Integrative analysis of hub genes for recurrent pregnancy loss with antiphospholipid syndrome: integrated bioinformatics analysis, machine learning and experimental validation - Scorecard - MDSpire
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Integrative analysis of hub genes for recurrent pregnancy loss with antiphospholipid syndrome: integrated bioinformatics analysis, machine learning and experimental validation
Clinical Scorecard: Comprehensive Analysis of Key Genes in Recurrent Pregnancy Loss Associated with Antiphospholipid Syndrome: A Combined Approach Using Bioinformatics, Machine Learning, and Experimental Methods
At a Glance
Category
Detail
Condition
Recurrent Pregnancy Loss (RPL) associated with Antiphospholipid Syndrome (APS)
Key Mechanisms
Imbalance of immune system-associated cells and molecules
Target Population
Women of childbearing age with recurrent pregnancy loss
Care Setting
Clinical and research settings focusing on reproductive health
Key Highlights
Identification of 10 common differentially expressed genes (DEGs) linked to RPL and APS
Three hub genes (NAA30, ARHGAP44, SUGT1) identified for potential diagnostic use
SUGT1 downregulated in RPL with APS, influencing trophoblast cell behavior
Use of machine learning and nomogram for diagnostic model construction
Exploration of associations between hub genes and pregnancy-related diseases
Guideline-Based Recommendations
Diagnosis
Utilize differential expression analysis to identify DEGs in RPL and APS patients
Employ ROC curves for validating diagnostic performance of hub genes
Management
Consider targeted therapeutic strategies based on identified biomarkers
Monitoring & Follow-up
Assess immune cell infiltration levels in RPL and APS patients
Risks
Monitor for recurrent pregnancy loss in women with positive antiphospholipid antibodies
Patient & Prescribing Data
Women of childbearing age with recurrent pregnancy loss and antiphospholipid syndrome
Potential for targeted therapies based on genetic insights
Clinical Best Practices
Incorporate genetic screening for high-risk variants in RPL patients
Utilize bioinformatics tools for comprehensive gene analysis
Implement machine learning techniques for improved diagnostic accuracy
