Abstract
Background: Kidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. Surgery is the preferred treatment option; however, the rate of distant metastasis is high. Mast cells in the tumor microenvironment promote or inhibit tumorigenesis depending on the cancer type; however, their role in KIRC is not well-established. Here, we used a bioinformatics approach to evaluate the roles of mast cells in KIRC. Methods: To quantify mast cell abundance based on gene sets, a single-sample gene set enrichment analysis (ssGSEA) was utilized to analyze three datasets. Weighted correlation network analysis (WGCNA) was used to identify the genes most closely related to mast cells. To identify new molecular subtypes, the nonnegative matrix factorization algorithm was used. GSEA and least absolute shrinkage and selection operator (LASSO) Cox regression were used to identify genes with high prognostic value. A multivariate Cox regression analysis was performed to establish a prognostic model based on mast cell-related genes. Promoter methylation levels of mast cell-related genes and relationships between gene expression and survival were evaluated using the UALCAN and GEPIA databases. Results: A prolonged survival in KIRC was associated with a high mast cell abundance. KIRC was divided into two molecular subtypes (cluster 1 and cluster 2) based on mast cell-related genes. Genes in Cluster 1 were enriched for various functions related to cancer development, such as the TGFβ signaling pathway, renal cell carcinoma, and mTOR signaling pathway. Based on drug sensitivity predictions, sensitivity to doxorubicin was higher for cluster 2 than for cluster 1. By a multivariate Cox analysis, we established a clinical prognostic model based on eight mast cell-related genes. Conclusion: We identified eight mast cell-related genes and constructed a clinical prognostic model. These results improve our understanding of the roles of mast cells in KIRC and may contribute to personalized medicine.
Highlights
Clear cell renal cell carcinoma (KIRC) accounts for approximately 65–70% of all renal cell carcinomas (Warren and Harrison, 2018; Siegel et al, 2020)
To quantify mast cell abundance based on a mast cell gene set in three Kidney renal clear cell carcinoma (KIRC) datasets (TCGA, ArrayExpress, and International Cancer Genome Consortium (ICGC) cohorts), single-sample gene set enrichment analysis (ssGSEA) was used
As determined by a Kaplan-Meier analysis, survival time was longer in cluster 1 than in cluster 2 (TCGA, p < 0.001; FIGURE 1 | (A–C) Kaplan–Meier curves for patients with bladder cancer (BLCA) showed that in the six cohorts, patients with a low fibroblast abundance have a better prognosis than that of patients with a high fibroblast abundance [(A): The Cancer Genome Atlas (TCGA); (B): E-MTAB-1980; (C): International Cancer Genome Consortium (ICGC)] (C) Using weighted correlation network analysis (WGCNA), eight modules were identified. (D) The brown module was most highly correlated with mast cells. (E, F) Functional enrichment analysis of 258 mast cell-related genes
Summary
Clear cell renal cell carcinoma (KIRC) accounts for approximately 65–70% of all renal cell carcinomas (Warren and Harrison, 2018; Siegel et al, 2020). Metastasis is the main cause of death in patients with KIRC (Li et al, 2018). Some patients with KIRC have metastases when they are first diagnosed (Motzer et al, 1996). Surgical treatment achieves good results, the 5-years survival rate for patients with metastatic KIRC is still low (Heidenreich et al, 2012; Sara et al, 2016). It is necessary to identify therapeutic targets and effective predictors for early diagnosis and treatment. Kidney renal clear cell carcinoma (KIRC) is a common malignant tumor of the urinary system. Mast cells in the tumor microenvironment promote or inhibit tumorigenesis depending on the cancer type; their role in KIRC is not wellestablished. We used a bioinformatics approach to evaluate the roles of mast cells in KIRC
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