molecular mechanisms of Erjing Pills, a prescription in the classic work of Traditional Chinese Medicine, "Shengji Zonglu," in the treatment of leukotrichia by utilizing network pharmacology and biomolecular docking techniques. The chemical components and potential targets of Chinese herbal medicines were analyzed through databases such as the Traditional Chinese Medicine Systems Pharmacology Database. The targets related to leukotrichia were collected using GeneCards. The intersection targets were obtained using RStudio. The protein-protein interaction (PPI) network map and the "drug-component-target-disease" visualization network were generated using Cytoscape and STRING to screen the core components and key targets. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were carried out using the Database for Annotation, Visualization and Integrated Discovery and RStudio. Finally, molecular docking verification was performed by AutoDock and PyMOL (Schrödinger LLC). The key active ingredients of Erjing Pills in the treatment of leukotrichia are β-sitosterol, quercetin, baicalein, and stigmasterol. The top 5 PPI core target proteins, in order, are AKT serine/threonine kinase 1, interleukin 6, tumor protein p53, cysteine-aspartic acid protease 3, and interleukin 1 beta. The Gene Ontology enrichment analysis suggests that the biological processes mainly include responses to exogenous stimuli, membrane rafts, and DNA-binding transcription factor binding. The Kyoto Encyclopedia of Genes and Genomes pathways involve signal pathways such as lipid and atherosclerosis, hepatitis B, Kaposi sarcoma virus infection, chemical carcinogenesis, and human cytomegalovirus infection. The molecular docking results indicate that most of the main active ingredients in Erjing Pills have relatively stable binding activities with the key targets, such as AKT serine/threonine kinase 1, interleukin 6, tumor protein p53, cysteine-aspartic acid protease 3, and interleukin 1 beta, in the PPI network. The active ingredients of Erjing Pills may interfere with the pathological process of leukotrichia by regulating key targets and signal pathways. This study provides a theoretical basis for the clinical application of Erjing Pills and indicates the direction for subsequent experimental research.

Drugs, Chinese Herbal↗