Integrative Genomic and Transcriptomic Profiling Reveals a Differential Molecular Signature in Uterine Leiomyoma versus Leiomyosarcoma.

Data de publicació: 16/02/2022 Data Ahead of Print: 16/02/2022

Autors de IIS La Fe

Autors aliens a IIS La Fe

Machado-Lopez A
Alonso R
Jimenez-Almazan J
Garcia M
Lopez S
Barcelo F
Torroba A
Ortiz S
Simon C
Mas A

Grups d'Investigació

Medicina Reproductiva

Investigador Principal

José Morales Roselló

Abstract

The absence of standardized molecular profiling to differentiate uterine leiomyosarcomas versus leiomyomas represents a current diagnostic challenge. In this study, we aimed to search for a differential molecular signature for these myometrial tumors based on artificial intelligence. For this purpose, differential exome and transcriptome-wide research was performed on histologically confirmed leiomyomas ( n = 52) and leiomyosarcomas ( n = 44) to elucidate differences between and within these two entities. We identified a significantly higher tumor mutation burden in leiomyosarcomas vs. leiomyomas in terms of somatic single-nucleotide variants (171,863 vs. 81,152), indels (9491 vs. 4098), and copy number variants (8390 vs. 5376). Further, we discovered alterations in specific copy number variant regions that affect the expression of some tumor suppressor genes. A transcriptomic analysis revealed 489 differentially expressed genes between these two conditions, as well as structural rearrangements targeting ATRX and RAD51B. These results allowed us to develop a machine learning approach based on 19 differentially expressed genes that differentiate both tumor types with high sensitivity and specificity. Our findings provide a novel molecular signature for the diagnosis of leiomyoma and leiomyosarcoma, which could be helpful to complement the current morphological and immunohistochemical diagnosis and may lay the foundation for the future evaluation of malignancy risk.

Dades de la publicació

ISSN/ISSNe:: 1661-6596, 1661-6596
Tipus:: Article
Pàgines:: -
DOI:: 10.3390/ijms23042190
PubMed:: 35216305
Factor d'Impacte:: 1,176 SCImago ℠
Quartil:: Q1 SCImago ℠

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Mètriques

Filiacions

Machado-Lopez A:: Igenomix Foundation, INCLIVA Biomedical Research Institute, 46980 Valencia, Spain
Alonso R:: Igenomix Foundation, INCLIVA Biomedical Research Institute, 46980 Valencia, Spain

Research and Development Department, Igenomix SL, 46980 Paterna, Spain
Lago V:: Gynecologic Oncology Department, University Hospital La Fe, 46026 Valencia, Spain
Jimenez-Almazan J:: Research and Development Department, Igenomix SL, 46980 Paterna, Spain
Garcia M:: Research and Development Department, Igenomix SL, 46980 Paterna, Spain
Monleon J:: Department of Obstetrics and Gynecology, Hospital Universitario La Fe, 46026 Valencia, Spain
Lopez S:: Department of Pathology, Hospital Universitario La Fe, 46026 Valencia, Spain
Barcelo F:: Department of Gynecology and Obstetrics, Gynecology Oncology Unit, Hospital Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
Torroba A:: Pathology Service, Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
Ortiz S:: Department of Pathology, Complejo Hospitalario de Cartagena, 30202 Murcia, Spain
Domingo S:: Gynecologic Oncology Department, University Hospital La Fe, 46026 Valencia, Spain
Simon C:: Igenomix Foundation, INCLIVA Biomedical Research Institute, 46980 Valencia, Spain

Department of Obstetrics and Gynecology, Universidad de Valencia, 46010 Valencia, Spain

Department of Obstetrics and Gynecology, BIDMC, Harvard University, Boston, MA 02215, USA
Mas A:: Igenomix Foundation, INCLIVA Biomedical Research Institute, 46980 Valencia, Spain

Keywords

classification model, diagnostic/prognostic biomarkers, differential gene expression, exome/transcriptome, integrative analysis, leiomyoma, leiomyosarcoma, machine learning, mutational pattern