A network-based signature to predict the survival of non-smoking lung adenocarcinoma.

ABSTRACT:

Background

A substantial increase in the number of non-smoking lung adenocarcinoma (LAC) patients has been drawing extensive attention in the past decade. However, effective biomarkers, which could guide the precise treatment, are still limited for identifying high-risk patients. Here, we provide a network-based signature to predict the survival of non-smoking LAC.

Materials and methods

Gene expression profiles were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus. Significant gene co-expression networks and hub genes were identified by Weighted Gene Co-expression Network Analysis. Potential mechanisms and pathways of co-expression networks were analyzed by Gene Ontology. The predictive signature was constructed by penalized Cox regression analysis and tes

SUBMITTER: Mao Q 

PROVIDER: S-EPMC6101016 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature