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T 细胞淋巴细胞和 Tregs 亚群的高密度与主要唾液腺癌较差的生存相关

英文原题:A high density of T-cell lymphocytes and Tregs subset correlate to a worse survival in major salivary gland carcinomas.

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A high density of T-cell lymphocytes and Tregs subset correlate to a worse survival in major salivary gland carcinomas.

PubMed 2026/03/03(内容时间) Sci Rep Q1 · IF 4.9(JCR 2025)

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中文摘要

涎腺癌(SGC)是罕见的异质性肿瘤,晚期治疗选择有限。近期证据提示肿瘤免疫微环境(TME)在疾病进展中可能发挥作用。本研究旨在通过分析TIL(肿瘤浸润淋巴细胞)(TILs)、肿瘤相关巨噬细胞(TAMs)和 PD-L1 表达来探讨 SGC 的免疫特征,并评估其与组织学分级和临床结局的相关性。对 103 例 SGC 病例进行了回顾性分析。进行了 CD3、CD4、CD8、CD20、CD56、PD-1、PD-L1、FOXP3、CD68 和 CD163 的免疫组化染色。使用 QuPath 软件对瘤内和瘤周区域进行了数字切片分析。高瘤内 FOXP3+ Tregs 与高级别肿瘤和更差的无进展生存期(PFS)显著相关(p = 0.009)。较高的瘤周 CD3+ T 细胞密度与不良预后相关(p = 0.046)。病理学家评估与 QuPath 定量之间的一致性为中高度(Cohen's K = 0.71)。

总之,瘤内 Tregs 和瘤周 T 淋巴细胞可作为负向预后生物标志物。未来的多中心研究和 AI(人工智能)驱动分析可增强 SGC 的免疫特征描述并指导免疫治疗策略。

展开英文摘要原文

Salivary gland carcinomas (SGC) are rare and heterogeneous tumors with limited therapeutic options in advanced stages. Recent evidence suggests a potential role of the tumor immune microenvironment (TME) in disease progression.

This study aimed to investigate the immune profile of SGCs by analyzing tumor-infiltrating lymphocytes (TILs), tumor-associated macrophages (TAMs), and PD-L1 expression, and to assess their correlation with histological grade and clinical outcome. A retrospective analysis was conducted on 103 SGC cases. Immunohistochemistry for CD3, CD4, CD8, CD20, CD56, PD-1, PD-L1, FOXP3, CD68, and CD163 was performed.

Digital slide analysis was carried out in intratumoral and peritumoral regions using QuPath software. High intratumoral FOXP3 + Tregs were significantly associated with high-grade tumors and worse progression-free survival (PFS) (p = 0. 009). A higher peritumoral CD3 + T cell density correlated with poor prognosis (p = 0. 046). Concordance between pathologist assessments and QuPath quantification was moderate to high (Cohen's K = 0. 71).

In conclusion, intratumoral Tregs and peritumoural T lymphocytes may be used as negative prognostic biomarkers. Future multicentric studies and AI (Artificial Intelligence)-driven analyses could enhance immune characterization and guide immunotherapeutic strategies in SGC.

论文信息

作者
Anconelli D、Vasuri F、Novelli L、Saragoni L、Messerini L、Saieva C、Cantone E、Muscatello L
第一作者单位
Pathology Unit, Santa Maria delle Croci Hospital, viale Randi 5, Ravenna, 48100, Italy.Italy
通讯作者单位
Pathology Unit, Santa Maria delle Croci Hospital, viale Randi 5, Ravenna, 48100, Italy. francesco.vasuri2@unibo.it.Italy
期刊
Scientific reports2026 Mar 3
原文标识
PubMed 41775752 · DOI 10.1038/s41598-026-39357-y