Screening of breast cancer diagnostic and prognostic biomarkers using bioinformatics analysis

Abstract

Abstract Background Breast cancer is one of the most common malignant tumors in women, and its incidence is increasing year by year and tends to be younger, which seriously threatens women's life and health. Therefore, the search for more sensitive and specific diagnostic and prognostic biomarkers for breast cancer is an urgent need. Object Screening potential diagnostic and prognostic biomarkers of breast cancer using bioinformatics analysis. Method GEO2R was used to analyze the gene differential expression of the breast cancer chips screened in the comprehensive gene expression database, and the DAVID online analysis website was used to conduct GO enrichment analysis and KEGG pathway enrichment analysis of the differential genes and visualized them by R language. Finally, the five genes with the most significant differences were screened for further analysis, and the survival analysis of the five most significant genes was carried out through the GEPIA online analysis website, and the expression levels of these five differential genes were verified in the TCGA database and GTEx database. Result Through differential analysis of cancer samples from breast cancer patients and normal breast samples, a total of 965 differential genes were obtained, 833 were down-regulated and 132 were up-regulated. Differentially expressed genes were enriched for different GO subsets such as angiogenesis, plasma membrane, and integrin binding, PPAR signaling pathways, regulation of lipolysis in adipocytes, and glycerollipid metabolism. The five genes with the most significant differences were CA4, PLIN4, GPD1, TUSC5, and S100B, and the expression levels of these five genes in breast cancer tissues were lower than those in normal breast tissues. GEPIA online analysis of the five most significantly differentially expressed genes, we found that the gene S100B has a significant relationship with the prognosis of patients. The higher the expression of the S100B gene, the better the prognosis of the patient. However, the expression levels of CA4, PLIN4, GPD1, and TUSC5 genes were not significantly associated with the prognosis of patients. The expression levels of these five genes in the TCGA database and GTEx database were down-regulated in breast cancer, and there was statistical significance. Conclusion The five most significantly differentially expressed genes, CA4, PLIN4, GPD1, TUSC5, and S100B, are expected to be potential diagnostic biomarkers for breast cancer patients. The gene S100B has prognostic value for breast cancer patients. The higher the expression of the S100B gene, the better the prognosis of patients.