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Updated: May 7, 2026
Evaluation of Tumor-infiltrating Leukocyte Subsets in a Subcutaneous Tumor Model
Published on: April 13, 2015
Constructing a chemokine-based model and identifying CCL17 as a core biomarker associated with immune infiltrates in
Mimi Zhang1, Bing Zou2, Qiang Li1
1Department of Thyroid and Breast Surgery, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, China.
Background:
Recent studies have highlighted the crucial role of chemokines in tumor progression and immune regulation, particularly in thyroid cancer (THCA). This study aims to construct a prognosis model related to chemokines and identify a potential biomarker in THCA.
Methods:
Hub genes were identified for a risk model construction using Cox analysis, which was evaluated by the Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curves. Enrichment analyses were used for functional annotation. CIBERSORT was used to calculate the immune cell infiltration, and Tumor Immune Dysfunction and Exclusion (TIDE) was applied to assess the immunotherapeutic value. Bioinformatics and experiments were employed to analyze the expressions and prognosis of the hub genes, yielding CCL17 as the core biomarker. The clinical relevance of CCL17 was then analyzed using the generalized additive models (GAM), restricted cubic spline (RCS), ROC, and decision curve analysis (DCA). In addition, we conducted cell experiments to explore the effect of CCL17 on the phenotype of tumor cells and its regulation of key pathways. Finally, connectivity map (CMap) analysis was conducted for drug prediction, and molecular docking analysis was conducted, and the effect of the drug was verified by cell experiments.
Results:
ACKR3, CCL2, and CCL17 were identified for the risk model construction with satisfactory predictive value in THCA. The immune cells were differentially expressed in the two risk groups and interacted with the hub genes. Besides, a higher TIDE score was observed in the high-risk group. Low expression of CCL17 was beneficial to prognosis (P=0.04). Elevated CCL17 level predicted lymph node metastasis and low thyroid differentiated score. Moreover, CCL17 was enriched in the JAK-STAT pathway and promoted the malignant phenotype of tumor cells by regulating the JAK-STAT pathway. This process can be inhibited by the drug TG-101348.
Conclusions:
We constructed a risk model with three chemokine-related genes (CRGs), which could effectively predict the prognosis of THCA. Notably, CCL17 expression had a considerable value to the risk model and may promote THCA progression by regulating the JAK-STAT pathway.
