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

Abstract

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.

Keywords: ccRCC; immune tumor environment; neutrophil extracellular traps; prognosis; subtypes.

Figure 1

The landscape of neutrophil extracellular trap-associated genes in the TCGA-KIRC. (A) Volcano plot and (B) heatmap of 69 NET-associated genes in ccRCC and non-tumor samples. (C) The location of the NET-associated genes on different chromosomes. (D) GeneMANIA gene–gene interaction network showed the correlation among different genes.

Figure 2

NET subtypes and clinicopathological features of three clusters. (A) Consensus matrix of ccRCC samples’ co-occurrence proportion for k = 3. (B, C) Consensus clustering CDF for k from 2 to 9. (D) The Kaplan–Meier plot showed the overall survival differences among the three subtypes in the ccRCC cohorts. (E) Principal component analysis of ccRCC samples grouped by clusters. (F) Heatmap showing the association of subtypes with clinical characteristics and expression of neutrophil extracellular trap-associated genes. (G) The boxplot of neutrophil extracellular trap-associated genes among different clusters. ns, no significance. *p < 0.05, **p < 0.01, ***p < 0.001.

Figure 3

The biological characteristics and landscape of immune status among different subtypes. (A) KEGG enrichment analysis of three NET subtypes. (B) The ESTIMATE proportion of stromal score, immune score, and ESTIMATE score among the three clusters. (C) The gene expression profiles of three common immune checkpoint genes, PDCD1, LAG3, and CD274. (D) The infiltration levels of 23 immune cell types among three subtypes. **p < 0.01, ***p < 0.001.

Figure 4

The different expression genes (DEGs), enrichment pathways among different clusters, and consensus clustering based on DEGs. (A) The GO and (B) KEGG enrichment of different subtypes. (C) The forest plot for ten core DEGs based on univariate Cox regression analysis. (D) Consensus matrix of ccRCC samples’ co-occurrence proportion for k = 2. (E) Kaplan–Meier curves for the two gene clusters of ccRCC patients. The log-rank test shows an overall p <0.001. (F) Heatmap showing the relationship among the clinicopathological characteristics of the gene clusters. (G) The boxplot of gene expression of ten core genes between the two subtypes. ***p < 0.001.

Figure 5

Construction of the NET-score system and clinical prognosis analysis in ccRCC patients. (A) Kaplan–Meier curves for high and low NET-score ccRCC patient groups (log-rank test, P <0.001). Differences in NET scores among the three clusters (B) (P <0.001) and two gene clusters (C) (P <0.001). (D) Alluvial diagram of NET-associated gene clusters in groups with different gene clusters, NET-score groups, and survival outcomes. (E) The correlation matrix of all infiltrating immune cells. Some fractions of immune cells were positively related and are represented in red, whereas others were negatively related and are represented in blue. p <0.05 was the cut-off. (F) Heatmap showing the relationship between scoring groups and chemokines, interferons, and cytokines. *p < 0.05, **p < 0.01, ***p < 0.001.

Figure 6

The correlation of NET-scores with clinic-pathological characteristics, hallmark and KEGG enrichment between high- and low-NET-score groups. The boxplot of different survival status (A), clinical grade (B), clinical stage (C), tumor stage (D), regional lymph node status (E), and distant metastasis (F). The hallmark (G) and (H) KEGG enrichment between high- and low-NET-score groups.

Figure 7

The mRNA expression of immune checkpoint genes and immunotherapeutic benefits. The PDCD1 (A), LAG3 (B), and CD274 (C) expression between different NET-score groups. The association between IPS and NET scores (D). The different immunotherapy responses between high- and low-NET-score groups in GSE135222 (E–G) and IMvigor210 (H–J) datasets. *p < 0.05, **p < 0.01, ***p < 0.001.

Figure 8

The pathway activity, drug sensitivity in ccRCC cohorts and pan cancer. (A–H) The drug sensitivity of eight common targeted compounds. (I) The associations of NET scores with activity pathways in the TCGA-KIRC dataset. (J) The correlation between gene expression and the sensitivity of GDSC drugs in pan-cancer. (K) The correlation between gene expression and the sensitivity of CTRP drugs in pan-cancer.