Leveraging machine learning and bioinformatics to identify diagnostic biomarkers connected to hypoxia-related genes in preeclampsia.

Abstract

PE is a serious form of pregnancy-related hypertension. Hypoxia can induce cellular dysfunction, adversely affecting both the infant and the mother. This study aims to investigate the relationship between HRGs and the diagnosis of PE, seeking to enhance our understanding of potential molecular mechanisms and offer new perspectives for the detection and treatment of the condition. A WGCNA network was established to identify key genes significantly associated with traits of PE. LASSO, SVM-RFE, and RF were utilized to identify feature genes. Calibration curves and DCA were employed to assess the diagnostic performance of the comprehensive nomogram. Consensus clustering was applied to identify subtypes of PE. GSEA and the construction of a ceRNA network were used to explore the potential biological functions and regulatory mechanisms of the identified feature genes. Furthermore, ssGSEA was conducted to investigate the immune landscape associated with PE. We successfully identified three potential diagnostic biomarkers for PE: P4HA1, NDRG1, and BHLHE40. Furthermore, the nomogram exhibited strong diagnostic performance. In patients with PE, the abundance of pro-inflammatory immune cells was significantly elevated, reflecting characteristics of high infiltration. The levels of immune cells infiltration were significantly correlated with the expression of the identified feature genes. Notably, these feature genes may be closely linked to mitochondrial-related biological functions. In conclusion, our findings enhance the understanding of the pathological mechanisms underlying PE and open innovative avenues for the diagnosis and treatment of PE.