Abstract

Background: Pancreatic cancer is highly lethal and aggressive with increasing trend of mortality in both genders. An effective prediction model is needed to assess prognosis of patients for optimization of treatment.Materials and Methods: Seven datasets of mRNA expression and clinical data were obtained from gene expression omnibus (GEO) database. Level 3 mRNA expression and clinicopathological data were obtained from The Cancer Genome Atlas pancreatic ductal adenocarcinoma (TCGA-PAAD) dataset. Differentially expressed genes (DEGs) between pancreatic tumor and normal tissue were identified by integrated analysis of multiple GEO datasets. Univariate and Lasso Cox regression analyses were applied to identify overall survival-related DEGs and establish a prognostic gene signature whose performance was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell's concordance index (C-index) and calibration curve. GSE62452 and GSE57495 were used for external validation. Gene set enrichment analysis (GSEA) and tumor immunity analysis were applied to elucidate the molecular mechanisms and immune relevance. Multivariate Cox regression analysis was used to identify independent prognostic factors in pancreatic cancer. Finally, a prognostic nomogram was established based on the TCGA PAAD dataset.Results: A nine-gene signature comprising MET, KLK10, COL17A1, CEP55, ANKRD22, ITGB6, ARNTL2, MCOLN3, and SLC25A45 was established to predict overall survival of pancreatic cancer. The ROC curve and C-index indicated good performance of the nine-gene signature at predicting overall survival in the TCGA dataset and external validation datasets relative to classic AJCC staging. The nine-gene signature could classify patients into high- and low-risk groups with distinct overall survival and differentiate tumor from normal tissue. Univariate Cox regression revealed that the nine-gene signature was an independent prognostic factor in pancreatic cancer. The nomogram incorporating the gene signature and clinical prognostic factors was superior to AJCC staging in predicting overall survival. The high-risk group was enriched with multiple oncological signatures and aggressiveness-related pathways and associated with significantly lower levels of CD4+ T cell infiltration.Conclusion: Our study identified a nine-gene signature and established a prognostic nomogram that reliably predict overall survival in pancreatic cancer. The findings may be beneficial to therapeutic customization and medical decision-making.

Highlights

  • Pancreatic cancer is lethal and aggressive with a 5-year survival rate of only 2–9% [1]

  • GSE57495 had 63 tumor tissues that were downloaded with their associated follow-up information for subsequent validation of the prognostic gene signature [16]

  • Seven sets of differentially expressed genes (DEGs) (GSE71729, GSE62165, GSE62452, GSE28735, GSE15471, GSE16515, and GSE32676) comprised of 453, 2,449, 285, 395, 948, 1,238, and 472 DEGs were identified between tumor and normal tissues (Figure 2A; Supplementary Figures 1A–G)

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Summary

Introduction

Pancreatic cancer is lethal and aggressive with a 5-year survival rate of only 2–9% [1]. An effective prediction model is needed for the accurate assessment of patient’s prognosis In this way, efficacious treatments may be selected to balance side effects and survival benefits and to decide whether to administer more aggressive treatment. Efficacious treatments may be selected to balance side effects and survival benefits and to decide whether to administer more aggressive treatment Clinicopathological parameters such as AJCC TNM staging have been used for predicting prognosis of patients [5]. The advancement of tumor molecular biology has facilitated the development of new prediction tools based on prognosis-related genes. These prognostic markers reflecting tumor progression at molecular level may be beneficial to realize individualized survival predictions with better accuracy. An effective prediction model is needed to assess prognosis of patients for optimization of treatment

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