Abstract

Pancreatic ductal adenocarcinoma (PDAC) is one of the most fatal malignancies worldwide. Extensive enhancement of glycolysis and reprogramming of lipid metabolism are both associated with the development and progression of PDAC. Previous studies have suggested that various gene signatures could convey prognostic information about PDAC. However, the use of these signatures has some limitations, perhaps because of a lack of knowledge regarding the genetic and energy supply backgrounds of PDAC. Therefore, we conducted multi-mRNA analysis based on metabolic reprogramming to identify novel signatures for accurate prognosis prediction in PDAC patients. In this study, a three-gene signature comprising MET, ENO3 and CD36 was established to predict the overall survival of PDAC patients. The three-gene signature could divide patients into high- and low-risk groups by disparities in overall survival verified by log-rank test in two independent validation cohorts and could differentiate tumors from normal tissues with excellent accuracy in four Gene Expression Omnibus (GEO) cohorts. We also found a positive correlation between the risk score of the gene signature and inherited germline mutations in PDAC predisposition genes. A glycolysis and lipid metabolism-based gene nomogram and corresponding calibration curves showed significant performance for survival prediction in the TCGA-PDAC dataset. The high-risk designation was closely connected with oncological signatures and multiple aggressiveness-related pathways, as determined by gene set enrichment analysis (GSEA). In summary, our study developed a three-gene signature and established a prognostic nomogram that objectively predicted overall survival in PDAC. The findings could provide a reference for the prediction of overall survival and could aid in individualized management for PDAC patients.

Highlights

  • Pancreatic ductal adenocarcinoma (PDAC) is one of the most invasive solid malignancies and could become the second leading source of cancer-related deaths in the United States

  • The detailed information in three eligible PDAC datasets in the Gene Expression Omnibus (GEO) database (GSE15471, GSE16515, and GSE32676) met our criteria. Analysis of these PDAC datasets revealed that 237 differentially expressed genes (DEGs) were shared among the 3 series of comparisons between tumor and adjacent paracancerous tissues in the Venn and UpSet diagrams; these DEGs were regarded as credible DEGs (Figure 2A, 2B)

  • Carbohydrate antigen 19-9 (CA19-9), CA50 and carcinoembryonic antigen (CEA) are classic serum biomarkers used for PDAC prognosis prediction

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Summary

Introduction

Pancreatic ductal adenocarcinoma (PDAC) is one of the most invasive solid malignancies and could become the second leading source of cancer-related deaths in the United States. Yan et al revealed a four-gene signature (with LYRM1, KNTC1, IGF2BP2, and CDC6) that is significantly related to the progression of pancreatic cancer through the same method [4]. This method is not suitable for highdimensional microarray data because of the limitation of overfitting. The most common transformation is enhancement of glycolysis, which enables vigorous growth of cells by generating a large variety of substrates and facilitates invasion and migration by affecting glycolytic enzymes to improve the supply of ATP [9, 10]. Lipid breakdown and fibrotic changes in the tumor microenvironment enhance the levels of FAs

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