Abstract
The study aimed to explore the molecular mechanism underlying triple-negative breast cancer (TNBC) and to identify their potential diagnostic/prognostic biomarkers. The differentially expressed lncRNAs (DElncRNAs) were identified by meta-analysis and machine learning feature selection methods. The dysregulated lncRNA-miRNA-mRNA network was constructed based on the competing endogenous RNA (ceRNA) hypothesis. A total of 26 DElncRNAs were identified with a meta-analysis approach of which 18 DElncRNAs attained high accuracy in training and test dataset by Support Vector Machine-Recursive Feature Elimination (SVM-RFE) which could act as diagnostic biomarkers. Among the identified DElncRNAs, LINC01315 and CTA-384D8.35 could act as prognostic biomarkers. Finally, two important sub-modules from lncRNA-miRNA-mRNA network were identified which consists of DElncRNAs (LINC01087, LINC01315, and SOX9-AS1) interacting with co-expressed DEmRNAs and DEmiRNAs. Thus, the study indicated the importance of DElncRNAs and highlighted the efficacy as potential biomarkers in TNBC.
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