Integrative analysis of hub genes for recurrent pregnancy loss with antiphospholipid syndrome: integrated bioinformatics analysis, machine learning and experimental validation.

Huan Zeng, Chengming Ding, Weilei Dong, Haochuan Long, Zhen Liu, Yulu Guo, Yuji Xiao, Wenyan Liao

Abstract read

In one paragraph

Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Huan Zeng *The First Affiliated Hospital, Department of Gynaecology and Obstetrics, Hengyang Medical School, University of South China, Hengyang, Hunan, China.

Chengming Ding *The First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China.

Weilei Dong *The First Affiliated Hospital, Department of Gynaecology and Obstetrics, Hengyang Medical School, University of South China, Hengyang, Hunan, China.

Haochuan LongThe First Affiliated Hospital, Department of Gynaecology and Obstetrics, Hengyang Medical School, University of South China, Hengyang, Hunan, China.

Zhen LiuThe First Affiliated Hospital, Department of Gynaecology and Obstetrics, Hengyang Medical School, University of South China, Hengyang, Hunan, China.

Yulu GuoThe First Affiliated Hospital, Department of Gynaecology and Obstetrics, Hengyang Medical School, University of South China, Hengyang, Hunan, China.

Yuji XiaoThe First Affiliated Hospital, Department of Hepatopancreatobiliary Surgery, Hengyang Medical School, University of South China, Hengyang, Hunan, China.

Wenyan LiaoThe First Affiliated Hospital, Department of Gynaecology and Obstetrics, Hengyang Medical School, University of South China, Hengyang, Hunan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Recurrent Pregnancy Loss (RPL) is significantly associated with Antiphospholipid Syndrome (APS), yet the shared pathogenic mechanism remains unclear. This study identifies potential hub genes for APS-related RPL, providing insights into its pathogenesis and potential diagnostic strategies. Methods: We retrieved APS and RPL datasets from the GEO database and performed differential expression analysis to identify differentially expressed genes. Thereafter, Gene Ontology (GO) analysis, pathway enrichment analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) analysis were carried out. For hub gene screening, Least Absolute Shrinkage and Selection Operator (LASSO) regression, Gaussian mixture model (GMM), and RandomForest (RF) algorithms were applied to analyze the gene expression profiles of RPL. The CIBERSORT tool was utilized to assess the immune cell infiltration levels in samples from RPL and APS patients, while the "clusterProfiler" R package was employed to perform single-gene Gene Set Enrichment Analysis (GSEA) for each hub gene in both cohorts. Three hub genes were validated for diagnostic performance via receiver operating characteristic (ROC) curves, with a nomogram model subsequently constructed and its efficacy assessed using ROC and calibration curves. Additionally, the Comparative Toxicogenomics Database was used to explore the associations between these hub genes and pregnancy-related diseases. Finally, quantitative polymerase chain reaction (qPCR) and cell function experiments were conducted to validate the expression and functional characteristics of the hub genes. Results: In this study, RPL and APS datasets were obtained from Gene Expression Omnibus (GEO) database, 10 common differentially expressed genes(DEGs)were identified, including eight downregulated and two upregulated genes. The analysis of these shared DEGs indicated that the imbalance of immune system-associated cells and molecules might represent a common characteristic in the pathophysiological processes of both APS and RPL. Through machine learning and the construction of a nomogram, we identified NAA30, ARHGAP44, and SUGT1 as hub genes. Further experiments revealed that SUGT1 was downregulated in RPL with APS and could influence the biological behavior of trophoblast cells. Conclusion: The present study provides valuable insights into the molecular mechanisms underlying RPL with APS, while also identifying potential biomarkers and therapeutic targets for this disease.

Indexed as

Abortion, HabitualAntiphospholipid SyndromeComputational BiologyMachine LearningDatabases, GeneticFemaleGene Expression ProfilingGene OntologyGene Regulatory NetworksHumansPregnancyProtein Interaction MapsTranscriptomeanti-phospholipid syndromebioinformatics analysesimmune infiltrationmachine learningnomogramrecurrent pregnancy lossregulatory networks

Identifiers

PMID42327749

DOI10.3389/fimmu.2026.1783244

PMCPMC13275653

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.