Integrative bioinformatics identifies NSCLC biomarkers associated with LPS metabolism and circadian disruption.

ABSTRACT:

Purpose

Non-small cell lung carcinoma (NSCLC) is a leading cause of cancer-related mortality worldwide, highlighting the urgent need for early detection and targeted therapies. While lipopolysaccharide (LPS) metabolism and circadian rhythm disruption are emerging as important factors in cancer progression, their specific roles in NSCLC remain poorly understood.

Methods

We integrated multiple GEO datasets to identify NSCLC-associated differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) identified key gene modules, followed by functional enrichment analysis. A hybrid machine learning approach combining Lasso regression and random forest was used to identify hub genes. Immune infiltration analysis evaluated associations with the tumor micr

SUBMITTER: Chen Y 

PROVIDER: S-EPMC12925077 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature