Dataset Information

Proteomic analysis of clear cell renal cell carcinoma tissue versus matched normal kidney tissue

ABSTRACT: We analyzed 84 tumor/normal pairs (177 total) using standard shotgun proteomic techniques in order to characterize the molecular landscape of clear cell renal cell carcinoma (ccRCC) and interrogated changes in protein abundance and biological pathways with ccRCC grade. These tissues were distributed across stage 1 (n = 34), 2 (n = 40), 3 (n = 42), and 4 (n = 52), with 9 pairs also including samples from metastasized tumor. These data can be (and were) combined with a previously published transcriptomic data set from the same sample cohort (NCBI GEO: GSE53757).

INSTRUMENT(S): LTQ

ORGANISM(S): Homo Sapiens (ncbitaxon:9606)

SUBMITTER: Dr Richard R. Drake

PROVIDER: MSV000079972 | MassIVE | Wed Jul 20 19:11:00 BST 2016

SECONDARY ACCESSION(S): PXD003271

REPOSITORIES: MassIVE

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Publications

Proteotranscriptomic Analysis Reveals Stage Specific Changes in the Molecular Landscape of Clear-Cell Renal Cell Carcinoma.

Neely Benjamin A BA Wilkins Christopher E CE Marlow Laura A LA Malyarenko Dariya D Kim Yunee Y Ignatchenko Alexandr A Sasinowska Heather H Sasinowski Maciek M Nyalwidhe Julius O JO Kislinger Thomas T Copland John A JA Drake Richard R RR

PloS one 20160429 4

Renal cell carcinoma comprises 2 to 3% of malignancies in adults with the most prevalent subtype being clear-cell RCC (ccRCC). This type of cancer is well characterized at the genomic and transcriptomic level and is associated with a loss of VHL that results in stabilization of HIF1. The current study focused on evaluating ccRCC stage dependent changes at the proteome level to provide insight into the molecular pathogenesis of ccRCC progression. To accomplish this, label-free proteomics was used ...[more]

PMID: 27128972

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Project description:Renal cell carcinoma (RCC) is the most common neoplasm of the adult kidney. Currently, there are no biomarkers for the diagnostic, prognostic, or predictive applications in RCC. MicroRNAs (miRNAs) are short non protein-coding RNAs that negatively regulate gene expression and have been shown to be involved in cancer. We analyzed a total of 70 matched pairs of clear cell RCC (ccRCC) and normal kidney tissues from the same patients by microarray analysis and validated our results by quantitative real time PCR. We identified 166 miRNAs significantly dysregulated in ccRCC. MiR-122, miR-155 and miR-210 had the highest fold changes of overexpression while miR-200c, miR-335, and miR-218 were the most downregulated. We performed extensive bioinformatics analysis including a combinatorial analysis of previously reported miRNAs dysregulated in RCC and extensive target prediction analysis. Many miRNAs were predicted to target a number of genes involved in RCC pathogenesis. Our results showed that miRNA dysregulation in RCC can be attributed in part, to chromosomal aberrations, the co-regulation of miRNA clusters, and co-expression with host genes. We also correlated miRNA expression with clinical characteristics and found miR-155 expression was correlated with ccRCC tumor size. In conclusion, our analysis showed that a number of miRNAs are dysregulated in ccRCC and may contribute to kidney cancer pathogenesis by targeting more than one key molecule. We identified mechanisms that may contribute to miRNA dysregulation in ccRCC. Dysregulated miRNAs represent potential biomarkers for kidney cancer. We preformed a miRNA microarray on 20 pairs of matched primary clear cell renal cell carcinoma (ccRCC) and normal kidney tissue from the same patient (St. Michael's Hospital, Toronto, Canada). One matched pair was used per array for a total of 20 arrays. Microarray results were validated by quantitative real time PCR using an independent set of 50 matched pairs of ccRCC and normal kidney tissue from the same patient.