Molecular classification of renal cell carcinoma subtypes using microRNA signatures

<p>Background: Renal cell carcinoma (RCC) is composed of various morphologically and cytogenetically distinct subtypes, the most prevalent of which are clear cell RCC (ccRCC, 75-80%), papillary RCC (papRCC, 10-15%) and chromophobe RCC (chRCC, 5%). Upper urinary tract urothelial cell carcinomas (UUT-UCCs) are uncommon and account for only 5-10% of urothelial carcinomas. Distinguishing between the subtypes is usually made by morphologic assessment, which is not always accurate.</p> <p>Objective: Our aim was to identify microRNA (miRNA) signatures that can distinguish the different RCC subtypes accurately.</p> <p>Methodology: A total of 27 different subtype cases were analyzed. MiRNA microarray analysis was performed on FFPE tissues of three common RCC subtypes (18 ccRCCs, 3 chRCC and 5 papRCC), on 1 UUT-UCC and were compared to 20 normal tissue samples. Results were validated using quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis with miRNA specific primers. Microarray data were analyzed by standard approaches. Relative expression for qRT-PCR was determined using the ΔΔCt method. Experiments were done in triplicate and an average was calculated. Fold change was expressed as a log2 value.</p> <p>Results: We identified 434 miRNAs that were significantly deregulated (DE) in all kidney tumours compared to the normal tissues. A total of 126 miRNAs (29%) had increased expression while 303 (69.8%) had decreased expression in RCC. Out of these, 94 were co-up-regulated and 218 were co-down-regulated among chRCC, papRCC and ccRCC. Of these, 89 and 203 were co-up- and co-down-regulated between RCCs and UUT-UCCs, respectively. We detected 11, 44 and 24 up-regulated miRNAs, which were specific for ccRCC, chRCC and papRCC, respectively. Furthermore, 19, 18 and 8 miRNAs were uniquelly down-regulated in ccRCC, chRCC and papRCC, respectively. We also detected 89 and 203 co-up- and co-down-regulated miRNAs between kidney cancer and UUT-UCCs. Five miRNAs were up-regulated specifically in renal tumours and 49 in UUT-UCCs, whereas 15 miRNAs were down-regulated specifically in renal tumours and 89 in UUT-UCCs, respectively.</p> <p>Discussion: We present novel deregulated miRNAs in RCC and we have built an accurate molecular classification method among its most prevalent RCC subtypes using microRNA signatures.</p

miRNA implication in the most common subtypes of renal cell carcinoma (RCC) and urothelial cell carcinoma of the upper urinary tract (UUT-UCC)

<p>The most common renal cell carcinoma (RCC) subtypes are ccRCC, papRCC and chRCC. Upper urinary tract-urothelial cell carcinomas (UUT-UCC) account for only 5-10% of urothelial carcinomas. MicroRNAs (miRNAs) are small non-coding RNAs, recently found to be deregulated in RCC. We identified miRNA signatures that can distinguish RCC subtypes, accurately. Furthermore we focused on biomarker discovery, identification of gene targets and consequences of miRNA deregulation. 27 FFPE RCCs and UUT-UCCs were profiled. Results of significantly deregulated (DE) miRNAs were validated using qRT-PCR and LNA-ISH. The ability to discriminate between the RCC subtypes and normal tissue was characterized by ROC. Chromosomal distribution of the DE miRNAs was compared with reported genomic alterations. MiRNA target prediction was performed by miRWalk. Enriched gene sets were grouped in functional categories by IPA and GO enrichments. The majority of the miRNAs (69.8%) was down-regulated in RCC. Unsupervised hierarchical clustering with Euclidian distance successfully managed to classify the various RCC subtypes among them. Microarray and qRT-PCR results revealed similar expression patterns. qRT-PCR validated the expression of miR-3648, miR-489, miR-638, miR-3656, miR-3687, miR-663b, miR-25-5p and miR-21-5p in ccRCC. MiR-25-5p high expression was confirmed in all ccRCC, papRCC and chRCC sections by LNA-ISH and its expression was significantly stronger compared to their corresponding normal tissues. More aggressive ccRCCs also stained stronger than the less aggressive ones. Chromosomal distribution analysis revealed that, for each RCC subtype, miRNAs had deregulated patterns that agreed with some of the previously reported chromosomal gains and losses. Four major gene networks were constructed by IPA for the DE miRNA targets. MiRNAs are deregulated in RCC and may contribute to kidney cancer pathogenesis by targeting key molecules involved in tissue development, cell cycle and proliferation.</p

Deregulated miRNAs in renal cell carcinoma: Diagnostic potential, chromosomal distribution, putative gene targets and molecular pathways in which they are implicated

<p>INTRODUCTION AND AIMS: Renal cell carcinoma (RCC) is composed of various distinct subtypes, the most prevalent of which are clear cell (ccRCC), papillary (papRCC) and chromophobe (chRCC). MicroRNAs (miRNAs) are small non-coding RNAs of approximately 22 nt size, and modulate differentiation, growth, apoptosis and proliferation of cells. More than 50% of miRNA genes are located in cancer-associated genomic regions or in fragile sites, frequently amplified or deleted in human cancer. Recognition of miRNAs that are differentially expressed (DE) between RCC and normal tissue may help to identify those miRNAs that are involved in this human malignancy.</p> <p>METHODS: MiRNA profiling was performed on 28 FFPE tissues of ccRCC, chRCC, papRCC and 20 normal cases. Results of DE miRNAs were validated using qRT-PCR and in-situ hybridization (ISH). The ability to discriminate between RCC subtypes and normal samples was characterized by ROC curves. The chromosomal distribution of the DE miRNAs was detected and compared with reported genomic alterations in each subtype. The miRNA targets were predicted using miRWalk. Enriched gene sets were grouped in functional categories by IPA and GO analysis.</p> <p>RESULTS: We identified 434 DE miRNAs in all kidney tumours and built a molecular signature that accurately classified RCC subtypes among them. Ten miRNAs (miR-10b-5p, miR-1257, miR-1303, miR-23c, miR-3171, miR-4270, miR-514b-3p, miR-515-5p, miR-620 and miR-98) were co-deregulated among RCC subtypes, whereas 270 miRNAs were identified uniquely in ccRCC, 33 in papRCC and 5 in chRCC, respectively. The expression of the most DE miRNAs was validated using qRT-PCR. The miRNAs exhibited deregulation patterns that agreed with previously reported chromosomal gains and losses, in each subtype. ISH for miR-25-5p revealed that its differential expression is cancer-cell associated. The top Canonical pathways of the putative gene targets of the deregulated miRNAs, included Molecular Mechanisms of Cancer (p=1.72E-04); PPARα/RXRα Activation (p=2.15E-03); Bladder Cancer Signaling (p=3.57E-03); Cell Cycle: G1/S Checkpoint Regulation (p=5.37E-03) and Estrogen-mediated S-phase Entry (p=6.25E-03). The major gene networks and their associated functions included Tissue Development, Cancer; Tumor Morphology, Cellular Movement; Cell Cycle, Cellular Growth and Proliferation, Cellular Development; and Cell Death and Survival, Tumor Morphology, Cell Morphology. VHL, HIF1A, EPAS1, SMARCB1, TP53 and PAWR constituted the most central nodes in these networks.</p> <p>CONCLUSIONS: Our comprehensive study highlights the dynamic role of miRNAs in the three most common renal cell carcinoma subtypes.</p

Transcription Factor Binding Motifs, Chromosome mapping and Gene Ontology analysis in Cross-platform microarray data from bladder cancer.

<p>We have previously analyzed the gene expression profile in urinary bladder cancer and determined the differentially expressed (DE) genes between cancer and healthy tissue. We aimed: 1) To identify the over-represented Transcription Factor Binding Motifs (TFBMs) in the promoters of the DE genes. 2) To map the DE genes on the chromosomal regions. 3) To gain more insight into the DE gene functions, using Gene Ontology (GO) analysis. We investigated the TFBMs in the Transcription Element Listening System Database (TELiS). The TRANSFAC TF database was used for the identification of TF binding sites. The Gene Ontology Tree Machine, WebGestalt web-tool and the Matlab ® (The Mathworks Inc.) computing environments were used for chromosome mapping. GO analysis was performed using the eGOn online tool. The WebGestalt web-tool was used for gene function classifications. Relations of the DE genes and the transcription factor binding motifs were further investigated using the Pubgene Ontology Database. The glucocorticoid receptor (GR) was predicted as one of the TFs in the common gene set. In order to find which gene was most commonly represented among the TFs, we plotted the incidence of each gene as a function of the times of appearance within the predicted TFs. The gene BMP4 (bone morphogenetic protein 4; ID: 652) exhibited the higher number of binding sites for the predicted TFs. The majority of the chromosomes in BC had inactivated (down-regulated) genes, compared to the normal tissue. However, two genes were significantly over-expressed: CDC20 (in chromosome 1) and HCCS (in chromosome X). Three main functions were outlined by GO for the DE genes: a) circulatory system regulation, b) reproductive organ and sex development, and c) catecholamine metabolism. This enrichment showed that the predicted gene set has more than a dual role. Through this study, we were able to identify several important factors that warrant further investigation both as prognostic markers and as therapeutic targets for bladder cancer. Such approaches may provide a better insight into tumorigenesis and tumor progression.</p

Linear correlations in chromosomal-based gene expression in urinary bladder cancer

<p>Introduction & Objectives: Gene expression is a very tidy and well coordinated procedure. Consecutive genes are often similarly expressed. We hypothesized that correlations might exist between genes of the same chromosome, yet belonging to different urinary bladder cancer (BC) samples, in order to indicate a common regulation for genes following this pattern.</p> <p>Materials & Methods: We analyzed BC gene expression profiles, with emphasis in linear correlations of gene expression based on their chromosomal locations. Samples from 10 human BCs and 5 normal tissues were analyzed by whole genome microarrays, along with a computational approach, for their expression profiles. After raw data normalization and classification, differentially expressed genes (DE) were sorted according to their chromosome distributions and were further investigated for linear correlations among them. Chromosomal activity in terms of gene expression was measured by calculating the average expression of all DE genes for each chromosome, both for tumour and control samples.</p> <p>Results: Chromosome-based expression analysis predicted that among the most active chromosomes were chromosomes 9 and X. Similarly, control samples also manifested high expression activity on the X chromosome. The genes that exhibited significant linear correlations (p<0.05) among tumor samples on chromosomes 4, 8, 13, 21 and 22, were as follows: TACR3, RNF150, ANXA10, CENTD1, EXOC1, GRSF1 for chromosome 4; ANXA13, DENND3, FGF20, EFHA2, DNAJC5B, MRPS28, FABP5 for chromosome 8; ITGBL1, RXFP2, KL, MYCBP2, FARP1 for chromosome 13; KRTAP19-1, IFNAR1, SON for chromosome 21; MORC2, PLA2G6, ACO2, ARHGAP8 for chromosome 22; SERPINA7, TMEM164, ARHGAP6, APLN, FHL1, PNMA6A, UBL4A, PRDX4, POLA1, MXRA5 for chromosome X.</p> <p>Conclusions: Despite the fact that linear correlations occurred among distinct patients, the expression of the genes appeared to be correlated among them, in a similar manner. We have previously reported that there are hints of common mechanisms between BCs of different stage/grade, employing microarray analysis. Chromosomal correlation analysis comes to support our previous findings, since it revealed genes bearing common regulation among samples of different histology. Gene expression correlations can further assist us to understand more in-depth the mechanisms underlying tumour progression and biology.</p

Cross-platform comparisons of microarray data. Elucidation of common differentially expressed genes in bladder cancer.

<p>INTRODUCTION: Parallel gene-expression monitoring is a powerful tool for analyzing relationships among tumors, discovering new tumor subgroups, assigning tumors to pre-defined classes, identifying co-regulated or tumor stage-specific genes and predicting disease outcome. Previous gene expression studies have focused on identifying differences between tumor samples of the same type.</p> <p>AIM OF STUDY: Using a reverse engineering approach, we searched for common expression profiles among tumor samples. We analyzed the gene expression profile of bladder cancer (BC) and determined the differentially expressed (DE) genes between cancer and healthy tissue, using cross-platform comparisons.</p> <p>MATERIALS AND METHODS: We performed cDNA microarray analysis, comprising both in-house experimental and publicly available GEO microarray data. In total, our pooled microarray analysis was composed of 17 control samples (n=5, for the CodeLink platform; and n=12, for the remaining microarray platforms) and 129 BC samples (n=10, for the CodeLink platform; and n=119, for the remaining microarray platforms). Tumor samples were separated into the following groups: Ta/T1 without CIS; Ta/T1 with CIS; Ta-grade 1; Ta-grade 3; T1-grade2; T1-grade 3; T2-grade 2-4. Each group was compared against all control samples and the DE genes were identified. Data were clustered with different algorithms.</p> <p>RESULTS:</p> <p>A two-sample T-test analysis for all tumor samples vs. all normal samples, revealed 434 DE genes between the two tissue groups. Hierarchical clustering (HCL) showed a clear distinction among tumor samples. In total, 17 genes appeared to be commonly expressed among all BC samples: BMP4, CRYGD, DBH, GJB1, KRT83, MPZ, NHLH1, TACR3, ACTC1, MFAP4, SPARCL1, TAGLN, TPM2, CDC20, LHCGR, TM9SF1 and HCCS. Three groups of genes were down-regulated in all samples: BMP4, CRYGD, DBH, GJB1, KRT83, MPZ, NHLH1, TACR3 in cluster 79; ACTC1, MFAP4, SPARCL1, TAGLN in cluster 81; and TPM2 in cluster 82. CDC20, TM9SF1 and HCCS appeared to be simultaneously over-expressed in all tumor groups. LHCGR was differentially expressed in 108/129 (83.7%) of the BC samples.</p> <p>DISCUSSION: Through this investigation we were able to identify several important factors that warrant further investigation both as prognostic markers and as therapeutic targets. Such approaches may provide a better insight into tumorigenesis and tumor progression.</p

Chromosome correlation maps of gene expression signatures could provide useful information on gene regulatory mechanisms in urinary bladder cancer

<p>Chromosome correlation maps display correlations between gene expression patterns on the same chromosome and are considered of major importance in the understanding of how gene expression is regulated. It has not yet been elucidated based on chromosome correlation, whether gene expression among same chromosomes from different tumor samples is governed by similar patterns; and if it exists we do not know whether it is of linear nature or not. In the present study we used urinary bladder carcinoma as the model of our hypothesis. Following microarray experimentation in combination with raw microarray data extraction from the GEO, we collected a data cohort of 129 bladder cancer and 17 normal samples and performed network analysis for the co-deregulated genes using Ingenuity Pathway Analysis (IPA). Chromosome mapping, mathematical modeling and data simulations were performed using the WebGestalt and Matlab software. The top deregulated molecules among all bladder cancer samples were implicated in the PI3K/AKT signaling, cell cycle, Myc-mediated apoptosis signaling and ERK5 signaling pathways. Their most prominent molecular and cellular functions were related to cell cycle, cell death, gene expression, molecular transport and cellular growth and proliferation. Chromosome correlation maps allowed us to detect significantly co-expressed genes along the chromosomes. We identified strong correlations among tumors of Tα-grade 1, as well as for those of Tα-grade 2, in chromosomes 1, 2, 3, 7, 12 and 19. Chromosomal domains of gene co-expression were revealed for the normal tissues, as well. The expression data were further simulated, exhibiting an excellent fit (0.7</p

Expression profile of oncomiRs and tumor-suppressor miRs in urothelial carcinoma of the bladder.

<p>Introduction & Objectives: Micro-RNAs are small, regulatory molecules approximately 21-24 nucleotides in length. They function at the post-transcriptional level by controlling the expression of more than 50% of human protein-coding genes and play an essential role in cell signaling pathways. Our goal was explore the expression profile of oncomiRs and tumor-suppressor miRs, and to define their possible correlations in urothelial carcinoma of the bladder (BC).</p> <p>Materials & Methods: Seventy-seven primary BCs, along with 77 matched tumor-associated normal samples were investigated for the expression of 12 micro-RNAs using qPCR. Relationships between the expression of miR-10b, miR19a, miR19b, miR-21, miR-122a, miR-145_1, miR-205_1, miR-210, miR-221, miR-222, miR-378-1 and miR-296-5p and the pathologic features of the tumors were also examined.</p> <p>Results: The majority of the micro-RNAs exhibited down-regulation in BC vs. normal tissue [miR-10b (p=0.0007), miR-19a (p=0.012), miR-19b (p=0.0361), miR-126_1 (p=0.0021), miR-145_1 (p<0.0001), miR-221 (p<0.0001), miR-296-5p (p<0.0001), miR-378-1 (p<0.0001)]. miR-21, miR-205_1 and miR-210 expression levels did not present difference between BC and normal tissue. However, we noticed a great range in the x-fold expression values of all micro-RNAs. The median x-fold expression (range) was as follows: miR-10b, 0.45 (0-12.58); miR-19a, 0.56 (0-25.63); miR-19b, 0.50 (0-18.90); miR-21, 0.96 (0-52.95); miR-126_1, 0.36 (0-42.62); miR-145_1, 0.04 (0-56.36); miR-205_1, 1.07 (0.01-36.42); miR-210, 1.09 (0-44.43); miR-221, 0.32 (0-33.51); miR-296-5p, 0.08 (0-75.24); miR-378-1, 0.17 (0-3.66). Significant correlations among all of the studied microRNAs were scored both in BC and control tissue.</p> <p>Conclusions: Different micro-RNAs are deregulated in BC through down-regulation. A synergistic involvement of these genes in the development of BC is implied.</p

MiR-21 can be used as independent prognostic factor for survival and metastasis in urinary bladder cancer.

<p>Introduction & Objectives: Our goal was to correlate the expression of 12 micro-RNAs with the corresponding expression of FGF2, OPN and VEGFA. Gene expression was correlated with the overall and cancer-specific survival of patients suffering from urinary bladder cancer (BC), as well as with recurrence and metastasis.</p> <p>Materials & Methods: Gene expression were acquired by qPCR, from 77 BC specimens. Correlation of the gene expression with survival, recurrence and metastasis was employed by SPSS.</p> <p>Results: High expression of miR-21 correlated with worse overall survival (p=0.0099). Univariate analysis showed that miR-21 and miR-210 can be used as independent prognostic factors for overall survival (p=0.015 and p=0.049, respectively). Moreover, univariate analysis revealed that miR-21 can be used as independent prognostic factor for metastasis (p=0.049). Multivariate analysis revealed that miR-21, miR-210 and miR-378_1 can be used as independent prognostic factors for overall survival (p=0.005, p=0.033 and p=0.012, respectively); miR-21 and miR-378_1 can be used as independent prognostic factors for recurrence (p=0.030 and p=0.031, respectively); and miR-21 can be used as independent prognostic factors for metastasis (p=0.049). FGF2 was positively correlated with the majority of the miRs both in BC and normal tissue (p<0.001). OPN was positively correlated with miR-145_1 (p=0.015) in BC, and with miR-296-5p (p=0.017) in normal tissue. VEGFA was positively correlated with miR-21 in BC (p=0.043), and with miR-205_1 (p=0.045), FGF2 (p=0.004) and OPN (p<0.001) in normal tissue.</p> <p>Conclusions: miR-21 can be used as independent prognostic factor both for overall patient survival and metastasis of BC. miR-210 is an independent prognostic factor for overall survival.</p

microRNA profiling.

<p>Four-hundred and thirty-four miRNAs were statistically significantly deregulated in all RCC subtypes and UT-UC versus the normal kidney. (A) Q-Q (quantile-quantile) plot. Red circles indicate the significantly deregulated miRNAs. (B) Frequencies of the t-scores and p-values. The deregulated miRNAs had a p<0.05. (C) The volcano-plot depicts the 434 statistically significantly deregulated miRNAs in ccRCC, papRCC, chRCC and UT-UC versus the normal kidney, of which the majority was significantly down-regulated in the cancerous tissue compared to the latter. (D) FDR diagram depicting the percentage of FDR with respect to p-value along with a plot of the estimated a priori probability that the null hypothesis π(0), is true versus the tuning parameter, lambda, λ, with a cubic polynomial fitting curve.</p