Volume 2,Issue 2
Fall 2024
Bioinformatics-Based Identification of Key Metabolic Genes in Breast Cancer and Survival Prognosis Analysis
Objective: To analyze key metabolic genes in breast cancer using bioinformatics methods and conduct survival prognosis analysis. Methods: Transcriptome data for breast cancer was obtained from the Cancer Genome Atlas (TCGA) database. Relevant metabolic genes were identified using the GSEA database and matched with genes in the TCGA database to determine the final metabolic genes. The Lasso model was constructed to obtain survival prognosis analysis results. Results: Three metabolic genes related to breast cancer were identified: POLR2K, NMNAT2, and SUCLA2. Survival analysis showed that the maximum survival time for both the high-risk and low-risk groups was 24 years. Age, status, and tumor stage were identified as independent prognostic factors. Conclusion: The POLR2K gene is the most significantly overexpressed and shows a preliminary correlation with the occurrence, development, and prognosis of breast cancer. However, further experimental validation is needed to confirm these findings.
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