Immunohistochemical-Based Molecular Typing of ACRG Combined With Immune-Associated PD-L1 Expression Can Predict the Prognosis of Gastric Cancer

IF 2.9 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2025-04-09 DOI:10.1002/cam4.70863

Fang Li, Huiyan Deng, Zeqing Hu, Zihao Chen, Huirui Zhang, Jiankun He, Xiaoxiao Wang, Yueping Liu

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引用次数: 0

Abstract

Background

Gastric cancer (GC) is a molecularly heterogeneous disease with diverse clinical outcomes. Traditional classifications lack predictive accuracy, necessitating alternative molecular subtyping approaches for effective prognosis prediction. The Asian Cancer Research Group (ACRG) molecular subtypes, combined with immune-associated PD-L1 expression, offer a promising framework to predict patient outcomes and potentially guide treatment strategies in GC.

Methods

This study retrospectively analyzed 1007 primary GC patients who underwent surgical resection between January 2017 and June 2019 at the Fourth Hospital of Hebei Medical University. Comprehensive immunohistochemical and fluorescent PCR-capillary electrophoresis analyses were conducted to determine ACRG molecular subtypes (microsatellite instability [MSI], microsatellite stability with epithelial–mesenchymal transition [MSS/EMT], MSS/TP53⁺, and MSS/TP53⁻) and PD-L1 expression. We assessed the relationship between these classifications and various clinicopathological parameters, including survival outcomes, using Cox regression and Kaplan–Meier analysis.

Results

The ACRG subtypes showed significant associations with clinicopathological features, including tumor invasion depth, Lauren classification, and HER2 status. The MSI subtype (6.7% of cases) was associated with higher PD-L1 positivity and a favorable prognosis, whereas the EMT subtype had the lowest 5-year survival rate (34.55%) and was predominantly linked to diffuse-type histology. PD-L1 positivity correlated with worse survival outcomes, with independent predictive value alongside ACRG subtypes (HR for PD-L1 = 1.759, p = 0.001; HR for ACRG = 5.144, p < 0.001).

Conclusion

The combination of ACRG molecular subtyping and PD-L1 expression serves as an effective predictor of GC prognosis, facilitating tailored clinical decision-making. The ACRG-PD-L1 classification system offers a practical, cost-effective approach for routine clinical application, providing critical insight into GC heterogeneity. Further multicenter studies are needed to validate these findings and explore the impact of ACRG subtypes on therapy responses, particularly in immunotherapy settings.

Abstract Image

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背景胃癌（GC）是一种分子异质性疾病，临床结果各不相同。传统的分类缺乏预测准确性，因此需要采用其他分子亚型方法来有效预测预后。亚洲癌症研究组（ACRG）分子亚型与免疫相关的 PD-L1 表达相结合，为预测患者预后和指导 GC 治疗策略提供了一个很有前景的框架。方法本研究回顾性分析了2017年1月至2019年6月期间在河北医科大学第四医院接受手术切除的1007例原发性GC患者。我们进行了全面的免疫组化和荧光 PCR-毛细管电泳分析，以确定 ACRG 分子亚型（微卫星不稳定性 [MSI]、微卫星稳定伴上皮-间质转化 [MSS/EMT]、MSS/TP53+ 和 MSS/TP53-）和 PD-L1 表达。我们使用 Cox 回归和 Kaplan-Meier 分析评估了这些分类与各种临床病理参数（包括生存结果）之间的关系。结果 ACRG 亚型与临床病理特征（包括肿瘤侵犯深度、劳伦分类和 HER2 状态）有显著关联。MSI亚型（6.7%的病例）与较高的PD-L1阳性率和良好的预后相关，而EMT亚型的5年生存率最低（34.55%），且主要与弥漫型组织学相关。PD-L1 阳性与较差的生存结果相关，与 ACRG 亚型一样具有独立的预测价值（PD-L1 的 HR = 1.759，p = 0.001；ACRG 的 HR = 5.144，p <0.001）。结论 ACRG 分子亚型和 PD-L1 表达的结合是预测 GC 预后的有效指标，有助于制定有针对性的临床决策。ACRG-PD-L1 分类系统为常规临床应用提供了一种实用、经济的方法，为了解 GC 的异质性提供了重要依据。需要进一步开展多中心研究来验证这些发现，并探索 ACRG 亚型对治疗反应的影响，尤其是在免疫疗法中。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.