MicroRNA as a promising molecular biomarker in the diagnosis of breast cancer

Abstract

Introduction: Breast cancer represents the most prevalent malignancy among women. Recent advancements in translational research have focused on the identification of novel biomarkers capable of providing valuable insights into patient outcomes. Furthermore, comprehensive investigations aimed at discovering novel miRNAs, unraveling their biological functions, and deciphering their target genes have significantly contributed to our understanding of the roles miRNAs play in tumorigenesis. Consequently, these investigations have facilitated the way for the development of miRNA-based approaches for breast cancer prognosis, diagnosis, and treatment. However, conducting a more extensive array of studies, particularly among diverse ethnic groups, is imperative to expand the scope of research and validate the significance of miRNAs. This study aimed to assess the expression patterns of circulating miRNAs in plasma as a prospective biomarker for breast cancer patients within a population primarily consisting of individuals from Black, Indigenous, and People of Color (BIPOC) communities. Methods: We evaluated 49 patients with breast cancer compared to 44 healthy women. Results and discussion: All miRNAs analyzed in the plasma of patients with breast cancer were downregulated. ROC curve analysis of miR-21 (AUC = 0.798, 95% CI: 0.682-0.914, p <0.0001), miR-1 (AUC = 0.742, 95% CI: 0.576-0.909, p = 0.004), miR-16 (AUC = 0.721, 95% CI: 0.581-0.861, p = 0.002) and miR-195 (AUC = 0.672, 95% CI: 0.553-0.792, p = 0.004) showed better diagnostic accuracy in discrimination of breast cancer patients in comparison with healthy women. miR-210, miR-21 showed the highest specificities values (97.3%, 94.1%, respectively). Following, miR-10b and miR-195 showed the highest sensitivity values (89.3%, and 77.8%, respectively). The panel with a combination of four miRNAs (miR-195 + miR-210 + miR-21 + miR-16) had an AUC of 0.898 (0.765-0.970), a sensitivity of 71.4%, and a specificity of 100.0%. Collectively, our results highlight the miRNA combination in panels drastically improves the results and showed high accuracy for the diagnosis of breast cancer displaying good sensitivity and specificity.

Keywords: cancer diagnosis; circulating miRNA; liquid biopsy; oncology; personalized medicine.

FIGURE 1

Circulating levels of miR-210 (A), miR-195 (B), miR-34a (C), miR-16 (D), miR-21 (E), miR-10b (F), miR-1 (G) in healthy control group compared with patients with breast cancer. Data are graphically represented as median and interquartile range (IQR). Abbreviation: *p < 0.05.

FIGURE 2

ROC curve for miR-210 (A), miR-195 (B), miR-34a (C), miR-16 (D), miR-21 (E), miR-10b (F), miR -1 (G), according to diagnosis. Abbreviation: AUC: Area under the ROC curve.

FIGURE 3

Correlogram representing the expression correlation matrix of the seven microRNAs. The color of the cells varies according to the magnitude of correlation, ranging from dark red for positive correlations to dark blue for negative correlations. Abbreviation: * Correlation is significant at 0.01 level.

FIGURE 4

ROC curve for miRNA panels miR-195 + miR-210 + miR-21 + miR-16 (A), miR-195 + miR-210 + miR-21 (B), miR-21 + miR-16 (C), according to diagnosis. Abbreviation: AUC: Area under the ROC curve.

FIGURE 5

Heat map of biological pathway analysis using Kyoto Encyclopedia of Genes and Genomes (A) and Gene Ontology (B). The color of the cells varies according to p-value, expressed in log10, ranging from yellow to red.

FIGURE 6

Simultaneous analysis of predicted target genes for miRNAs. Blue squares represent miR-210, miR-195, miR-21, and miR-16. Pink circles represent target genes. Each gray line represents a link between a gene and a miRNA in this network map.