Copy Number Alterations with Prognostic Potential in Clear Cell Renal Cell Carcinoma

Abstract

Objectives: To detect chromosomal aberrations in a genome-wide manner with potential value for prognosis in groups of patients with different histopathological grading in clear cell renal carcinoma (ccRCC). Material and Methods: We performed a copy number alteration analysis using the Affymetrix platform and SNP 6.0 mapping arrays with samples from 48 ccRCC-patients. The data analysis was done using 3 different Software Platforms: Affymetrix Genotyping Console (version 4.1.3.840) and 2 open-source packages for validation: PennCNV and PICNIC. Results: Consistent changes were found to divide the tumors into 4 groups: first group showed typical losses on 3p, second group losses on 3p plus gains on 5q, third group gains on chromosome 7 plus losses on chromosome 8; fourth group did not show any major changes. We selected the affected genes with the highest consistency and identified 13 different genes mapping in the SNP 6.0 results and Kyoto Encyclopedia of Genes and Genomes. Remarkable for further consideration were the phosphatidylinositol 3-kinase pathway, BRAF, MET, EGLN1; growth factors, for example, HGF, PGF and TGFB2. Conclusion: A multimodal approach with a well-defined workflow for detecting genomic aberrations by using array technologies and comparing the findings with different comprehensive databases may provide insights into functional tumor processes and help to identify potential new targets for more individualized future treatment.

In silico DNA methylation analysis identifies potential prognostic biomarkers in type 2 papillary renal cell carcinoma

“…Copy number analysis based on the raw 450k data, using adjacent‐normal tissue as control data, reveals differences between localized and advanced‐stage type 2 PRCC (Figure A,B, Table ). Among the genes showing CNV, three ( PTK7 , EGLN1 , SMYD3 ) have been reported to be related to RCC or cancer cell invasion . Gain of PTK7 is observed in three of 51 cases (5.9%) of localized‐stage and 18 of 35 cases (51.4%) of advanced‐stage type 2 PRCC, and survival analysis demonstrates that PTK7 gain is associated with poor survival ( P < 0.0001).…”

Comprehensive analysis of somatic copy number alterations in clear cell renal cell carcinoma

“…Interestingly, we showed that 3p24.3 mixed type was an independent factor that predicted an excellent disease‐free prognosis in ccRCC. Although it is well known that a specific SCNA can be a prognostic factor to predict poor patient prognosis in various cancers including gastric, 26 colorectal, 27 and renal cell cancers, 28 a prediction of good prognosis in ccRCC has not been reported as far as we know. Such association was supported by the finding that the 3p24.3 mixed type was correlated with disease‐free survival in the validation cohort (second cohort).…”

“…Interestingly, we showed that 3p24.3 mixed type was an independent factor that predicted an excellent disease-free prognosis in ccRCC. Although it is well known that a specific SCNA can be a prognostic factor to predict poor patient prognosis in various cancers including gastric, 26 colorectal, 27 and renal cell cancers, 28 One report showed that high expression of RFTN1 was closely associated with poor prognosis of ccRCC, compared with low expression (The Human Genome Atlas). 29 Previous study has shown that RFTN1 controls toll-like receptor adaptor molecule 1 (TICAM-1) signaling that regulates protein-protein interactions between the tolllike receptors and signal-transduction components.…”

“…9,10 Several studies have demonstrated that the presence of multiple SCNAs is associated with overall patient survival, tumor stage, and development of metastasis. 11,12 Genome-wide assessment using somatic SCNAs provides useful information for identifying overall genomic profiles of cancer cells. 11,12 The accumulation of SCNAs contributes to tumor heterogeneity and consequently striking differences in the presence of SCNAs between primary and metastatic sites.…”

“…11,12 Genome-wide assessment using somatic SCNAs provides useful information for identifying overall genomic profiles of cancer cells. 11,12 The accumulation of SCNAs contributes to tumor heterogeneity and consequently striking differences in the presence of SCNAs between primary and metastatic sites. 13 The accumulation of SCNAs plays important roles in tumor progression in RCC as well as other types of cancer.…”