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Updated: Jan 25, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Identification of aberrantly methylated differentially expressed genes in breast cancer by integrated bioinformatics
Lilan Yi1, Peng Luo1, Jian Zhang1
1Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, People's Republic of China.
Background:
Abnormal DNA methylation has been demonstrated to drive breast cancer tumorigenesis. Thus, this study aimed to explore differentially expressed biomarkers driven by aberrant methylation in breast cancer and explore potential pathological mechanisms using comprehensive bioinformatics analysis.
Methods:
Gene microarray datasets of expression (GSE45827) and methylation (GSE32393) were extracted from the Gene Expression Omnibus database. Abnormally methylated differentially expressed genes (DEGs) were obtained by overlapping datasets. Functional enrichment analysis of screened genes and protein-protein interaction (PPI) networks were executed with the Search Tool for the Retrieval of Interacting Genes database. PPI networks were visualized, and hub genes were screened using Cytoscape software. The results were further verified using Oncomine and The Cancer Genome Atlas (TCGA) databases. Finally, the genetic alterations and prognostic roles of hub genes were analyzed.
Results:
In total, we found 18 hypomethylated upregulated oncogenes and 21 hypermethylated downregulated tumor suppressor genes (TSGs). These genes were mainly linked to the biological process categories of cellular component movement and cellular metabolism as well as nuclear factor-κB (NF-κB) and ataxia telangiectasia mutated (ATM) signaling pathways. Six hub genes were identified: three hypomethylated upregulated oncogenes (BCL2, KIT, and RARA) and three hypermethylated downregulated TSGs (ATM, DICER1, and DNMT1). The expression and methylation status of hub genes validated in Oncomine and TCGA databases were significantly altered and were consistent with our findings. Downregulation of BCL2, KIT, ATM, and DICER1 was closely associated with shorter overall survival in breast cancer patients. In addition, the expression levels of ATM and DICER1 were significantly distinct among different subgroups of clinical stages, molecular subtypes, and histological types.
Conclusions:
Our study reveals possible methylation-based DEGs and involved pathways in breast cancer, which could provide novel insights into underlying pathogenesis mechanisms. Abnormally methylated oncogenes and TSGs, especially ATM and DICER1, may emerge as novel biomarkers and therapeutic targets for breast cancer in the future.
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