TITLE:
Cross-Dataset Transcriptomic Analysis Reveals Distinct Immune Regulatory Networks in Non-Tuberculous Mycobacterial Disease
AUTHORS:
Chaoyue Liang, Hongyun Luo, Ni Zhou, Wenmei Mu, Shouqiang Ma, Shumin Yu, Zhicheng Yang, Siyi Hu
KEYWORDS:
Non-Tuberculous Mycobacteria, Transcriptomics, Immune Networks, CD36, Biomarkers, Precision Medicine
JOURNAL NAME:
Advances in Bioscience and Biotechnology,
Vol.16 No.7,
July
21,
2025
ABSTRACT: Background: Non-tuberculous mycobacterial (NTM) infections present increasing global health challenges with heterogeneous clinical manifestations and variable immune responses. Despite the growing incidence worldwide, the molecular mechanisms underlying systemic immune dysfunction in NTM disease remain poorly understood. Methods: We performed comprehensive cross-dataset transcriptomic analysis using two independent RNA-seq datasets (GSE97298 and GSE290289) comprising 65 peripheral blood samples from NTM patients and controls. Differential gene expression analysis was conducted using stringent criteria (|log2FC| > 1.3, P Results: Our analysis identified 10 commonly dysregulated genes across both datasets, forming a highly connected regulatory network with a network density of 0.267. CD36 emerged as the central hub with the highest degree centrality (0.556) and betweenness centrality (0.722), showing dataset-specific regulation patterns. The network revealed coordinated immune dysfunction characterized by downregulation of T-cell signaling components (CD3E, GZMK) and variable innate immune responses. Functional analysis demonstrated enrichment in pathogen recognition pathways, lipid metabolism, and inflammatory response regulation. Conclusions: This study provides the first comprehensive cross-dataset analysis of systemic immune networks in NTM disease, identifying CD36 as a central network hub with variable expression patterns. Our findings suggest molecular heterogeneity in NTM disease and identify potential biomarkers that warrant further validation in clinically well-characterized patient cohorts.