Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis

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dc.contributor.authorMosharaf, Md. Parvez
dc.contributor.authorReza, Md. Selim
dc.contributor.authorGov, Esra
dc.contributor.authorMahumud, Rashidul Alam
dc.contributor.authorMollah, Md. Nurul Haque
dc.date.accessioned2025-01-06T17:30:25Z
dc.date.available2025-01-06T17:30:25Z
dc.date.issued2022
dc.description.abstractNon-small-cell lung cancer (NSCLC) is considered as one of the malignant cancers that causes premature death. The present study aimed to identify a few potential novel genes highlighting their functions, pathways, and regulators for diagnosis, prognosis, and therapies of NSCLC by using the integrated bioinformatics approaches. At first, we picked out 1943 DEGs between NSCLC and control samples by using the statistical LIMMA approach. Then we selected 11 DEGs (CDK1, EGFR, FYN, UBC, MYC, CCNB1, FOS, RHOB, CDC6, CDC20, and CHEK1) as the hub-DEGs (potential key genes) by the protein–protein interaction network analysis of DEGs. The DEGs and hub-DEGs regulatory network analysis commonly revealed four transcription factors (FOXC1, GATA2, YY1, and NFIC) and five miRNAs (miR-335-5p, miR-26b-5p, miR-92a-3p, miR-155-5p, and miR-16-5p) as the key transcriptional and post-transcriptional regulators of DEGs as well as hub-DEGs. We also disclosed the pathogenetic processes of NSCLC by investigating the biological processes, molecular function, cellular components, and KEGG pathways of DEGs. The multivariate survival probability curves based on the expression of hub-DEGs in the SurvExpress web-tool and database showed the significant differences between the low-and high-risk groups, which indicates strong prognostic power of hub-DEGs. Then, we explored top-ranked 5-hub-DEGs-guided repurposable drugs based on the Connectivity Map (CMap) database. Out of the selected drugs, we validated six FDA-approved launched drugs (Dinaciclib, Afatinib, Icotinib, Bosutinib, Dasatinib, and TWS-119) by molecular docking interaction analysis with the respective target proteins for the treatment against NSCLC. The detected therapeutic targets and repurposable drugs require further attention by experimental studies to establish them as potential biomarkers for precision medicine in NSCLC treatment. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
dc.identifier.doi10.3390/vaccines10050771
dc.identifier.issn2076-393X
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85130361267
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/vaccines10050771
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1600
dc.identifier.volume10
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofVaccines
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241211
dc.subjectgene expression profiles
dc.subjectintegrated bioinformatics approaches
dc.subjectmolecular signatures
dc.subjectnon-small cell lung cancer
dc.subjecttherapeutic targets and agents
dc.titleDisclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis
dc.typeArticle

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