Prediction and immune landscape study of potentially key autophagyrelated biomarkers in preeclampsia with gestational diabetes mellitus

Qin Wang¹Xiaoqi Li^1,2Wen Ye³Lin Lin⁴Kejun Ye⁴Mengjia Peng^4*

• ¹Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, Henan Province, China
• ²Xinjiang Medical University, Ürümqi, Xinjiang Uyghur Region, China
• ³Second Affiliated Hospital, Xinjiang Medical University, Urumqi, China
• ⁴Third Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China

Gestational diabetes mellitus and preeclampsia are prevalent pregnancy complications that threaten maternal and infant health and impose a substantial socioeconomic burden. Although several interventions have been implemented, shortcomings in individualised treatment and other limiting factors require urgent in-depth research. This study aimed to examine alterations in autophagy-related gene expression in preeclampsia combined with gestational diabetes mellitus. Through bioinformatics analyses, such as gene expression profiling, weighted gene co-expression network analysis, gene ontology and KEGG enrichment analyses, machine learning modelling, immune infiltration analyses, and single-cell analyses, we identified differentially expressed autophagy-related genes linked to preeclampsia with gestational diabetes mellitus. The expression levels of the four key genes in placental samples were validated using reverse transcription quantitative polymerase chain reaction. Our findings identified potential biomarkers and mechanisms of preeclampsia with gestational diabetes mellitus, supported by single-cell analysis, thus enhancing our understanding of their molecular mechanisms. This study details the molecular mechanisms between gestational diabetes mellitus and preeclampsia, identifies potential biomarkers and therapeutic targets, and provides a valuable reference for future research and clinical applications.