HS3ST3A1 and CAPN8 Serve as Immune-Related Biomarkers for Predicting the Prognosis in Thyroid Cancer

Zhao-Hui Chen; Hao-Ran Yue; Jun-Hui Li; Ruo-Yu Jiang; Xiao-Ning Wang; Xue-Jie Zhou; Yue Yu; Xu-Chen Cao

doi:10.1155/2022/6724295

HS3ST3A1 and CAPN8 Serve as Immune-Related Biomarkers for Predicting the Prognosis in Thyroid Cancer

J Oncol. 2022 Dec 22:2022:6724295. doi: 10.1155/2022/6724295. eCollection 2022.

Authors

Zhao-Hui Chen^{1

2

3

4}, Hao-Ran Yue^{1

2

3

4}, Jun-Hui Li^{1

2

3

4}, Ruo-Yu Jiang⁵, Xiao-Ning Wang⁵, Xue-Jie Zhou^{1

2

3

4}, Yue Yu^{1

2

3

4}, Xu-Chen Cao^{1

2

3

4}

Affiliations

¹ The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060, China.
² Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin 300060, China.
³ Key Laboratory of Cancer Prevention and Therapy, Tianjin 300060, China.
⁴ Tianjin's Clinical Research Center for Cancer, Tianjin 300060, China.
⁵ Department of General Surgery, Tianjin Medical University General Hospital, Tianjin 300052, China.

Abstract

Background: Thyroid cancer (TC) tends to be a common malignancy worldwide and results in various outcomes due to its different subtypes. The tumor microenvironment (TME) was demonstrated to play crucial roles in various malignancies, including thyroid cancer. This study combined the ESTIMATE and CIBERSORT algorithms, identified four TME-related genes, and evaluated their correlation with clinical characteristics. These findings revealed the malignant performance of TME in TC, and the TME-related DEGs might serve as prognostic biomarkers, which can be utilized for the prediction of immunotherapy effects in patients with TC.

Methods: The clinical and gene expression profiles of TC patients were collected from the TCGA dataset. The ESTIMATE algorithm was utilized to estimate stromal and immune scores and predict the level of stromal and immune cell infiltration. The differential expressed genes related to TME were filtered by the "limma" package in R, and the PPI network was constructed by a string website. KEGG pathway and GO analyses were performed to investigate the biological progression and molecular functions of TME-related DEGs. Then, univariate Cox regression analysis was employed to screen four genes correlated with clinical characteristics. GSEA was conducted to assess their roles in the TME of TC. To further investigate the association between TME-related genes and tumor-infiltrating immune cells (TIICs), the CIBERSORT algorithm was performed. Finally, the malignancy behaviors of the two genes were verified by RT-qPCR, IHC, MTT, colony formation, and transwell assays.

Results: Four TME-related DEGs, LRRN4CL, HS3ST3A1, PCOLCE2, and CAPN8, were identified and were significantly predictive of poor overall survival. KEGG and GO pathway analysis established that the TME-related DEGs were involved in immune responses and pathways in cancer. Furthermore, the malignancy behaviors of HS3ST3A1 and CAPN8 were verified by cellular functional experiments. These results revealed that the TME-related genes HS3ST3A1 and CAPN8 were able to serve as predictors of prognosis in patients with TC.

Conclusion: HS3ST3A1 and CAPN8 may serve as valuable prognostic biomarkers and TME indicators, which can be utilized for the prediction of immunotherapy effects and provide novel treatment strategies for patients with TC.