Growth Hormone‐Related Gene Signatures Reveal Diagnostic Biomarkers, Immune Landscapes, and Potential Therapeutic Targets in Sepsis Through WGCNA and Machine Learning Analyses
Xiuhua Li, Ang Liu, Yingwei Ding, Gang Chen, Bingwen ZhangABSTRACT
Background
Sepsis reshapes inflammatory, immune, and metabolic activity, yet the behavior of genes linked to growth hormone biology within this response has not been well defined. We therefore searched for growth hormone‐related genes (GHRGs) that could distinguish sepsis and asked whether the resulting markers also reflected variation in host immune state.
Methods
For model development, two Gene Expression Omnibus (GEO) microarray cohorts were combined, while GSE26378 and GSE28750 were kept separate for validation. Sepsis‐associated expression signals were first defined by differential analysis and weighted gene co‐expression network analysis (WGCNA), then intersected with 1205 GHRGs. A protein–protein interaction step and four machine‐learning procedures were used to reduce the candidate set. Classification was examined with receiver operating characteristic (ROC) curves and a two‐gene nomogram, with calibration and decision‐curve analyses used as complementary checks. We also examined pathway enrichment, immune‐cell patterns, predicted regulatory interactions, candidate compounds, and expression‐based molecular subgroups.
Results
The integrated cohort yielded 2552 DEGs and 1873 genes from sepsis‐associated WGCNA modules; their overlap with the GHRG collection contained 40 genes. PPI module analysis reduced this set to 23 hub candidates. Machine‐learning screening consistently retained JAK1 and GATA3, both of which were downregulated in sepsis and showed stable diagnostic performance across the training and external validation cohorts. Their combined model provided stronger discrimination than either gene alone. Functional analyses linked the two genes to T‐cell signaling, antigen processing, immune regulation, mitochondrial metabolism, and translational processes. Immune profiling revealed reduced lymphocyte‐associated features together with enhanced myeloid signatures in sepsis. When septic samples were grouped by JAK1/GATA3 expression, two subtypes with different immune and transcriptional profiles emerged.
Conclusions
JAK1 and GATA3 emerged as promising growth hormone‐related candidate diagnostic biomarkers associated with immune heterogeneity in sepsis. Their combined signature may provide a useful framework for molecular stratification and warrants further experimental and clinical validation.