Preeclampsia Endoplasmic Reticulum Stress Gene Expression Analysis

Four endoplasmic reticulum stress-related feature genes (HTRA1, GBA1, KL, PC) were identified from placental samples in a bioinformatic study of preeclampsia. The dataset includes results from differential expression analysis, WGCNA, and machine learning model construction, achieving an AUC of 0.846 for diagnostic prediction. Author Chunzi Xu published the supporting data in 2026.

Use Cases

• Validate the diagnostic model using the four identified feature genes (HTRA1, GBA1, KL, PC) on independent placental transcriptomics data.
• Analyze correlations between gene expression levels of KL and PC and immune infiltration scores for M1 and M2 macrophages.
• Investigate the enrichment of HTRA1, GBA1, KL, and PC in metabolic reprogramming pathways like amino acid and central energy metabolism.
• Perform molecular docking simulations using the protein structures encoded by the signature genes to screen for potential drug candidates.

Strengths

• Includes results from multiple analytical methods (LASSO, RF, SVM-RFE, WGCNA) applied to a specific cohort of 20 PE and 21 control samples.
• The diagnostic model built from the four feature genes achieved a reported AUC of 0.846.
• Data is published under a permissive CC BY 4.0 license, facilitating reuse.

Limitations

• The underlying sample size for the original gene expression analysis is small (41 total placental samples).
• Dataset consists of processed analytical results, not raw sequencing reads or expression matrices, limiting re-analysis scope.
• Temporal and geographic coverage of the original biological samples is not specified.

Provenance

Source

figshare, authored by Chunzi Xu.

Collection Method

Bioinformatic analysis of publicly available gene expression dataset GSE114691 using R, WGCNA, and machine learning algorithms.

Time Range

null

Freshness

Last updated in March 2026.

Geography

null