Revealing ANXA6 as a Novel Autophagy-related Target for Pre-eclampsia Based on the Machine Learning

Abstract

Background: Preeclampsia (PE) is a severe pregnancy complication associated with autophagy. Objective: This research sought to uncover autophagy-related genes in pre-eclampsia through bioinformatics and machine learning. Methods: GSE75010 from the GEO series was subjected to WGCNA to identify key modular genes in PE. Autophagy genes retrieved from the THANATOS overlapped with the modular genes to yield PE-related autophagy genes. Furthermore, the crucial step involved the utilization of two machine learning algorithms (LASSO and SVM-RFE) for dimensionality reduction. The candidate gene was further verified by quantitative reverse transcription polymerase chain reaction, western blot, and immunohistochemistry. Preliminary experiments were conducted on HTR-8/SVneo cell lines to explore the role of candidate genes in autophagy regulation. Results: WGCNA identified 291 genes from 5 hubs, and after overlapping with 1087 autophagy-related genes obtained from THANATOS, 42 PE-related ARGs were identified. ANXA6 was recognized as a potential target through SVM-RFE and LASSO analyses. The mRNA and protein expression of ANXA6 were verified in placenta samples. In HTR8/SVneo cells, modulating ANXA6 expression altered autophagy levels. Knocking down ANXA6 resulted in an anti-autophagy effect, which was reversed by treatment with CAL101, an inhibitor of PI3K, Akt, and mTOR. Conclusion: We observed that ANXA6 may serve as a possible PE action target and that autophagy may be crucial to the pathogenesis of PE.