Abstract

BACKGROUND: With the development of research on cancer genomics and microenvironment, a new era of oncology focusing on the complicated gene regulation of pan-cancer research and cancer immunotherapy is emerging. This study aimed to identify the common gene expression characteristics of multiple cancers – lung cancer, liver cancer, kidney cancer, cervical cancer, and breast cancer – and the potential therapeutic targets in public databases.

MATERIAL AND METHODS: Gene expression analysis of GSE42568, GSE19188, GSE121248, GSE63514, and GSE66272 in the GEO database of multitype cancers revealed differentially expressed genes (DEGs). Then, GO analysis, KEGG function, and path enrichment analyses were performed. Hub-genes were identified by using the degree of association of protein interaction networks. Moreover, the expression of hub-genes in cancers was verified, and hub-gene-related survival analysis was conducted. Finally, infiltration levels of tumor immune cells with related genes were explored.

RESULTS: We found 12 cross DEGs in the 5 databases (screening conditions: “adj p<0.05” and “logFC>2 or logFC<–2”). The biological processes of DEGs were mainly concentrated in cell division, regulation of chromosome segregation, nuclear division, cell cycle checkpoint, and mitotic nuclear division. Furthermore, 10 hub-genes were obtained using Cytoscape: TOP2A, ECT2, RRM2, ANLN, NEK2, ASPM, BUB1B, CDK1, DTL, and PRC1. The high expression levels of the 10 genes were associated with the poor survival of these multiple cancers, as well as ASPM, may be associated with immune cell infiltration.

CONCLUSIONS: Analysis of the common DEGs of multiple cancers showed that 10 hub-genes, especially ASPM and CDK1, can become potential therapeutic targets. This study can serve as a reference to understand the characteristics of different cancers, design basket clinical trials, and create personalized treatments.

Keywords: Antineoplastic Agents, DNA, Neoplasm, Genes, vif, Immunity, Active, Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, gene ontology, Gene Regulatory Networks, Meta-Analysis as Topic, Neoplasms, Protein Interaction Maps, Reproducibility of Results, Survival Analysis