Integrating transcriptomics and hybrid machine learning enables high-accuracy diagnostic modeling for nasopharyngeal carcinoma.

ABSTRACT:

Background

Nasopharyngeal carcinoma (NPC) lacks biomarkers demonstrating both high specificity and sensitivity for early diagnosis. This study aimed to develop robust machine learning (ML)-driven diagnostic models and identify key biomarkers through integrated analysis of multi-cohort transcriptomic data.

Methods

Seven NPC transcriptomic datasets (GSE12452, GSE40290, GSE53819, and GSE64634 were merged to form the training cohort, while GSE13597, GSE34573, and GSE61218 served as independent external validation sets) were integrated and preprocessed using ComBat for batch effect correction. Differential expression analysis identified 293 differentially expressed genes (DEGs). Twelve ML algorithms (including Stepglm, glmBoost, and RF) were systematically combined into 113 disti

SUBMITTER: Wang H 

PROVIDER: S-EPMC12162433 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature