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

A 2026 study by Huan Zeng integrates multiple Gene Expression Omnibus (GEO) datasets to investigate shared pathogenic mechanisms between Recurrent Pregnancy Loss (RPL) and Antiphospholipid Syndrome (APS). The analysis identified 10 common differentially expressed genes, applied machine learning algorithms including LASSO regression and RandomForest, and validated three hub genes via qPCR and cell function experiments. The dataset is a 30.5 KB document summarizing the bioinformatics workflow and results.

Use Cases

• Identifying potential diagnostic biomarkers for APS-related RPL based on the three identified hub genes (NAA30, ARHGAP44, SUGT1).
• Analyzing immune cell infiltration patterns in RPL and APS patient samples using the CIBERSORT tool mentioned in the description.
• Training machine learning models for gene selection in reproductive disorders using the LASSO regression and RandomForest methods described.
• Performing pathway enrichment analysis on differentially expressed genes common to APS and RPL.
• Validating gene expression findings in trophoblast cell models based on the experimental validation approach outlined.

Strengths

• Integrates multiple analysis methods including differential expression, WGCNA, PPI, and three machine learning algorithms.
• Includes experimental validation via qPCR and cell function assays for the identified hub genes.
• Applies immune cell infiltration analysis using the CIBERSORT tool on patient samples.
• Released under a permissive CC-BY-4.0 license for reuse.

Limitations

• The dataset is a 30.5 KB document; the underlying raw gene expression data is not included and must be sourced separately from GEO.
• Column-level documentation is absent; field semantics must be inferred from the document's narrative description.
• Row count for any underlying expression matrices is unknown, which may limit suitability assessment for large-scale modeling.

Provenance

Source

Gene Expression Omnibus (GEO) database.

Collection Method

Integrated bioinformatics analysis combining differential expression, co-expression networks, protein-protein interaction, and machine learning.

Freshness

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