scholarly journals Identification of Mast Cell-Based Molecular Subtypes and a Predictive Signature in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Hanxiang Liu ◽  
Yi Yang
Keyword(s):  
Renal Cell Carcinoma ◽  
Mast Cell ◽  
Mast Cells ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Model ◽  
Cox Regression ◽  
Clear Cell ◽  
Molecular Subtypes ◽  
Cluster 2

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.

scholarly journals MP72-17 THE ASSOCIATION BETWEEN SARCOPENIA AND MOLECULAR SUBTYPES OF CLEAR CELL RENAL CELL CARCINOMA

2018 ◽  
Vol 199 (4S) ◽  
Author(s):  
Alejandro Sanchez ◽  
Stacey Petruzella ◽  
Marguerite Samson ◽  
Oguz Akin ◽  
Michael Paris ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Molecular Subtypes ◽  
Cell Renal Cell Carcinoma

scholarly journals A novel 10 glycolysis-related genes signature could predict overall survival for clear cell renal cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qianwei Xing ◽  
Tengyue Zeng ◽  
Shouyong Liu ◽  
Hong Cheng ◽  
Limin Ma ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Low Risk ◽  
The Cancer Genome Atlas ◽  
Regression Analyses ◽  
Cell Renal Cell Carcinoma

Abstract Background The role of glycolysis in tumorigenesis has received increasing attention and multiple glycolysis-related genes (GRGs) have been proven to be associated with tumor metastasis. Hence, we aimed to construct a prognostic signature based on GRGs for clear cell renal cell carcinoma (ccRCC) and to explore its relationships with immune infiltration. Methods Clinical information and RNA-sequencing data of ccRCC were obtained from The Cancer Genome Atlas (TCGA) and ArrayExpress datasets. Key GRGs were finally selected through univariate COX, LASSO and multivariate COX regression analyses. External and internal verifications were further carried out to verify our established signature. Results Finally, 10 GRGs including ANKZF1, CD44, CHST6, HS6ST2, IDUA, KIF20A, NDST3, PLOD2, VCAN, FBP1 were selected out and utilized to establish a novel signature. Compared with the low-risk group, ccRCC patients in high-risk groups showed a lower overall survival (OS) rate (P = 5.548Ee-13) and its AUCs based on our established signature were all above 0.70. Univariate/multivariate Cox regression analyses further proved that this signature could serve as an independent prognostic factor (all P < 0.05). Moreover, prognostic nomograms were also created to find out the associations between the established signature, clinical factors and OS for ccRCC in both the TCGA and ArrayExpress cohorts. All results remained consistent after external and internal verification. Besides, nine out of 21 tumor-infiltrating immune cells (TIICs) were highly related to high- and low- risk ccRCC patients stratified by our established signature. Conclusions A novel signature based on 10 prognostic GRGs was successfully established and verified externally and internally for predicting OS of ccRCC, helping clinicians better and more intuitively predict patients’ survival.

A prognostic nomogram based on competing endogenous RNA network for clear-cell renal cell carcinoma

2020 ◽  
Author(s):  
Yun Peng ◽  
Shangrong Wu ◽  
Zihan Xu ◽  
Dingkun Hou ◽  
Nan Li ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Rna Sequencing ◽  
Renal Cell ◽  
Cox Regression ◽  
Clear Cell ◽  
Gene Signature ◽  
Expression Data ◽  
Competing Endogenous Rna ◽  
Cell Renal Cell Carcinoma

Abstract Backgroud Clear-cell renal cell carcinoma (ccRCC) is stubborn to traditional chemotherapy and radiation treatment, which makes its clinical management a major challenge. Recently, we have made efforts to understand the etiology of ccRCC. Increasing evidence revealed that the competing endogenous RNA (ceRNA) were involved in the development of various tumor. However, it’s scant for studying on ccRCC, and a comprehensive analysis of prognostic model based on lncRNA-miRNA-mRNA ceRNA regulatory network of ccRCC with large-scale sample size and RNA‐sequencing expression data is still limited. Methods RNA‐sequencing expression data were taken out from GTEx database and TCGA database, A total of 354 samples with ccRCC and 157 normal controlled samples were included in our study. The ccRCC-specific genes were obtained from WGCNA and differential expression analysis. Following, the communication between mRNAs and lncRNAs and target miRNAs were predicted by MiRcode, starBase, miRTarBase, and TargetScan. A gene signature of eight genes was constructed by univariate Cox regression, lasso methods and multivariate Cox regression analysis. Results A total of 2191 mRNAs and 1377 lncRNAs was identified, and a dys-regulated ceRNA network for ccRCC was established using 7 mRNAs, 363 lncRNAs, and 3 miRNAs. Further, a gene signature in cluding 8 genes based on this ceRNA was constructed, meanwhile, a nomogram predicting 1-, 3-, 5-year survival probability containing both clinical characteristics and ccRCC-specific gene signatures was developed. Conclusion It could contribute to a better understanding of ccRCC tumorigenesis mechanism and guide clinicians to make a more accurate treatment decision.

scholarly journals Pathological significance and prognostic roles of densities of CD57+ cells, CD68+ cells, and mast cells, and their ratios in clear cell renal cell carcinoma

2018 ◽  
Vol 79 ◽  
pp. 102-108 ◽  
Author(s):  
Hiromi Nakanishi ◽  
Yasuyoshi Miyata ◽  
Yasushi Mochizuki ◽  
Takuji Yasuda ◽  
Yuichiro Nakamura ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Mast Cells ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma

scholarly journals Prognostic Value of DNA Methylation-Driven Genes in Clear Cell Renal Cell Carcinoma: A Study Based on Methylation and Transcriptome Analyses

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Maolin Hu ◽  
Jiangling Xie ◽  
Huiming Hou ◽  
Ming Liu ◽  
Jianye Wang
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Methylation Level ◽  
Cox Regression ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma

Background. Few previous studies have comprehensively explored the level of DNA methylation and gene expression in ccRCC. The purpose of this study was to identify the key clear cell renal cell carcinoma- (ccRCC-) related DNA methylation-driven genes (MDG) and to build a prognostic model based on the level of DNA methylation. Methods. RNA-seq transcriptome data and DNA methylation data were obtained from The Cancer Genome Atlas. Based on the MethylMix algorithm, we obtain ccRCC-related MDG. The univariate and multivariate Cox regression analyses were employed to investigate the correlation between patient overall survival and the methylation level of each MDG. Finally, a prognosis risk score was established based on a linear combination of the regression coefficient derived from the multivariate Cox regression model (β) multiplied with the methylation level of the gene. Results. 19 ccRCC-related MDG were identified. Three MDG (NCKAP1L, EVI2A, and BATF) were further screened and integrated into a prognostic risk score model, risk score=3.710∗methylation level of NCKAP1L+−3.892∗methylation level of EVI2A+−3.907∗methylation level of BATF. The risk model was independent from conventional clinical characteristics as a prognostic factor for ccRCC (HR=1.221, 95% confidence interval: 1.063–1.402, and P=0.005). The joint survival analysis showed that the gene expression and methylation levels of the prognostic genes EVI2A and BATF were significantly related with prognosis. Conclusion. This study provided an important bioinformatics foundation for in-depth studies of ccRCC DNA methylation.

Proteomic stratification of clear cell renal cell carcinoma utilizing The Cancer Genome Atlas (TCGA) with external validation.

2015 ◽  
Vol 33 (7_suppl) ◽  
pp. 406-406
Author(s):  
Samuel D. Kaffenberger ◽  
Giovanni Ciriello ◽  
Andrew G. Winer ◽  
Martin Henner Voss ◽  
Jodi Kathleen Maranchie ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
External Validation ◽  
Mtor Pathway ◽  
The Cancer Genome Atlas ◽  
Genomic Alterations ◽  
Cell Renal Cell Carcinoma ◽  
Cluster 2

406 Background: Proteomics represents the ultimate convergence of DNA and expression alterations. We therefore sought to leverage TCGA reverse phase protein array (RPPA) data with an independent proteomic platform to identify druggable targets and pathways associated with prognosis in clear cell renal cell carcinoma (ccRCC). Methods: Unsupervised hierarchical consensus clustering was performed and differentially expressed proteins were identified for pathway analysis. Associations with clinicogenomic factors were assessed and Cox proportional hazards models were performed for disease-specific survival (DSS). Results: RPPA clustering of 324 patients from the ccRCC TCGA revealed 5 robust clusters characterized by alterations in specific pathways and divergent prognoses. Cluster 1 was characterized by poor DSS, decreased expression of receptor tyrosine kinases (RTK) and upregulation of the mTOR pathway. It was also associated with mTOR pathway genomic alterations, sarcomatoid histology and the ccb prognostic mRNA signature (all p<0.001). Cluster 2 was characterized by increased expression of RTKs and interestingly, had upregulation of the mTOR pathway with excellent DSS. After accounting for stage and grade, cluster designation remained independently associated with DSS (HR 0.23 for cluster 2, 95% CI 0.08-0.68; p=0.008). External validation was performed on a separate cohort of 189 patients with a different quantitative proteomics platform. A panel of phosphoproteins (pHER1, pHER2, pHER3, pSHC, pMEK, pAKT), highly discriminant between the most divergent RPPA clusters (1 and 2) was evaluated. Those at the highest quartile of activation in > 3 proteins were associated with improved DSS (HR 0.19, 95% CI 0.05-0.082; p=0.03). Patients with mTOR pathway activation segregated to those with coincident RTK activation (n=83) and those without (n=13). Conclusions: We have identified and validated proteomic signatures which cluster ccRCC patients into 5 prognostic groups. Furthermore, two distinct mTOR-activated clusters—one with high RTK activity and one with increased mTOR pathway genomic alterations were revealed, which may have prognostic and therapeutic implications.

Improving IMDC criteria in patients with metastatic renal cell carcinoma through the addition of initial metastatic site in bone, brain, and liver.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 754-754
Author(s):  
Vincenzo Di Nunno ◽  
Francesco Massari ◽  
Annalisa Guida ◽  
Carolina Alves Costa Silva ◽  
Lisa Derosa ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Metastatic Renal Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Patients ◽  
Prognostic Stratification ◽  
Patient Prognosis ◽  
Significant Factors

754 Background: IMDC criteria are largely adopted to estimate patient prognosis. In a retrospective study we assessed that the addition of first site of metastases in brain bone and liver (bbl) metastases as variable improves prognostic stratification of patients (pts) with metastatic renal cell carcinoma (mRCC). We performed a validation study in an external cohort of patients with mRCC. Methods: All consecutive mRCC pts treated at a single institute and included in the local IGReCC database. All pts received at least one line of targeted therapy or ICIs. Primary outcome was OS from first line start. Univariate and multivariate analysis (cox regression model) were performed. Bootstrap was also carried out for validation. Results: 1073 mRCC pts were analysed (808 with known IMDC score). Of 13 initial variables considered for prognostic model, the presence of bbl as first site/s of metastases plus the 6 variables included in IMDC resulted as statistically significant factors associated with OS (table). By including this new parameter we developed a 7-variables based score (IMDC-7). Patients with 0, 1-2 and≥3 positive variable/s were considered as good, intermediate and poor risk patients respectively. 56 (22%) pts moved from good (IMDC) to intermediate (IMDC-7) and 66 (15.4%) pts moved from intermediate (IMDC) to poor (IMDC-7) group (table). Final bias corrected concordance c-index was 0.69 and 0.71 in overall cohort and in pts treated with ICI (n=241). Conclusions: The addition of bbl primary metastases to the others IMDC variables improves prognostic predictive power of the model.[Table: see text]

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