scholarly journals Identification of EGFR as a Novel Key Gene in Clear Cell Renal Cell Carcinoma (ccRCC) through Bioinformatics Analysis and Meta-Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Sheng Wang ◽  
Zhi-hong Yu ◽  
Ke-qun Chai
Keyword(s):  
Renal Cell Carcinoma ◽  
Survival Analysis ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Meta Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma ◽  
Kegg Analysis

Clear cell renal cell carcinoma (ccRCC) was the most aggressive histological type of renal cell carcinoma (RCC) and accounted for 70–80% of cases of all RCC. The aim of this study was to identify the potential biomarker in ccRCC and explore their underlying mechanisms. Four profile datasets were downloaded from the GEO database to identify DEGs. GO and KEGG analysis of DEGs were performed by DAVID. A protein–protein interaction (PPI) network was constructed to predict hub genes. The hub gene expression within ccRCC across multiple datasets and the overall survival analysis were investigated utilizing the Oncomine Platform and UALCAN dataset, separately. A meta-analysis was performed to explore the relationship between the hub genes: EGFR and ccRCC. 127 DEGs (55 upregulated genes and 72 downregulated genes) were identified from four profile datasets. Integrating the result from PPI network, Oncomine Platform, and survival analysis, EGFR, FLT1, and EDN1 were screened as key factors in the prognosis of ccRCC. GO and KEGG analysis revealed that 127 DEGs were mainly enriched in 21 terms and 4 pathways. The meta-analysis showed that there was a significant difference of EGFR expression between ccRCC tissues and normal tissues, and the expression of EGFR in patients with metastasis was higher. This study identified 3 importance genes (EGFR, FLT1, and EDN1) in ccRCC, and EGFR may be a potential prognostic biomarker and novel therapeutic target for ccRCC, especially patients with metastasis.

scholarly journals Bioinformatics analysis of C3 and CXCR4 demonstrates their potential as prognostic biomarkers in clear cell renal cell carcinoma (ccRCC)

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jing Quan ◽  
Yuchen Bai ◽  
Yunbei Yang ◽  
Er Lei Han ◽  
Hong Bai ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Therapeutic Targets ◽  
Disease Free Survival ◽  
Prognostic Biomarkers ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma ◽  
Upregulated Genes

Abstract Background The molecular prognostic biomarkers of clear cell renal cell carcinoma (ccRCC) are still unknown. We aimed at researching the candidate biomarkers and potential therapeutic targets of ccRCC. Methods Three ccRCC expression microarray datasets (include GSE14762, GSE66270 and GSE53757) were downloaded from the gene expression omnibus (GEO) database. The differentially expressed genes (DEGs) between ccRCC and normal tissues were explored. The potential functions of identified DEGs were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). And then the protein - protein interaction network (PPI) was established to screen the hub genes. After that, the expressions of hub genes were identified by the oncomine database. The hub genes’ prognostic values of patients with ccRCC were analyzed by GEPIA database. Results A total of 137 DEGs were identified by utilizing the limma package and RRA method, including 63 upregulated genes and 74 downregulated genes. It is found that 137 DEGs were mainly enriched in 82 functional terms and 24 pathways in accordance with the research results. Thirteen highest-scoring genes were screened as hub genes (include 10 upregulated genes and 3 downregulated candidate genes) by utilizing the PPI network and module analysis. Through integrating the oncoming database and GEPIA database, the author found that C3 and CXCR4 are not only overexpressed in ccRCC, but also associated with the prognosis of ccRCC. Further results could reveal that patients with high C3 expression had a poor overall survival (OS), while patients with high CTSS and TLR3 expressions had a good OS; patients with high C3 and CXCR4 expressions had a poor disease-free survival (DFS), while ccRCC patients with high TLR3 expression had a good DFS. Conclusion These findings suggested that C3 and CXCR4 were the candidate biomarkers and potential therapeutic targets of ccRCC patients.

Response to systemic therapy in non-clear cell renal cell carcinomas: A systematic review and meta-analysis.

2014 ◽  
Vol 32 (4_suppl) ◽  
pp. 425-425 ◽  
Author(s):  
Francisco Emilio Vera-Badillo ◽  
Arnoud Templeton ◽  
Alberto Ocana ◽  
Paulo deGouveia ◽  
Priya Aneja ◽  
...  
Keyword(s):  
Systematic Review ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Systemic Therapy ◽  
Renal Cell ◽  
Overall Response Rate ◽  
Response Rate ◽  
Clear Cell ◽  
Meta Analysis ◽  
Cell Renal Cell Carcinoma

425 Background: Clinical data supporting the efficacy of systemic therapy in non-clear cell renal cell carcinoma (non-ccRCC) are limited and based on retrospective analyses, expanded access programs and single arm phase II trials. Therefore the optimal treatment for this subgroup remains uncertain. Methods: A systematic review of electronic databases was conducted to identify publications evaluating the outcomes of patients with non-ccRCC (excluding those with sarcomatoid tumors) treated with different systemic approaches (immunotherapy, chemotherapy, targeted agents, small molecules). The primary endpoint was response rate and secondary endpoints were median progression free (PFS) and overall survival (OS). Where possible, data were pooled in a meta-analysis using the Mantel-Haenszel random-effect modeling. For studies comprising of unselected patients, outcomes of those with non-ccRCC were compared with clear cell renal cell carcinoma (ccRCC). Results: Forty-nine studies comprising 7,799 patients were included: 471 patients were enrolled on studies conducted exclusively in non-ccRCC and 7,328 patients on studies of unselected renal cell carcinoma. Among these, 903 (12%) had non-ccRCC and 6,425 (88%) had ccRCC. For non-ccRCC, overall response rate, median PFS and median OS were 9%, 7.9 and 13.4 months, respectively. By comparison, the overall response rate for ccRCC was 15% (Risk Ratio for response [RR] 0.67, 95% CI 0.52-0.86, p=0.002). This association was independent of type of treatment administered. Among the different novel agents (bevacizumab, lenalidomide, linefanib, sorafenib, sunitinib, pazopanib, everolimus and temsirolimus), sunitinib was significantly less efficacious in non-ccRCC than ccRCC (RR 0.56, 95% CI 0.42-0.72), but there was no significant difference in response rates for sorafenib (RR 0.64, 95% CI 0.31-1.35) or other agents (RR 1.10, 95% CI 0.50-2.44), However, confidence intervals were wide. Results of further analyses will be presented at the meeting. Conclusions: Patients with non-ccRCC have lower response rates than those with ccRCC, but the absolute difference between them is modest. Further study of targeted therapy in non-ccRCC is warranted.

scholarly journals Meta-analysis of Clear Cell Renal Cell Carcinoma Gene Expression Defines a Variant Subgroup and Identifies Gender Influences on Tumor Biology

2012 ◽  
Vol 61 (2) ◽  
pp. 258-268 ◽  
Author(s):  
A. Rose Brannon ◽  
Scott M. Haake ◽  
Kathryn E. Hacker ◽  
Raj S. Pruthi ◽  
Eric M. Wallen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Meta Analysis ◽  
Tumor Biology ◽  
Cell Renal Cell Carcinoma ◽  
Gender Influences

scholarly journals Predicting Clear Cell Renal Cell Carcinoma Survival Using Kurtosis of Cytoplasm in the Hematoxylin Channel from Histology Slides

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Jun Wang ◽  
Jianhui Chen ◽  
Liren Jiang ◽  
Qi Wu ◽  
Dawei Wang
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Survival Analysis ◽  
Cell Carcinoma ◽  
Survival Time ◽  
Renal Cell ◽  
Clear Cell ◽  
Progression Free Survival ◽  
Cell Renal Cell Carcinoma ◽  
Free Survival

Purpose. Grade-dependent decrease of lipid storage in clear cell renal cell carcinoma (ccRCC) leads to morphology changes in HE sections. This study investigated the role of cytoplasmic features in frozen sections of ccRCC on prognosis using the digital pathology approach. Methods. We established an automatic pipeline that performed tumor region selection, stain vector normalization, nuclei segmentation, and feature extraction based on the pathologic data from Shanghai General Hospital and The Cancer Genome Atlas database. Extracted features were subjected to survival analysis. Results. Kurtosis of the cytoplasm in the hematoxylin channel was correlated with progression-free survival (HR 0.10, 95% CI: 0.04–0.24, p = 6.52 ∗ 10 − 7 ) and overall survival (HR 0.11, 95% CI: 0.05–0.31, p = 1.72 ∗ 10 − 5 ) in ccRCC, which outperformed other texture features in this analysis. Multivariate Cox regression analysis revealed that low kurtosis of cytoplasm in the hematoxylin channel was an independent predictor for a shorter progression-free survival time ( p = 0.044 ) and overall survival time (p = 0.01). Kaplan–Meier survival analysis of progression-free survival and overall survival also showed a significantly worse prognosis in patients with low kurtosis of the cytoplasm in the hematoxylin channel (both p < 0.0001 ). Lower kurtosis of cytoplasm in the hematoxylin channel was associated with higher pathologic grade, less cholesterol ester, and more mitochondrial DNA content. Conclusion. Kurtosis of the cytoplasm in the hematoxylin channel predicts survival in clear cell renal cell carcinoma.

scholarly journals Functional Assessment of Four Novel Immune-Related Biomarkers in the Pathogenesis of Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 9 ◽  
Author(s):  
Daojun Lv ◽  
Xiangkun Wu ◽  
Ming Wang ◽  
Wenzhe Chen ◽  
Shuxin Yang ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Rank Aggregation ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma ◽  
Network Analyses

BackgroundClear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma whose pathogenesis is not well understood. We aimed at identifying novel immune-related biomarkers that could be valuable in the diagnosis and prognosis of ccRCC.MethodsThe Robust Rank Aggregation (RRA) method was used to integrate differently expressed genes (DEGs) of 7 Gene Expression Omnibus (GEO) datasets and obtain robust DEGs. Weighted gene co-expression network analyses (WGCNA) were performed to identify hub genes associated with clinical traits in The Cancer Genome Atlas (TCGA) database. Comprehensive bioinformatic analyses were used to explore the role of hub genes in ccRCC.ResultsFour hub genes IFI16, LMNB1, RHBDF2 and TACC3 were screened by the RRA method and WGCNA. These genes were found to be up-regulated in ccRCC, an upregulation that could be due to their associations with late TNM stages and tumor grades. The Receiver Operating Characteristic (ROC) curve and Kaplan-Meier survival analysis showed that the four hub genes had great diagnostic and prognostic values for ccRCC, while Gene Set Enrichment Analysis (GSEA) showed that they were involved in immune signaling pathways. They were also found to be closely associated with multiple tumor-infiltrating lymphocytes and critical immune checkpoint expressions. The results of Quantitative Real-time PCR (qRT-PCR) and immunohistochemical staining (IHC) analysis were consistent with bioinformatics analysis results.ConclusionThe four hub genes were shown to have great diagnostic and prognostic values and played key roles in the tumor microenvironment of ccRCC.

scholarly journals Identification of Four Pathological Stage-Relevant Genes in Association with Progression and Prognosis in Clear Cell Renal Cell Carcinoma by Integrated Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Dengyong Xu ◽  
Yuzi Xu ◽  
Yiming Lv ◽  
Fei Wu ◽  
Yunlong Liu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Early Diagnosis ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Clear Cell ◽  
Clinical Stage ◽  
Hub Genes ◽  
Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a major histological subtype of renal cell carcinoma and can be clinically divided into four stages according to the TNM criteria. Identifying clinical stage-related genes is beneficial for improving the early diagnosis and prognosis of ccRCC. By using bioinformatics analysis, we aim to identify clinical stage-relevant genes that are significantly associated with the development of ccRCC. First, we analyzed the gene expression microarray data sets: GSE53757 and GSE73731. We divided these data into five groups by staging information—normal tissue and ccRCC stages I, II, III, and IV—and eventually identified 500 differentially expressed genes (DEGs). To obtain precise stage-relevant genes, we subsequently applied weighted gene coexpression network analysis (WGCNA) to the GSE73731 dataset and KIRC data from The Cancer Genome Atlas (TCGA). Two modules from each dataset were identified to be related to the tumor TNM stage. Several genes with high inner connection inside the modules were considered hub genes. The intersection results between hub genes of key modules and 500 DEGs revealed UBE2C, BUB1B, RRM2, and TPX2 as highly associated with the stage of ccRCC. In addition, the candidate genes were validated at both the RNA expression level and the protein level. Survival analysis also showed that 4 genes were significantly correlated with overall survival. In conclusion, our study affords a deeper understanding of the molecular mechanisms associated with the development of ccRCC and provides potential biomarkers for early diagnosis and individualized treatment for patients at different stages of ccRCC.

Immune checkpoint inhibitors plus tyrosine kinase inhibitors combination in the first-line setting of metastatic clear cell renal cell carcinoma: A meta-analysis of randomized clinical trials.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 704-704
Author(s):  
Maria Cruz Martin Soberón ◽  
Alberto Carretero-González ◽  
Guillermo de Velasco ◽  
Lucia Carril ◽  
Daniel Castellano
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Subgroup Analysis ◽  
Renal Cell ◽  
Randomized Clinical Trials ◽  
Clear Cell ◽  
Meta Analysis ◽  
Objective Response ◽  
Significant Benefit ◽  
Cell Renal Cell Carcinoma

704 Background: ICIs + TKIs have shown to improve outcomes in treatment-naïve metastatic clear-cell renal cell carcinoma (ccRCC). We aimed to analize the efficacy of all combinations published including the subgroup analysis based on age, sex and IMDC prognostic factors score. Methods: We searched published RCTs in MEDLINE and EMBASE comparing ICIs + TKIs vs TKIs in 1L metastatic ccRCC. Outcomes selected to assess efficacy were progression-free survival (PFS), overall survival (OS) and objective response rate (ORR) in the intent-to-treat population. Hazard ratios (HR) for PFS and OS, and relative risk (RR) for ORR with 95% confidence intervals (CI) were used as efficacy measures. Subgroup-based meta-analysis was afterwards performed according to randomized-effect model. Results: We identified two eligible RCTs of ICIs + TKIs (avelumab [avelu] or pembrolizumab [pembro] + axitinib [axi]) versus TKIs (sunitinib). Combined sample size was 1,747 patients (avelu + axi arm 442 patients; pembro + axi arm 432 patients; sunitinib arm 873 patients). Globally, three outcomes favored the combination. Improved HRs for PFS (0.69), OS (0.64) and ORR (1.81) were found for combination (Table). Regarding subgroup analysis HRs for PFS were favorable for combination in male (0.665) and female (0.66). Benefit in combination arms was confirmed in terms of age < 65 years (0.68) and ≥ 65 years (0.66). Intermediate and poor IMDC subgroups showed statistically significant benefit for combination (HR 0.68 and 0.56), whereas PFS in favorable group (0.68) was not statistically significant. Conclusions: ICIs + TKIs combination therapy has consistently demonstrated to be superior in terms of OS, PFS and ORR in 1L ccRCC to TKIs alone. We hereby confirm statistically significant benefit per subgroups except for favorable IMDC subgroup.[Table: see text]

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