Data Sheet 3_Comprehensive profiling of chemokine and NETosis-associated genes in sarcopenia: construction of a machine learning-based diagnostic nomogram.pdf

BackgroundChemokines and neutrophil extracellular trap formation (NETosis) are critical drivers of inflammatory responses. However, the molecular characteristics and interaction mechanisms of these processes in sarcopenia remain incompletely understood.
MethodsUtilizing the mRNA expression profile dataset GSE226151 (including 19 sarcopenia, 19 pre-sarcopenia, and 20 healthy control samples), enrichment analysis was performed to identify differentially expressed NETosis-related genes (DENRGs) and chemokine-related genes (DECRGs). Two machine learning algorithms and univariate analysis were integrated to screen signature genes, which were subsequently used to construct diagnostic nomogram models for sarcopenia. Single-gene Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were used to investigate pathway associations, followed by the construction of a gene interaction network.
ResultsA total of 7 DECRGs and DENRGs were identified, primarily enriched in chemokine signaling pathways, cytokine-cytokine receptor interactions, and sarcopenia-related diseases. Machine learning and univariate analysis revealed three signature genes (CXCR1, CXCR2, and LPL). The nomogram models demonstrated high predictive accuracy in distinguishing sarcopenia from both healthy and pre-sarcopenic states, as evidenced by AUC values of 0.837 (95% CI 0.703–0.947) and 0.903 (95% CI 0.789–0.989), respectively. Single-gene GSEA highlighted significant associations between these genes and the JAK-STAT and PPAR signaling pathways. GSVA indicated that sarcopenia was closely linked to upregulated chemokine signaling, cytokine-receptor interaction activities, and leukocyte transendothelial migration.
ConclusionThe research pinpointed three genes associated with chemokines and NETosis (CXCR1, CXCR2, LPL) and developed highly accurate diagnostic models, offering a new and preliminary approach to differentiate sarcopenia and its early stages.