scholarly journals Identification of Gene Signature for Renal Cell Carcinoma-Associated Fibroblasts Mediating Cancer Progression and Affecting Prognosis

2020 ◽  
Author(s):  
Bitian Liu ◽  
Xiaonan Chen ◽  
Yunhong Zhan ◽  
Bin Wu ◽  
Shen Pan
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Cancer Progression ◽  
Renal Cell ◽  
Gene Signature ◽  
Carcinoma Associated Fibroblasts

scholarly journals Identification Of Gene Signature For Renal Cell Carcinoma-Associated Fibroblasts Mediating Cancer Progression And Affecting Prognosis

2020 ◽  
Author(s):  
Bitian Liu ◽  
Xiaonan Chen ◽  
Yunhong Zhan ◽  
Bin Wu ◽  
Shen Pan
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Single Cell ◽  
Clinical Significance ◽  
Cell Lines ◽  
Cancer Progression ◽  
Renal Cell ◽  
Gene Signature ◽  
Pathological Grade ◽  
Single Cell Sequencing

Abstract Background: Cancer-associated fibroblasts (CAFs) are most abundant in stroma and are critically involved in cancer progression. However, the specific signature of CAFs and related clinicopathological parameters in renal cell carcinoma (RCC) remain unclear. Methods: In this work, methods using recognized gene signatures were employed to roughly assess the infiltration level of the stroma and CAFs in RCC based on the data in The Cancer Genome Atlas. Weighted gene co-expression network analysis (WGCNA) was used to cluster transcriptomes and correlate with CAFs to identify specific markers. A comparison of fibroblast versus urothelial carcinoma cell lines and correlation with previously reported CAF markers were performed to demonstrate the specific expressed of the gene signature. The gene signature was used to compare fibroblast infiltration of each sample through single sample gene set enrichment analysis, and the clinical significance of fibroblasts was analyzed via Cox risk assessment and the chi-square test. Finally, we used validation data to verify the clinical significance of the fibroblast gene signature in RCC. Results: Roughly calculated tumor matrix and CAF levels were significantly higher in kidney cancer than in normal tissues. More than 85% of fibroblast-specific markers identified by WGCNA were consistent with markers obtained via single-cell sequencing. These markers were more highly expressed in fibroblast cell lines and were significantly correlated with canonical CAFs makers. Data validation also showed that CAFs were significant correlation with survival and pathological grade. Conclusions: In summary, our findings indicate that the gene signature potentially serves as a biomarker of CAFs in RCC and that infiltration of fibroblasts in RCC is an independent prognostic factor associated with pathological grade and stage of tumor. The ability to recognize specific CAF markers using WGCNA is comparable to single-cell sequencing.

scholarly journals Identification of a Gene Signature for Renal Cell Carcinoma–Associated Fibroblasts Mediating Cancer Progression and Affecting Prognosis

2021 ◽  
Vol 8 ◽  
Author(s):  
Bitian Liu ◽  
Xiaonan Chen ◽  
Yunhong Zhan ◽  
Bin Wu ◽  
Shen Pan
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Cancer Progression ◽  
Renal Cell ◽  
Gene Signature ◽  
Clinicopathological Parameters ◽  
Advanced Tumor Stage ◽  
Advanced Tumor ◽  
Potential Biomarker ◽  
Key Genes

Background: Cancer-associated fibroblasts (CAFs) are mainly involved in cancer progression and treatment failure. However, the specific signature of CAFs and their related clinicopathological parameters in renal cell carcinoma (RCC) remain unclear. Here, methods to recognize gene signatures were employed to roughly assess the infiltration of CAFs in RCC, based on the data from The Cancer Genome Atlas (TCGA). Weighted Gene Coexpression Network Analysis (WGCNA) was used to cluster transcriptomes and correlate with CAFs to identify the gene signature. Single-cell and cell line sequencing data were used to verify the expression specificity of the gene signature in CAFs. The gene signature was used to evaluate the infiltration of CAFs in each sample, and the clinical significance of each key gene in the gene signature and CAFs was analyzed. We observed that the CAF infiltration was higher in kidney cancer and advanced tumor stage and grade than in normal tissues. The seven key genes of the CAF gene signature identified using WGCNA showed high expression of CAF-related characteristics in the cell clustering landscape and fibroblast cell lines; these genes were found to be associated with extracellular matrix function, collagen synthesis, cell surface interaction, and adhesion. The high CAF infiltration and the key genes were verified from the TCGA and Gene Expression Omnibus data related to the advanced grade, advanced stage, and poor prognosis of RCC. In summary, our findings indicate that the clinically significant gene signature may serve as a potential biomarker of CAFs in RCC, and the infiltration of CAFs is associated with the pathological grade, stage, and prognosis of RCC.

scholarly journals Development of a novel gene signature to predict prognosis and response to PD-1 blockade in clear cell renal cell carcinoma

2021 ◽  
Vol 10 (1) ◽  
pp. 1933332
Author(s):  
Xiaomao Yin ◽  
Zaoyu Wang ◽  
Jianfeng Wang ◽  
Yunze Xu ◽  
Wen Kong ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Gene Signature ◽  
Cell Renal Cell Carcinoma ◽  
Novel Gene

scholarly journals Metabolomic Analysis to Elucidate Mechanisms of Sunitinib Resistance in Renal Cell Carcinoma

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 1
Author(s):  
Tomonori Sato ◽  
Yoshihide Kawasaki ◽  
Masamitsu Maekawa ◽  
Shinya Takasaki ◽  
Kento Morozumi ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Energy Metabolism ◽  
Cell Carcinoma ◽  
Cell Line ◽  
Cell Lines ◽  
Cancer Progression ◽  
Renal Cell ◽  
Treatment Resistance ◽  
Tca Cycle ◽  
Glutamine Uptake

Metabolomics analysis possibly identifies new therapeutic targets in treatment resistance by measuring changes in metabolites accompanying cancer progression. We previously conducted a global metabolomics (G-Met) study of renal cell carcinoma (RCC) and identified metabolites that may be involved in sunitinib resistance in RCC. Here, we aimed to elucidate possible mechanisms of sunitinib resistance in RCC through intracellular metabolites. We established sunitinib-resistant and control RCC cell lines from tumor tissues of RCC cell (786-O)-injected mice. We also quantified characteristic metabolites identified in our G-Met study to compare intracellular metabolism between the two cell lines using liquid chromatography-mass spectrometry. The established sunitinib-resistant RCC cell line demonstrated significantly desuppressed protein kinase B (Akt) and mesenchymal-to-epithelial transition (MET) phosphorylation compared with the control RCC cell line under sunitinib exposure. Among identified metabolites, glutamine, glutamic acid, and α-KG (involved in glutamine uptake into the tricarboxylic acid (TCA) cycle for energy metabolism); fructose 6-phosphate, D-sedoheptulose 7-phosphate, and glucose 1-phosphate (involved in increased glycolysis and its intermediate metabolites); and glutathione and myoinositol (antioxidant effects) were significantly increased in the sunitinib-resistant RCC cell line. Particularly, glutamine transporter (SLC1A5) expression was significantly increased in sunitinib-resistant RCC cells compared with control cells. In this study, we demonstrated energy metabolism with glutamine uptake and glycolysis upregulation, as well as antioxidant activity, was also associated with sunitinib resistance in RCC cells.

CD151 expression can predict cancer progression in clear cell renal cell carcinoma

2011 ◽  
Vol 58 (2) ◽  
pp. 191-197 ◽  
Author(s):  
Seong H Yoo ◽  
Kyoungbun Lee ◽  
Ji Y Chae ◽  
Kyung C Moon
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Cancer Progression ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma

scholarly journals B7-H4 expression in renal cell carcinoma and tumor vasculature: Associations with cancer progression and survival

2006 ◽  
Vol 103 (27) ◽  
pp. 10391-10396 ◽  
Author(s):  
A. E. Krambeck ◽  
R. H. Thompson ◽  
H. Dong ◽  
C. M. Lohse ◽  
E. S. Park ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Cancer Progression ◽  
Renal Cell ◽  
Tumor Vasculature

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 A four-gene signature predicts survival in clear-cell renal-cell carcinoma

Oncotarget ◽  
2016 ◽  
Vol 7 (50) ◽  
pp. 82712-82726 ◽  
Author(s):  
Jun Dai ◽  
Yuchao Lu ◽  
Jinyu Wang ◽  
Lili Yang ◽  
Yingyan Han ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Gene Signature ◽  
Cell Renal Cell Carcinoma
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