A LncRNA-miRNA-mRNA ceRNA regulatory network based tuberculosis prediction model. (September 2021) - Feng, Jinfang; Bian, Qin; He, Xianwei; Zhang, Han; He, Jiujiang
Abstract: The high incidence of tuberculosis (TB) has brought serious social burdens and it is urgent to explore the mechanism of TB development. This study was conducted to analyze the role of lncRNA-miRNA-mRNA regulatory network and its contained nodes involved in TB to identify crucial biomarkers for early diagnosis of TB. Long-noncoding RNAs (lncRNAs), messenger RNA (mRNAs) and microRNAs (miRNAs) expression profiles of TB patients and healthy individuals were downloaded from the GSE34608 dataset. Weighted gene co-expression network analysis (WGCNA) was performed to identified the key modules related to TB and the highly related mRNA-lncRNA pair in the module. Based on highly related mRNAs and lncRNAs in greenyellow module, lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network was constructed. The DE-mRNAs in the network were functionally enriched with Gene ontology (GO) and Gene set enrichment analysis (GSEA). Least absolute shrinkage and selection operator (LASSO) algorithm and receiver operating characteristic curve (ROC) were used to construct and evaluate the prediction model of TB. We identified 3267 DE-mRNAs, 484 DE-lncRNAs and 69 DE-miRNAs between the TB and healthy subjects, from which 8 DE-mRNAs, 14 DE-lncRNAs and 3 DE-miRNAs were used to construct the ceRNA network. The genes contained in the ceRNA network were mainly enriched in neutrophil mediated immune response, including neutrophil activation, degradation and signal transduction. ROC analysis revealed that has-miR-140–5p, has-miR-142–3p and the LASSO cox prediction model based on HMGA1 and CAPN1 have potential value for forecasting TB (AUC > 0.7). Hence, our study provides a new perspective from the lncRNA-miRNA-mRNA ceRNA regulatory network for TB diagnosis and treatment. Highlights: We identified a TB-related lncRNA-miRNA-mRNA regulatory network through bioinformatics analysis. LASSO-cox prediction model was an effective tool for predicting TB. We determined the predictive performance of biomarkers for predicting TB in an expanded sample.