Senescence-driven molecular subtyping in pancreatic cancer: a multi-omics framework for precision medicine.
[BACKGROUND] Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy with a five-year survival rate below 15%, largely due to tumor heterogeneity and limited therapeutic options.
APA
Shi M, Li P, et al. (2025). Senescence-driven molecular subtyping in pancreatic cancer: a multi-omics framework for precision medicine.. BMC cancer, 26(1), 99. https://doi.org/10.1186/s12885-025-15341-z
MLA
Shi M, et al.. "Senescence-driven molecular subtyping in pancreatic cancer: a multi-omics framework for precision medicine.." BMC cancer, vol. 26, no. 1, 2025, pp. 99.
PMID
41398222
Abstract
[BACKGROUND] Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy with a five-year survival rate below 15%, largely due to tumor heterogeneity and limited therapeutic options. While senescence-related genes (SRGs) are implicated in cancer progression, their pancreas-specific roles in PDAC subtyping and treatment remain unexplored.
[METHODS] We integrated multi-omics data (RNA-seq, ATAC-seq, and whole-genome sequencing) from 402 pancreas-specific SRGs to classify PDAC subtypes through unsupervised clustering. Independent validation cohorts (TCGA-PAAD, = 183; patient-derived organoids, = 40) and drug sensitivity screens were used to define subtype-specific therapeutic vulnerabilities. A machine learning-based random forest model identified key SRG biomarkers for clinical stratification.
[RESULTS] Three distinct PDAC subtypes were identified: Cluster 1, characterized by extensive immune infiltration; Cluster 2, mixed features with moderate prognosis; and Cluster 3, defined by significant metabolic reprogramming. Drug screens revealed Cluster 3 as uniquely sensitive to Metformin and Trametinib, suggesting combinatory therapy potential. A 20-gene random forest classifier achieved high accuracy in subtype prediction (AUC = 0.96).
[CONCLUSION] This study establishes the first pancreas-specific SRG-driven classification of PDAC, resolving prior inconsistencies in Metformin trial outcomes. Our framework enables risk stratification and subtype-guided therapy, with immediate clinical implications: Metabolic-targeting agents (Metformin) may benefit the high-risk Cluster 3, while immunotherapy could be prioritized for Cluster 1.
[SUPPLEMENTARY INFORMATION] The online version contains supplementary material available at 10.1186/s12885-025-15341-z.
[METHODS] We integrated multi-omics data (RNA-seq, ATAC-seq, and whole-genome sequencing) from 402 pancreas-specific SRGs to classify PDAC subtypes through unsupervised clustering. Independent validation cohorts (TCGA-PAAD, = 183; patient-derived organoids, = 40) and drug sensitivity screens were used to define subtype-specific therapeutic vulnerabilities. A machine learning-based random forest model identified key SRG biomarkers for clinical stratification.
[RESULTS] Three distinct PDAC subtypes were identified: Cluster 1, characterized by extensive immune infiltration; Cluster 2, mixed features with moderate prognosis; and Cluster 3, defined by significant metabolic reprogramming. Drug screens revealed Cluster 3 as uniquely sensitive to Metformin and Trametinib, suggesting combinatory therapy potential. A 20-gene random forest classifier achieved high accuracy in subtype prediction (AUC = 0.96).
[CONCLUSION] This study establishes the first pancreas-specific SRG-driven classification of PDAC, resolving prior inconsistencies in Metformin trial outcomes. Our framework enables risk stratification and subtype-guided therapy, with immediate clinical implications: Metabolic-targeting agents (Metformin) may benefit the high-risk Cluster 3, while immunotherapy could be prioritized for Cluster 1.
[SUPPLEMENTARY INFORMATION] The online version contains supplementary material available at 10.1186/s12885-025-15341-z.
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