Integrative analysis of TCGA data identifies hsa-miR-200a as a breast cancer-associated microRNA biomarker with...

Breast cancer is a biologically heterogeneous disease, and reliable non-invasive biomarkers are needed to improve early detection and clinical risk stratification. MicroRNAs (miRNAs) represent promising diagnostic candidates due to their regulatory roles in tumor-associated gene expression.

In this study, an integrative analysis of miRNA and mRNA expression profiles was performed using paired tumor and normal samples from the TCGA-BRCA cohort to identify diagnostically relevant miRNAs.

Differential expression analysis and receiver operating characteristic (ROC) curve analysis were used to identify miRNAs with high discriminatory capacity. Priority was given to candidates showing strong inverse correlation with predicted target genes. A logistic regression model was developed for internal validation using a held-out test set, and exploratory clinical serum analysis was conducted using qRT-PCR on serum samples from breast cancer patients and healthy controls.

Multiple upregulated miRNAs with strong discriminatory capacity (AUC values > 0.90) were identified. Among these, hsa-miR-200a was prioritized based on consistently high diagnostic accuracy and a strong inverse correlation with its predicted target gene, TNS1. hsa-miR-200a expression was elevated across pathological stages and molecular subtypes, whereas TNS1 expression was progressively reduced. The logistic regression model achieved an accuracy of 92.9% and a sensitivity of 96.7% in a held-out internal test set. Exploratory serum analysis suggested altered circulating hsa-miR-200a-5p patterns, with distinct perioperative dynamics according to clinical risk.

These findings support hsa-miR-200a as a potential breast cancer-associated miRNA biomarker candidate and highlight the utility of integrative transcriptomic and exploratory clinical serum approaches for future precision diagnostics.