PDF(Pubmed)
Abstract：
Neutrophil extracellular traps (NETs) are implicated in gastric cancer (GC) growth, metastatic dissemination, cancer-associated thrombosis, etc. 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 C5AR1, CSF1R, CSF2RB, CYBB, HCK, ITGB2, LILRB2, MNDA, MPEG1, PLEK, SRGN, and STAB1 are proven to be aberrantly expressed in GC cells. Specific knockdown of C5AR1 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.
摘要：
中性粒细胞胞外诱捕网（NETs）与胃癌（GC）的生长有关，转移性播散，癌症相关的血栓形成，等。进行这项工作是为了阐明GC中NETs的异质性。通过共识聚类算法在TCGA-STAD中研究了NETs的转录组异质性，随后在GSE88433和GSE88437队列中进行外部验证。临床和分子特征，免疫微环境，和药物反应在识别的基于NET的集群中表征。基于NET的特征基因，构建了一个分类器，用于通过机器学习估计基于NET的集群。利用多个实验来验证特征基因在GC中的表达和含义。提出了一种新的基于NET的分类系统，以反映GC中NET的异构性。两个基于NET的集群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疗法的反应。逻辑回归模型可靠地区分基于NET的集群。特征基因C5AR1，CSF1R,CSF2RB,CYBB,HCK,ITGB2，LILRB2，MNDA，MPEG1,PLEK,SRGN,和STAB1被证明在GC细胞中异常表达。C5AR1的特异性敲除有效阻碍GC细胞生长并引起细胞内ROS积累。此外,其抑制抑制GC细胞的侵袭性和EMT表型。总之,NETs是GC肿瘤内异质性和不同药物敏感性的主要贡献者，和C5AR1已显示触发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。
