Coexpression Network Analysis-Based Identification of Critical Genes Differentiating between Latent and Active Tuberculosis

Abstract

Background and Objectives. The identification of reliable biomarkers is critical to the diagnosis and prevention of progression from latent tuberculosis infection (LTBI) to active tuberculosis (ATB). This study was thus developed to identify key hub genes capable of differentiating between LTBI and ATB through a weighted gene coexpression network analysis (WGCNA) approach. Methods. Three Gene Expression Omnibus (GEO) microarray datasets (GSE19491, GSE98461, and GSE152532) were downloaded, with GSE19491 and GSE98461 then being merged to form a training dataset. Hub genes capable of differentiating between ATB and LTBI were then identified through differential expression analyses and a WGCNA analysis of this training dataset. Receiver operating characteristic (ROC) curves were then used to gauge to the diagnostic accuracy of these hub genes in the test dataset (GSE152532). Gene expression-based immune cell infiltration and the relationship between such infiltration and hub gene expression were further assessed via a single-sample gene set enrichment analysis (ssGSEA). Results. In total, 485 differentially expressed genes were analyzed, with the WGCNA approach yielding 8 coexpression models. Of these, the black module was the most closely correlated with ATB. In total, five hub genes (FBXO6, ATF3, GBP1, GBP4, and GBP5) were identified as potential biomarkers associated with LTBI progression to ATB based on a combination of differential expression and LASSO analyses. The area under the ROC curve values for these five genes ranged from 0.8 to 0.9 in the test dataset, and ssGSEA revealed the expression of these genes to be negatively correlated with lymphocyte activity but positively correlated with myeloid and inflammatory cell activity. Conclusion. The five hub genes identified in this study may play a novel role in tuberculosis-related immunopathology and offer value as novel biomarkers differentiating LTBI from ATB.

Significance of Oxidative Stress in the Diagnosis and Subtype Classification of Intervertebral Disc Degeneration

“…Therefore, targeting OS to restore redox homeostasis in IVD cells and thus improve IVDD is considered a promising therapeutic approach ( In our research, we comprehensively evaluated the role of oxidative stress-related genes (OSRGs) in the diagnosis and subtype classi cation of IVDD based on the Gene Expression Omnibus (GEO). Then we built a nomogram model for the six core OSRGs (ATP7A, MELK, NCF1, NOX1, RHOB, and SP1) to predict the prevalence of IVDD (Chen et al, 2022;Wu et al, 2022). Finally, we developed different OSRG clusters and gene clusters and found that they are in agreement with the expression landscape of IVDD-related genes, which suggests that OSRGs could be exploited for early diagnosis and classi cation of IVDD.…”