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CDK1, B3GNT7, S100A9, and MMP9 Expression Associated with Thyroid Cancer Prognosis

CDK1, B3GNT7, S100A9, and MMP9 Expression Associated with Thyroid Cancer Prognosis
Photo by Logan Voss / Unsplash
Key Takeaway
Note CDK1, B3GNT7, S100A9, and MMP9 expression correlates with thyroid cancer prognosis in this retrospective cohort.

This retrospective cohort study included 180 patients with thyroid cancer treated in a hospital between May 2022 and April 2025. Researchers analyzed expression levels of CDK1, B3GNT7, S100A9, and MMP9 genes alongside immune infiltration analysis.

Patients were stratified into 126 good prognosis cases and 54 poor prognosis cases. Gene expression levels were higher in the poor prognosis group than the good prognosis group (P < 0.05). The prognostic prediction model achieved an average C-index of 0.919 (95% CI: 0.882–0.961) and an AUC of 0.880.

The poor prognosis group had lower infiltration abundance of B lymphocytes, CD4+T lymphocytes, and CD8+T lymphocytes, but higher infiltration abundance of neutrophils and macrophages (P < 0.05). CDK1, B3GNT7, S100A9, and MMP9 were negatively correlated with infiltration abundance of B lymphocytes, CD4+T lymphocytes, and CD8+T lymphocytes. High expression of S100A9 and MMP9 was correlated with advanced lymph node metastasis, distant metastasis, and overall TNM stage.

Safety data were not reported. The study is limited by its overall retrospective design and reliance on TCGA-THCA database analysis. Gene expression levels are described as independent risk factors, implying association rather than proven causation.

The prognostic prediction model may provide objective evidence for early screening of high-risk cases in specific clinical practice.

Study Details

Study typeCohort
EvidenceLevel 3
PublishedApr 2026
View Original Abstract ↓
ObjectivesTo construct a prognostic risk model for thyroid cancer based on immune genes and analyze the correlation between immune genes and immune infiltration.MethodsA retrospective study was conducted on 180 patients with thyroid cancer treated in our hospital during May 2022 to April 2025. Based on the prognosis, the subjects were graded as good prognosis group of 126 cases and poor prognosis group of 54 cases. The influencing factors were analyzed by a binary logistic regression model, receiver operating characteristic curve and goodness of fit test. Single sample gene set enrichment analysis was used to perform immune infiltration analysis on the expression matrix of peripheral blood mononuclear cells. The GSEA algorithm was used to calculate the abundance of tumor associated immune cell infiltration. Pearson correlation analysis was used to investigate the correlation. The TCGA-THCA database was used to analyze the differential expression of genes, as well as the correlation with clinical pathological features.ResultsThe expression levels of CDK1, B3GNT7, S100A9, and MMP9 genes were higher in the poor prognosis group than the good prognosis group (P < 0.05). A prognostic prediction model was constructed according to formula [1/1 + exp (4.125 + 1.250 × CDK1 + 1.880 × B3GNT7 + 0.920 × S100A9 + 1.050 × MMP9)]. The average C-index of the model was 0.919 (95% CI: 0.882–0.961). The AUC of the prognosis prediction model was 0.880. The poor prognosis group had much lower infiltration abundance of B lymphocytes, CD4+T lymphocytes, and CD8+T lymphocytes, and higher infiltration abundance of neutrophils and macrophages than the good prognosis group (P < 0.05). CDK1, B3GNT7, S100A9, and MMP9 were negatively correlated with the infiltration abundance of B lymphocytes, CD4+T lymphocytes, and CD8+T lymphocytes, and positively correlated with the infiltration abundance of neutrophils and macrophages (P < 0.05). Further analysis from the TCGA-THCA database showed that the high expression of S100A9 and MMP9 was correlated with advanced lymph node metastasis (pN stage), distant metastasis (pM stage) and overall TNM stage (P < 0.05).ConclusionCDK1, B3GNT7, S100A9, and MMP9 were independent risk factors for poor prognosis in thyroid cancer. The prognostic prediction model may provide objective evidence for early screening of high-risk cases in clinical practice.
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