Distinguish active tuberculosis with an immune-related signature and molecule subtypes: a multi-cohort analysis.

ABSTRACT:

Background

Distinguishing latent tuberculosis infection (LTBI) from active tuberculosis (ATB) is very important. This study aims to analyze cases from multiple cohorts and get the signature that can distinguish LTBI from ATB.

Methods

Thirteen datasets were downloaded from the gene expression omnibus (GEO) database. Three datasets were selected as discovery datasets, and the hub genes were discovered through WGCNA. In the training cohort, we use machine learning to establish the signature, verify the authentication ability of the signature in the remaining datasets, and compare it with other signatures. Cluster analysis was carried out on ATB cases, immune cell infiltration analysis, GSVA analysis, and drug sensitivity analysis were carried out on different clusters.

Resul

SUBMITTER: Shan Q 

PROVIDER: S-EPMC11605007 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature