Identification of potential models for predicting progestin insensitivity in patients with endometrial atypical hyperplasia and endometrioid endometrial cancer based on ATAC-Seq and RNA-Seq integrated analysis.
Front Genet
; 13: 952083, 2022.
Article
en En
| MEDLINE
| ID: mdl-36092919
ABSTRACT
Objective:
The aim of this study was to establish predictive models based on the molecular profiles of endometrial lesions, which might help identify progestin-insensitive endometrial atypical hyperplasia (EAH) or endometrioid endometrial cancer (EEC) patients before progestin-based fertility-preserving treatment initiation.Methods:
Endometrial lesions from progestin-sensitive (PS, n = 7) and progestin-insensitive (PIS, n = 7) patients were prospectively collected before progestin treatment and then analyzed by ATAC-Seq and RNA-Seq. Potential chromatin accessibility and expression profiles were compared between the PS and PIS groups. Candidate genes were identified by bioinformatics analyses and literature review. Then expanded samples (n = 35) were used for validating bioinformatics data and conducting model establishment.Results:
ATAC-Seq and RNA-Seq data were separately analyzed and then integrated for the subsequent research. A total of 230 overlapping differentially expressed genes were acquired from ATAC-Seq and RNA-Seq integrated analysis. Further, based on GO analysis, REACTOME pathways, transcription factor prediction, motif enrichment, Cytoscape analysis and literature review, 25 candidate genes potentially associated with progestin insensitivity were identified. Finally, expanded samples were used for data verification, and based on these data, three predictive models comprising 9 genes (FOXO1, IRS2, PDGFC, DIO2, SOX9, BCL11A, APOE, FYN, and KLF4) were established with an overall predictive accuracy above 90%.Conclusion:
This study provided potential predictive models that might help identify progestin-insensitive EAH and EEC patients before fertility-preserving treatment.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Revista:
Front Genet
Año:
2022
Tipo del documento:
Article
País de afiliación:
China