A LncRNA-miRNA-mRNA ceRNA regulatory network based tuberculosis prediction model

Area of Science:

• Molecular Biology
• Genomics
• Immunology

Background:

• Tuberculosis (TB) poses a significant global health burden, necessitating research into its developmental mechanisms.
• Identifying reliable biomarkers is crucial for the early diagnosis and effective management of TB.

Purpose of the Study:

• To analyze the lncRNA-miRNA-mRNA regulatory network in TB.
• To identify key molecular nodes and potential biomarkers for early TB diagnosis.

Main Methods:

• Utilized gene expression profiles (GSE34608) for TB patients and healthy individuals.
• Applied Weighted Gene Co-expression Network Analysis (WGCNA) to construct a lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network.
• Employed Gene Ontology (GO), Gene Set Enrichment Analysis (GSEA), LASSO, and ROC curve analysis for functional enrichment and model evaluation.

Main Results:

• Identified significant differentially expressed mRNAs, lncRNAs, and miRNAs between TB patients and healthy controls.
• Constructed a ceRNA network involving 8 DE-mRNAs, 14 DE-lncRNAs, and 3 DE-miRNAs, enriched in neutrophil-mediated immune responses.
• Developed a prediction model with potential diagnostic value (AUC > 0.7) using has-miR-140-5p, has-miR-142-3p, HMGA1, and CAPN1.

Conclusions:

• The lncRNA-miRNA-mRNA ceRNA regulatory network offers novel insights into TB pathogenesis.
• Specific miRNAs and mRNAs show potential as biomarkers for early TB detection.
• This network provides a new perspective for TB diagnosis and therapeutic strategies.