ArticleComputational and structural biotechnology journal2025
Machine learning and gene network integration reveal prognostic subnetworks and biomarkers in pancreatic cancer.
Article in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- An Interpretable Omics-to-Image Transformer Framework for Cancer Prognosis Prediction.Computational and structural biotechnology journal · 2026Article
- Knowledge Discovery and Drug-Repurposing Framework for Pancreatic Ductal Adenocarcinoma: Molecular Networking and Computational Docking.Computational and structural biotechnology journal · 2026Article
- MUC13-Associated Molecular Interactome in Pancreatic Cancer.Computational and structural biotechnology journal · 2026Article
- Multi-cohort machine learning identifies a ferroptosis-linked prognostic signature in lung adenocarcinoma.Frontiers in bioinformatics · 2026Article
- Mathematical strategies for predicting resistant subpopulations from scRNAseq data of a PANC-1 3D tissue model: Insight into gemcitabine resistance and TGFB1-induced invasion and EMT.Computational and structural biotechnology journal · 2025Article
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Abstract
Background: Pancreatic cancer has a high mortality rate and lacks early detection markers. Advanced methods, such as machine learning (ML) and network analysis, identify central cancer networks with potential diagnostic and prognostic biomarkers, leading to improved tumor targeting strategies Methods: We systematically collected pancreatic cancer transcriptome datasets from the databases TCGA, GTEx, and GEO. Weighted gene co-expression network analysis (WGCNA) identified gene modules associated with clinical traits. Multiple machine learning-based feature selection methods (Random Forest, Support Vector Machine, LASSO, ReliefF) and differential gene expression (DGE) analysis prioritized candidate genes. Functional enrichment (Gene Ontology and KEGG pathway database) examined biological processes involved in tumor progression and immune evasion. Survival analyses evaluated prognostic significance. Results: WGCNA identified pancreatic cancer networks from key gene modules strongly associated with cancer stage and survival. Common biomarkers, including transcripts from genes Conclusion: This study identified novel regulatory cancer networks and associated biomarkers for pancreatic cancer prognosis and diagnosis by integrating WGCNA with ML, DGE, pathway, and survival analyses. An interactive web portal to explore the full results and visualizations is available at pc-biomarkers.de. Future work will further validate these biomarkers to improve early detection, prognosis, and treatment strategies.
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