Hub Gene Analysis for Recurrent Pregnancy Loss with Antiphospholipid Syndrome

Integrated bioinformatics and machine learning identified three hub genes linked to recurrent pregnancy loss in patients with antiphospholipid syndrome. The study by Huan Zeng analyzed gene expression profiles from the GEO database, applying LASSO regression and RandomForest algorithms. Experimental validation via qPCR confirmed the downregulation of the SUGT1 gene in affected trophoblast cells.

Use Cases

• Identifying diagnostic biomarkers for recurrent pregnancy loss based on hub gene expression profiles
• Analyzing immune cell infiltration patterns in patient samples using the CIBERSORT tool
• Building predictive nomogram models for disease diagnosis based on machine learning results
• Exploring gene-disease associations for pregnancy-related conditions via the Comparative Toxicogenomics Database

Strengths

• Analysis integrates results from multiple machine learning algorithms (LASSO, GMM, RandomForest) for hub gene screening
• Includes experimental validation via qPCR and cell function assays to support bioinformatics findings
• Published under a permissive CC-BY-4.0 license, allowing for reuse and redistribution

Limitations

• The primary data file is a 47.0 KB DOC document, suggesting the dataset likely contains summarized analysis results rather than raw expression data
• Row and column counts are unknown, limiting assessment of the underlying data's scale and structure
• Description metadata is limited; actual data quality requires manual inspection after download

Provenance

Source

Gene Expression Omnibus (GEO) database

Collection Method

Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) analysis performed on retrieved datasets

Freshness

Last updated 2026-06-04 05:44:44; freshness should be verified