Prediction and immune landscape study of potentially key autophagy-related biomarkers in preeclampsia with gestational diabetes mellitus

Abstract

Introduction: Gestational diabetes mellitus (GDM) and preeclampsia are prevalent pregnancy complications that threaten maternal and infant health while imposing substantial socioeconomic burdens. Although several interventions exist, shortcomings in individualized treatment and other limitations necessitate urgent in-depth research. This study aimed to examine alterations in autophagy-related gene expression in preeclampsia combined with GDM. Methods: We conducted bioinformatics analyses including gene expression profiling, weighted gene co-expression network analysis (WGCNA), gene ontology (GO) and KEGG enrichment analyses, machine learning modeling, immune infiltration analyses, and single-cell RNA sequencing. Differentially expressed autophagy-related genes linked to preeclampsia with GDM were identified. Expression levels of four key genes were validated in placental samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR). Results: Our findings identified potential biomarkers and molecular mechanisms underlying preeclampsia with GDM. Single-cell analysis corroborated these results, revealing distinct autophagy-related gene signatures and enhancing understanding of the pathophysiology. Discussion: This study elucidates molecular mechanisms connecting GDM and preeclampsia, identifies novel biomarkers and therapeutic targets, and provides a valuable reference for future research and clinical applications. The integration of multi-omics approaches advances precision medicine strategies for these comorbid conditions.