Proteomic analysis to identification of hypoxia related markers in spinal tuberculosis: a study based on weighted gene co-expression network analysis and machine learning

Abstract Objective This article aims at exploring the role of hypoxia-related genes and immune cells in spinal tuberculosis and tuberculosis involving other organs. Methods In this study, label-free quantitative proteomics analysis was performed on the intervertebral discs (fibrous cartilaginous tissues) obtained from five spinal tuberculosis (TB) patients. Key proteins associated with hypoxia were identified using molecular complex detection (MCODE), weighted gene co-expression network analysis(WGCNA), least absolute shrinkage and selection operator (LASSO), and support vector machine recursive feature Elimination (SVM-REF) methods, and their diagnostic and predictive values were assessed. Immune cell correlation analysis was then performed using the Single Sample Gene Set Enrichment Analysis (ssGSEA) method. In addition, a pharmaco-transcriptomic analysis was also performed to identify targets for treatment. Results The three genes, namely proteasome 20 S subunit beta 9 (PSMB9), signal transducer and activator of transcription 1 (STAT1), and transporter 1 (TAP1), were identified in the present study. The expression of these genes was found to be particularly high in patients with spinal TB and other extrapulmonary TB, as well as in TB and multidrug-resistant TB (p-value