Bioinformatics-Based Identification of Key Metabolic Genes in Breast Cancer and Survival Prognosis Analysis
Keywords:
Bioinformatics, Breast cancer, Metabolic genes, Survival analysis, PrognosisAbstract
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.
References
Chen X, Wang J, 2013, Surgery, People’s Health Publishing House, Beijing, 256.
Bai S, 2017, Epidemiological Status and Disease Characteristics of Breast Cancer in China. World Latest Medicine Information, 17(41): 253–256.
Li W, Zhao T, Jiang X, et al., 2022, Visual Analysis of Knowledge Map of Traditional Chinese Medicine Treatment of Breast Cancer Based on CNKI. Western Journal of Traditional Chinese Medicine, 35(3): 79–83.
Ge J, Jia Y, Li J, et al., 2020, Discussion on the Pathway Related to the Onset of Breast Cancer Based on Enrichment Analysis. Systems Medicine, 5(11): 29–33.
Kan B, Sun M, Yan G, et al., 2016, Clinicopathological Features and Prognostic Analysis of Breast-Conserving Surgery Patients with Breast Cancer in Different Age Groups. Progress in Modern Biomedicine, 16(34): 6733–6737.
Song C, Meng Y, Shang P, et al., 2018, Related Influencing Factors of the Occurrence, Development, and Prognosis of Breast Cancer. Journal of Hebei North University (Natural Science Edition), 34(8): 54–56.
Yu C, Pei J, 2020, Prediction of the Proportion of Infiltrating Immune Cells in Breast Cancer and Prognostic Analysis of Subtypes Based on CIBERSORT. Journal of Yunnan Minzu University (Natural Science Edition), 29(1): 78–83.
Duan Y, 2013, Clinicopathological Features and Prognostic Analysis of Different Molecular Subtypes of Breast Cancer, dissertation, Ningxia Medical University.
Wu X, Zhao M, 2005, Bioinformatics - A Guide to Basic and Clinical Medical Applications, Science Press, Beijing, 3–5.
Chu H, 2018, Application of Data Mining in Bioinformatics. Digital Technology and Application, 36(10): 123–124.
Li J, Huo F, Li Z, et al., 2023, Exploring the Mechanism of Qianghuoshenshi Decoction in the Treatment of Ankylosing Spondylitis Based on Bioinformatics and Molecular Docking Technology. Western Journal of Traditional Chinese Medicine, 36(2): 72–79.
Liu GM, Xie WX, Zhang CY, et al., 2020, Identification of a Four-Gene Metabolic Signature Predicting Overall Survival for Hepatocellular Carcinoma. Cell Physiol, 235(2): 1624–1636.
Li Z, Li F, Peng Y, et al., 2020, Identification of Three m6A-Related mRNAs Signature and Risk Score for the Prognostication of Hepatocellular Carcinoma. Cancer Med, 9(5): 1877–1889.
Zhang M, Zhu K, Pu H, et al., 2019, An Immune-Related Signature Predicts Survival in Patients with Lung Adenocarcinoma. Front Oncol, 10(9): 1314–1327.
Zhang C, Wang F, Guo F, et al., 2020, A 13-Gene Risk Score System and a Nomogram Survival Model for Predicting the Prognosis of Clear Cell Renal Cell Carcinoma. Urol Oncol, 38(3): 71–74.
Ma N, Tie C, Yu B, et al., 2020, Identifying lncRNA-miRNA-mRNA Networks to Investigate Alzheimer’s Disease Pathogenesis and Therapy Strategy. Aging (Albany NY), 12(3): 2897–2920.
George B, Seals S, Aban I, 2014, Survival Analysis and Regression Models. J Nucl Cardiol, 21(4): 686–694.
Wang J, Chen X, Tian Y, et al., 2020, Six-Gene Signature for Predicting Survival in Patients with Head and Neck Squamous Cell Carcinoma. Aging (Albany NY), 12(1): 767–783.
Jung CR, Choi S, Im DS, 2007, The NS5A Protein of Hepatitis C Virus Represses Gene Expression of hRPB10alpha, a Common Subunit of Host RNA Polymerases, Through Interferon Regulatory Factor-1 Binding Site. Virus Res, 129(2): 155–165.
Lin Y, Li Z, Ozsolak F, et al., 2012, An In-Depth Map of Polyadenylation Sites in Cancer. Nucleic Acids Res, 40(17): 8460–8471.
Liu Y, Zhu X, Zhu J, et al., 2007, Identification of Differential Expression of Genes in Hepatocellular Carcinoma by Suppression Subtractive Hybridization Combined cDNA Microarray. Oncol Rep, 18(4): 943–951.
Kontorovich T, Levy A, Korostishevsky M, et al., 2010, Single Nucleotide Polymorphisms in miRNA Binding Sites and miRNA Genes as Breast/Ovarian Cancer Risk Modifiers in Jewish High-Risk Women. Int J Cancer, 127(3): 589–597.
Pottorf T, Mann A, Fross S, et al., 2018, Nicotinamide Mononucleotide Adenylyltransferase 2 Maintains Neuronal Structural Integrity Through the Maintenance of Golgi Structure. Neurochem Int, 12(1): 86–97.
Mayer PR, Huang N, Dewey CM, et al., 2010, Expression, Localization, and Biochemical Characterization of Nicotinamide Mononucleotide Adenylyltransferase 2. J Biol Chem, 285(51): 40387–40396.
Cui C, Qi J, Deng Q, et al., 2016, Nicotinamide Mononucleotide Adenylyl Transferase 2: A Promising Diagnostic and Therapeutic Target for Colorectal Cancer. Biomed Res Int, 29(8): 180–196.
Zhao Y, Tian J, Sui S, et al., 2017, Loss of succinyl-CoA Synthase ADP-Forming β Subunit Disrupts mtDNA Stability and Mitochondrial Dynamics in Neurons. Sci Rep, 7(1): 7169–7177.
Miller C, Wang L, Ostergaard E, et al., 2011, The Interplay Between SUCLA2, SUCLG2, and Mitochondrial DNA Depletion. Biochim Biophys Acta, 1812(5): 625–629.