Identification and validation of a T-cell-related MIR600HG/hsa-mir-21-5p competing endogenous RNA network in tuberculosis activation based on integrated bioinformatics approaches.

T cells play critical roles in the progression of tuberculosis (TB); however, knowledge regarding these molecular mechanisms remains inadequate. This study constructed a critical ceRNA network was constructed to identify the potentially important role of TB activation T-cell regulation. We performed integrated bioinformatics analysis in a randomly selected training set from the GSE37250 dataset. After estimating the abundance of 18 types of T cells using ImmuCellAI, critical T-cell subsets were determined by their diagnostic accuracy in distinguishing active from latent TB. We then identified the critical genes associated with T-cell subsets in TB activation through co-expression analysis and PPI network prediction. Then, the ceRNA network was constructed based on RNA complementarity detection on the DIANA-LncBase and mirDIP platform. The gene biomarkers included in the ceRNA network were lncRNA, miRNA, and targeting mRNA. We then applied an elastic net regression model to develop a diagnostic classifier to assess the significance of the gene biomarkers in clinical applications. Internal and external validations were performed to assess the repeatability and generalizability. We identified CD4 T, Tr1, nTreg, iTreg, and Tfh as T cells critical for TB activation. A ceRNA network mediated by the MIR600HG/hsa-mir-21-5p axis was constructed, in which the significant gene cluster regulated the critical T subsets in TB activation. MIR600HG, hsa-mir-21-5p, and five targeting mRNAs (BCL11B, ETS1, EPHA4, KLF12, and KMT2A) were identified as gene biomarkers. The elastic net diagnostic classifier accurately distinguished active TB from latent. The validation analysis confirmed that our findings had high generalizability in different host background cases. The findings of this study provided novel insight into the underlying mechanisms of TB activation and identifying prospective biomarkers for clinical applications.