ArticleFrontiers in medicine2025
Identification of shared biomarkers and potential therapeutic targets for antiphospholipid syndrome and recurrent miscarriage by integrated bioinformatics analysis and machine learning.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 Conclusion:
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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 OpenQuestion graph.