Bioinformatics and experimental analysis identify CHN2 and MEF2C as diagnostic biomarkers for tuberculosis

The diagnosis, prevention, and treatment of tuberculosis (TB) are crucial for controlling its spread. This study aimed to identify potential pathogenic mechanisms and biomarkers for differentiating active pulmonary TB (PTB) and latent TB (LTB).

Microarray expression profiles (GSE19439, GSE19442, GSE19444) were retrieved from the GEO database, integrated, and normalized. Differentially expressed genes (DEGs) were obtained by comparing the PTB, LTB, and control groups. Functional enrichment analysis was performed, and potential biomarkers were identified using LASSO regression model, nomogram, and ROC curve analysis. Key candidate genes were further verified in a THP-1-derived macrophage infection system using qRT-PCR, Western blotting and biological assays.

A total of 4044 PTB-associated and 312 LTB-associated DEGs were identified. PTB-associated genes were significantly enriched in viral transcription and NF-κB signaling pathways, whereas LTB-related genes were associated with cellular protective responses and NK cell-mediated cytolysis. Through the 18-gene PTB panel and the 39-gene LTB panel, 13 potential biomarkers were identified, along with 12 genes validated for the second time. Experimental validation confirmed that Chimerin 2 (CHN2) is highly expressed in the PTB model and induces inflammatory responses; However, myocyte enhancer factor 2C (MEF2C) is upregulated in the LTB model and contributes to immune regulation. CHN2 may serve as a primary biomarker for PTB, while LOC653809 and MEF2C may be potential biomarkers for LTB.

Integrated bioinformatics and experimental analyses revealed distinct molecular profiles between PTB and LTB. The diagnostic models performed well, and candidate genes, particularly CHN2 and MEF2C, show promise as potential biomarkers for differential TB diagnosis.