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The Landscape of Disulfidptosis in Preeclampsia Reveals a Novel 5-gene diagnostic signature via Machine Learning

bioinformatics
genetics
genomics
gynecology-and-obstetrics
womens-health

Abstract

Background Preeclampsia ( PE), a pregnancy-specific pathological condition, has shown a growing incidence over recent decades. Disulfidptosis is a newly discovered mode of programmed cell death that differs from traditional cell death pathways in its molecular mechanisms. Numerous studies have reported the association between disulfidptosis and various diseases; however, the role of disulfidptosis 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 conducted specifically on the PE samples included in this dataset. Weighted gene co-expression network analysis (WCGNA) and machine learning algorithms were utilized to identify hub genes related to PE and disulfidptosis clusters. Ultimately, the expression profiles of these hub genes were validated using the independent datasets GSE4707, GSE30186, and GSE54618, as well as quantitative PCR (qPCR).

Results 9 DRGs showed abnormal expressions in the PE samples. Subsequently, two disulfidptosis clusters were identified, each with its own unique functional pathway. Among the four algorithms examined, the support vector machine (SVM) delivered the most dependable predictions. In addition, the genes SASH1, CST6, CCBL1, FSTL3, and SPAG4 were determined as the central genes. A diagnostic model was established using these five genes, which achieved excellent diagnostic performance in the independent dataset and qPCR validation.

Conclusion This study proposes a new diagnostic model for PE, which can serve as a framework for studying disease heterogeneity and provides a basis for understanding the role of disulfidptosis in the occurrence of PE.
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