抽象的

Genetic influences of major lung diseases on lung cancer: A systematic and bioinformatics approach.

Amina Rownaq, Shahinul Islam SM, Md Nurul Haque Mollah

Background: Cancer comorbidity with lung diseases is evident from epidemiological and clinical data. Fatality relationships analysis is tough to check intimately victimization by using conventional endocrinological strategies. We tend to developed tissue transcript method to determine genetic influences of lung diseases on lung cancer. Different lung diseases have significant association with the threat of lung cancer

Methods: By using two lung carcinoma microarray datasets and four lung diseases (Asthma, Bronchitis, COPD and COVID) microarray datasets, Differentially Expressed Genes (DEGs) were determined comparing normal and diseases-related lung tissue. The betweeness and interaction of DEGs was detected through the STRING tool in Cytoscape software. Gene Ontology (GO) and enriched molecular pathway analysis of DEGs were analyzed via DAVID software. We also constructed a DEG-TF and DEG-miRNA interactions networks for analysis of the gene targets of Transcription Factors (TFs) and microRNA (miRNAs) through miRNet. For survival analysis we used online SurvExpress database.

Results: One hundred nineteen DEGs are common between lung carcinoma and other lung diseases associated with the recovery of tumoral characteristics. By using protein-protein interaction network we identified 10 hub genes including RHOA, CREB1, ACLY, AGO2, DNMT3A, CD44, BCL6, MMP3, PSMA6 and HNRNPA2B1. The DEG-TF network and DEG-miRNA interactions network analyses disclosed variety of TFs (CREBBP, SP1, JUN and HDAC1) and miRNAs (has-mir-20a-5p, has-mir-155-5p, has-mir-34a-5p and has-mir-16-5p) that regulate as a key transcriptional and post-transcriptional regulators on hub DEGs. Depend on the expression of hub-DEGs the multivariate survival probability curves displayed the significant differences between the low-risk and high-risk groups in the SurvExpress web-tool and database, which reveal effective prognostic power of hub-DEGs. The comparison of the coexpression networks of carcinoma and different respiratory organ diseases allowed the identification of common property patterns with DEGs and TFs related to big tumoral processes and sign pathways, that haven´t been studied through an experiment, validate their role within the early stages of carcinoma.

Conclusion: Our studies identified and validated common cell pathways and regulatory biomolecules (TFs and miRNAs) that may contribute to the link between lung carcinoma and other lung diseases. Thus our data-driven approaches can produce new insights into disease interaction mechanisms.