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

A bioinformatics study from 2026 identifies potential hub genes for recurrent pregnancy loss associated with antiphospholipid syndrome. The analysis, authored by Huan Zeng, integrates differential gene expression from GEO datasets with machine learning algorithms like LASSO regression and RandomForest. It includes experimental validation via qPCR and cell function experiments.

Use Cases

• Identify diagnostic biomarkers for recurrent pregnancy loss based on hub gene expression profiles.
• Analyze immune cell infiltration patterns in patient samples using the CIBERSORT tool.
• Validate potential therapeutic targets through in-silico and experimental methods described in the study.
• Construct predictive nomogram models for disease risk assessment based on the identified hub genes.

Strengths

• Includes experimental validation via qPCR and cell function experiments, as stated in the description.
• Integrates multiple analytical methods: differential expression analysis, WGCNA, PPI analysis, and three machine learning algorithms.
• Identifies three specific hub genes (NAA30, ARHGAP44, SUGT1) with diagnostic performance assessed via ROC curves.

Limitations

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

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.