Identification of Potential Exosomal miRNA-mRNA Regulatory Network Relevant to Tuberculous-Associated Lung Cancer

Background/Objectives: Tuberculosis is a risk factor for lung cancer. Previous studies have demonstrated that miRNAs derived from tuberculous pleural effusion (TPE) exosomes are involved in lung cancer progression. This study aimed to identify potential miRNA–mRNA regulatory networks associated with TPE-derived exosome exposure in the context of lung cancer. Methods: Exploratory differential expression analysis was performed using RNA-seq data from TPE-derived exosomes to select candidate miRNAs showing differential expression patterns. In an in vivo mouse xenograft tumor model, TPE-derived exosomes were administered intratumorally, followed by RNA-seq analysis to select candidate genes showing differential expression patterns. Predicted target genes of the candidate miRNAs were then overlapped with the candidate genes from the xenograft analysis to obtain overlapping common genes (OCGs). The regulation of these genes by candidate miRNAs was validated through in vitro experiments, and a miRNA-mRNA network was constructed using Cytoscape. Results: Exploratory analyses suggested differential expression patterns in 27 candidate miRNAs (19 showing increased and 8 showing decreased expression) in TPE-derived exosomes and 2340 candidate genes in xenograft tissues. A total of 4715 and 700 target genes were predicted for candidate miRNAs using miRDB and miRTarBase, respectively. Following a stepwise filtering strategy, five candidate target genes (RRAS2, RAC1, RAP1B, NOTCH2, and SMARCA4) were prioritized from the overlapping common genes (OCGs). The seven candidate miRNAs predicted to regulate these genes were subsequently used to prioritize a candidate 7-miRNA–5-mRNA regulatory network for further investigation. Conclusions: This study prioritized a candidate miRNA–mRNA regulatory network associated with TPE-derived exosome exposure in lung cancer models, providing a basis for further investigation in larger clinical cohorts and more physiologically relevant models.