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

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 Transcription Factors BARX1 and DLX4 Contribute to Progression of Clear Cell Renal Cell Carcinoma via Promoting Proliferation and Epithelial–Mesenchymal Transition

2021 ◽  
Vol 8 ◽  
Author(s):  
Guoliang Sun ◽  
Yue Ge ◽  
Yangjun Zhang ◽  
Libin Yan ◽  
Xiaoliang Wu ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Transcription Factors ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Epithelial Mesenchymal Transition ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Cell Renal Cell Carcinoma ◽  
Mesenchymal Transition

Dysregulation of transcription factors contributes to the carcinogenesis and progression of cancers. However, their roles in clear cell renal cell carcinoma remain largely unknown. This study aimed to evaluate the clinical significance of TFs and investigate their potential molecular mechanisms in ccRCC. Data were accessed from the cancer genome atlas kidney clear cell carcinoma cohort. Bioinformatics algorithm was used in copy number alterations mutations, and differentially expressed TFs’ analysis. Univariate and multivariate Cox regression analyses were performed to identify clinically significant TFs and construct a six-TF prognostic panel. TFs’ expression was validated in human tissues. Gene set enrichment analysis (GSEA) was utilized to find enriched cancer hallmark pathways. Functional experiments were conducted to verify the cancer-promoting effect of BARX homeobox 1 (BARX1) and distal-less homeobox 4 (DLX4) in ccRCC, and Western blot was performed to explore their downstream pathways. As for results, many CNAs and mutations were identified in transcription factor genes. TFs were differentially expressed in ccRCC. An applicable predictive panel of six-TF genes was constructed to predict the overall survival for ccRCC patients, and its diagnostic efficiency was evaluated by the area under the curve (AUC). BARX1 and DLX4 were associated with poor prognosis, and they could promote the proliferation and migration of ccRCC. In conclusion, the six-TF panel can be used as a prognostic biomarker for ccRCC patients. BARX1 and DLX4 play oncogenic roles in ccRCC via promoting proliferation and epithelial–mesenchymal transition. They have the potential to be novel therapeutic targets for 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 Identification of novel differentially expressed lncRNA and mRNA transcripts in clear cell renal cell carcinoma by expression profiling

Genomics Data ◽  
2015 ◽  
Vol 5 ◽  
pp. 173-175 ◽  
Author(s):  
Mario Deng ◽  
Jasmine J. Blondeau ◽  
Doris Schmidt ◽  
Sven Perner ◽  
Stefan C. Müller ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Expression Profiling ◽  
Renal Cell ◽  
Clear Cell ◽  
Differentially Expressed ◽  
Cell Renal Cell Carcinoma ◽  
Mrna Transcripts

Histology-guided proteomic analysis to investigate the molecular profiles of clear cell Renal Cell Carcinoma grades

2019 ◽  
Vol 191 ◽  
pp. 38-47 ◽  
Author(s):  
Martina Stella ◽  
Clizia Chinello ◽  
Anna Cazzaniga ◽  
Andrew Smith ◽  
Manuel Galli ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Proteomic Analysis ◽  
Renal Cell ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Molecular Profiles

scholarly journals Construction and Validation of a Prognostic Nomogram for Clear Cell Renal Cell Carcinoma Based on DNA Methylation-driven Differentially Expressed Genes

2020 ◽  
Author(s):  
Zheng Wang ◽  
Yanlong Zhang ◽  
Shuaishuai Fan ◽  
Yuan Ji ◽  
Jianchao Ren ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Clear Cell ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Differentially Expressed ◽  
Cell Renal Cell Carcinoma ◽  
Score Model

Abstract Background: Clear cell renal cell carcinoma (ccRCC) is the most frequent type of kidney cancer. This study aimed to establish a nomogram to predict ccRCC prognosis.Methods: By integrating DNA methylation (DNAm) data and gene expression profiles of ccRCC obtained from The Cancer Genome Atlas (TCGA), DNAm-driven genes were identified by differential and correlation analyses. Next, risk genes were selected by multiple algorithms (univariate Cox and Kaplan-Meier survival analyses) and various databases (TCGA, Clinical Proteomic Tumor Analysis Consortium (CPTAC), and The Human Protein Atlas (HPA)). A risk score model was established by multivariate Cox analyses. ConsensusPathDB and Gene Set Enrichment Analysis (GSEA) were used to identify the biological functions of the selected genes. After comprehensively evaluating the clinical data, we established and assessed a dynamic nomogram available on a webserver.Results: In total, 220 differentially expressed DNAm-driven genes were identified, and five-gene signature (EPB41L4B, HHLA2, IFI16, CMTM3, and XAF1) was related to overall survival (OS). Next, we integrated the DNAm-driven genes into the prognostic risk score model and found that age, histologic grade, pathological stage, and risk level were correlated with OS in ccRCC patients. Based on these variables, a dynamic nomogram was established to predict the ccRCC prognosis. Finally, Functional enrichment analysis showed that the functions of these genes were relevant to immune reactions.Conclusions: We identified a 5 DNAm-driven gene signature whose altered status was highly correlated with ccRCC patient OS. We constructed a dynamic nomogram to provide individualized survival predictions for ccRCC patients.

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.

Homologous repair deficiency in VHL-mutated clear cell renal cell carcinoma.

2018 ◽  
Vol 36 (6_suppl) ◽  
pp. 585-585 ◽  
Author(s):  
Patrick Glen Pilie ◽  
Lijun Zhou ◽  
Christine B Peterson ◽  
Yang Peng ◽  
Guang Peng ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Proteomic Analysis ◽  
In Silico ◽  
Renal Cell ◽  
Clear Cell ◽  
Early Stage ◽  
Gene Signature ◽  
Cell Renal Cell Carcinoma ◽  
Tumor Stages

585 Background: Clear cell renal cell carcinoma (ccRCC) displays genomic instability across all tumor stages, indicative of increased replicative stress and defects in DNA damage response (DDR) pathways including homologous repair (HR); however ccRCC does not display mutations in canonical DDR genes and advanced ccRCC is not traditionally sensitive to DNA damaging agents. We hypothesized that biallelic VHL loss is sufficient to cause HR deficiency (HRD) and that early stage ccRCC is more likely to display HRD than late stage disease. Methods: We performed whole-exome (WES) sequencing of 15 small ccRCC tumors to assess for mutational burden, mutational signature, and cancer-related driver gene mutations. We performed in silico genomic, transcriptomic, and proteomic analysis of tumors in KIRC TCGA to assess for HRD. Finally, we assessed HR efficiency as a product of biallelic VHL loss in engineered cell line models. Results: 15/15 early stage ccRCC had VHL mutations, approximately 100 additional mutations per tumor, but no common mutations found in KIRC TCGA. In silico analysis showed 67% of KIRC TCGA displayed HRD gene signature, and was significantly higher in stage I disease (p = 2.21e-08). Patients with VHL-mutated tumors are more commonly HRD than HR intact (HRI) (p = 0.03), with frameshift/nonsense variants in VHL more likely to result in HRD than missense variants (p = 0.02). Multivariate analysis showed HRD predicted for better overall survival compared to HRI (p < 0.0001). Proteomic analysis of KIRC TCGA revealed HRI samples had significantly higher protein expression of Rad51 and RAPTOR when compared to HRD samples. Lastly, etoposide-treated MEF Vhl-/- cell lines displayed reduced HR efficiency when compared to Vhl intact controls. Conclusions: Biallelic loss of VHL is sufficient to cause HRD. HRD gene signature predicts for significantly better OS in ccRCC patients, and early stage ccRCC is more likely to be HRD. mTOR/PI3K signaling pathway proteins are differentially expressed in HRI patient samples from TCGA compared to HRD, suggesting a possible role for these oncogenes driving a HRI phenotype. Treatment strategies combining PARP inhibitors with PI3K/mTOR inhibitors should be tested in advanced VHL-mutated ccRCC.

Differentially expressed micro-RNAs in clear cell renal cell carcinoma

2010 ◽  
Vol 203 (1) ◽  
pp. 71
Author(s):  
Klaas Kok ◽  
Gerben Duns ◽  
Anke van den Berg ◽  
Geert Harms ◽  
Inge van Duivenbode ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Clear Cell ◽  
Differentially Expressed ◽  
Cell Renal Cell Carcinoma ◽  
Micro Rnas
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