Table 3_Integrative analysis of hub genes for recurrent pregnancy loss with antiphospholip

Gene expression data analysis identifies potential biomarkers for recurrent pregnancy loss associated with antiphospholipid syndrome. The study by Huan Zeng integrates differential expression analysis from the GEO database with machine learning techniques. It was last updated on June 4, 2026.

Use Cases

• Identifying diagnostic biomarkers for APS-related RPL based on hub gene expression profiles.
• Analyzing immune cell infiltration patterns in RPL and APS patient samples using the CIBERSORT tool.
• Training machine learning models for gene prioritization using LASSO regression and RandomForest algorithms.
• Validating gene function through in vitro experiments on trophoblast cell behavior.

Strengths

• Integrated analysis combines multiple bioinformatics methods (WGCNA, PPI) and machine learning models (LASSO, RF, GMM).
• Includes experimental validation via qPCR and cell function experiments for identified hub genes.
• Data sourced from the authoritative Gene Expression Omnibus (GEO) database.

Limitations

• The dataset is very small at 34.5 KB, indicating limited scope.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.

Provenance

Source

Gene Expression Omnibus (GEO) database.

Collection Method

Integrated bioinformatics analysis including differential expression, WGCNA, PPI network construction, and machine learning.

Freshness

Last updated 2026-06-04 05:44:43