Integrated miRNA Signatures: Advancing Breast Cancer Diagnosis and Prognosis

Breast cancer ranks first in incidence and second in deaths worldwide, presenting alarmingly rising mortality rates. Imaging methodologies and/or invasive biopsies are routinely used for screening and detection, although not always with high sensitivity/specificity. MicroRNAs (miRNAs) could serve as diagnostic and prognostic biomarkers for breast cancer. We have designed a computational approach combining transcriptome profiling, survival analyses, and diagnostic power calculations from 1165 patients with breast invasive carcinoma from The Cancer Genome Atlas (TCGA-BRCA). Our strategy yielded two separate miRNA signatures consisting of four up-regulated and five down-regulated miRNAs in breast tumors, with cumulative diagnostic strength of AUC 0.93 and 0.92, respectively. We provide direct evidence regarding the breast cancer-specific expression of both signatures through a multicancer comparison of >7000 biopsies representing 19 solid cancer types, challenging their diagnostic potency beyond any of the current diagnostic methods. Our signatures are functionally implicated in cancer-related processes with statistically significant effects on overall survival and lymph-node invasion in breast cancer patients, which underlie their strong prognostic implication. Collectively, we propose two novel miRNA signatures with significantly elevated diagnostic and prognostic power as a functionally resolved tool for binary and accurate detection of breast cancer and other tumors of the female reproductive system.

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Publication Details

Journal
Biomolecules
Published
2024-10-24
DOI
https://doi.org/10.3390/biom14111352
Citations
6
Primary Topic
MicroRNA in disease regulation
Type
article
Field-Weighted Citation Impact
1.71

Funders

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article

Integrated miRNA Signatures: Advancing Breast Cancer Diagnosis and Prognosis

Eleni Thodou, Antonis Giakountis, Antigoni Poultsidi, Μαρία Σαμαρά et al.
6 citations
Biomolecules
MicroRNA in disease regulation
1.71
article

Integrated miRNA Signatures: Advancing Breast Cancer Diagnosis and Prognosis

Eleni Thodou, Antonis Giakountis, Antigoni Poultsidi, Μαρία Σαμαρά, Georgia Eleni Thomopoulou, Marina Patoulioti
article en
6 citations

Abstract

Breast cancer ranks first in incidence and second in deaths worldwide, presenting alarmingly rising mortality rates. Imaging methodologies and/or invasive biopsies are routinely used for screening and detection, although not always with high sensitivity/specificity. MicroRNAs (miRNAs) could serve as diagnostic and prognostic biomarkers for breast cancer. We have designed a computational approach combining transcriptome profiling, survival analyses, and diagnostic power calculations from 1165 patients with breast invasive carcinoma from The Cancer Genome Atlas (TCGA-BRCA). Our strategy yielded two separate miRNA signatures consisting of four up-regulated and five down-regulated miRNAs in breast tumors, with cumulative diagnostic strength of AUC 0.93 and 0.92, respectively. We provide direct evidence regarding the breast cancer-specific expression of both signatures through a multicancer comparison of >7000 biopsies representing 19 solid cancer types, challenging their diagnostic potency beyond any of the current diagnostic methods. Our signatures are functionally implicated in cancer-related processes with statistically significant effects on overall survival and lymph-node invasion in breast cancer patients, which underlie their strong prognostic implication. Collectively, we propose two novel miRNA signatures with significantly elevated diagnostic and prognostic power as a functionally resolved tool for binary and accurate detection of breast cancer and other tumors of the female reproductive system.

BiomoleculesVol. 14(11)
University of Thessaly (GR), National and Kapodistrian University of Athens (GR), University Hospital of Larissa (GR)
European Commission, University of Thessaly, European Regional Development Fund
Good health and well-being
Openalex Percentile: Top 15%
MicroRNA in disease regulation
1.71
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