Leveraging PANoptosis-associated genes for unraveling implication of decidualization deficiency in pre-eclampsia via transcriptome data and experiment validation.

Background: Decidualization deficiency is a key pathological feature of pre-eclampsia (PE) and is closely associated with aberrant regulation of cell fate. PANoptosis is a recently characterized form of inflammatory programmed cell death that has been implicated in several pregnancy-related disorders. However, its potential involvement in decidualization deficiency in PE remains poorly understood. This study aimed to explore the association between PANoptosis-related genes and decidualization deficiency in PE, and to identify candidate biomarkers and potential therapeutic targets related to PANoptosis. Methods: Datasets containing decidual tissue samples derived from women with PE and normal controls were acquired in the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and subjected to enrichment analysis. After that, PANoptosis-related genes were intersected with DEGs derived from the decidual tissue of PE, followed by protein-protein interaction (PPI) network construction and correlation analysis. Next, the immune infiltration landscape and its association with PANoptosis-related DEGs were assessed. Furthermore, three machine learning algorithms, including support vector machine-recursive feature elimination (SVM-RFE), the least absolute shrinkage and selection operator (LASSO), and the random forest (RF) algorithms, were adopted to identify potential diagnostic biomarkers for PE. Artificial neural network (ANN) and nomogram models were then constructed and evaluated in testing datasets, which included decidual stromal cell samples derived from women with PE and normal controls. Additionally, the expression of PANoptosis-related signature genes and decidualization-related markers was experimentally validated in primary human decidual stromal cells (HDSCs) derived from PE patients and healthy controls. In addition, consensus clustering analysis was conducted on the basis of signature genes, and immune infiltration landscape analysis of different subtypes of PE was performed. Ultimately, the candidate compounds targeting the signature genes were screened and then further verified Results: 430 DEGs were determined, and enrichment analysis indicated that these DEGs were mainly involved in inflammation, apoptosis, and dysfunction of decidual tissue in PE. Then, 10 PANoptosis-related DEGs in PE were further screened. Following that, immune landscape analysis revealed an aberrant abundance of various immunocytes and the levels of immune checkpoints in the decidual tissue of PE, which were closely associated with the PANoptosis-related DEGs. Next, through machine learning, nine PANoptosis-related signature genes ( Conclusion: This study suggests a potential association between PANoptosis-related molecular dysregulation and decidualization deficiency in PE. The identified PANoptosis-related signature genes may serve as candidate biomarkers with predictive relevance, and melatonin may represent a potential therapeutic candidate targeting PANoptosis-related pathways. These findings provide a foundation for future mechanistic and translational studies on PE.