A Pathway-Based Strategy to Identify Biomarkers for Lung Cancer Diagnosis and Prognosis.

ABSTRACT: Current research has identified several potential biomarkers for lung cancer diagnosis or prognosis. However, most of these biomarkers are derived from a relatively small number of samples using algorithms at the gene level. Hence, gene expression signatures discovered in these studies have little overlaps. In this study, we proposed a new strategy to identify biomarkers from multiple datasets at the pathway level. We integrated the genome-wide expression data of lung cancer tissues from 13 published studies and applied our strategy to identify lung cancer diagnostic and prognostic biomarkers. We identified a 32-gene signature that differentiates lung adenocarcinomas from other lung cancer subtypes. We also discovered a 43-gene signature that can predict the outcome of human lung cancers.

SUBMITTER: Sheng M 

PROVIDER: S-EPMC6431770 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature