Placental transcriptomics identifies a candidate HLA-DQA2-FGL2 immune signature in gestational diabetes mellitus.
Gestational diabetes mellitus (GDM) is a major public health challenge characterized by placental immunometabolic dysregulation. This study aimed to identify molecular signatures associated with GDM-related placental pathology and evaluate their diagnostic and therapeutic implications.
Two bulk placental transcriptomic datasets (GSE70493 and GSE263483) and one single-cell dataset (GSE173193) were analyzed. Differential expression analysis, WGCNA, and machine-learning feature selection (LASSO and SVM-RFE) were used to identify candidate gene signatures. A two-gene logistic regression model was constructed and externally validated. Immune-cell correlation rewiring and single-cell in silicoperturbation analysis explored immune-network alterations and regulatory roles of identified genes. A drug-prioritization framework was used for therapeutic screening.
Intersecting WGCNA modules with differentially expressed genes identified 29 candidates enriched in antigen presentation and mononuclear phagocyte functions. Dual-algorithm selection converged on HLA-DQA2 and FGL2. The two-gene model achieved an AUC of 0.786 in the discovery cohort and 0.813 in the external validation cohort. Immune rewiring analysis indicated a shift toward M1 macrophage polarization. Single-cell perturbation analysis supported regulatory roles of HLA-DQA2 and FGL2 within the macrophage lineage. Drug analysis reaffirmed insulin, metformin, and glyburide as stable therapeutic anchors.
The HLA-DQA2/FGL2 signature captures key aspects of placental immunometabolic dysregulation in GDM, including altered antigen presentation and macrophage-associated immune rewiring. This integrated framework provides candidate biomarkers for GDM risk stratification and generates computational hypotheses for future mechanistic and therapeutic studies.
Two bulk placental transcriptomic datasets (GSE70493 and GSE263483) and one single-cell dataset (GSE173193) were analyzed. Differential expression analysis, WGCNA, and machine-learning feature selection (LASSO and SVM-RFE) were used to identify candidate gene signatures. A two-gene logistic regression model was constructed and externally validated. Immune-cell correlation rewiring and single-cell in silicoperturbation analysis explored immune-network alterations and regulatory roles of identified genes. A drug-prioritization framework was used for therapeutic screening.
Intersecting WGCNA modules with differentially expressed genes identified 29 candidates enriched in antigen presentation and mononuclear phagocyte functions. Dual-algorithm selection converged on HLA-DQA2 and FGL2. The two-gene model achieved an AUC of 0.786 in the discovery cohort and 0.813 in the external validation cohort. Immune rewiring analysis indicated a shift toward M1 macrophage polarization. Single-cell perturbation analysis supported regulatory roles of HLA-DQA2 and FGL2 within the macrophage lineage. Drug analysis reaffirmed insulin, metformin, and glyburide as stable therapeutic anchors.
The HLA-DQA2/FGL2 signature captures key aspects of placental immunometabolic dysregulation in GDM, including altered antigen presentation and macrophage-associated immune rewiring. This integrated framework provides candidate biomarkers for GDM risk stratification and generates computational hypotheses for future mechanistic and therapeutic studies.