Integrative modeling of malignant epithelial programs in EGFR-mutant LUAD via single-cell transcriptomics and multi-algorithm machine learning.

ABSTRACT:

Background

Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer, with EGFR mutations serving as key oncogenic drivers. However, patients harboring EGFR mutations exhibit considerable heterogeneity in clinical outcomes and treatment responses. Characterizing the malignant features of EGFR-mutant epithelial cells may facilitate improved stratification and personalized therapeutic strategies.

Methods

Using publicly available single-cell RNA sequencing data, malignant epithelial cells were identified in EGFR-mutant LUAD samples via inferCNV and k-means clustering. Pseudotime trajectories were constructed using Monocle2, and branch-specific genes were extracted for functional analysis. Differentially expressed genes were integrated with TCGA bulk tr

SUBMITTER: Zhang W 

PROVIDER: S-EPMC12558860 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature