Neutrophil extracellular traps-associated modification patterns depict the tumor microenvironment, precision immunotherapy, and prognosis of clear cell renal cell carcinoma

BACKGROUND: Neutrophil extracellular traps (NETs) are web-like structures formed by neutrophils, and their main function is antimicrobial defense. Moreover, NETs have numerous roles in the pathogenesis and progression of cancers. However, the potential roles of NET-related genes in renal cell carcinoma remain unclear. In this study, we comprehensively investigated the NETs patterns and their relationships with tumor environment (TME), clinicopathological features, prognosis, and prediction of therapeutic benefits in the clear cell renal cell carcinoma (ccRCC) cohort. METHODS: We obtained the gene expression profiles, clinical characteristics, and somatic mutations of patients with ccRCC from The Cancer Genome Atlas database (TCGA), Gene Expression Omnibus (GEO), and ArrayExpress datasets, respectively. ConsensusCluster was performed to identify the NET clusters. The tumor environment scores were evaluated by the “ESTIMATE,” “CIBERSORT,” and ssGSEA methods. The differential analysis was performed by the “limma” R package. The NET-scores were constructed based on the differentially expressed genes (DEGs) among the three cluster patterns using the ssGSEA method. The roles of NET scores in the prediction of immunotherapy were investigated by Immunophenoscores (TCIA database) and validated in two independent cohorts (GSE135222 and IMvigor210). The prediction of targeted drug benefits was implemented using the “pRRophetic” and Gene Set Cancer Analysis (GSCA) datasets. Real-time quantitative reverse transcription polymerase chain reaction (RT-PCR) was performed to identify the reliability of the core genes’ expression in kidney cancer cells. RESULTS: Three NET-related clusters were identified in the ccRCC cohort. The patients in Cluster A had more metabolism-associated pathways and better overall survival outcomes, whereas the patients in Cluster C had more immune-related pathways, a higher immune score, and a poorer prognosis than those in Cluster B. Based on the DEGs among different subtypes, patients with ccRCC were divided into two gene clusters. These gene clusters demonstrated significantly different immune statuses and clinical features. The NET scores were calculated based on the ten core genes by the Gene Set Variation Analysis (GSVA) package and then divided ccRCC patients into two risk groups. We observed that high NET scores were associated with favorable survival outcomes, which were validated in the E-MTAB-1980 dataset. Moreover, the NET scores were significantly associated with immune cell infiltration, targeted drug response, and immunotherapy benefits. Subsequently, we explored the expression profiles, methylation, mutation, and survival prediction of the 10 core genes in TCGA-KIRC. Though all of them were associated with survival information, only four out of the 10 core genes were differentially expressed genes in tumor samples compared to normal tissues. Finally, RT-PCR showed that MAP7, SLC16A12, and SLC27A2 decreased, while SLC3A1 increased, in cancer cells. CONCLUSION: NETs play significant roles in the tumor immune microenvironment of ccRCC. Identifying NET clusters and scores could enhance our understanding of the heterogeneity of ccRCC, thus providing novel insights for precise individual treatment.