scholarly journals Bioinformatic analysis identifies potentially key differentially expressed genes in oncogenesis and progression of clear cell renal cell carcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8096 ◽  
Author(s):  
Haiping Zhang ◽  
Jian Zou ◽  
Ying Yin ◽  
Bo Zhang ◽  
Yaling Hu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Renal Cell ◽  
Molecular Mechanisms ◽  
Clear Cell ◽  
Clinical Stage ◽  
Stage I ◽  
Differentially Expressed ◽  
Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common and lethal types of cancer within the urinary system. Great efforts have been made to elucidate the pathogeny. However, the molecular mechanism of ccRCC is still not well understood. The aim of this study is to identify key genes in the carcinogenesis and progression of ccRCC. The mRNA microarray dataset GSE53757 was downloaded from the Gene Expression Omnibus database. The GSE53757 dataset contains tumor and matched paracancerous specimens from 72 ccRCC patients with clinical stage I to IV. The linear model of microarray data (limma) package in R language was used to identify differentially expressed genes (DEGs). The protein–protein interaction (PPI) network of the DEGs was constructed using the search tool for the retrieval of interacting genes (STRING). Subsequently, we visualized molecular interaction networks by Cytoscape software and analyzed modules with MCODE. A total of 1,284, 1,416, 1,610 and 1,185 up-regulated genes, and 932, 1,236, 1,006 and 929 down-regulated genes were identified from clinical stage I to IV ccRCC patients, respectively. The overlapping DEGs among the four clinical stages contain 870 up-regulated and 645 down-regulated genes. The enrichment analysis of DEGs in the top module was carried out with DAVID. The results showed the DEGs of the top module were mainly enriched in microtubule-based movement, mitotic cytokinesis and mitotic chromosome condensation. Eleven up-regulated genes and one down-regulated gene were identified as hub genes. Survival analysis showed the high expression of CENPE, KIF20A, KIF4A, MELK, NCAPG, NDC80, NUF2, TOP2A, TPX2 and UBE2C, and low expression of ACADM gene could be involved in the carcinogenesis, invasion or recurrence of ccRCC. Literature retrieval results showed the hub gene NDC80, CENPE and ACADM might be novel targets for the diagnosis, clinical treatment and prognosis of ccRCC. In conclusion, the findings of present study may help us understand the molecular mechanisms underlying the carcinogenesis and progression of ccRCC, and provide potential diagnostic, therapeutic and prognostic biomarkers.

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.

scholarly journals Genome-Wide Analysis of Differentially Expressed Genes and Splicing Isoforms in Clear Cell Renal Cell Carcinoma

PLoS ONE ◽  
2013 ◽  
Vol 8 (10) ◽  
pp. e78452 ◽  
Author(s):  
Alessio Valletti ◽  
Margherita Gigante ◽  
Orazio Palumbo ◽  
Massimo Carella ◽  
Chiara Divella ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Renal Cell ◽  
Clear Cell ◽  
Differentially Expressed ◽  
Cell Renal Cell Carcinoma ◽  
Genome Wide Analysis ◽  
Genome Wide ◽  
Splicing Isoforms

scholarly journals Expression of AOX1 Predicts Prognosis of Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Luyang Xiong ◽  
Yuchen Feng ◽  
Wei Hu ◽  
Jiahong Tan ◽  
Shusheng Li ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Renal Cell ◽  
Clear Cell ◽  
Metabolic Reprogramming ◽  
Differentially Expressed ◽  
Normal Kidney ◽  
Cell Renal Cell Carcinoma ◽  
Cancer Management

Clear cell renal cell carcinoma (ccRCC) is the most prevalent kidney cancer worldwide, and appropriate cancer biomarkers facilitate early diagnosis, treatment, and prognosis prediction in cancer management. However, an accurate biomarker for ccRCC is lacking. This study identified 356 differentially expressed genes in ccRCC tissues compared with normal kidney tissues by integrative analysis of eight ccRCC datasets. Enrichment analysis of the differentially expressed genes unveiled improved adaptation to hypoxia and metabolic reprogramming of the tumor cells. Aldehyde oxidase 1 (AOX1) gene was identified as a biomarker for ccRCC among all the differentially expressed genes. ccRCC tissues expressed significantly lower AOX1 than normal kidney tissues, which was further validated by immunohistochemistry at the protein level and The Cancer Genome Atlas (TCGA) data mining at the mRNA level. Higher AOX1 expression predicted better overall survival in ccRCC patients. Furthermore, AOX1 DNA copy number deletion and hypermethylation were negatively correlated with AOX1 expression, which might be the potential mechanism for its dysregulation in ccRCC. Finally, we illustrated that the effect of AOX1 as a tumor suppressor gene is not restricted to ccRCC but universally exists in many other cancer types. Hence, AOX1 may act as a potential prognostic biomarker and therapeutic target for ccRCC.

scholarly journals Differentially Expressed Genes in Clear Cell Renal Cell Carcinoma as a Potential Marker for Prognostic and Immune Signatures

2021 ◽  
Vol 11 ◽  
Author(s):  
Ying Tong ◽  
Yiwen Yu ◽  
Hui Zheng ◽  
Yanchun Wang ◽  
Suhong Xie ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differentially Expressed Genes ◽  
Renal Cell ◽  
Clear Cell ◽  
Differentially Expressed ◽  
High Expression ◽  
Molecular Networks ◽  
Cell Renal Cell Carcinoma ◽  
Potential Marker

Clear cell renal cell carcinoma (ccRCC) is characterized by the inactivation of the von Hippel–Lindau (VHL) gene. Of note, no other gene is mutated as frequently as VHL in ccRCC, turning out that patients with inactivated VHL constitute the majority of ccRCC-related character. Thus, differentially expressed genes (DEGs) and their molecular networks caused by VHL mutation were considered as important factors for influencing the prognosis of ccRCC. Here, we first screened out six DEGs (GSTA1, GSTA2, NAT8, FABP7, SLC17A3, and SLC17A4) which downregulated in ccRCC patients with VHL non-mutation than with the mutation. Generally, most DEGs with high expression were associated with a favorable prognosis and low-risk score. Meanwhile, we spotted transcription factors and their kinases as hubs of DEGs. Finally, we clustered ccRCC patients into three subgroups according to the expression of hub proteins, and analyzed these subgroups with clinical profile, outcome, immune infiltration, and potential Immune checkpoint blockade (ICB) response. Herein, DEGs might be a promising biomarker panel for immunotherapy and prognosis in ccRCC. Moreover, the ccRCC subtype associated with high expression of hubs fit better for ICB therapy.

scholarly journals Glycolysis-Related Genes Serve as Potential Prognostic Biomarkers in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yan Zhang ◽  
Mingying Chen ◽  
Meihong Liu ◽  
Yingkun Xu ◽  
Guangzhen Wu
Keyword(s):  
Renal Cell Carcinoma ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Predictive Model ◽  
Differentially Expressed Genes ◽  
Renal Cell ◽  
Drug Sensitivity ◽  
Clear Cell ◽  
Differentially Expressed ◽  
Cell Renal Cell Carcinoma

Metabolic rearrangement is a marker of cancer that has been widely studied in recent years. One of the major metabolic characteristics of tumor cells is the high levels of glycolysis, even under aerobic conditions, a phenomenon that is called the “Warburg effect.” We investigated the expression and copy number variation (CNV) frequency of all glycolysis-related genes in multiple cancer types and found many differentially expressed genes, particularly in clear cell renal cell carcinoma (ccRCC). Single nucleotide variants (SNVs) showed that the overall average mutation frequency for all genes was low. The purpose of this study was to establish a predictive model by studying glycolysis-related genes in ccRCC. We compared the expression of glycolysis-related genes in 539 ccRCC tissues and 72 normal renal tissues from The Cancer Genome Atlas dataset and identified 17 upregulated and 26 downregulated genes. Pathway analysis revealed that PSAT1 and SDHB could activate the cell cycle, RPIA could activate the DNA damage response, and HK3 could activate apoptosis and EMT signaling, while PDK2 could inhibit apoptosis. The results of the drug sensitivity analysis suggested that some of these differentially expressed genes were positively correlated with drug sensitivity. Thirteen genes were selected from the gene coexpression network and the LASSO regression analysis. The Kaplan-Meier overall survival curves showed that the expression of upregulated genes in ccRCC patients was associated with lower overall survival. We established a predictive model consisting of 13 genes (RPIA, G6PD, PSAT1, ENO2, HK3, IDH1, PDK4, PGM2, PGK1, FBP1, OGDH, SUCLA2, and SUCLG2). This predictive model correlated well with the development and progression of ccRCC. Thus, it is of great value in the diagnosis and prognostic evaluation of ccRCC and may aid the identification of potential prognostic biomarkers and drug targets.

scholarly journals Long Noncoding RNA HOTTIP Serves as an Independent Predictive Biomarker for the Prognosis of Patients with Clear Cell Renal Cell Carcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Zhanxin Liu ◽  
Zichun Wang ◽  
Xiaoxiong Wang ◽  
Meisong Lu ◽  
Guang Chen
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Noncoding Rna ◽  
Clear Cell ◽  
Clinical Stage ◽  
Predictive Biomarker ◽  
The Cancer Genome Atlas ◽  
Cell Renal Cell Carcinoma ◽  
Advanced Clinical Stage

Several studies have indicated that HOXA transcript at the distal tip (HOTTIP) play important roles in the tumorigenesis and development of various cancers. We aim to investigate the expression and prognostic value of HOTTIP in clear cell renal cell carcinoma (ccRCC). A systematic review of PubMed, Embase, Medline, and Web of Science databases was performed to select eligible literatures relevant to the correlation between HOTTIP expression and clinical outcome of different cancers. The association between the HOTTIP level and overall survival (OS), lymph node metastasis (LNM), or clinical stage was subsequently analyzed. Survival analyses were performed in a large cohort of more than 500 patients with ccRCC from The Cancer Genome Atlas (TCGA) using bioinformatic methods. Seventeen studies with a total of 1594 patients with thirteen kinds of carcinomas were included in this analysis. The result showed that high HOTTIP expression could predict worse outcome in cancer patients, with the pooled hazard ratio (HR) of 2.34 (95% confidence interval (CI) 1.96–2.79, p<0.0001). The result also showed that elevated HOTTIP expression was correlated with more LNM (OR=2.61, 95% CI 1.91-3.58, p<0.0001) and advanced clinical stage (OR=3.57, 95% CI 2.58-4.93, p<0.0001). We further validated that ccRCC patients with higher HOTTIP expression tend to have unsatisfactory outcomes both in the entire TCGA dataset and different clinical stratums, like age, grade, and stage. The tumor of those patients was associated with a larger size, easier to metastasis, advanced clinical stage, and a higher pathological grade. These findings suggested that increased HOTTIP expression might act as a novel prognostic marker for ccRCC patients.

Survival outcomes and patterns of utilization of cytoreductive nephrectomy in the tyrosine kinase inhibitors (TKI)-era in metastatic clear cell renal cell carcinoma (ccRCC) and non-clear cell renal cell carcinoma (nccRCC): Analyses from the National Cancer Database (NCDB).

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e16068-e16068
Author(s):  
Jeanny B. Aragon-Ching ◽  
Hongkun Wang ◽  
Donald L. Trump
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Median Survival ◽  
Renal Cell ◽  
Clear Cell ◽  
Stage Iv ◽  
Stage I ◽  
Survival Outcomes ◽  
Cytoreductive Nephrectomy ◽  
Cell Renal Cell Carcinoma

e16068 Background: Use of TKIs is standard of care for metastatic ccRCC and cytoreductive nephrectomy has improved survival even in metastatic ccRCC. The use of cytoreductive nephrectomy in the TKI era post-2005 for nccRCC histologies is unknown. We sought to determine trends and explore differences in characteristics of ccRCC and nccRCC, use of cytoreductive nephrectomy and survival outcomes for varying stages. Methods: Using a de-identified dataset acquired from the NCDB from 2004 to 2014, extraction of demographic information on patients divided into ccRCC versus nccRCC. Descriptive statistics was used for summarizing patients’ characteristics. Chi-Square test was used for comparing categorical variables. Two-sample t-test was used for comparing continuous variables. Kaplan-Meier method was used to analyze patients’ survival data. Results: 302,339 (82%) ccRCC and 66,530 (18%) nccRCC patients were identified. nccRCC included papillary (n = 42,251), sarcomatoid (n = 5769), chromophobe (n = 17,671) and collecting duct (n = 839). The median age for both groups was 63 years, more common in males (ccRCC = 61%; nccRCC = 69%), and mostly Caucasians (ccRCC = 86%; nccRCC = 75%). Most patients were treated in comprehensive community and academic research programs (ccRCC = 81%; nccRCC = 83%). Majority of patients were diagnosed with AJCC Stage I (ccRCC = 57%, nccRCC = 62%). Stage IV was diagnosed in 14% of ccRCC and 9% of nccRCC. The utilization of nephrectomy declined for ccRCC at 36% in the year 2014 compared to 44% in 2005, whereas it declined more for nccRCC with 47% in 2014 compared to 70% in 2005. The median survival time for Stage I was comparable in both groups (ccRCC = 140 mos, 95% CI 138.8 – 142.16; nccRCC = 140 mos, 95% CI 138 – unestimable). However, median survival was worse for stage IV in nccRCC (ccRCC = 9 mos; 95% CI 8.8 – 9.17; nccRCC = 7.43 mos; 95% CI 7.16 – 7.79). Conclusions: In stage IV patients with nccRCC, cytoreductive nephrectomy is used less often and overall survival appears inferior compared to ccRCC.

Proteomic analysis in clear cell renal cell carcinoma: identification of differentially expressed protein by 2-D DIGE

2012 ◽  
Vol 8 (4) ◽  
pp. 1040 ◽  
Author(s):  
Francesca Raimondo ◽  
Claudia Salemi ◽  
Clizia Chinello ◽  
Daniela Fumagalli ◽  
Lavinia Morosi ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Proteomic Analysis ◽  
Renal Cell ◽  
Clear Cell ◽  
Differentially Expressed ◽  
Cell Renal Cell Carcinoma ◽  
Differentially Expressed Protein
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