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. 2025 Jun 5;28(7):112828.
doi: 10.1016/j.isci.2025.112828. eCollection 2025 Jul 18.

Olink proteomics reveals TNFRSF9 as a biomarker for abdominal aortic aneurysms

Affiliations

Olink proteomics reveals TNFRSF9 as a biomarker for abdominal aortic aneurysms

Runze Chang et al. iScience. .

Abstract

Abdominal aortic aneurysm (AAA) is a serious cardiovascular disease associated with chronic inflammation. The purpose of this study was to use the Olink proteomics to reveal serum inflammatory markers in AAA. We examined the expression levels of 92 inflammation-related proteins in patients with AAA (n = 18) and healthy individuals (n = 10) using the Olink proximity extension assay (PEA) inflammatory plate. Olink proteomics identified 38 differential proteins. Combined analysis of Olink proteomics and GSE183464 showed interleukin-6 (IL-6) and tumor necrosis factor receptor superfamily member 9 (TNFRSF9) were upregulated at both gene and protein levels in AAA patients. The ELISA results were consistent with the Olink proteomics results, and the receiver operating characteristic (ROC) curve analysis revealed that the binding of TNFRSF9 and IL-6 has high diagnostic value (Olink AUC = 0.9056; ELISA AUC = 0.950). Subsequently, elevated TNFRSF9 expression in AAA was confirmed by animal models, suggesting that TNFRSF9 may serve as a potential biomarker for AAA.

Keywords: Cardiovascular medicine; Proteomics.

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Conflict of interest statement

The authors declare no competing interests.

Figures

None
Graphical abstract
Figure 1
Figure 1
All DEPs of inflammation-related markers between the AAA (n = 18) and control groups (n = 10) (A) Protein expression in the AAA group and the control group. The x axis represents sample names, and the y axis shows NPX values. Different colors indicate different groups. Each boxplot displays five statistical measures (from top to bottom: maximum, upper quartile, median, lower quartile, and minimum. (B) Volcano plot of 92 inflammation-related proteins between the two groups. The horizontal axis in the figure represents the differential changes in protein expression (expressed as Log2(FC)), whereas the vertical axis represents the statistical significance of the differences in protein abundance (expressed as -Log210(pval)). (C) Heatmap of DEPs between the two groups.
Figure 2
Figure 2
Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analyses of differentially expressed inflammation-related proteins (A and B) Bar and bubble plots based on differential protein GO enrichment analysis. (C and D) Bar and bubble plots based on differential protein KEGG enrichment analysis.
Figure 3
Figure 3
Screening for specific inflammation-related proteins between the two groups (A) LASSO regression path of differential inflammatory proteins. (B) Cross-validation curve of LASSO regression. (C) Bar plot of regression coefficients for important feature proteins. (D) Volcano plot of differentially expressed genes in the AAA group vs. control group in GSE183464. (E) Venn diagram of LASSO and GSE183464.
Figure 4
Figure 4
Correlation of TNFRSF9 and IL-6 with all DEPs (A) Correlation analysis between DEPs. (B) Visualization of the PPI network analysis of DEPs.
Figure 5
Figure 5
Predictive value of NPX values and ELISA values for IL-6 and TNFRSF9 (A) Olink data visualization of TNFRSF9. (B) Olink data visualization of IL-6. (C) Receiver operating characteristic (ROC) curve of TNFRSF9 concentrations based on Olink data between AAA and control groups. (D) ROC curve of IL-6 concentrations based on Olink data in the AAA and control groups. (E) ROC curve of IL-6+TNFRSF9 concentrations based on Olink data in the AAA and control groups. (F) ELISA data visualization of TNFRSF9. (G) ELISA data visualization of IL-6. (H) ROC curve of TNFRSF9 based on ELISA data in the AAA and control groups. (I) ROC curve of IL-6 concentrations based on ELISA data in the AAA and control groups. (J) ROC curve of IL-6+TNFRSF9 concentrations based on ELISA data in the AAA and control groups. Data are represented as mean ± SEMs. Statistical analysis between the two groups was performed using a t test. Significance levels are annotated as follows: ns = not significant, ∗ = p value < 0.05, ∗∗ = p value < 0.01, ∗∗∗ = p value < 0.001, ∗∗∗∗ = p value < 0.0001.
Figure 6
Figure 6
Validation of TNFRSF9 expression in AAA (A) Ordinary photographs of mouse aortic tissue. (B) Ultrasound images of the aortas of the mice before sacrifice. (C) Hematoxylin and eosin (H&E) staining of the abdominal aortas of the control and AAA groups. Scale bars: 50 μm (main image), 20 μm (inset). (D) Elastica van Gieson (EVG) staining of the abdominal aortas of the control and AAA groups. Scale bars: 200 μm (main image), 100 μm (inset). (E) Serum TNFRSF9 expression levels in the control and AAA groups (n = 6). (F) Comparison of TNFRSF9 expression between the two groups by immunohistochemistry. Scale bars: 20 μm. (G) Western blot analysis of TNFRSF9 protein and β-actin proteins. Data are represented as mean ± SEM. Statistical analysis between the two groups was performed using a t test. Significance levels are annotated as follows: ns = not significant, ∗ = p value < 0.05, ∗∗ = p value < 0.01, ∗∗∗ = p value < 0.001, ∗∗∗∗ = p value < 0.0001.
Figure 7
Figure 7
Single-cell data analysis of AAA (A) Uniform manifold approximation and projection (UMAP) images of single cells isolated from the abdominal aortas of control and AAA mice. (B) Tnfrsf9 expression in the T cells of AAA mice. Data are represented as mean ± SEMs. Significance levels are annotated as follows: ns = not significant, ∗ = p value < 0.05, ∗∗ = p value < 0.01, ∗∗∗ = p value < 0.001, ∗∗∗∗ = p value < 0.0001.

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References

    1. DeRoo E., Stranz A., Yang H., Hsieh M., Se C., Zhou T. Endothelial Dysfunction in the Pathogenesis of Abdominal Aortic Aneurysm. Biomolecules. 2022;12 doi: 10.3390/biom12040509. - DOI - PMC - PubMed
    1. Abdominal aortic aneurysms. Nat. Rev. Dis. Primers. 2018;4:35. doi: 10.1038/s41572-018-0036-1. - DOI - PubMed
    1. Wanhainen A., Van Herzeele I., Bastos Goncalves F., Bellmunt Montoya S., Berard X., Boyle J.R., D'Oria M., Prendes C.F., Karkos C.D., Kazimierczak A., et al. Editor's Choice -- European Society for Vascular Surgery (ESVS) 2024 Clinical Practice Guidelines on the Management of Abdominal Aorto-Iliac Artery Aneurysms. Eur. J. Vasc. Endovasc. Surg. 2024;67:192–331. doi: 10.1016/j.ejvs.2023.11.002. - DOI - PubMed
    1. Kniemeyer H.W., Kessler T., Reber P.U., Ris H.B., Hakki H., Widmer M.K. Treatment of ruptured abdominal aortic aneurysm, a permanent challenge or a waste of resources? Prediction of outcome using a multi-organ-dysfunction score. Eur. J. Vasc. Endovasc. Surg. 2000;19:190–196. doi: 10.1053/ejvs.1999.0980. - DOI - PubMed
    1. Kent K.C. Clinical practice. Abdominal aortic aneurysms. N. Engl. J. Med. 2014;371:2101–2108. doi: 10.1056/NEJMcp1401430. - DOI - PubMed

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