Comprehensive analysis of mitochondrial unfolded protein response related genes for prognosis and therapeutic response in pancreatic cancer.
Pancreatic cancer (PC) is a highly aggressive malignancy of the digestive system, with an extremely poor prognosis. The mitochondrial unfolded protein response (UPRmt) can maintain mitochondrial homeostasis and promote tumor progression and chemotherapy resistance. Nevertheless, the functions of UPRmt-related genes (MRGs) in PC remain undefined.
Gene expression data were obtained from TCGA, GEO, and CPTAC databases. Consensus clustering was performed based on MRGs, with subsequent evaluation of immune infiltration patterns across clusters. Prognostic MRGs were identified using three machine learning algorithms: LASSO regression, Random Survival Forest (RSF), and Extreme Gradient Boosting (XGBoost), combined with Cox regression analysis to establish a MRGs risk score (MRS). Quantitative real-time PCR (qRT-PCR) and western blotting were employed to validate potential mechanisms. Drug sensitivity profiling distinguished therapeutic responses between risk groups. Finally, we developed an MRS-based prognostic nomogram and validated it in multiple cohorts.
PC patients were stratified into two distinct UPRmt clusters with notable differences in overall survival (OS) and immune cell infiltration. Through screening, we established a novel MRS based on three prognostic core genes (CAT, CEBPB, and PRKN). High MRS patients showed significantly poorer OS compared to low MRS patients. We observed marked differences in drug sensitivity between subgroups and further predicted potential therapeutic agents targeting MRS. The prognostic nomogram based on MRS demonstrated strong predictive accuracy for 1-, 2-, and 3-year OS across both training and validation PC cohorts. Furthermore, western blot analysis preliminarily validated the potential association between UPRmt and both P53 signaling and glycolysis pathways.
Our study systematically characterizes the prognostic and therapeutic implications of MRGs in PC, establishing a 3-gene MRS capable of reliably predicting OS in PC patients and exploring UPRmt potential oncogenic mechanisms. These findings provide a valuable reference for individualized therapeutic strategies in PC management.
Gene expression data were obtained from TCGA, GEO, and CPTAC databases. Consensus clustering was performed based on MRGs, with subsequent evaluation of immune infiltration patterns across clusters. Prognostic MRGs were identified using three machine learning algorithms: LASSO regression, Random Survival Forest (RSF), and Extreme Gradient Boosting (XGBoost), combined with Cox regression analysis to establish a MRGs risk score (MRS). Quantitative real-time PCR (qRT-PCR) and western blotting were employed to validate potential mechanisms. Drug sensitivity profiling distinguished therapeutic responses between risk groups. Finally, we developed an MRS-based prognostic nomogram and validated it in multiple cohorts.
PC patients were stratified into two distinct UPRmt clusters with notable differences in overall survival (OS) and immune cell infiltration. Through screening, we established a novel MRS based on three prognostic core genes (CAT, CEBPB, and PRKN). High MRS patients showed significantly poorer OS compared to low MRS patients. We observed marked differences in drug sensitivity between subgroups and further predicted potential therapeutic agents targeting MRS. The prognostic nomogram based on MRS demonstrated strong predictive accuracy for 1-, 2-, and 3-year OS across both training and validation PC cohorts. Furthermore, western blot analysis preliminarily validated the potential association between UPRmt and both P53 signaling and glycolysis pathways.
Our study systematically characterizes the prognostic and therapeutic implications of MRGs in PC, establishing a 3-gene MRS capable of reliably predicting OS in PC patients and exploring UPRmt potential oncogenic mechanisms. These findings provide a valuable reference for individualized therapeutic strategies in PC management.
Authors
Li Li, Lu Lu, Ma Ma, Zhao Zhao, Xiao Xiao, Xu Xu, Sun Sun, Wu Wu, Wang Wang, Zhao Zhao
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