Table 4_Integrative analysis of hub genes for recurrent pregnancy loss with antiphospholip

A bioinformatics study identifies potential hub genes for recurrent pregnancy loss associated with antiphospholipid syndrome. The analysis integrates differential expression, machine learning, and experimental validation from datasets retrieved from the Gene Expression Omnibus (GEO) database. The work was authored by Huan Zeng and last updated on June 4, 2026.

Use Cases

• Identify diagnostic biomarkers for recurrent pregnancy loss with antiphospholipid syndrome based on hub gene expression profiles.
• Analyze immune cell infiltration levels in patient samples using the CIBERSORT tool methodology described.
• Validate potential therapeutic targets through gene set enrichment analysis (GSEA) of candidate hub genes.
• Build predictive models for disease diagnosis using machine learning algorithms like LASSO regression and RandomForest as applied in the study.

Strengths

• The study employs a multi-method validation approach, including bioinformatics analysis, machine learning, and experimental qPCR.
• Identifies three specific hub genes (NAA30, ARHGAP44, SUGT1) with diagnostic performance assessed via ROC curves.
• Data is openly shared under a CC-BY-4.0 license, facilitating reuse and verification.

Limitations

• The dataset is very small at 47.0 KB, suggesting limited scope, likely containing summary results rather than raw expression data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale ML training.

Provenance

Source

Gene Expression Omnibus (GEO) database.

Collection Method

Integrated bioinformatics analysis of differential expression, WGCNA, PPI networks, and machine learning.

Freshness

Last updated 2026-06-04 05:47:31; freshness should be verified.