Molecular Subtyping and Prognostic Prediction in Pancreatic Cancer Based on Mitophagy-Related Genes

TL;DR

This study identifies molecular subtypes of pancreatic cancer based on mitophagy-related genes and develops a prognostic model to predict patient survival.

Contribution

A novel prognostic model for pancreatic cancer based on mitophagy-related genes and their expression patterns.

Findings

Three distinct molecular clusters were identified based on differentially expressed mitophagy-related genes.

A prognostic model using six genes showed strong predictive performance for overall survival in pancreatic cancer patients.

The model correlated with immune cell infiltration and drug sensitivity in high-risk patients.

Abstract

Background: Pancreatic cancer (PaC) is characterized by poor prognosis. This study aimed to identify mitophagy-related clusters and develop a prognostic model for PaC.

Methods: Differentially expressed genes (DEGs) between PaC and normal tissues were identified from the TCGA and GTEx cohorts. Mitophagy-related genes (MRGs) were sourced from Reactome, GO, and KEGG databases. The intersection of DEGs and MRGs identified differentially expressed MRGs (DeMRGs). Consensus clustering identified PaC subtypes based on DeMRG expression. Univariate Cox analysis was used to find prognosis-related genes, and LASSO regression analysis was employed to develop the prognostic model. A nomogram was constructed to predict survival probabilities.

Results: A total of 7,240 DEGs were identified between PaC tissues and normal controls. From these, 12 DeMRGs were identified, and consensus clustering revealed three distinct molecular clusters. A prognostic model based on six significant genes (PAPPA, NBPF12, CXCL11, CKLF-CMTM1, CCDC6, AHNAK) was developed using LASSO regression analysis. This model demonstrated good predictive performance for overall survival in the TCGA cohort, with AUC values of 0.78, 0.74, and 0.82 for 1-, 2-, and 3-year survival in the training set, and 0.73, 0.82, and 0.73 in the validation set. External validation in independent GEO cohorts demonstrated moderate predictive performance. The nomogram demonstrated good calibration and accuracy in predicting survival. Significant correlations were found between the risk model and immune cell infiltration. High-risk patients showed higher sensitivity to dasatinib and staurosporine.

Conclusions: The study identified mitophagy-related molecular clusters and developed a prognostic model for PaC. This model may help predict overall survival and guide personalized treatment strategies for PaC patients.

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