AUTHOR=Hong Guo-Hu , Guan Qing , Peng Hong , Luo Xin-Hua , Mao Qing 

TITLE=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

JOURNAL=Frontiers in Genetics

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2022.979213

DOI=10.3389/fgene.2022.979213

ISSN=1664-8021

ABSTRACT=Backgroud: T cells play critical roles in TB progression, however, the investigation of molecular mechanisms remain inadequate. A critical ceRNA network was constructed, which probably played important role in TB activation via regulating T cells.
Methods: We performed integrated bioinformatics analysis in a randomly selected training set from GSE37250 datasets. After estimated the abundance of 18 types of T cell using ImmuCellAI, the critical T cell subsets were determined by their diagnostic accuracy in distinguishing active TB from latent. Through co-expression analysis and PPI network prediction, we obtained the critical genes associated with T subsets in TB activation. Then, the ceRNA network was identified base on RNA complementarity detection in DIANA-LncBase and mirDIP platform. For assessing the significance of our findings in clinical application, the gene biomarkers were determined as the lncRNA, miRNA and targeting mRNA in this ceRNA network, and the Elastic Net regression model was employed to develop a diagnostic classifier. The internal and external validation were performed to assess the repeatability and generalizability.
Results: The CD4+ T, Tr1, nTreg, iTreg and Tfh were identified as the critical T cells in TB activation. The ceRNA network mediated by MIR600HG/hsa-mir-21-5p axis was constructed, and the major gene cluster could regulated these critical T subsets in TB activation. MIR600HG, hsa-mir-21-5p and 5 targeting mRNAs (BCL11B, ETS1, EPHA4, KLF12 and KMT2A) were identified as the gene biomarkers. The Elastic Net diagnostic classifier performed excellent accuracy in distinguishing active TB from latent. The validation analysis confirmed that our findings had high generalizability in different host background cases.
Conclusion: The research provide novel insight in revealing the underlying mechanisms of TB activation and identify prospective biomarkers in clinical application.