Model-Based Integration Analysis Revealed Presence of Novel Prognostic miRNA Targets and Important Cancer Driver Genes in Triple-Negative Breast Cancers

Abstract

Background: miRNAs (microRNAs) play a key role in triple-negative breast cancer (TNBC) progression, and its heterogeneity at the expression, pathological and clinical levels. Stratification of breast cancer subtypes on the basis of genomics and transcriptomics profiling, along with the known biomarkers’ receptor status, has revealed the existence of subgroups known to have diverse clinical outcomes. Recently, several studies have analysed expression profiles of matched mRNA and miRNA to investigate the underlying heterogeneity of TNBC and the potential role of miRNA as a biomarker within cancers. However, the miRNA-mRNA regulatory network within TNBC has yet to be understood. Results and Findings: We performed model-based integrated analysis of miRNA and mRNA expression profiles on breast cancer, primarily focusing on triple-negative, to identify subtype-specific signatures involved in oncogenic pathways and their potential role in patient survival outcome. Using univariate and multivariate Cox analysis, we identified 25 unique miRNAs associated with the prognosis of overall survival (OS) and distant metastases-free survival (DMFS) with “risky” and “protective” outcomes. The association of these prognostic miRNAs with subtype-specific mRNA genes was established to investigate their potential regulatory role in the canonical pathways using anti-correlation analysis. The analysis showed that miRNAs contribute to the positive regulation of known breast cancer driver genes as well as the activation of respective oncogenic pathway during disease formation. Further analysis on the “risk associated” miRNAs group revealed significant regulation of critical pathways such as cell growth, voltage-gated ion channel function, ion transport and cell-to-cell signalling. Conclusion: The study findings provide new insights into the potential role of miRNAs in TNBC disease progression through the activation of key oncogenic pathways. The results showed previously unreported subtype-specific prognostic miRNAs associated with clinical outcome that may be used for further clinical evaluation.

An Insight into miR-1290: An Oncogenic miRNA with Diagnostic Potential

“…Moreover, NAT1 was positively correlated with ER and PR expression, negatively correlated with BC tumor grade and size, and associated with longer survival in BC patients treated with tamoxifen and in patients with lymph node metastasis [237]. Interestingly, integrated analysis indicated that miR-1290 was downregulated in TNBC; however, its expression was correlated with poor prognosis, which suggests the high variability of miRNA expression within this subtype [238]. The above conclusion was supported by a significant inverse correlation of miR-1290 with FOXA1, one of the highly upregulated genes in the analyzed TNBC group.…”

Overview of triple negative breast cancer prognostic signatures in the context of data science-driven clinico-genomics research

“…For instance, an 8-miRNAs signature (miR-139-5p, miR-10b-5p, miR-486-5p, miR-455-3p, miR-107, miR-146b-5p, miR-324-5p and miR-20a-5p) supported an accurate predictive model of relapse in TNBC patients highly correlated with prognosis (AUC of 0.80), independently validated twice (AUCs of 0.89 and 0.90) ( 32 ). In study of Zaka et al ( 33 ), Cox analysis was employed to identify 25 miRNAs associated with prognosis with both risky and protective outcomes. Some miRNAs were associated with OS (hsa-miR-342-3p, hsa-miR-195, hsa-miR-155, and hsa-miR-497) and others with distant metastases-free survival (hsa-miR-29c, hsa-miR-342-3p, hsa-let-7c, hsa-let-7b, and hsa-miR-497).…”

Identification of stage-associated exosome miRNAs in colorectal cancer by improved robust and corroborative approach embedded miRNA-target network

“…The omics-driven and network-based approaches, composed of biological and computational processes, can lead to the more precise discovery of potential novel biomarkers due to corroborative evidence integrated with interacting molecular entities and incorporating measurements of molecule expression and biological function ( 18 – 21 ). The miRNA targeting topology networks are frequently used to extract key miRNAs from noisy backgrounds for further translational studies ( 22 , 23 ). In contrast to this approach, Chen et al ( 24 ) increased our understanding of miRNA biology by developing a single-line regulatory miRNA vulnerability network and identifying the corresponding biomarkers.…”