Molecular signatures of tumor progression in pancreatic adenocarcinoma identified by energy metabolism characteristics

• Published in: BMC cancer Vol. 22; no. 1; p. 404
• Main Authors: Tan, Cong, Wang, Xin, Wang, Xu, Weng, Weiwei, Ni, Shu-Juan, Zhang, Meng, Jiang, Hesheng, Wang, Lei, Huang, Dan, Sheng, Weiqi, Xu, Mi-Die
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 13.04.2022; BioMed Central; BMC
• Subjects: Adenocarcinoma; Adenocarcinoma - genetics; Analysis; Cancer; Care and treatment; Cell metabolism; Clinical trials; Cluster analysis; Datasets; Diagnosis; Energy; Energy metabolism; Energy Metabolism - genetics; Energy metabolism-related genes; Gene expression; Gene set enrichment analysis; Genomes; Health aspects; Humans; Medical prognosis; Metabolism; Metabolites; Molecular structure; Molecular subtype; Neoplastic Processes; Nomograms; Pancreas; Pancreatic adenocarcinoma; Pancreatic cancer; Pancreatic Neoplasms; Pancreatic Neoplasms - genetics; Prognosis; Prognosis signature; Risk factors; Risk groups; Software packages; Survival; Survival analysis; Tumors; Taiwan; Pancreatic adenocarcinoma; Molecular subtype; Prognosis signature; Energy metabolism-related genes
• ISSN: 1471-2407; 1471-2407
• DOI: 10.1186/s12885-022-09487-3

In this study, we performed a molecular evaluation of primary pancreatic adenocarcinoma (PAAD) based on the comprehensive analysis of energy metabolism-related gene (EMRG) expression profiles. Molecular subtypes were identified by nonnegative matrix clustering of 565 EMRGs. An overall survival (OS) predictive gene signature was developed and internally and externally validated based on three online PAAD datasets. Hub genes were identified in molecular subtypes by weighted gene correlation network analysis (WGCNA) coexpression algorithm analysis and considered as prognostic genes. LASSO cox regression was conducted to establish a robust prognostic gene model, a four-gene signature, which performed better in survival prediction than four previously reported models. In addition, a novel nomogram constructed by combining clinical features and the 4-gene signature showed high-confidence clinical utility. According to gene set enrichment analysis (GSEA), gene sets related to the high-risk group participate in the neuroactive ligand receptor interaction pathway. In summary, EMRG-based molecular subtypes and prognostic gene models may provide a novel research direction for patient stratification and trials of targeted therapies.