Construction of a 6-gene prognostic signature to assess prognosis of patients with pancreatic cancer. Issue 37 (11th September 2020)

• Record Type: Journal Article
• Authors: Yang, Jiayue; Shi, Wei; Zhu, Shengwei; Yang, Cheng
• Other Names: Pezzilli. Raffaele section editor.
• Is Part Of: Medicine. Volume 99:Issue 37(2020)
• Publication Date: 2020-09-11
• Subjects: Cox regression analysis -- pancreatic cancer -- prognosis -- WGCNA; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000022092
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract : Abstract: Pancreatic cancer (PaCa) is one of the most fatal cancers in the world. Although great efforts have made to explore the mechanisms of PaCa oncogenesis, the prognosis of PaCa patients is still unsatisfactory. Thus, it is imperative to further understand the potential carcinogenesis of PaCa and reliable prognostic models. The gene expression profile and clinical information of GSE21501 were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was applied to explore the potent genes associated with the overall survival (OS) events of PaCa patients. Cox regression model was applied to selecting prognostic genes and establish prognostic model. The prognostic values of six-gene signature were validated in TCGA-PAAD cohort. According to the WGCNA analysis, a total of 19 modules were identified and 115 hub genes in the mostly associated module were reserved for next analysis. According to the univariate and multivariate Cox regression analysis, we established a six-gene signature ( FTSJ3, STAT1, STX2, CDX2, RASSF4, MACF1 ) which could effectively evaluate the overall survival (OS) of PaCa patients. In validated patients' cohorts, the six-gene signature exhibited excellent prognostic value in TCGA-PAAD cohort as well. We developed a six-gene signature to exactly predict OS of PaCa patients and provide a novel personalized strategy for evaluating prognosis. The findings may be contributed to medical customization and therapeutic decision in clinical practice. Abstract : Supplemental Digital Content is available in the text