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Updated: Aug 6, 2026
Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
Published on: May 17, 2019
Expression Levels of DNMT1, HDAC1, and MT1E Genes as Diagnostic Biomarkers for Breast Cancer Clinicopathological
Sedigheh Akhtartavan1, Hamid Madanchi1, Raheb Ghorbani2
1Department of Medical Biotechnology, Semnan University of Medical Sciences, Semnan, Iran, semums.ac.ir.
Background:
Breast cancer (BC) is the second leading cause of cancer-related deaths worldwide and has a high recurrence rate. This study aimed to evaluate the expression levels of three biomarkers: DNA methyltransferase 1 (DNMT1), histone deacetylase 1 (HDAC1), and metallothionein 1E (MT1E) in BC patients.
Methods:
Peripheral blood and tissue samples from 95 female BC patients and 50 age-matched (±5 years) healthy female controls were analyzed using an enzyme-linked immunosorbent assay (ELISA). Diagnostic potential was assessed through receiver operating characteristic (ROC) curve analysis. In silico analyses identified differentially expressed genes (DEGs), Gene Ontology (GO) terms, pathway enrichment, correlation analysis, miRNA-mRNA interactions, and drug-gene networks.
Results:
Both experimental and computational results revealed significantly higher levels of DNMT1 and HDAC1 but lower levels of MT1E in BC patients compared to controls (p-value < 0.0001). Furthermore, tumor expression of these genes correlated with molecular subtypes, showing distinct expression patterns in triple-negative BC (TNBC). Advanced-stage tumors also exhibited increased HDAC1 and DNMT1 expression. GO enrichment analysis indicated that these DEGs are involved in cell division, differentiation, and nuclear functions. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis linked DNMT1 to the p53 pathway, HDAC1 to mismatch repair, and MT1E to cytokine receptor interactions. The strongest gene correlations were DNMT1-ILF3, HDAC1-RBBP4, and MT1E-MT2A. Additionally, miRNA-mRNA interaction analysis revealed that DNMT1, HDAC1, and MT1E are targeted by multiple miRNAs, with the top interacting miRNAs being hsa-miR-103a-3p for DNMT1, hsa-miR-34a-5p for HDAC1, and hsa-miR-126-3p for MT1E. DNMT1 showed the highest number of miRNA interactions among the three genes. Moreover, 98 drugs were found to interact with these three genes. ROC analysis demonstrated promising diagnostic performance, with areas under the curve (AUC) of 0.916 for DNMT1, 0.792 for HDAC1, and 0.683 for MT1E (95% confidence interval [CI]).
Conclusion:
These findings suggest that DNMT1, HDAC1, and MT1E show potential as complementary biomarkers in BC diagnosis. However, further validation in larger cohorts and functional studies are warranted.
