ORIGINAL RESEARCH article
Front. Med.
Sec. Geriatric Medicine
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1606430
This article is part of the Research TopicMechanisms and Novel Treatments of Muscle WastingView all 3 articles
Comprehensive Profiling of Chemokine and NETosis-Associated Genes in Sarcopenia: Construction of a Machine Learning-Based Diagnostic Nomogram
Provisionally accepted
- 1Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China., Liuzhou, Guangxi Zhuang Region, China
- 2The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, China., jinan, China
- 3Department of Gastroenterology, The 960th Hospital of Chinese PLA Joint Logistics Support Force, Jinan 250031, China, jinan, China
- 4Gastroenterology Department, Liuzhou Peoples' Hospital Affiliated to Guangxi Medical University, Liuzhou, China., Liuzhou, China
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Background: Chemokines 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.Utilizing 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.Results: A 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 predictive accuracy of the nomogram models for differentiating sarcopenia from pre-sarcopenia was high, with AUC values of 0.837 (95% CI 0.703-0.947) and 0.903 (95% CI 0.789-0.989). 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.The 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.
Keywords: Sarcopenia, NEtosis, chemokine, machine learning, nomogram, bioinformatics
Received: 05 Apr 2025; Accepted: 28 May 2025.
Copyright: © 2025 Wang, Wang, Zhang, Wang, Zhao, Huang, Cai and Mo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Huaiyang Cai, Liuzhou People's Hospital Affiliated to Guangxi Medical University, Liuzhou, China., Liuzhou, Guangxi Zhuang Region, China
Shuangyang Mo, Gastroenterology Department, Liuzhou Peoples' Hospital Affiliated to Guangxi Medical University, Liuzhou, China., Liuzhou, China
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