Neutrophil extracellular traps-associated modification patterns depict the tumor microenvironment, precision immunotherapy, and prognosis of clear cell renal cell carcinoma - Teng, Zhi-Hai; Li, Wen-Ce; Li, Zhi-Chao; Wang, Ya-Xuan; Han, Zhen-Wei; Zhang, Yan-Ping (Frontiers in Oncology, 2022)
Background Increasing studies have reported that NETs play multiple roles in the pathogenesis and progression of cancers. However, the potential roles of NETs-related genes in the renal cell carcinoma remain still 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 ccRCC cohorts. Methods The gene expression profiles, clinical characteristics of ccRCC patients were acquired from TCGA and GEO datasets, respectively. ConsensusCluster was performed to identify the NETs clusters. The tumor environment scores were evaluated by “ESTIMATE”, “CIBERSORT” and ssGSEA methods. The differential analysis were performed by “limma” R package. The NET-scores was constructed on the different expression genes (DEGs) among three cluster patterns by ssGSEA method. The roles of NET-scores in the prediction of immunotherapy were investigated by Immunophenoscores (IPS) (TCIA database) and validated in two independent cohorts (GSE135222, IMvigor210). The prediction of targeted drug benefits was implemented by "pRRophetic" and GSCA dataset. qRT-PCR was performed to identify the reliability of the core genes expression in kidney cancer cells. Results Three NETs-related clusters were identified in ccRCC cohorts. The patients in Cluster A had more metabolism-associated pathways and better overall survival outcomes, while Cluster C patients had more immune-related pathways, higher immune score and poor prognosis. The NET-scores was then calculated on the ten core genes by GSVA method and divided ccRCC patients into two risk groups. Moreover, it was also validated that NET-scores was significantly correlated with immune cell infiltration, targeted drug response and immunotherapy benefits. Subsequence, we explored the expression profiles, methylation, mutation and survival prediction of ten core genes in TCGA-KIRC. Though all of them were associated with survival information, 4/10 were different expressed genes in tumor samples compared to normal tissues. Finally, qPCR showed that MAP7, SLC16A12 and SLC27A2 decreased, while SLC3A1 increased in cancer cells. Conclusion NETs plays critical roles in the tumor immune microenvironment of ccRCC. The identification of NETs clusters and NET-scores could promote our understanding the heterogeneity of ccRCC, which might also provide novel insights for precise individual treatment.