Enhanced prognostic signature for lung adenocarcinoma through integration of adjacent normal and tumor gene expressions.

ABSTRACT:

Background

Cancer prognosis-related signatures have traditionally been constructed based on gene expression profiles derived from tumor or normal tissues. However, the potential benefits of incorporating gene expression profiles from both tumor and normal tissues to improve signature performance have not been explored.

Methods

In this study, we developed three prognostic models for lung adenocarcinoma (LUAD) using gene expression profiles from tumor tissues, normal tissues, and a combination (COM) of both, sourced from The Cancer Genome Atlas (TCGA). To ensure comparability, the same workflow was followed for all three models.

Results

When applied to the TCGA LUAD dataset, the tumor-derived model exhibited the best overall performance, except in calibration analysis,

SUBMITTER: Hao M 

PROVIDER: S-EPMC11466592 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature