Identification of an autophagy-related novel signature for spinal tuberculosis: a multi-cohort machine learning study and Mendelian randomization analysis.

BACKGROUND: Spinal tuberculosis (STB) is an infectious disease caused by Mtb with unclear diagnosis and molecular mechanisms. Autophagy is reported to be associated with the pathology of spinal tuberculosis. The present study intends to elucidate the role of autophagy-related miRNAs and genes in STB.

METHODS: Core miRNAs were identified through WGCNA and differential analysis. A total of 113 machine learning algorithms were used to develop a diagnostic model. Target genes were predicted and overlapped by TargetScan, miRDB, and miRTarBase. The two-sample Mendelian randomization analysis was utilized to explore the association between genes and tuberculosis.

RESULTS: Nine autophagy-related miRNAs were identified. The GBM model yielded the best performance with the highest AUC (0.816). A signature comprising eight miRNAs, specifically miR-27b-3p and miR-27a-3p, was constructed accordingly. A nomogram was established to facilitate clinical implementation. ZFHX3 gene was indicated to be significantly associated with sequelae tuberculosis. Notably, the ZFHX3/miR-27 axis has never been reported in the realm of tuberculosis.

CONCLUSIONS: The present research established an optimal machine learning model to predict the possibility of STB, which might provide valuable insights into the diagnosis and treatment of STB. ZFHX3/miR-27 may serve as a novel potential molecular pathway in Mtb pathophysiology.