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The landscape of disulfidptosis in preeclampsia reveals a novel five-gene diagnostic signature via machine learning

bioinformatics
gynecology-and-obstetrics
pathology
medical-genetics
data-mining-and-machine-learning

Abstract

Background: Preeclampsia (PE), a pregnancy-specific pathological condition, has shown a growing incidence in recent decades. Disulfidptosis is a newly discovered form of programmed cell death that differs from traditional pathways in its molecular mechanisms. While numerous studies have linked disulfidptosis to various diseases, its role in the pathogenesis of PE remains unknown.

Methods: This study first analyzed the expression patterns of disulfidptosis-related genes (DRGs) using the GSE75010 dataset. Based on these results, unsupervised consensus clustering was performed on the PE samples within the dataset. Weighted gene co-expression network analysis (WGCNA) and machine learning algorithms were employed to identify hub genes associated with PE and disulfidptosis clusters. The expression profiles of these hub genes were then validated using independent datasets (GSE4707, GSE30186, and GSE54618) and quantitative PCR (qPCR).

Results: Nine DRGs were found to be abnormally expressed in PE samples. Two distinct disulfidptosis clusters were identified, each associated with unique functional pathways. Among the four machine learning algorithms tested, the support vector machine (SVM) provided the most reliable predictions. Five hub genes—SASH1, CST6, CCBL1, FSTL3, and SPAG4—were identified. A diagnostic model based on these genes demonstrated strong performance in both independent datasets and qPCR validation.

Conclusion: This study proposes a novel diagnostic model for PE based on five disulfidptosis-related genes. The findings offer a new framework for exploring disease heterogeneity and provide insights into the potential role of disulfidptosis in the development of PE.

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