miRNA Biomarkers of Renal Cancer

miRNA microarray analysis for comparison of miRNA expression levels between RCC tissues and their normal counterpart revealed several dysregulated miRNAs, which were validated by quantitative RT-PCR and bioinformatics analysis (Chow et al. 2010). Some of these miRNAs are dysregulated in other malignancies as well and have a potential role in RCC pathogenesis. The miRNAs showed a significant correlation with reported chromosomal aberration sites. Target prediction algorithms were to identify gene targets. These miRNAs are potential biomarkers of RCC. A study has demonstrated the usefulness of miRNA expression profiling for identifying a signature unique to various RCC subtypes at a single anatomic locus (Petillo et al. 2009).

Use of Proteomics for Detection of RCC Biomarkers

Quantitative MS analysis has been used to identify proteins that are dysregulated in RCC (Siu et al. 2009). Protein expression of kidney cancer tissues was compared to their normal counterparts from the same patient using LC-MS/MS. iTRAQ labeling that enabled simultaneous quantitative analysis. These dysregulated proteins in RCC were statistically significantly different from those of transitional cell carcinoma and end-stage glomerulonephritis. These results were validated using different tools and databases including Serial Analysis of Gene Expression (SAGE), UniGene EST ProfileViewer, Cancer Genome Anatomy Project, and Gene Ontology consortium analysis.

Second mitochondria-derived activator of caspase/direct inhibitor of apoptosisbinding protein with low pI (Smac/DIABLO) has been identified as a protein that is released from mitochondria in response to apoptotic stimuli and promotes apoptosis by antagonizing inhibitor of apoptosis proteins. Detection and quantification of Smac/DIABLO activity (i.e. protein transcription and expression) in cancer tissue following biopsy or surgery may be accomplished through IHC, RT-PCR, Western blot, flow cytometry, and/or HPLC.

Multivariate analyses using proteomic data obtained by SELDI-MS have been reported to be highly successful for detection of various tumors by examination of serum samples. CE-MS has been used to analyze urine samples from patients with RCC and 86 peptides were found to be specifically associated to RCC, of which sequence could be obtained for 40 (Frantzi et al. 2014). A classifier based on these peptides was evaluated in an independent set of samples and results showed 80% sensitivity and 87% specificity. Thus RCC can be detected with a high degree of accuracy based on specific urinary peptides.

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