阐明细胞焦亡在甲状腺乳头状癌中的作用：预后、免疫学及治疗方面的观点

Elucidating the role of Pyroptosis in papillary thyroid cancer: prognostic, immunological, and therapeutic perspectives.

作者信息

Li Fang, Du Rui, Kou Jiedong, Li Jingting, Zhou Le, Zhang Daqi, Fu Yantao, Dionigi Gianlorenzo, Bertoli Simona, Sun Hui, Liang Nan

机构信息

Division of Thyroid Surgery, The China-Japan Union Hospital of Jilin University, Jilin Provincial Key Laboratory of Surgical Translational Medicine, Jilin Provincial Precision Medicine Laboratory of Molecular Biology and Translational Medicine on Differentiated Thyroid Carcinoma, Changchun, 130031, Jilin, China.

Division of General Surgery, Endocrine Surgery Unit, Endocrine Surgery Research Institute, Istituto Auxologico Italiano Capitanio (IRCCS), University of Milan, 20122, Milan, Italy.

出版信息

Cancer Cell Int. 2024 Jan 29;24(1):45. doi: 10.1186/s12935-024-03229-0.

DOI:10.1186/s12935-024-03229-0

PMID:38287330

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10823616/

Abstract

BACKGROUND

Pyroptosis, an inflammatory form of programmed cell death, has been implicated in the pathogenesis and progression of several cancers. However, the significance of pyroptosis-related genes (PRGs) in papillary thyroid cancer (PTC) remains unclear.

METHODS

Transcriptome and clinical data of PTC patients were obtained from The Cancer Genome Atlas. The expression patterns of PRGs were identified by consensus clustering. A prognostic model for predicting the thyroid cancer-free interval (TCFi) employed five machine learning methods. Enrichment and immune-related analyses were performed to elucidate the role of pyroptosis. The responses to radioactive iodine (RAI), immune checkpoint inhibitors (ICIs), molecular targeted therapy (MTT), and chemotherapy (CTx) were predicted based on pyroptosis-derived features. Additionally, the expression of prognostic PRGs was validated via six external datasets, 16 cell lines, and 20 pairs of clinical samples.

RESULTS

PTC patients were classified into three PyroClusters, C1 exhibited BRFA-like tumors with the highest invasiveness and the worst prognosis, C2 presented RAS-like tumors, and C3 was characterized by gene fusion. Nine PRGs (CXCL8, GJA1, H2BC8, IFI27, PRDM1, PYCARD, SEZ6L2, SIGLEC15, TRAF6) were filtered out to construct a PyroScore prognostic model. A derived nomogram demonstrated superior predictive performance than four clinical staging systems. A strong correlation between pyroptosis and tumor immune microenvironment (TIME) remodeling was observed in mechanistic analyses. Patients with a high PyroScore exhibited "hot" tumor immunophenotypes and had a poorer prognosis but could benefit more from ICIs and CTx (such as paclitaxel). Patients with a low PyroScore were more sensitive to RAI and MTT (such as pazopanib and sorafenib).

CONCLUSIONS

PyroScore model can effectively predict TCFi in patients with PTC. Dysregulated expression of PRGs is associated with the TIME modeling. Pyroptosis features have potential significance for developing novel therapeutic strategies for PTC patients.

摘要

背景

细胞焦亡是一种程序性细胞死亡的炎症形式，与多种癌症的发病机制和进展有关。然而，细胞焦亡相关基因（PRGs）在甲状腺乳头状癌（PTC）中的意义仍不清楚。

方法

从癌症基因组图谱获取PTC患者的转录组和临床数据。通过一致性聚类确定PRGs的表达模式。采用五种机器学习方法构建预测无甲状腺癌生存期（TCFi）的预后模型。进行富集分析和免疫相关分析以阐明细胞焦亡的作用。基于细胞焦亡衍生特征预测对放射性碘（RAI）、免疫检查点抑制剂（ICIs）、分子靶向治疗（MTT）和化疗（CTx）的反应。此外，通过六个外部数据集、16个细胞系和20对临床样本验证预后PRGs的表达。

结果

PTC患者被分为三个细胞焦亡簇（PyroClusters），C1表现为侵袭性最高、预后最差的BRAF样肿瘤，C2呈现RAS样肿瘤，C3以基因融合为特征。筛选出九个PRGs（CXCL8、GJA1、H2BC8、IFI27、PRDM1、PYCARD、SEZ6L2、SIGLEC15、TRAF6）构建细胞焦亡评分（PyroScore）预后模型。衍生的列线图显示出比四种临床分期系统更好的预测性能。在机制分析中观察到细胞焦亡与肿瘤免疫微环境（TIME）重塑之间存在强烈相关性。高细胞焦亡评分的患者表现出“热”肿瘤免疫表型，预后较差，但可能从ICIs和CTx（如紫杉醇）中获益更多。低细胞焦亡评分的患者对RAI和MTT（如帕唑帕尼和索拉非尼）更敏感。