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Establishment of a diagnostic model of endometriosis based on disulfidptosis‐related genes.
- Source :
-
Journal of Obstetrics & Gynaecology Research . Jul2024, Vol. 50 Issue 7, p1201-1207. 7p. - Publication Year :
- 2024
-
Abstract
- Objectives: We aimed to establish a diagnostic model of endometriosis (EM) based on disulfidptosis‐related genes (DRGs). Materials and Methods: The mRNA expression data of EM were downloaded from the gene expression omnibus database and subjected to differential analysis, and co‐expression analysis was performed based on 10 disulfidptosis genes to acquire DRGs. The differentially expressed DRGs were subjected to biofunctional analysis. Lasso analysis and support vector machine‐recursive feature elimination (SVM‐RFE) analysis were employed to extract the intersection of feature genes as biomarkers, and the diagnostic values of biomarkers for EM were evaluated based on receiver operating characteristic curves. The correlations between biomarkers and the immune microenvironment were assessed by Pearson analysis of biomarkers and immune cell infiltration levels. Results: Transforming growth factor β stimulated protein clone 22 domain family member 4 (TSC22D4), and F‐box/SPRY domain‐containing protein 1 (FBXO45) worked as the diagnostic classifiers in EM, with an obvious decrease in FBXO45 expression and an evident increase in TSC22D4 expression. The areas under the curves of FBXO45 and TSC22D4 were 0.752 and 0.706, respectively, and the area of FBXO45 combined with TSC22D4 reached 0.865, suggesting that TSC22D4 and FBXO45 had high predictive values. The diagnostic markers were closely correlated with immune cell infiltration. Conclusion: The diagnostic markers constructed based on disulfidptosis are good predictors for EM, which have close correlations with EM. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DIAGNOSIS of endometriosis
*PEARSON correlation (Statistics)
*PREDICTION models
*RECEIVER operating characteristic curves
*RESEARCH funding
*APOPTOSIS
*DESCRIPTIVE statistics
*GENES
*SUPPORT vector machines
*BIOINFORMATICS
*MESSENGER RNA
*GENE expression
*GENE expression profiling
*BIOMARKERS
*TRANSFORMING growth factors-beta
Subjects
Details
- Language :
- English
- ISSN :
- 13418076
- Volume :
- 50
- Issue :
- 7
- Database :
- Academic Search Index
- Journal :
- Journal of Obstetrics & Gynaecology Research
- Publication Type :
- Academic Journal
- Accession number :
- 178317718
- Full Text :
- https://doi.org/10.1111/jog.15945