Identification of shared biomarkers and potential therapeutic targets for antiphospholipid syndrome and recurrent miscarriage by integrated bioinformatics analysis and machine learning.

Background: Antiphospholipid syndrome (APS) is a group of clinical syndromes of thrombosis or adverse pregnancy outcomes caused by antiphospholipid antibodies that can increase the probability of miscarriage occurring in pregnant women. However, the mechanism of recurrent miscarriage (RM) induced by APS is not fully understood. The aim of this study was searching for potential shared genes in RM and APS.

Methods: We downloaded the APS and RM datasets from the GEO database and conducted differential expression analysis to obtain differentially expressed genes (DEGs). Their common DEGs were then identified. Functional enrichment analyses were performed on the common DEGs, follow by the construction of protein-protein interaction (PPI) networks. Next, machine learning was utilized to screen for their common key genes. Receiver operating characteristic curves (ROC) were applied to assess the diagnostic value of key genes. In addition, we performed immune infiltration analysis to understand the changes in their immune microenvironment. Subsequently, the Drug Gene Interaction Database (DGIdb) was searched for potential therapeutic drugs. Finally, the expression of key genes was verified by clinical samples.

Results: We identified a total of 52 common DEGs. Functional enrichment analyses indicated that neutrophil extracellular trap formation, cellular and molecular imbalances in the immune system may be a common mechanism in the pathophysiology of APS and RM. Machine learning identified CCR1, MNDA, S100A8 and CXCR2 as common key genes. The key genes were highly expressed in both APS and RM. In addition, we utilized the Drug Gene Interaction Database (DGIdb) to screen for potential therapeutic drugs targeting the key genes. Finally, we validated the expression of key genes by immunohistochemical staining and RT-qPCR.

Conclusion: CCR1, MNDA, S100A8 and CXCR2 are shared biomarkers between RM and APS. Meanwhile, our study further elucidated the biological mechanism between APS and RM.