ArticleFrontiers in immunology2023
Olink proteomics analysis uncovers the landscape of inflammation-related proteins in patients with acute compartment syndrome.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Plasma proteomics improves thrombosis prediction in patients with cancer and identifies targetable IL-17-driven endothelial activation.Science translational medicine · 2026Article
- Exploration and Validation of the Diagnostic Potential of the Circadian Rhythm-Related Genes CCL23 and VNN1 in Adolescents with Depressive Disorder.Molecular neurobiology · 2026Article
- A plasma proteomics-based candidate biomarker panel predictive of amyotrophic lateral sclerosis.Nature medicine · 2025Article
- The causal relationship between 91 inflammatory cytokines and chronic pancreatitis, and the mediating role of 1400 metabolites.Medicine · 2025Article
- Large-scale plasma proteomics uncovers novel targets linking ambient air pollution and depression.Molecular psychiatry · 2025Article
- Liquid biopsy using plasma proteomics in predicting efficacy and tolerance of PD-1/PD-L1 blockades in NSCLC: a prospective exploratory study.Molecular biomedicine · 2025Article
- Novel Proteomic Insights into Hip Fractures in the Elderly: Unraveling Immunologic Biomarkers, Temporal Expression Patterns, and Clinical Correlations.Journal of inflammation research · 2025Article
- Development of Predictive Models for Long-Term Endoscopic Response to Ustekinumab in Crohn's Disease Based on Plasma Proteomics.Journal of inflammation research · 2025Article
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6 authors.
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Abstract
Purpose: Our primary purpose was to explore the landscape of inflammation-related proteins, and our second goal was to investigate these proteins as potential biomarkers of acute compartment syndrome (ACS), which is a serious complication of tibial fractures. Methods: We collected sera from 15 healthy subjects (control group, CG) and 30 patients with tibial fractures on admission day, comprising 15 patients with ACS (ACS group, AG) and 15 patients without ACS (fracture group, FG). Ten samples in each group were analyzed by the inflammation panel of Olink Proteomics Analysis, and all samples were verified by an ELISA. Receiver-operating characteristic (ROC) curve analysis was performed to identify the diagnostic ability and cutoff values of potential biomarkers. Results: Our findings showed that the levels of IL6, CSF-1, and HGF in the FG were significantly higher than those in the CG. Similar results were found between the AG and CG, and their cutoff values for predicting ACS compared with the CG were 9.225 pg/ml, 81.04 pg/ml, and 0.3301 ng/ml, respectively. Furthermore, their combination had the highest diagnostic accuracy. Notably, compared with FG, we only found a higher expression of CCL23 in the AG. Additionally, we identified 35.75 pg/ml as the cutoff value of CCL23 for predicting ACS in patients with tibial fractures. Conclusion: We identified CCL23 as a potential biomarker of ACS in comparison with tibial fracture patients and the significance of the combined diagnosis of IL6, CSF-1, and HGF for predicting ACS compared with healthy individuals. Furthermore, we also found their cutoff values, providing clinicians with a new method for rapidly diagnosing ACS. However, we need larger samples to verify our results.
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