ArticleJournal of gastrointestinal oncology2025
The value of lipid metabolism-related genes in pancreatic cancer immunotherapy and drug prediction.
Article in Journal of gastrointestinal oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Authors and funding
6 authors.
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Abstract
Background: Pancreatic cancer represents a significant global health burden. Although dysregulated lipid metabolism and its associated inflammation drive tumorigenesis, their molecular interplay remains incompletely understood. This bioinformatics study investigates lipid metabolism-related genes (LMRGs) for prognostic prediction and treatment guidance in pancreatic cancer. Methods: LMRGs were obtained from the Gene Set Enrichment Analysis (GSEA) database, while messenger ribonucleic acid (mRNA) expression profiles and clinical information were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and International Cancer Genome Consortium (ICGC) databases. Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression analysis were employed to screen prognosis-related genes, followed by the construction of a risk prediction model. Patients were stratified into high- and low-risk groups for prognosis and immune infiltration comparison. Potential therapeutic drugs for pancreatic cancer were predicted using the DSigDB database based on the identified LMRGs. Results: We successfully established and validated a prognostic prediction model for pancreatic cancer patients based on six LMRGs ( Conclusions: The risk score based on the six LMRGs provides prognostic insights for pancreatic cancer. High-risk pancreatic cancer populations are potentially associated with an immunosuppressive microenvironment. Candidate drugs screened based on LMRGs offer new possibilities for personalized treatment of pancreatic cancer.
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