Background: Lung cancer is one of the most dangerous cancers and tuberculosis is one of the deadliest infectious diseases in the world. Many studies have confirmed the connection between lung cancer and tuberculosis, and also the microRNAs (miRNAs) that play a major role in the development of these two diseases. This study aims to use different databases to find effective miRNAs and their role in different genes in lung and tuberculosis diseases. It also aims to determine the role of miR-34a and miR-182 in lung cancer and tuberculosis. Methods: Using the Gene Expression Omnibus (GEO) database, the influential miRNA databases were studied in the two diseases. Finally, considering bioinformatics results and literature studies, two miR-34a and miR-182 were selected. The role of these miRNAs and their target genes was carefully evaluated using bioinformatics. The expression of miRNAs in the plasma of patients with lung cancer and tuberculosis and healthy individuals was investigated. Results: According to the GEO database, miR-34a and miR-182 are miRNAs that affect tuberculosis and lung cancer. By checking the miRBase, miRcode, DIANA, miRDB, galaxy, Kyoto Encyclopedia of Genes and Genomes databases, the role of these miRNAs on genes and different molecular pathways and their effect on these miRNAs were mentioned. The results of the present study showed that the expression of miR-34a and miR-182 was lower than that of healthy people. The p-value for miR-182 was <0.0001 and for miR-34a was 0.3380. Conclusion Reducing the expression pattern of these miRNAs indicates their role in lung cancer and tuberculosis occurrence. Therefore, these miRNAs can be used as a biomarker for prognosis, diagnosis, and treatment methods.

Non–small cell lung cancer (NSCLC) is an important cause of cancer-associated deaths worldwide. Therefore, the exact molecular mechanisms of NSCLC are unidentified. The present investigation aims to identify the miRNAs with predictive value in NSCLC. The two datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DEmiRNA) and mRNAs (DEmRNA) were selected from the normalized data. Next, miRNA-mRNA interactions were determined. Then, co-expression network analysis was completed using the WGCNA package in R software. The co-expression network between DEmiRNAs and DEmRNAs was calculated to prioritize the miRNAs. Next, the enrichment analysis was performed for DEmiRNA and DEmRNA. Finally, the drug-gene interaction network was constructed by importing the gene list to dgidb database. A total of 3,033 differentially expressed genes and 58 DE miRNA were recognized from two datasets. The co-expression network analysis was utilized to build a gene co-expression network. Next, four modules were selected based on the Zsummary score. In the next step, a bipartite miRNA-gene network was constructed and hub miRNAs (let-7a-2-3p, let-7d-5p, let-7b-5p, let-7a-5p, and let-7b-3p) were selected. Finally, a drug-gene network was constructed while SUNITINIB, MEDROXYPROGESTERONE ACETATE, DOFETILIDE, HALOPERIDOL, and CALCITRIOL drugs were recognized as a beneficial drug in NSCLC. The hub miRNAs and repurposed drugs may act a vital role in NSCLC progression and treatment, respectively; however, these results must validate in further clinical and experimental assessments.