Identification of Common Differentially Expressed Genes in Urinary Bladder Cancer

BACKGROUND: Current diagnosis and treatment of urinary bladder cancer (BC) has shown great progress with the utilization of microarrays. PURPOSE: Our goal was to identify common differentially expressed (DE) genes among clinically relevant subclasses of BC using microarrays. METHODOLOGY/PRINCIPAL FINDINGS: BC samples and controls, both experimental and publicly available datasets, were analyzed by whole genome microarrays. We grouped the samples according to their histology and defined the DE genes in each sample individually, as well as in each tumor group. A dual analysis strategy was followed. First, experimental samples were analyzed and conclusions were formulated; and second, experimental sets were combined with publicly available microarray datasets and were further analyzed in search of common DE genes. The experimental dataset identified 831 genes that were DE in all tumor samples, simultaneously. Moreover, 33 genes were up-regulated and 85 genes were down-regulated in all 10 BC samples compared to the 5 normal tissues, simultaneously. Hierarchical clustering partitioned tumor groups in accordance to their histology. K-means clustering of all genes and all samples, as well as clustering of tumor groups, presented 49 clusters. K-means clustering of common DE genes in all samples revealed 24 clusters. Genes manifested various differential patterns of expression, based on PCA. YY1 and NFκB were among the most common transcription factors that regulated the expression of the identified DE genes. Chromosome 1 contained 32 DE genes, followed by chromosomes 2 and 11, which contained 25 and 23 DE genes, respectively. Chromosome 21 had the least number of DE genes. GO analysis revealed the prevalence of transport and binding genes in the common down-regulated DE genes; the prevalence of RNA metabolism and processing genes in the up-regulated DE genes; as well as the prevalence of genes responsible for cell communication and signal transduction in the DE genes that were down-regulated in T1-Grade III tumors and up-regulated in T2/T3-Grade III tumors. Combination of samples from all microarray platforms revealed 17 common DE genes, (BMP4, CRYGD, DBH, GJB1, KRT83, MPZ, NHLH1, TACR3, ACTC1, MFAP4, SPARCL1, TAGLN, TPM2, CDC20, LHCGR, TM9SF1 and HCCS) 4 of which participate in numerous pathways. CONCLUSIONS/SIGNIFICANCE: The identification of the common DE genes among BC samples of different histology can provide further insight into the discovery of new putative markers

Spotlight on Differentially Expressed Genes in Urinary Bladder Cancer

Introduction: We previously identified common differentially expressed (DE) genes in bladder cancer (BC). In the present study we analyzed in depth, the expression of several groups of these DE genes. Materials and Methods: Samples from 30 human BCs and their adjacent normal tissues were analyzed by whole genome cDNA microarrays, qRT-PCR and Western blotting. Our attention was focused on cell-cycle control and DNA damage repair genes, genes related to apoptosis, signal transduction, angiogenesis, as well as cellular proliferation, invasion and metastasis. Four publicly available GEO Datasets were further analyzed, and the expression data of the genes of interest (GOIs) were compared to those of the present study. The relationship among the GOI was also investigated. GO and KEGG molecular pathway analysis was performed to identify possible enrichment of genes with specific biological themes. Results: Unsupervised cluster analysis of DNA microarray data revealed a clear distinction in BC vs. control samples and low vs. high grade tumors. Genes with at least 2-fold differential expression in BC vs. controls, as well as in non-muscle invasive vs. muscle invasive tumors and in low vs. high grade tumors, were identified and ranked. Specific attention was paid to the changes in osteopontin (OPN, SPP1) expression, due to its multiple biological functions. Similarly, genes exhibiting equal or low expression in BC vs. the controls were scored. Significant pair-wise correlations in gene expression were scored. GO analysis revealed the multi-facet character of the GOIs, since they participate in a variety of mechanisms, including cell proliferation, cell death, metabolism, cell shape, and cytoskeletal re-organization. KEGG analysis revealed that the most significant pathway was that of Bladder Cancer (p = 1.5x10(-31)). Conclusions: The present work adds to the current knowledge on molecular signature identification of BC. Such works should progress in order to gain more insight into disease molecular mechanisms

Altered metabolic pathways in clear cell renal cell carcinoma: A meta-analysis and validation study focused on the deregulated genes and their associated networks.

<p>Clear cell renal cell carcinoma (ccRCC) is the predominant subtype of renal cell carcinoma (RCC). It is one of the most therapy-resistant carcinomas, responding very poorly or not at all to radiotherapy, hormonal therapy and chemotherapy. A more comprehensive understanding of the deregulated pathways in ccRCC can lead to the development of new therapies and prognostic markers. We performed a meta-analysis of 5 publicly available gene expression datasets and identified a list of co-deregulated genes, for which we performed extensive bioinformatic analysis coupled with experimental validation on the mRNA level. Gene ontology enrichment showed that many proteins are involved in response to hypoxia/oxygen levels and positive regulation of the VEGFR signaling pathway. KEGG analysis revealed that metabolic pathways are mostly altered in ccRCC. Similarly, Ingenuity Pathway Analysis showed that the antigen presentation, inositol metabolism, pentose phosphate, glycolysis/gluconeogenesis and fructose/mannose metabolism pathways are altered in the disease. Cellular growth, proliferation and carbohydrate metabolism, were among the top molecular and cellular functions of the co-deregulated genes. qRT-PCR validated the deregulated expression of several genes in Caki-2 and ACHN cell lines and in a cohort of ccRCC tissues. <i>NNMT</i> and <i>NR3C1</i> increased expression was evident in ccRCC biopsies from patients using immunohistochemistry. ROC curves evaluated the diagnostic performance of the top deregulated genes in each dataset. We show that metabolic pathways are mostly deregulated in ccRCC and we highlight those being most responsible in its formation. We suggest that these genes are candidate predictive markers of the disease.</p&gt

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

New miRNA profiles accurately distinguish renal cell carcinomas and upper tract urothelial carcinomas from the normal kidney.

BACKGROUND: Upper tract urothelial carcinomas (UT-UC) can invade the pelvicalyceal system making differential diagnosis of the various histologically distinct renal cell carcinoma (RCC) subtypes and UT-UC, difficult. Correct diagnosis is critical for determining appropriate surgery and post-surgical treatments. We aimed to identify microRNA (miRNA) signatures that can accurately distinguish the most prevalent RCC subtypes and UT-UC form the normal kidney. METHODS AND FINDINGS: miRNA profiling was performed on FFPE tissue sections from RCC and UT-UC and normal kidney and 434 miRNAs were significantly deregulated in cancerous vs. the normal tissue. Hierarchical clustering distinguished UT-UCs from RCCs and classified the various RCC subtypes among them. qRT-PCR validated the deregulated expression profile for the majority of the miRNAs and ROC analysis revealed their capability to discriminate between tumour and normal kidney. An independent cohort of freshly frozen RCC and UT-UC samples was used to validate the deregulated miRNAs with the best discriminatory ability (AUC>0.8, p<0.001). Many of them were located within cytogenetic regions that were previously reported to be significantly aberrated. miRNA targets were predicted using the miRWalk algorithm and ingenuity pathway analysis identified the canonical pathways and curated networks of the deregulated miRNAs. Using the miRWalk algorithm, we further identified the top anti-correlated mRNA/miRNA pairs, between the deregulated miRNAs from our study and the top co-deregulated mRNAs among 5 independent ccRCC GEO datasets. The AB8/13 undifferentiated podocyte cells were used for functional assays using luciferase reporter constructs and the developmental transcription factor TFCP2L1 was proved to be a true target of miR-489, which was the second most upregulated miRNA in ccRCC. CONCLUSIONS: We identified novel miRNAs specific for each RCC subtype and UT-UC, we investigated their putative targets, the networks and pathways in which they participate and we functionally verified the true targets of the top deregulated miRNAs

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

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