Identification of differentially methylated genes as diagnostic and prognostic biomarkers of breast cancer

• Published in: World journal of surgical oncology Vol. 19; no. 1; p. 29
• Main Authors: Mao, Xiao-Hong, Ye, Qiang, Zhang, Guo-Bing, Jiang, Jin-Ying, Zhao, Hong-Ying, Shao, Yan-Fei, Ye, Zi-Qi, Xuan, Zi-Xue, Huang, Ping
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 26.01.2021; BioMed Central; BMC
• Subjects: Bioinformatics; Biological markers; Biomarkers; Biomarkers, Tumor - genetics; Breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - genetics; Care and treatment; Datasets; Deoxyribonucleic acid; Development and progression; Diagnosis; DNA; DNA Methylation; Gene Expression Regulation, Neoplastic; Genes; Genetic aspects; Health aspects; Humans; Identification and classification; Kaplan-Meier Estimate; Kinases; Methylation; Prognosis; Survival; China; Biomarkers; Breast cancer; Prognosis; Diagnosis; Methylation
• ISSN: 1477-7819; 1477-7819
• DOI: 10.1186/s12957-021-02124-6

Aberrant DNA methylation is significantly associated with breast cancer. In this study, we aimed to determine novel methylation biomarkers using a bioinformatics analysis approach that could have clinical value for breast cancer diagnosis and prognosis. Firstly, differentially methylated DNA patterns were detected in breast cancer samples by comparing publicly available datasets (GSE72245 and GSE88883). Methylation levels in 7 selected methylation biomarkers were also estimated using the online tool UALCAN. Next, we evaluated the diagnostic value of these selected biomarkers in two independent cohorts, as well as in two mixed cohorts, through ROC curve analysis. Finally, prognostic value of the selected methylation biomarkers was evaluated breast cancer by the Kaplan-Meier plot analysis. In this study, a total of 23 significant differentially methylated sites, corresponding to 9 different genes, were identified in breast cancer datasets. Among the 9 identified genes, ADCY4, CPXM1, DNM3, GNG4, MAST1, mir129-2, PRDM14, and ZNF177 were hypermethylated. Importantly, individual value of each selected methylation gene was greater than 0.9, whereas predictive value for all genes combined was 0.9998. We also found the AUC for the combined signature of 7 genes (ADCY4, CPXM1, DNM3, GNG4, MAST1, PRDM14, ZNF177) was 0.9998 [95% CI 0.9994-1], and the AUC for the combined signature of 3 genes (MAST1, PRDM14, and ZNF177) was 0.9991 [95% CI 0.9976-1]. Results from additional validation analyses showed that MAST1, PRDM14, and ZNF177 had high sensitivity, specificity, and accuracy for breast cancer diagnosis. Lastly, patient survival analysis revealed that high expression of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 were significantly associated with better overall survival. Methylation pattern of MAST1, PRDM14, and ZNF177 may represent new diagnostic biomarkers for breast cancer, while methylation of ADCY4, CPXM1, DNM3, PRDM14, PRKCB, and ZNF177 may hold prognostic potential for breast cancer.