<i>In Silico</i> Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis
Personalized medicine relies on the integration and consideration of specific characteristics of the patient, such as tumor phenotypic and genotypic profiling. Background: Radiogenomics aim to integrate phenotypes from tumor imaging data with genomic data to discover genetic mechanisms underlying tu...
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doaj-76e9d8757a73431fb9a5c3ec20d2d70e2020-11-25T02:00:17ZengMDPI AGInternational Journal of Molecular Sciences1422-00672019-11-012023582510.3390/ijms20235825ijms20235825<i>In Silico</i> Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential DiagnosisFrancesca Gallivanone0Claudia Cava1Fabio Corsi2Gloria Bertoli3Isabella Castiglioni4Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F. Cervi 93, 20090 Segrate-Milan, Milan, ItalyInstitute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F. Cervi 93, 20090 Segrate-Milan, Milan, ItalyLaboratory of Nanomedicine and Molecular Imaging, Istituti Clinici Scientifici Maugeri IRCCS, via Maugeri 4, 27100 Pavia, ItalyInstitute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F. Cervi 93, 20090 Segrate-Milan, Milan, ItalyInstitute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Via F. Cervi 93, 20090 Segrate-Milan, Milan, ItalyPersonalized medicine relies on the integration and consideration of specific characteristics of the patient, such as tumor phenotypic and genotypic profiling. Background: Radiogenomics aim to integrate phenotypes from tumor imaging data with genomic data to discover genetic mechanisms underlying tumor development and phenotype. Methods: We describe a computational approach that correlates phenotype from magnetic resonance imaging (MRI) of breast cancer (BC) lesions with microRNAs (miRNAs), mRNAs, and regulatory networks, developing a radiomiRNomic map. We validated our approach to the relationships between MRI and miRNA expression data derived from BC patients. We obtained 16 radiomic features quantifying the tumor phenotype. We integrated the features with miRNAs regulating a network of pathways specific for a distinct BC subtype. Results: We found six miRNAs correlated with imaging features in Luminal A (<i>miR-1537</i>, <i>-205</i>, <i>-335</i>, <i>-337</i>, <i>-452</i>, and <i>-99a</i>), seven miRNAs (<i>miR-142</i>, <i>-155</i>, <i>-190</i>, <i>-190b</i>, <i>-1910</i>, <i>-3617</i>, and <i>-429</i>) in HER2+, and two miRNAs (<i>miR-135b</i> and <i>-365-2</i>) in Basal subtype. We demonstrate that the combination of correlated miRNAs and imaging features have better classification power of Luminal A versus the different BC subtypes than using miRNAs or imaging alone. Conclusion: Our computational approach could be used to identify new radiomiRNomic profiles of multi-omics biomarkers for BC differential diagnosis and prognosis.https://www.mdpi.com/1422-0067/20/23/5825radiogenomicsradiomirnomicsbreast cancermagnetic resonance imagingmrimicrornas/mirnaspathwaysnetwork |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Francesca Gallivanone Claudia Cava Fabio Corsi Gloria Bertoli Isabella Castiglioni |
spellingShingle |
Francesca Gallivanone Claudia Cava Fabio Corsi Gloria Bertoli Isabella Castiglioni <i>In Silico</i> Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis International Journal of Molecular Sciences radiogenomics radiomirnomics breast cancer magnetic resonance imaging mri micrornas/mirnas pathways network |
author_facet |
Francesca Gallivanone Claudia Cava Fabio Corsi Gloria Bertoli Isabella Castiglioni |
author_sort |
Francesca Gallivanone |
title |
<i>In Silico</i> Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis |
title_short |
<i>In Silico</i> Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis |
title_full |
<i>In Silico</i> Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis |
title_fullStr |
<i>In Silico</i> Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis |
title_full_unstemmed |
<i>In Silico</i> Approach for the Definition of radiomiRNomic Signatures for Breast Cancer Differential Diagnosis |
title_sort |
<i>in silico</i> approach for the definition of radiomirnomic signatures for breast cancer differential diagnosis |
publisher |
MDPI AG |
series |
International Journal of Molecular Sciences |
issn |
1422-0067 |
publishDate |
2019-11-01 |
description |
Personalized medicine relies on the integration and consideration of specific characteristics of the patient, such as tumor phenotypic and genotypic profiling. Background: Radiogenomics aim to integrate phenotypes from tumor imaging data with genomic data to discover genetic mechanisms underlying tumor development and phenotype. Methods: We describe a computational approach that correlates phenotype from magnetic resonance imaging (MRI) of breast cancer (BC) lesions with microRNAs (miRNAs), mRNAs, and regulatory networks, developing a radiomiRNomic map. We validated our approach to the relationships between MRI and miRNA expression data derived from BC patients. We obtained 16 radiomic features quantifying the tumor phenotype. We integrated the features with miRNAs regulating a network of pathways specific for a distinct BC subtype. Results: We found six miRNAs correlated with imaging features in Luminal A (<i>miR-1537</i>, <i>-205</i>, <i>-335</i>, <i>-337</i>, <i>-452</i>, and <i>-99a</i>), seven miRNAs (<i>miR-142</i>, <i>-155</i>, <i>-190</i>, <i>-190b</i>, <i>-1910</i>, <i>-3617</i>, and <i>-429</i>) in HER2+, and two miRNAs (<i>miR-135b</i> and <i>-365-2</i>) in Basal subtype. We demonstrate that the combination of correlated miRNAs and imaging features have better classification power of Luminal A versus the different BC subtypes than using miRNAs or imaging alone. Conclusion: Our computational approach could be used to identify new radiomiRNomic profiles of multi-omics biomarkers for BC differential diagnosis and prognosis. |
topic |
radiogenomics radiomirnomics breast cancer magnetic resonance imaging mri micrornas/mirnas pathways network |
url |
https://www.mdpi.com/1422-0067/20/23/5825 |
work_keys_str_mv |
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