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Identification of Prognostic Genes and Pathways in Lung Adenocarcinoma Using a Bayesian Approach
Journal article   Open access   Peer reviewed

Identification of Prognostic Genes and Pathways in Lung Adenocarcinoma Using a Bayesian Approach

Yu Jiang, Yuan Huang, Yinhao Du, Yinjun Zhao, Jie Ren, Shuangge Ma and Cen Wu
Cancer informatics, Vol.16(15), pp.1176935116684825-1176935116684825
12/10/2020
DOI: 10.1177/1176935116684825
PMCID: PMC7736146
PMID: 33354107
url
https://doi.org/10.1177/1176935116684825View
Published (Version of record) Open Access

Abstract

Lung cancer is the leading cause of cancer-associated mortality in the United States and the world. Adenocarcinoma, the most common subtype of lung cancer, is generally diagnosed at the late stage with poor prognosis. In the past, extensive effort has been devoted to elucidating lung cancer pathogenesis and pinpointing genes associated with survival outcomes. As the progression of lung cancer is a complex process that involves coordinated actions of functionally associated genes from cancer-related pathways, there is a growing interest in simultaneous identification of both prognostic pathways and important genes within those pathways. In this study, we analyse The Cancer Genome Atlas lung adenocarcinoma data using a Bayesian approach incorporating the pathway information as well as the interconnections among genes. The top 11 pathways have been found to play significant roles in lung adenocarcinoma prognosis, including pathways in mitogen-activated protein kinase signalling, cytokine-cytokine receptor interaction, and ubiquitin-mediated proteolysis. We have also located key gene signatures such as RELB , MAP4K1 , and UBE2C . These results indicate that the Bayesian approach may facilitate discovery of important genes and pathways that are tightly associated with the survival of patients with lung adenocarcinoma.
 Bayesian approach cancer prognosis lung adenocarcinoma Methodology pathway analysis TCGA

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