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, 8 (1), 16138

Identifying a miRNA Signature for Predicting the Stage of Breast Cancer

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Identifying a miRNA Signature for Predicting the Stage of Breast Cancer

Srinivasulu Yerukala Sathipati et al. Sci Rep.

Abstract

Breast cancer is a heterogeneous disease and one of the most common cancers among women. Recently, microRNAs (miRNAs) have been used as biomarkers due to their effective role in cancer diagnosis. This study proposes a support vector machine (SVM)-based classifier SVM-BRC to categorize patients with breast cancer into early and advanced stages. SVM-BRC uses an optimal feature selection method, inheritable bi-objective combinatorial genetic algorithm, to identify a miRNA signature which is a small set of informative miRNAs while maximizing prediction accuracy. MiRNA expression profiles of a 386-patient cohort of breast cancer were retrieved from The Cancer Genome Atlas. SVM-BRC identified 34 of 503 miRNAs as a signature and achieved a 10-fold cross-validation mean accuracy, sensitivity, specificity, and Matthews correlation coefficient of 80.38%, 0.79, 0.81, and 0.60, respectively. Functional enrichment of the 10 highest ranked miRNAs was analysed in terms of Kyoto Encyclopedia of Genes and Genomes and Gene Ontology annotations. Kaplan-Meier survival analysis of the highest ranked miRNAs revealed that four miRNAs, hsa-miR-503, hsa-miR-1307, hsa-miR-212 and hsa-miR-592, were significantly associated with the prognosis of patients with breast cancer.

Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
SVM-BRC performance evaluation using the ROC curve. The area under the ROC curve is 0.87 using a 386-patient breast cancer cohort.
Figure 2
Figure 2
Feature knockout analysis. Prediction performance difference for individual miRNAs using feature knockout analysis.
Figure 3
Figure 3
KEGG pathway analysis of the 10 highest ranked miRNAs.
Figure 4
Figure 4
Gene ontology (GO) annotations for the 10 highest ranked miRNAs. GO enrichment analysis was performed for the 10 highest ranked miRNAs at three levels: biological process (a), molecular functions (b), and cellular component (c).
Figure 5
Figure 5
Kaplan-Meier plots of hsa-miR-503, hsa-miR-1307, hsa-miR-212, and hsa-miR-592 for the systemically treated breast cancer cohort.

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