M. Youssef

Differential expression profiling of microRNAs and their potential involvement in renal cell carcinoma pathogenesis.

OBJECTIVE We seek to identify the differentially expressed miRNAs in the clear cell subtype (ccRCC) of kidney cancer. DESIGN AND METHODS We performed a miRNA microarray analysis to compare the miRNA expression levels between ccRCC tissues and their normal counterpart. The top dysregulated miRNAs were validated by quantitative RT-PCR analysis. Bioinformatics analysis was also performed. RESULTS A total of 33 dysregulated miRNAs were identified in ccRCC, including 21 upregulated miRNAs and many of these miRNAs have been reported to be dysregulated in other malignancies and have a potential role in cancer pathogenesis. The miRNAs showed a significant correlation with reported chromosomal aberration sites. We also utilized target prediction algorithms to identify gene targets. Preliminary analyses showed these targets can be directly involved in RCC pathogenesis. CONCLUSION We identified miRNAs that are dysregulated in ccRCC and bioinformatics analysis suggests that these miRNAs may be involved in cancer pathogenesis and have the potential to be biomarkers.

Accurate Molecular Classification of Kidney Cancer Subtypes Using MicroRNA Signature

BACKGROUND Renal cell carcinoma (RCC) encompasses different histologic subtypes. Distinguishing between the subtypes is usually made by morphologic assessment, which is not always accurate. OBJECTIVE Our aim was to identify microRNA (miRNA) signatures that can distinguish the different RCC subtypes accurately. DESIGN, SETTING, AND PARTICIPANTS A total of 94 different subtype cases were analysed. miRNA microarray analysis was performed on fresh frozen tissues of three common RCC subtypes (clear cell, chromophobe, and papillary) and on oncocytoma. Results were validated on the original as well as on an independent set of tumours, using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis with miRNA-specific primers. MEASUREMENTS Microarray data were analysed by standard approaches. Relative expression for qRT-PCR was determined using the ΔΔC(T) method, and expression values were normalised to small nucleolar RNA, C/D box 44 (SNORD44, formerly RNU44). Experiments were done in triplicate, and an average was calculated. Fold change was expressed as a log(2) value. The top-scoring pairs classifier identified operational decision rules for distinguishing between different RCC subtypes and was robust under cross-validation. RESULTS AND LIMITATIONS We developed a classification system that can distinguish the different RCC subtypes using unique miRNA signatures in a maximum of four steps. The system has a sensitivity of 97% in distinguishing normal from RCC, 100% for clear cell RCC (ccRCC) subtype, 97% for papillary RCC (pRCC) subtype, and 100% accuracy in distinguishing oncocytoma from chromophobe RCC (chRCC) subtype. This system was cross-validated and showed an accuracy of about 90%. The oncogenesis of ccRCC is more closely related to pRCC, whereas chRCC is comparable with oncocytoma. We also developed a binary classification system that can distinguish between two individual subtypes. CONCLUSIONS MiRNA expression patterns can distinguish between RCC subtypes.

miRNA profiling for clear cell renal cell carcinoma: biomarker discovery and identification of potential controls and consequences of miRNA dysregulation.

PURPOSE Renal cell carcinoma is the most common neoplasm of the adult kidney. Currently to our knowledge there are no biomarkers for diagnostic, prognostic or predictive applications for renal cell carcinoma. miRNAs are nonprotein coding RNAs that negatively regulate gene expression and are potential biomarkers for cancer. MATERIALS AND METHODS We analyzed 70 matched pairs of clear cell renal cell carcinoma and normal kidney tissues from the same patients by microarray analysis and validated our results by quantitative real-time polymerase chain reaction. We also performed extensive bioinformatic analysis to explore the role and regulation of miRNAs in clear cell renal cell carcinoma. RESULTS We identified 166 miRNAs that were significantly dysregulated in clear cell renal cell carcinoma, including miR-122, miR-155 and miR-210, which had the highest over expression, and miR-200c, miR-335 and miR-218, which were most down-regulated. Analysis of previously reported miRNAs dysregulated in RCC showed overall agreement in the direction of dysregulation. Extensive target prediction analysis revealed that many miRNAs were predicted to target genes involved in renal cell carcinoma pathogenesis. In renal cell carcinoma miRNA dysregulation can be attributed in part to chromosomal aberrations, co-regulation of miRNA clusters and co-expression with host genes. We also performed a preliminary analysis showing that miR-155 expression correlated with clear cell renal cell carcinoma size. This finding must be validated in a larger independent cohort. CONCLUSIONS Analysis showed that miRNAs are dysregulated in clear cell renal cell carcinoma and may contribute to kidney cancer pathogenesis by targeting more than 1 key molecule. We identified mechanisms that may contribute to miRNA dysregulation in clear cell renal cell carcinoma. Dysregulated miRNAs represent potential biomarkers for kidney cancer.

The miR-17-92 Cluster is Over Expressed in and Has an Oncogenic Effect on Renal Cell Carcinoma

PURPOSE miRNAs are small, nonprotein coding RNAs that are differentially expressed in many malignancies. We previously identified 80 miRNAs that are dysregulated in clear cell renal cell carcinoma. In this study we validated over expression of the miR-17-92 cluster in clear cell renal cell carcinoma and tested the effect of 2 members of this cluster (miR-17-5p and miR-20a) on tumor proliferation. We also elucidated the role of miRNA in clear cell renal cell carcinoma pathogenesis with bioinformatics. MATERIALS AND METHODS miRNA expression was validated by quantitative reverse transcriptase-polymerase chain reaction. The cell proliferation effect of miR-17-5p and miR-20a was tested in a renal adenocarcinoma cell line model. Multiple in silico analyses were done of dysregulated miRNAs. RESULTS We validated miR-71-92 cluster over expression in clear cell renal cell carcinoma by quantitative reverse transcriptase-polymerase chain reaction. Transfection of miR-20a inhibitor significantly decreased cell proliferation in a dose dependent manner. Transfection of miR-17-5p, which is not endogenously expressed in the ACHN cell line, led to increased cell proliferation compared to control values. This effect was suppressed by miR-17-5p inhibitor. Bioinformatics analysis identified 10 clusters of miRNAs dysregulated in clear cell renal cell carcinoma that followed the same expression patterns. We also identified matching patterns between reported chromosomal aberration in clear cell renal cell carcinoma and miRNA dysregulation for 37.5% of the miRNAs. Target prediction analysis was done using multiple algorithms. Many key molecules in clear cell renal cell carcinoma pathogenesis, including HIFs, mTOR, VEGF and VHL, were potential targets for dysregulated miRNAs. CONCLUSIONS A significant number of dysregulated proteins in clear cell renal cell carcinoma are potential miRNA targets. Also, many clear cell renal cell carcinoma dysregulated miRNAs are phylogenetically conserved.

The Clinical Utility of miR-21 as a Diagnostic and Prognostic Marker for Renal Cell Carcinoma

Renal cell carcinoma (RCC) is the most common neoplasm of the kidney. Increasing evidence suggests that microRNAs are dysregulated in RCC and are important factors in RCC pathogenesis. miR-21 is a known oncogene with tumor-promoting effects in many types of cancer. In this study, we analyzed miR-21 in 121 cases of healthy kidney and different RCC subtypes, including clear cell (ccRCC), papillary (pRCC), chromophobe (chRCC), and oncocytoma. Total RNA was extracted, and the expression of miR-21 was measured with real-time quantitative RT-PCR using miR-21-specific probes. The expression of miR-21 was significantly up-regulated in RCC compared with healthy kidney. There was a significant difference in the expression levels between RCC subtypes, with the highest levels of expression in ccRCC and pRCC subtypes. miR-21 expression distinguished ccRCC and pRCC from chRCC and oncocytoma with 90% specificity (95% CI, 63.9% to 98.1%) and 83% sensitivity (95% CI, 53.5% to 97.6%). Significantly higher miR-21 levels were associated with higher stage and grade. Patients who were miR-21 positive had statistically significant shorter disease-free and overall survival rates. Thus, miR-21 is up-regulated in RCC, and its expression levels can be used as a diagnostic marker to distinguish ccRCC and pRCC from chRCC and oncocytoma. Moreover, it has potential as a prognostic marker in RCC, although it is not independent of tumor stage and grade.

Differential protein expressions in renal cell carcinoma: new biomarker discovery by mass spectrometry.

Renal cell carcinoma (RCC) is the most common neoplasm in the adult kidney. Unfortunately, there are currently no biomarkers for the diagnosis of RCC. In addition to early detection, biomarkers have a potential use for prognosis, for monitoring recurrence after treatment, and as predictive markers for treatment efficiency. In this study, we identified proteins that are dysregulated in RCC, utilizing a quantitative mass spectrometry analysis. We compared the protein expression of kidney cancer tissues to their normal counterparts from the same patient using LC-MS/MS. iTRAQ labeling permitted simultaneous quantitative analysis of four samples (cancer, normal, and two controls) by separately tagging the peptides in these samples with four cleavable mass-tags (114, 115, 116, and 117 Da). The samples were then pooled, and the tagged peptides resolved first by strong cation exchange chromatography and then by nanobore reverse phase chromatography coupled online to nanoelectrospray MS/MS. We identified a total of 937 proteins in two runs. There was a statistically significant positive correlation of the proteins identified in both runs (r(p) = 0.695, p < 0.001). Using a cutoff value of 0.67 fold for underexpression and 1.5 fold for overexpression, we identified 168 underexpressed proteins and 156 proteins that were overexpressed in RCC compared to normal tissues. These dysregulated proteins in RCC were statistically significantly different from those of transitional cell carcinoma and end-stage glomerulonephritis. We performed an in silico validation of our results using different tools and databases including Serial Analysis of Gene Expression (SAGE), UniGene EST ProfileViewer, Cancer Genome Anatomy Project, and Gene Ontology consortium analysis.

Multilevel Whole-Genome Analysis Reveals Candidate Biomarkers in Clear Cell Renal Cell Carcinoma

Renal cell carcinoma (RCC) is the most common neoplasm of the kidney. We conducted an integrated analysis of copy number, gene expression (mRNA and miRNA), protein expression, and methylation changes in clear cell renal cell carcinoma (ccRCC). We used a stepwise approach to identify the most significant copy number aberrations (CNA) and identified regions of peak and broad copy number gain and loss, including peak gains (3q21, 5q32, 5q34-q35, 7p11, 7q21, 8q24, 11q13, and 12q14) and deletions (1p36, 2q34-q37, 3p25, 4q33-q35, 6q23-q27, and 9p21). These regions harbor novel tumor-related genes and miRNAs not previously reported in renal carcinoma. Integration of genome-wide expression data and gene set enrichment analysis revealed 75 gene sets significantly altered in tumors with CNAs compared with tumors without aberration. We also identified genes located in peak CNAs with concordant methylation changes (hypomethylated in copy number gains such as STC2 and CCND1 and hypermethylated in deletions such as CLCNKB, VHL, and CDKN2A/2B). For other genes, such as CA9, expression represents the net outcome of opposing forces (deletion and hypomethylation) that also significantly influences patient survival. We also validated the prognostic value of miRNA let-7i in RCCs. miR-138, located in chromosome 3p deletion, was also found to have suppressive effects on tumor proliferation and migration abilities. Our findings provide a significant advance in the delineation of the ccRCC genome by better defining the impact of CNAs in conjunction with methylation changes on the expression of cancer-related genes, miRNAs, and proteins and their influence on patient survival.

Dysregulation of kallikrein-related peptidases in renal cell carcinoma: potential targets of miRNAs

Abstract Renal cell carcinoma (RCC) accounts for 3% of all adult malignancies and currently no diagnostic marker exists. Kallikrein-related peptidases (KLKs) have been implicated in numerous cancers including ovarian, prostate, and breast carcinoma. KLKs 5, 6, 10, and 11 have decreased expression in RCC when compared to normal kidney tissue. Our bioinformatic analysis indicated that the KLK 1, 6, and 7 genes have decreased expression in RCC. We experimentally verified these results and found that decreased expression of KLKs 1 and 3 were significantly associated with the clear cell RCC subtype (p<0.001). An analysis of miRNAs differentially expressed in RCC showed that 61 of the 117 miRNAs that were reported to be dysregulated in RCC were predicted to target KLKs. We experimentally validated two targets using two independent approaches. Transfection of miR-224 into HEK-293 cells resulted in decreased KLK1 protein levels. A luciferase assay demonstrated that hsa-let-7f can target KLK10 in the RCC cell line ACHN. Our results, showing differential expression of KLKs in RCC, suggest that KLKs could be novel diagnostic markers for RCC and that their dysregulation could be under miRNA control. The observation that KLKs could represent targets for miRNAs suggests a post-transcriptional regulatory mechanism with possible future therapeutic applications.

Microvascular density as an independent predictor of clinical outcome in renal cell carcinoma: an automated image analysis study

Tumor microvascular density (MVD) has been shown to correlate with the aggressiveness of several cancers. With the introduction of targeted anti-angiogenic therapy, assessment of MVD has the potential not only as a prognostic but also as a therapeutic marker. The significance of tumor vascularity in clear cell renal cell carcinoma (ccRCC) has been debated, with studies showing contradictory results. Previous studies were limited by manual quantification of MVD within a small area of tumor. Since then, the validity of this method has been questioned. To avoid the inaccuracies of manual quantification, we employed a computerized image analysis, which allowed assessment of large areas of tumor and adjacent normal tissue. The latter was used as an internal reference for normalization. MVD and vascular endothelial growth factor (VEGF) were assessed in 57 cases of ccRCC. Sections were immunostained for CD34 and VEGF. Areas of ccRCC and normal kidney medulla were analyzed within scanned images using software that counted CD34-positive vessels and measured the intensity of VEGF staining. We obtained unadjusted values from tumoral areas and calculated adjusted values as tumor/normal ratios. Unadjusted MVD had no association with clinical outcome. However, similarly to tumor stage, higher adjusted MVD was associated with shorter disease-free survival (log-rank P=0.037, Cox P=0.02). This was significant in univariate and multivariate analyses. MVD did not correlate with tumor stage, pointing to its independent prognostic value. As expected due to the known molecular abnormalities in ccRCC, most tumors showed higher VEGF expression than normal tissue. Higher adjusted VEGF was associated with high tumor grade (P=0.049). The finding of increased MVD as an independent marker of tumor aggressiveness may prove useful in the development of new tests for prognostic and therapeutic guidance. Digital techniques can provide more accurate assessment of immunomarkers and may reveal less obvious associations.

The miRNA-kallikrein axis of interaction: a new dimension in the pathogenesis of prostate cancer.

Abstract Kallikrein-related peptidases (KLKs) are a family of serine proteases that were shown to be useful cancer biomarkers. KLKs have been shown to be dysregulated in prostate cancer (PCa). microRNAs (miRNAs) are short RNA nucleotides that negatively regulate gene expression and have been reportedly dysregulated in PCa. We compiled a comprehensive list of 55 miRNAs that are differentially expressed in PCa from previous microarray analysis and published literature. Target prediction analyses showed that 29 of these miRNAs are predicted to target 10 KLKs. Eight of these miRNAs were predicted to target more than one KLK. Quantitative real-time (qRT)-PCR demonstrated that there was an inverse correlation pattern in the expression (normal vs. cancer) between dysregulated miRNAs and their target KLKs. In addition, we experientially validated the miRNA-KLK interaction by transfecting miR-331-3p and miR-143 into a PCa cell line. Decreased expression of targets KLK4 and KLK10, respectively, and decreased cellular growth were observed. In addition to KLKs, dysregulated miRNAs were predicted to target other genes involved in the pathogenesis of PCa. These data show that miRNAs can contribute to KLK regulation in PCa. The miRNA-KLK axis of interaction projects a new element in the pathogenesis of PCa that may have therapeutic implications.