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. 2019 Sep 23;11(18):7525-7536.
doi: 10.18632/aging.102268. Epub 2019 Sep 23.

Identification of a Novel microRNA Recurrence-Related Signature and Risk Stratification System in Breast Cancer

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Free PMC article

Identification of a Novel microRNA Recurrence-Related Signature and Risk Stratification System in Breast Cancer

Jianguo Lai et al. Aging (Albany NY). .
Free PMC article

Abstract

Increasing evidence has revealed that microRNAs (miRNAs) play vital roles in breast cancer (BC) prognosis. Thus, we aimed to identify recurrence-related miRNAs and establish accurate risk stratification system in BC patients. A total of 381 differentially expressed miRNAs were confirmed by analyzing 1044 BC tissues and 102 adjacent normal samples from The Cancer Genome Atlas (TCGA). Then, based on the association between each miRNAs and disease-free survival (DFS), we identified miRNA recurrence-related signature to construct a novel prognostic nomogram using Cox regression model. Target genes of the four miRNAs were analyzed via Gene Ontology and KEGG pathway analyses. Time-dependent receiver operating characteristic analysis indicated that a combination of the miRNA signature and tumor-node-metastasis (TNM) stage had better predictive performance than that of TNM stage (0.710 vs 0.616, P<0.0001). Furthermore, risk stratification analysis suggested that the miRNA-based model could significantly classify patients into the high- and low-risk groups in the two cohorts (all P<0.0001), and was independent of other clinical features. Functional enrichment analysis demonstrated that the 46 target genes mainly enrichment in important cell biological processes, protein binding and cancer-related pathways. The miRNA-based prognostic model may facilitate individualized treatment decisions for BC patients.

Keywords: breast cancer; microRNA; model; recurrence; survival.

Conflict of interest statement

CONFLICTS OF INTEREST: The authors declare that there is no conflict of interest to disclose.

Figures

Figure 1
Figure 1
Volcano plot of 273 up-regulated and 108 down-regulated. Blue color represents up-regulated expression, and red color reveals down-regulated expression.
Figure 2
Figure 2
miRNA-based prognostic model to predict 5-year disease-free survival in breast cancer patients.
Figure 3
Figure 3
Time-dependent receiver operating characteristic curves at 5-years based on the miRNA-based prognostic model in the derivation cohort (A) and validation cohort (B). Calibration curves of the miRNA-based prognostic model in the derivation cohort (C) and validation cohort (D).
Figure 4
Figure 4
The distribution of risk score, DFS, and DFS status in the derivation cohort (A) and validation cohort (B). The black line indicates the optimal cutoff point of the nomogram score used to stratify patients into the low- and high-risk group. Kaplan–Meier curves of the low- and high-risk patients based on the miRNA-based prognostic model in the derivation cohort (C) and validation cohort (D). DFS, disease-free survival.
Figure 5
Figure 5
Stratified analysis of the miRNA-based prognostic model for breast cancer patients in T stage, N stage, TNM stage, HR, and Her2 status.
Figure 6
Figure 6
Comparisons of the predictive accuracy at 5-years DFS using time-dependent receiver operating characteristic curves in miRNA-based model with clinical risk factors (A), and miRNA-based model with single prognostic miRNA (B). DFS, disease-free survival.
Figure 7
Figure 7
Functional enrichment analysis for 46 target genes of the four miRNAs. (A) Gene ontology (GO) enrichment analysis. (B) Kyoto Encyclopedia of Genes and Genomes analyses (KEGG) enrichment analysis. The x-axis indicates the number of genes, and the y-axis represents the GO terms and KEGG pathway names. The color represents the P value.

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