Mining transcriptomic data for gestational diabetes mellitus: What public datasets reveal

Figure 1 Species and tissues coverage in gestational diabetes mellitus transcriptomic studies. A: Rattus norvegicus. Datasets most frequently sample pancreatic islets, cardiac tissue and placenta; B: Mus musculus. Commonly profiled tissues include hippocampus, pancreatic islets, liver, skeletal muscle and uterus; C: Ovis aries. Sheep studies primarily focus on pancreatic islets; D: Homo sapiens. Human datasets encompass placenta, maternal peripheral blood, chorionic villi, amniocytes and fetal umbilical cord blood (e.g., venous blood).

Figure 2 The dataset counts and sample sizes across four major categories in gestational diabetes mellitus transcriptomic studies. From the innermost to the outermost ring, the concentric layers represent: (1) Broad tissue categories (placenta-related, blood and its components, pancreas/islet-related and others); (2) The number of datasets assigned to each subcategory; and (3) The cumulative sample size for each category. For example, within the placenta-related category, 19 datasets focusing on placenta were identified, together comprising 1189 samples. PBMCs: Peripheral blood mononuclear cells; sEVs: Secreted extracellular vesicles.

Figure 3 Various transcriptomic technologies in gestational diabetes mellitus research. A: Overall proportions of transcriptomic technologies represented in gestational diabetes mellitus (GDM) datasets. RNA-sequencing (RNA-seq) remains the most prevalent (43%), followed by DNA microarrays (28%), non-coding RNA-seq (13%), multi-omics (6%) and single-cell RNA-seq (scRNA-seq) (10%). These distributions highlight both the persistence of classical platforms and the growing role of emerging modalities in GDM research; B: Distribution of dataset types across research themes in GDM. Placenta-related datasets dominate, followed by circulating biomarkers and β-cell/islet, while predictive modeling studies remain fewer. RNA-seq (blue) and microarray (light blue) are the most widely used platforms overall, whereas non-coding RNA-seq (light green) and scRNA-seq (dark green) contribute substantially to biomarker and islet-related studies. Multi-omics approaches (pink), though relatively limited, are increasingly adopted in biomarker discovery and predictive modeling. RNA-seq: RNA-sequencing; scRNA-seq: Single-cell RNA-sequencing; ncRNA-seq: Non-coding RNA-sequencing.