Zhao Jing, Li Xiangyu, Li Liming, Chen Beibei, Xu Weifeng, He Yunduan, Chen Xiaobing
Department of Medical Oncology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450003, China.
Department of Radiation Oncology, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou 450003, China.
Acta Biochim Biophys Sin (Shanghai). 2024 Apr 25;56(4):538-550. doi: 10.3724/abbs.2023290.
Neutrophil extracellular traps (NETs) are implicated in gastric cancer (GC) growth, metastatic dissemination, cancer-associated thrombosis, . This work is conducted to elucidate the heterogeneity of NETs in GC. The transcriptome heterogeneity of NETs is investigated in TCGA-STAD via a consensus clustering algorithm, with subsequent external verification in the GSE88433 and GSE88437 cohorts. Clinical and molecular traits, the immune microenvironment, and drug response are characterized in the identified NET-based clusters. Based upon the feature genes of NETs, a classifier is built for estimating NET-based clusters via machine learning. Multiple experiments are utilized to verify the expressions and implications of the feature genes in GC. A novel NET-based classification system is proposed for reflecting the heterogeneity of NETs in GC. Two NET-based clusters have unique and heterogeneous clinical and molecular features, immune microenvironments, and responses to targeted therapy and immunotherapy. A logistic regression model reliably differentiates the NET-based clusters. The feature genes , , , , , , , , , , , and are proven to be aberrantly expressed in GC cells. Specific knockdown of effectively hinders GC cell growth and elicits intracellular ROS accumulation. In addition, its suppression suppresses the aggressiveness and EMT phenotype of GC cells. In all, NETs are the main contributors to intratumoral heterogeneity and differential drug sensitivity in GC, and C5AR1 has been shown to trigger GC growth and metastatic spread. These findings collectively provide a theoretical basis for the use of anti-NETs in GC treatment.
中性粒细胞胞外陷阱（NETs）与胃癌（GC）的生长、转移扩散、癌症相关血栓形成有关。本研究旨在阐明GC中NETs的异质性。通过共识聚类算法在TCGA-STAD中研究NETs的转录组异质性，并随后在GSE88433和GSE88437队列中进行外部验证。在识别出的基于NETs的簇中表征临床和分子特征、免疫微环境及药物反应。基于NETs的特征基因，通过机器学习构建用于估计基于NETs的簇的分类器。利用多个实验验证特征基因在GC中的表达及意义。提出一种新的基于NETs的分类系统以反映GC中NETs的异质性。两个基于NETs的簇具有独特且异质的临床和分子特征、免疫微环境以及对靶向治疗和免疫治疗的反应。逻辑回归模型能可靠地区分基于NETs的簇。已证实特征基因、、、、、、、、、、和在GC细胞中异常表达。特异性敲低有效地阻碍GC细胞生长并引发细胞内活性氧积累。此外，对其抑制可抑制GC细胞的侵袭性和上皮-间质转化表型。总之，NETs是GC肿瘤内异质性和药物敏感性差异的主要促成因素，并且已表明C5AR1可触发GC生长和转移扩散。这些发现共同为在GC治疗中使用抗NETs提供了理论依据。
