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 PErelated 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.