A scoring model based on bacterial lipopolysaccharide-related genes to predict prognosis in NSCLC.

ABSTRACT:

Background

Non-small cell lung cancer (NSCLC) has high incidence and mortality rates. The discovery of an effective biomarker for predicting prognosis and treatment response in patients with NSCLC is of great significance. Bacterial lipopolysaccharide-related genes (LRGs) play a critical role in tumor development and the formation of an immunosuppressive microenvironment; however, their relevance in NSCLC prognosis and immune features is yet to be discovered.

Methods

Differentially expressed LRGs associated with NSCLC prognosis were identified in the TCGA dataset. Prognostic LRG scoring and nomogram models were established using single-variable Cox regression, Least Absolute Shrinkage, and Selection Operator (LASSO) regression. The prognostic value of the scoring and nomogra

SUBMITTER: Bao N 

PROVIDER: S-EPMC11602480 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature