Objectives: Clear cell renal cell carcinoma (ccRCC) is a highly prevalent subtype of malignant renal tumor, but unfortunately, the survival rate remains unsatisfactory. The aim of the present study is to explore genomic features that are correlated with cancer stage, allowing for the identification of subgroups of ccRCC patients with high risk of unfavorable outcomes and enabling prompt intervention and treatment.
Methods: We compared the gene expression levels across ccRCC patients with diverse cancer stages from The Cancer Genome Atlas (TCGA) database, which revealed characteristic genes associated with tumor stage. We then extracted prognostic genes and used least absolute shrinkage selection operator (LASSO) regression to select four genes for feature extraction and the construction of a prognostic risk model.
Results: We have identified a total of 171 differentially expressed genes (DEGs) that are closely linked to the tumor stage of ccRCC through difference analysis. A prognostic risk model constructed based on the expression levels of ZIC2, TFAP2A-AS1, ITPKA, and SLC16A12 holds significant prognostic value in ccRCC. The results of the functional enrichment analysis imply that the DEGs are mainly involved in the regulation of immune-related signaling pathways, and therefore may have a significant function in immune system regulation of ccRCC.
Conclusions: Our study has successfully identified significant DEGs between high- and low-staging groups of ccRCC using bioinformatics methods. The construction of a prognostic risk model based on the expression levels of ZIC2, TFAP2A-AS1, ITPKA, and SLC16A12 has displayed promising prognostic significance, indicating its valuable potential for clinical application.
Keywords: Clear cell renal cell carcinoma; bioinformatics; cancer staging; differentially expressed genes; prognosis.
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