Integrated machine learning and causal inference reveal CHRDL1 as a diagnostic and functional biomarker in NSCLC.

ABSTRACT:

Background

Non-small cell lung cancer (NSCLC) accounts for the majority of lung cancer-related deaths, largely due to late diagnosis and limited early biomarkers. Integrating machine learning (ML) with causal inference may improve biomarker discovery and translational potential.

Methods

Six NSCLC transcriptomic datasets were integrated and normalized. Differentially expressed genes were intersected with WGCNA modules to identify candidate features. A total of 114 ML models were constructed using various feature selection and classification algorithms, with model performance evaluated by area under the ROC curve (AUC). Immune cell infiltration was assessed using CIBERSORT. Causal relationships were evaluated via two-sample Mendelian randomization (MR) using pQTL and GWAS data

SUBMITTER: Shen C 

PROVIDER: S-EPMC12748480 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature