Identification of Novel Metabolism-Associated Subtypes for Pancreatic Cancer to Establish an Eighteen-Gene Risk Prediction Model

Abstract

Pancreatic cancer (PanC) is an intractable malignancy with a high mortality. Metabolic processes contribute to cancer progression and therapeutic responses, and histopathological subtypes are insufficient for determining prognosis and treatment strategies. In this study, PanC subtypes based on metabolism-related genes were identified and further utilized to construct a prognostic model. Using a cohort of 171 patients from The Cancer Genome Atlas (TCGA) database, transcriptome data, simple nucleotide variants (SNV), and clinical information were analyzed. We divided patients with PanC into metabolic gene-enriched and metabolic gene-desert subtypes. The metabolic gene-enriched subgroup is a high-risk subtype with worse outcomes and a higher frequency of SNVs, especially in <i>KRAS</i>. After further characterizing the subtypes, we constructed a risk score algorithm involving multiple genes (i.e., <i>NEU2</i>, <i>GMPS</i>, <i>PRIM2</i>, <i>PNPT1</i>, <i>LDHA</i>, <i>INPP4B</i>, <i>DPYD</i>, <i>PYGL</i>, <i>CA12</i>, <i>DHRS9</i>, <i>SULT1E1</i>, <i>ENPP2</i>, <i>PDE1C</i>, <i>TPH1</i>, <i>CHST12</i>, <i>POLR3GL</i>, <i>DNMT3A</i>, and <i>PGS1</i>). We verified the reproducibility and reliability of the risk score using three validation cohorts (i.e., independent datasets from TCGA, Gene Expression Omnibus, and Ensemble databases). Finally, drug prediction was completed using a ridge regression model, yielding nine candidate drugs for high-risk patients. These findings support the classification of PanC into two metabolic subtypes and further suggest that the metabolic gene-enriched subgroup is associated with worse outcomes. The newly established risk model for prognosis and therapeutic responses may improve outcomes in patients with PanC.