Ji Guo, Sun Hanlin, Chen Simo, Sun Xuechen, Chang Le, Xie Ruting, Huang Runzhi, Zheng Lijun, Chang Zhengyan
Department of Pathology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Middle 301 Yanchang Road, Shanghai, 200072, China.
Department of Burn Surgery, the First Affiliated Hospital of Naval Medical University, 168 Changhai Road, Shanghai, 200433, China.
Cancer Immunol Immunother. 2025 Jul 12;74(8):267. doi: 10.1007/s00262-025-04101-4.
Papillary thyroid cancer (PTC) is the most common thyroid cancer, but current molecular features inadequately stratify its risk. Whether distinct underlying mechanisms can further classify PTC and improve prognostic precision remains unclear.
We integrated single-cell RNA sequencing data (158,577 cells from 11 PTC patients; GEO: GSE184362) with bulk-RNA sequencing data from The Cancer Genome Atlas Thyroid Carcinoma (TCGA-THCA) cohort (501 patients). Multi-omics analyses were employed to elucidate PTC heterogeneity, identify malignant cell differentiation and prognosis-related genes (MCD&PRGs), and construct a novel molecular classification, the Oncogenic Signature Of Papillary Thyroid Carcinoma Classification (OSPTCC). A prognostic risk score was developed, and the classification's prognostic relevance was further explored in an independent institutional cohort using qRT-PCR.
Single-cell analysis revealed three malignant cell differentiation states (PTC1-3) and a 34-gene signature (MCD&PRGs). This formed the basis of our Oncogenic Signature Of Papillary Thyroid Carcinoma Classification (OSPTCC), defining three subtypes: Inflammation-associated (IPTCC), BRAF/autophagy-related (BAPTCC), and lipid metabolism-related (LPTCC). These subtypes showed distinct molecular profiles and significantly different progression-free survival (IPTCC poorest, P = 0.044). A 7-gene risk score derived from MCD&PRGs independently predicted prognosis (multivariate HR = 21.511, P < 0.001). qRT-PCR validation in an independent cohort (n = 48) using key markers (DEPTOR, APOE, APOC1) confirmed that OSPTCC-based risk stratification correlated with adverse clinical features, including higher recurrence rates in the high-risk group (P = 0.007).
This study introduces OSPTCC, a prognostically significant molecular classification for PTC based on tumor cell differentiation states. The identified subtypes, characterized by distinct biological mechanisms, provide deeper insights into PTC's molecular pathology and offer a framework for improved risk stratification and potential precision therapies.
甲状腺乳头状癌(PTC)是最常见的甲状腺癌,但目前的分子特征不足以对其风险进行分层。不同的潜在机制是否能进一步对PTC进行分类并提高预后预测的准确性仍不清楚。
我们将单细胞RNA测序数据(来自11例PTC患者的158,577个细胞;GEO:GSE184362)与来自癌症基因组图谱甲状腺癌(TCGA-THCA)队列(501例患者)的批量RNA测序数据进行整合。采用多组学分析来阐明PTC的异质性,识别恶性细胞分化和预后相关基因(MCD&PRGs),并构建一种新的分子分类,即甲状腺乳头状癌致癌特征分类(OSPTCC)。制定了一个预后风险评分,并在一个独立的机构队列中使用qRT-PCR进一步探讨该分类的预后相关性。
单细胞分析揭示了三种恶性细胞分化状态(PTC1-3)和一个34基因特征(MCD&PRGs)。这构成了我们的甲状腺乳头状癌致癌特征分类(OSPTCC)的基础,定义了三种亚型:炎症相关型(IPTCC)、BRAF/自噬相关型(BAPTCC)和脂质代谢相关型(LPTCC)。这些亚型表现出不同的分子特征和显著不同的无进展生存期(IPTCC最差,P = 0.044)。从MCD&PRGs得出的7基因风险评分独立预测预后(多变量HR = 21.511,P < 0.001)。在一个独立队列(n = 48)中使用关键标志物(DEPTOR、APOE、APOC1)进行的qRT-PCR验证证实,基于OSPTCC的风险分层与不良临床特征相关,包括高风险组中更高的复发率(P = 0.007)。
本研究引入了OSPTCC,这是一种基于肿瘤细胞分化状态对PTC具有预后意义的分子分类。所识别的亚型具有不同的生物学机制,为PTC的分子病理学提供了更深入的见解,并为改进风险分层和潜在的精准治疗提供了一个框架。