AMG-319

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.