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Identification of early diagnostic biomarkers in venous thromboembolism: A bioinformatic analysis based on crosstalk between pyroptosis and venous thromboembolism

  • Authors:
    • Shengbin Han
    • Jingzhe Xu
    • Chenchen Yu
    • Hongxi Guan
    • Shun Ding
  • View Affiliations / Copyright

    Affiliations: Department of Vascular Surgery, The First Affiliated Hospital, Kunming Medical University, Kunming, Yunnan 650032, P.R. China
    Copyright: © Han et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 24
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    Published online on: November 18, 2025
       https://doi.org/10.3892/etm.2025.13019
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Abstract

Venous thromboembolism (VTE) is a common vascular disease and a major cause of mortality. Development of early diagnostic biomarkers that accurately predict the occurrence of VTE is key for its initial management. The present study was designed to identify potential early diagnostic biomarkers based on the crosstalk between pyroptosis and VTE. The GSE19151 and GSE48000 datasets were utilized as the training and validation cohorts, respectively. Pyroptosis‑related genes (PRGs) were sourced from the existing literature. Multiple bioinformatic analyses were conducted to pinpoint key PRGs in VTE. The possible functions of these genes were elucidated through gene set enrichment analysis (GSEA). Molecular regulatory networks were synthesized to probe into the underlying molecular mechanism of VTE. Moreover, a total of 5 pairs of frozen blood samples were analyzed quantifiably by reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR) to evaluate the expression levels of these biomarkers. a total of Five critical biomarkers (RPL31, RPL34, RPL9, RPS27L and HINT1) were eventually screened, with significantly elevated expression levels observed in VTE samples in both the training and validation cohorts compared with control. The RT‑qPCR results further confirmed that expression trends of these genes were consistent with those in the GSE19151 and GSE48000 datasets. GSEA indicated a correlation between the five biomarkers and ribosomal proteins as well as oxidative phosphorylation signaling pathways, suggesting their potential role in triggering VTE by regulating pyroptosis‑inflammation‑coagulation axis. A total of five critical pyroptosis‑related biomarkers have been initially characterized, showing potential for early diagnosis of VTE. While these findings are promising, further investigation into the precise mechanisms and clinical thresholds is warranted.
View Figures

Figure 1

Inter group differences of pyroptosis
score. Pyroptosis score in the VTE group was notably elevated
compared with the control group, suggesting a substantial impact of
pyroptosis on the onset and progression of VTE.
**P<0.01. VTE, venous thromboembolism.

Figure 2

Relationships between modules and
traits shown on heatmap. Following the generation of the weighted
gene co-expression network analysis (network, the relationships
between modules and traits were shown on heatmap. VTE, venous
thromboembolism. ME, module eigen-gene. The hierarchical clustering
tree analysis showed distinct co-expression blocks for the filtered
genes, resulting in the identification of a total of 10 modules.
These 10 modules are represented by 10 different colors on the
left. Moreover, their relationships with pyroptosis score and VTE
were demonstrated by P-value. The darker the color (middle and
right columns), the more significant the statistical difference.
Cold and warm colors represent negative and positive correlations,
respectively.

Figure 3

Volcano map of differentially
expressed genes. The distinction of gene expression between VTE
samples and normal samples was analyzed. VTE, venous
thromboembolism.

Figure 4

Venn Diagram (intersection of DEGs and
hub genes). A number of 52 DE-PRGs were filtrated by feat of
intersection of DEGs and hub genes. DEGs, differentially expressed
genes; WGCNA, weighted gene co-expression network analysis.

Figure 5

PPI network. The PPI network
demonstrated the interaction among 46 key differentially
expressed-pyroptosis-related genes at the protein level. PPI,
protein-protein interaction.

Figure 6

Venn diagram based on six algorithms.
A total of 18 candidate genes were identified by intersecting the
top 20 genes from each of the six algorithms utilized in the
analysis. MCC, maximal clique centrality. MNC, maximum neighborhood
component. EPC, edge percolated component.

Figure 7

LASSO algorithm. A total of 11
feature genes were further verified through LASSO logistic
regression algorithm. LASSO, least absolute shrinkage and selection
operator.

Figure 8

SVM-RFE algorithm. 14 feature genes
were confirmed via the SVM-RFE algorithm. CV, copy variant;
SVM-RFE, support vector machine-recursive feature elimination.

Figure 9

Venn diagram based on LASSO and
SVM-REF. A total of 10 candidate biomarkers were subsequently
screened when these genes were intersected. LASSO, least absolute
shrinkage and selection operator; SVM-REF, support vector
machine-recursive feature elimination

Figure 10

Receiver operating characteristics
analysis of five final biomarkers. A total of five key biomarkers
(RPL31, RPL34, RPL9, RPS27L and HINT1) were screened under the
condition of ROC >0.7. AUC, area under the curve.

Figure 11

Expression levels of five biomarkers
in the training cohort (GSE19151). The expression levels of these
five biomarkers in the training cohort were significantly increased
in VTE samples. ****P<0.0001. VTE, venous
thromboembolism.

Figure 12

Expression levels of five biomarkers
in the validation cohort (GSE48000). The expression levels of these
five biomarkers in the validation cohort were significantly
increased in VTE samples. *P<0.05 and
**P<0.01. VTE, venous thromboembolism.

Figure 13

Expression levels of five biomarkers
in the western blotting experiments. The result showed that all
five biomarkers significantly increased in the VTE group.
**P<0.01, ***P<0.001 and
****P<0.0001. VTE, venous thromboembolism.

Figure 14

Gene set enrichment analysis of the
five biomarkers. Ribosome and oxidative phosphorylation signaling
pathways exhibited significant enrichment in the context of these
five biomarkers.
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Copy and paste a formatted citation
Spandidos Publications style
Han S, Xu J, Yu C, Guan H and Ding S: Identification of early diagnostic biomarkers in venous thromboembolism: A bioinformatic analysis based on crosstalk between pyroptosis and venous thromboembolism. Exp Ther Med 31: 24, 2026.
APA
Han, S., Xu, J., Yu, C., Guan, H., & Ding, S. (2026). Identification of early diagnostic biomarkers in venous thromboembolism: A bioinformatic analysis based on crosstalk between pyroptosis and venous thromboembolism. Experimental and Therapeutic Medicine, 31, 24. https://doi.org/10.3892/etm.2025.13019
MLA
Han, S., Xu, J., Yu, C., Guan, H., Ding, S."Identification of early diagnostic biomarkers in venous thromboembolism: A bioinformatic analysis based on crosstalk between pyroptosis and venous thromboembolism". Experimental and Therapeutic Medicine 31.1 (2026): 24.
Chicago
Han, S., Xu, J., Yu, C., Guan, H., Ding, S."Identification of early diagnostic biomarkers in venous thromboembolism: A bioinformatic analysis based on crosstalk between pyroptosis and venous thromboembolism". Experimental and Therapeutic Medicine 31, no. 1 (2026): 24. https://doi.org/10.3892/etm.2025.13019
Copy and paste a formatted citation
x
Spandidos Publications style
Han S, Xu J, Yu C, Guan H and Ding S: Identification of early diagnostic biomarkers in venous thromboembolism: A bioinformatic analysis based on crosstalk between pyroptosis and venous thromboembolism. Exp Ther Med 31: 24, 2026.
APA
Han, S., Xu, J., Yu, C., Guan, H., & Ding, S. (2026). Identification of early diagnostic biomarkers in venous thromboembolism: A bioinformatic analysis based on crosstalk between pyroptosis and venous thromboembolism. Experimental and Therapeutic Medicine, 31, 24. https://doi.org/10.3892/etm.2025.13019
MLA
Han, S., Xu, J., Yu, C., Guan, H., Ding, S."Identification of early diagnostic biomarkers in venous thromboembolism: A bioinformatic analysis based on crosstalk between pyroptosis and venous thromboembolism". Experimental and Therapeutic Medicine 31.1 (2026): 24.
Chicago
Han, S., Xu, J., Yu, C., Guan, H., Ding, S."Identification of early diagnostic biomarkers in venous thromboembolism: A bioinformatic analysis based on crosstalk between pyroptosis and venous thromboembolism". Experimental and Therapeutic Medicine 31, no. 1 (2026): 24. https://doi.org/10.3892/etm.2025.13019
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