Machine learning-driven discovery of NETs-associated diagnostic biomarkers and molecular subtypes in tuberculosis.

ABSTRACT:

Object

NETs constitute a pivotal mechanism in the pathogenesis and progression of TB. Despite their recognized importance, the genetic underpinnings of NETs in TB remain inadequately elucidated. Accordingly, the present study endeavors to delineate the molecular characteristics of NRGs in TB, with the objective of reliably identifying associated molecular clusters and biomarkers.

Methods

Gene expression profiles were analyzed from integrated datasets retrieved from the GEO database. Differential analysis, WGCNA, and an ensemble of 113 machine learning algorithms were employed to identify the core NETs genes. Subsequently, TB patients were stratified into distinct subtypes based on the expression profiles of these core genes, and the differences in immune infiltration charact

SUBMITTER: Ding S 

PROVIDER: S-EPMC12521227 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature