Gestational Diabetes Placental Transcriptomics with Mitochondrial and Immune Cell Analysis

Transcriptomic data from the GEO repository was analyzed to identify mitochondrial-related genes associated with gestational diabetes mellitus. The study used machine learning to identify hub genes and single-cell RNA sequencing to characterize immune cell proportions in placental tissue. The dataset, authored by Rui Zhao and last updated in March 2026, is a 1.3 MB PDF containing bioinformatics analysis and validation results.

Use Cases

• Identify mitochondrial dysfunction biomarkers for gestational diabetes based on hub genes DHRS2, STX17, and TIMM44.
• Analyze pro-inflammatory remodeling of the placental immune microenvironment based on single-cell RNA sequencing data.
• Validate predictive models for GDM based on the described Mito-RGs score and nomogram.
• Investigate pathway enrichment in specific placental cell types based on GSVA and GSEA results mentioned.

Strengths

• Includes experimental validation in human placental tissues and a GDM mouse model.
• Analysis leverages multiple machine learning algorithms (LASSO, RF, XGBoost) for feature selection.
• Data is openly available under a CC-BY-4.0 license.

Limitations

• The primary data file is a 1.3 MB PDF, which may require extraction of underlying tabular data.
• Row and column counts for the underlying transcriptomic data are unknown.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

Transcriptomic data obtained from the GEO repository.

Collection Method

Combined bioinformatics analysis (machine learning, GSVA, GSEA) with experimental validation.

Freshness

Last updated 2026-03-18 07:34:17; freshness should be verified.