DPP4 (dipeptidyl peptidase 4) is expressed in many cancers, but the relationship between DPP4 and thyroid carcinoma (THCA) is incompletely understood. We aim to explore the expression of DPP4 in THCA and the correlation between DPP4 expression with the prognosis of THCA and antitumor immunity. We systematically analyzed data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases and explored DPP4 expression, its impact on prognosis, and its relationship with antitumor immunity in THCA. Next, we collected 18 pairs of fresh THCA and adjacent paracancerous tissues and performed RT-qPCR to validate the DPP4 mRNA level. Concurrently, immunohistochemistry (IHC) analysis was performed on 12 pairs of paraffin-embedded tissues of medullary thyroid carcinoma (MTC) and paracancerous tissues to validate the DPP4 protein level. Bioinformatics analysis showed that DPP4 mRNA expression in THCA was significantly higher than that in paracancerous tissues ( p < 0.01 ). DPP4 was expressed at the highest levels in MTC than in other pathological types. The DPP4 expression level was different between groups with different clinical characteristics. The higher the DPP4 expressed in THCA, the lower the disease-free survival (DFS) was (HR = 1.8, p = 0.048 ). DPP4 was significantly correlated with immune cell infiltration and immune response and was positively associated with 21 immune checkpoint genes (ICGs) in THCA ( p < 0.05 ). The results of RT-qPCR showed that the relative mRNA expression of DPP4 was significantly upregulated in 18 THCA tissues compared to that in paracancerous tissues ( p = 0.011 ). IHC results showed that the DPP4 protein level was higher in 12 MTC tissues than in paracancerous tissues ( p = 0.011 ). In conclusion, DPP4 is a potential prognostic marker of THCA and may become an effective target for immunotherapy.
Background: S100 calcium-binding protein A11 (S100A11) has important roles in tumorigenesis and multiple cancer progression. In this study, we aimed to analyze the expression and prognostic value of S100A11 across cancers and further explore the relationship between S100A11 and the tumor immune microenvironment. Methods: We analyzed the differential expression of S100A11 in the TIMER, GEPIA, and BioGPS databases and searched for its prognostic impact in the GEPIA and Kaplan-Meier plotter databases. We used the SangerBox database to investigate the relationship between S100A11 expression and the tumor immune microenvironment. The TIMER database explored the relationship between S100A11 expression and tumor immune-infiltrated cells (TILs). Correlation analysis of S100A11 expression with clinical parameters in thyroid carcinoma (THCA) was performed using the UALCAN database. The co-expression network of S100A11 in THCA was explored through the LinkedOmics database. RT‒qPCR and immunohistochemical (IHC) staining were used to analyze the expression level of S100A11 in THCA. Results: S100A11 expression was higher in many tumors than in paired normal tissues, and increased expression was associated with poor prognosis, including overall survival (OS), recurrence-free survival (RFS), and disease-free survival (DFS). S100A11 was differentially expressed in immune subtypes and molecular subtypes of some cancers. The expression of S100A11 was correlated with immune checkpoints (ICP), tumor mutational burden (TMB), microsatellite instability (MSI), neoantigens, and TILs. The methylation level of S100A11 was negatively correlated with mRNA expression. S100A11 expression had a specific correlation with the clinical parameters of THCA. In THCA, the coexpression network of S100A11 was mainly involved in regulating inflammation and immune responses. RT‒qPCR and IHC staining confirmed that S100A11 was upregulated in THCA. Conclusion: S100A11 may be related to the regulation of the tumor microenvironment. S100A11 may serve as a potential pan-cancer biomarker for prognosis. S100A11 could be a potential target for THCA immunotherapy.
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