Establishing a diagnostic model for recurrent spontaneous abortion based on the levels of autophagy-related genes in the endometrium

Abstract

BACKGROUND: The etiology of recurrent spontaneous abortion is complex. With the development of genetics and other fields, it has been found that the abnormal expression of autophagy-related genes may lead to the imbalance of cell homeostasis, thus triggers pathological processes, such as apoptosis, inflammatory response and immunosuppressive response, and affects the endometrial microenvironment, trophoblastic function and immune cell function, thereby leading to recurrent spontaneous abortion. By using the recurrent spontaneous abortion samples in the Gene Expression Omnibus database, the expression changes and regulatory mechanisms of autophagy-related genes were analyzed, which is helpful to reveal the mechanism of recurrent spontaneous abortion and develop new therapeutic strategies. However, the specific mechanism of autophagy-related genes in recurrent spontaneous abortion and their interaction with other biological processes still need to be further studied. OBJECTIVE: To establish a risk score prognostic model for patients with recurrent spontaneous abortion based on autophagy-related genes. METHODS: Endometrial gene expression matrix of patients with recurrent abortion was obtained from the Gene Expression Omnibus database, autophagy-related genes were obtained from the Human Autophagy Database (HADb), and 30 differentially co-expressed autophagy-related genes were identified. The biological functions of autophagy-related genes were analyzed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and DisGeNET enrichment. LASSO and logistic regression analysis identified 16 autophagy-related genes as potential biomarkers. A nomogram model was then constructed, and the predictive accuracy was evaluated using receiver operating characteristic curves. Six machine learning models (random forest, support vector machine, generalized linear model, etc.) were compared to select the optimal model. Nomogram, calibration curves, and decision curve analysis were used to validate the predictive efficacy. RESULTS AND CONCLUSION: GO analysis showed that autophagy-related genes in recurrent spontaneous abortion patients were mainly enriched in autophagy regulation, catabolic processes, and formation of mitochondrial or other organelle membranes. KEGG analysis showed enrichment in autophagy regulation, phosphatidylinositol 3-kinase/protein kinase B signaling pathway, human papillomavirus infection, and neurodegeneration pathways. GSEA analysis indicated involvement in copper detoxification, proton transmembrane transport, sarcoplasmic reticulum components, and regulation of nucleoside diphosphate phosphatase activity. A predictive risk model was constructed, identifying 16 specific autophagy-related genes as predictive targets. Based on the detection efficacy of machine models, the optimal neural network model was selected, and five most important autophagy-related gene variables (MAP2K7, CALCOCO2, SAR1A, TUSC1, and STK11) were identified. Using recurrent spontaneous abortion samples from the European population in the GEO database, the gene expression patterns, differentially expressed genes, and signaling pathways in the endometrium were analyzed from the genetic single nucleotide polymorphism level. The predictive model and machine learning model with good predictive efficacy can screen new therapeutic targets and potential biomarkers for recurrent spontaneous abortion, which has certain guiding significance for clinical work and mechanism research.

1. Introduction

Recurrent spontaneous abortion (RSA) is defined as two or more consecutive spontaneous miscarriages with the same partner. Its etiology is complex and diverse, with incidence increasing with the number of miscarriages. Known causes include genetic factors, chromosomal abnormalities, uterine anatomical abnormalities, endocrine disorders, and immune factors. However, more than half of the cases remain unexplained. As a common issue in female reproductive system diseases, RSA currently lacks effective treatments to improve adverse pregnancy outcomes. Therefore, in-depth research and elucidation of the pathogenesis of RSA are of paramount importance for discovering effective therapeutic targets and developing innovative diagnostic and therapeutic strategies.

Autophagy is a multi-step process finely regulated by autophagy-related genes. It promotes the dynamic self-digestion of intracellular proteins and aged organelles under physiological and pathological conditions, which is crucial for cell survival and health. Autophagy is mainly divided into three types: microautophagy, macroautophagy, and chaperone-mediated autophagy, with macroautophagy being the most predominant, achieved by forming double-membrane vesicles that engulf and degrade unnecessary organelles and cytoplasmic proteins. As a programmed intracellular degradation mechanism, autophagy degrades vesicle contents by fusing with lysosomes, clearing accumulated proteins and damaged organelles. Recent studies have shown that autophagy plays an important role in improving oocyte quality and regulating endometrial growth. In RSA patients, decreased glutamine metabolism in the endometrium is accompanied by enhanced autophagy, and this abnormal metabolic microenvironment may adversely affect pregnancy maintenance by influencing endometrial receptivity. There is a close correlation between autophagy and RSA, and modulating autophagy levels may help improve reproductive outcomes in RSA patients.