Prognostic microRNA/mRNA signature from the integrated analysis of patients with invasive breast cancer
Open accessAbstract
The optimal management of breast cancer (BC) presents challenges due to the heterogeneous molecular classification of the disease. We performed survival analysis on a cohort of 466 patients with primary invasive ductal carcinoma (IDC), the most frequent type of BC, by integrating mRNA, microRNA (miRNA), and DNA methylation next-generation sequencing data from The Cancer Genome Atlas (TCGA). Expression data from eight other BC cohorts were used for validation. The prognostic value of the resulting miRNA/mRNA signature was compared with that of other prognostic BC signatures. Thirty mRNAs and seven miRNAs were associated with overall survival across different clinical and molecular subclasses of a 466-patient IDC cohort from TCGA. The prognostic RNAs included PIK3CA , one of the two most frequently mutated genes in IDC, and miRNAs such as hsa-miR-328, hsa-miR-484, and hsa-miR-874. The area under the curve of the receiver-operator characteristic for the IDC risk predictor in the TCGA cohort was 0.74 at 60 mo of overall survival ( P < 0.001). Most relevant for clinical application, the integrated signature had the highest prognostic value in early stage I and II tumors (receiver-operator characteristic area under the curve = 0.77, P value < 0.001). The genes in the RNA risk predictor had an independent prognostic value compared with the clinical covariates, as shown by multivariate analysis. The integrated RNA signature was successfully validated on eight BC cohorts, comprising a total of 2,399 patients, and it had superior performance for risk stratification with respect to other RNA predictors, including the mRNAs used in MammaPrint and Oncotype DX assays.
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RNA-seq data have been used to develop gene expression-based predictive models in cancer research.
“RNA-seq data have also been used to develop gene expression-based predictive models in cancer research [13, 14].”
From Comparison of RNA-seq and microarray-based models for clinical endpoint prediction (2015) · cites this paper as [14]
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- Authors
- Volinia, S., & Croce, C. M.
- Affiliations
- University of Ferrara (IT)
- The Ohio State University (US)
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2013-04-15
- Volume
- 110
- Issue
- 18
- Pages
- 7413-7417
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC3645522
- OpenAlex
- W2068976735
- Cited by
- 149
- References
- 32
- Citations by year
- 2026: 5 · 2025: 1 · 2024: 2 · 2023: 2 · 2022: 6 · 2021: 8 · 2020: 13 · 2019: 14 · 2018: 14 · 2017: 10 · 2016: 15 · 2015: 26 · 2014: 27 · 2013: 6
- FWCI
- 8.67
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Institutional Research Information System University of Ferrara (University of Ferrara) (submittedVersion)
- Funders
- Associazione Italiana per la Ricerca sul Cancro
- National Cancer Institute (U01CA152758)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- invasive ductal carcinoma, breast cancer, prognostic signature, microRNA, mRNA, DNA methylation, overall survival, PIK3CA, risk stratification, early breast cancer, MammaPrint, Oncotype DX
- MeSH terms
- Breast Neoplasms, Female, Humans, Neoplasm Invasiveness, Prognosis, Risk Factors, RNA, Messenger, ROC Curve, Reproducibility of Results, Cohort Studies, Genome, Human, Gene Expression Regulation, Neoplastic, Carcinoma, Ductal, Breast, DNA Methylation, Gene Expression Profiling, MicroRNAs, Genes, Neoplasm, Kaplan-Meier Estimate
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