Multi-omics analysis reveals common differentialexpressed hub genes cross multiple cancer types

Background: The utilization of pan-cancer analysis presentsa comprehensive strategy for investigating genes of interest invarious types of cancer. By identifying common differentiallyexpressed genes (DEGs) across multiple cancer types, thismethod holds promise for elucidating the relationship betweengenes or molecules and potential theories of carcinogenesis andcancer progression. Therefore, our goal is to leverage this approachto pinpoint key DEGs that may contribute to cancer developmentand progression.Materials and Methods: RNA-seq datasets associated to ۳۳different cancer types were analysed in cancer tissues comparedto normal control tissues using data from The Cancer GenomeAtlas (TCGA) and Genotype-Tissue Expression (GTEx). Further,integrative bioinformatics analysis was applied throughDAVID, STRING, Enrichr, GEPIA, cBioPortal, DIANA-microT,miRNet, TISIDB and DGIdb to unveil the hub genes rolein tumor progression and cancer-associated immunology.Results: The gene expression profiling analysis established ۲۲down-regulated and ۲۷ up-regulated genes (|log۲FC| > ۱ andadjusted q-value < ۰.۰۵) among ۱۳ cancer types. The identifieddifferentially expressed genes were mainly enriched in celldivision, regulation of chromosome segregation, nuclear division,and mitotic nuclear division. Top significant module with۲۱ nodes and ۲۰۴ edges was identified in the protein-proteininteraction network. Finally, ۴ hub-genes, including TOP۲A,RRM۲, BUB۱B and BUB۱ were detected. The high expressionlevels of the hub genes were associated with the poor survivalof the studied cancers. There were significant positive correlationsbetween BUB۱, BUB۱B, RRM۲, and TOP۲A expressionand activated CD۴ T cell (Act-CD۴) in all studied cancers.Conclusion: The pan-cancer analysis indicated TOP۲A, BUBI,BUB۱B and RRM۲ as highly expressed genes significantly correlatedwith cancer development and patient survival prognosisin multiple cancer types.

کلیدواژه ها:

Cancer ، Pan-Cancer Analysis ، Multi-omics Analysis ، Biomarker ، Differential expression