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HS3ST3A1和CAPN8作为预测甲状腺癌预后的免疫相关生物标志物。

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

作者信息

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

机构信息

The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin 300060, China.

Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education, Tianjin 300060, China.

出版信息

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

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.

摘要

背景

甲状腺癌(TC)在全球范围内往往是一种常见的恶性肿瘤,由于其不同的亚型而导致各种不同的结果。肿瘤微环境(TME)在包括甲状腺癌在内的各种恶性肿瘤中发挥着关键作用。本研究结合ESTIMATE和CIBERSORT算法,鉴定了四个与TME相关的基因,并评估了它们与临床特征的相关性。这些发现揭示了TME在TC中的恶性表现,且与TME相关的差异表达基因(DEGs)可能作为预后生物标志物,可用于预测TC患者的免疫治疗效果。

方法

从TCGA数据集中收集TC患者的临床和基因表达谱。利用ESTIMATE算法估计基质和免疫评分,并预测基质和免疫细胞浸润水平。通过R语言中的“limma”包筛选与TME相关的差异表达基因,并通过string网站构建蛋白质-蛋白质相互作用(PPI)网络。进行KEGG通路和基因本体(GO)分析,以研究与TME相关的DEGs的生物学进程和分子功能。然后,采用单因素Cox回归分析筛选出四个与临床特征相关的基因。进行基因集富集分析(GSEA)以评估它们在TC的TME中的作用。为了进一步研究与TME相关的基因和肿瘤浸润免疫细胞(TIICs)之间的关联,运行CIBERSORT算法。最后,通过实时定量聚合酶链反应(RT-qPCR)、免疫组织化学(IHC)、MTT法、集落形成实验和Transwell实验验证了这两个基因的恶性行为。

结果

鉴定出四个与TME相关的DEGs,即LRRN4CL、HS3ST3A1、PCOLCE2和CAPN8,它们显著预测总体生存率较差。KEGG和GO通路分析表明,与TME相关的DEGs参与免疫反应和癌症相关通路。此外,通过细胞功能实验验证了HS3ST3A1和CAPN8的恶性行为。这些结果表明,与TME相关的基因HS3ST3A1和CAPN8能够作为TC患者预后的预测指标。

结论

HS3ST3A1和CAPN8可能作为有价值的预后生物标志物和TME指标,可用于预测免疫治疗效果,并为TC患者提供新的治疗策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9fea/9800087/e4060406e78f/JO2022-6724295.001.jpg

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