Table 2_Integrative analysis of hub genes for recurrent pregnancy loss with antiphospholip

10 common differentially expressed genes were identified from RPL and APS datasets retrieved from the GEO database. The study by Huan Zeng, last updated in June 2026, used machine learning and experimental validation to identify hub genes. It suggests an imbalance of immune system-associated cells and molecules may be a common characteristic in the pathophysiological processes of both conditions.

Use Cases

• Identify potential diagnostic biomarkers for APS-related RPL based on hub gene expression profiles.
• Analyze immune cell infiltration levels in patient samples using the CIBERSORT tool mentioned in the description.
• Validate gene function through cell behavior experiments on trophoblast cells as described.
• Build predictive models for disease risk using the LASSO regression and RandomForest algorithms applied in the study.

Strengths

• Integrates multiple analysis methods including differential expression, WGCNA, PPI, and three machine learning algorithms.
• Includes experimental validation via qPCR and cell function experiments.
• Uses publicly available data from the GEO database.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.

Provenance

Source

Gene Expression Omnibus (GEO) database.

Collection Method

Integrated bioinformatics analysis of retrieved datasets, followed by machine learning and experimental validation.

Freshness

Last updated 2026-06-04 05:47:30; freshness should be verified.