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CDK1, B3GNT7, S100A9, and MMP9 Expression Associated with Thyroid Cancer PrognosisImmune Genes Could Predict Thyroid Cancer Outcomes Better

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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.

Imagine getting a thyroid diagnosis and wondering what comes next. Most patients face uncertainty about how the disease will progress. This new research offers a clearer picture of the future.

Why Thyroid Cancer Risk Is Hard To See

Thyroid cancer is common but varies wildly in severity. Some cases are mild, while others spread quickly. Doctors need better ways to tell the difference.

Current methods often rely on how big a tumor looks. This can miss subtle signs of danger inside the body. Many patients worry about recurrence after surgery.

The Surprising Shift In Cell Behavior

Traditionally, doctors looked at tumor size and spread. But here’s the twist: biology inside the cells matters more.

Scientists found that immune activity changes how cancer grows. It is not just about the tumor itself. It is about how the body reacts to it.

How The Body Fights Or Fails

Think of genes as switches controlling cell behavior. This study found four specific switches linked to danger.

High levels of these switches signal a weaker immune response. Imagine a security system that stops working before a break-in.

The immune system usually sends soldiers to fight cancer cells. In this case, those soldiers are confused or missing.

Researchers looked at 180 patients over three years. They compared blood samples from those with good and bad outcomes.

They used a computer model to find patterns in the data. This helped them build a prediction tool.

The Numbers Behind The Discovery

The model predicted outcomes with high accuracy. Patients with high gene levels had fewer helpful immune cells.

Specifically, they had fewer B cells and T cells. These are the troops that usually stop cancer growth.

This doesn’t mean this treatment is available yet.

The poor prognosis group had more neutrophils and macrophages. These cells can sometimes help cancer spread instead of stopping it.

What Experts Say About The Future

Experts say this adds a new layer to patient care. It helps doctors plan more personalized treatment paths.

Knowing the risk early allows for closer monitoring. It does not change the diagnosis, but it changes the plan.

How To Use This Information Today

You cannot get this test at your doctor today. Talk to your specialist about current standard options.

This research is a step toward better tools. It is not a replacement for current care.

Why We Must Wait For More Data

The study group was relatively small. Results need confirmation in larger, diverse populations.

Different groups of people might react differently to the genes. Science requires proof before changing practice.

What Happens Next In Research

More trials are needed before approval. Scientists are working to validate these findings globally.

If successful, this could become a standard blood test. It would help identify high-risk cases much earlier.

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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