Identification of neutrophil extracellular trap-driven gastric cancer heterogeneity and C5AR1 as a therapeutic target

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.

1. Introduction

Gastric cancer (GC) represents a global health-care challenge [1]. The therapeutic landscape of GC has markedly evolved. Although the effectiveness of chemotherapy and surgical removal has improved, patient prognosis remains unsatisfactory [2]. Chemicalotherapeutic agents with low toxicity, along with molecular-driven targeted treatment, are valuable in sequential therapeutic regimens for the optimization of patient survival. In advanced disease, only trastuzumab and immune checkpoint blockade (ICB), e.g., nivolumab and pembrolizumab in combination with chemotherapy, have shown durable and superior effects in treating HER2-positive and PD-L1-positive patients, respectively [3‒5]. Biomarkers currently utilized for therapeutic choices include HER2 overexpression and amplification, combined positive PD-L1 score, and microsatellite instability [6]. Intra- and intertumor heterogeneity are notable features of GC and are partially responsible for unfavorable survival [7,8]. Nonetheless, only histological classification is not sufficient to potently stratify patients for personalized therapy or to prolong survival time. Large-scale molecular characterization of GC patients is important for identifying candidate treatment options [9].

Neutrophils are the dominating leukocytes in peripheral blood and are the initial defense against invading pathogens [10‒12]. Neutrophil extracellular traps (NETs) are network structures comprising decondensed DNA strands coated with granule proteins that were first discovered in 1996 [13], and these NETs were subsequently named NETosis [14,15]. Based upon mounting evidence, circulating NET levels are aberrantly increased in GC patients, and NETs have indispensable implications for GC growth [16], the metastatic cascade [17‒19] and cancer-associated thrombosis [20]. However, additional studies on NET formation and compounds that hinder or destroy NETs are needed, providing a theoretical basis for the use of NETs in GC treatment.

In the present study, a novel NET-based molecular classification system for GC was proposed, revealing the heterogeneity of NET formation. In addition, a classifier was built for the differentiation of NET-based clusters. Among the feature genes in the classifier, C5AR1 was experimentally proven to mediate GC growth and metastatic spread, indicating the potential of C5AR1 as a treatment target in GC.