Abstract. Peptide growth factors play an important role in several intracellular processes, such as cellular growth and differentiation, angiogenesis and apoptosis, as well as in carcinogenesis, since they contribute significantly to the malignant transformation. The prostate gland is abundant in growth factors. The two most known prostatic diseases, prostate cancer (PCa) and benign prostatic hyperplasia (BPH), are among the most common diseases that affect elderly men. This study was conducted using a quantitative real-time RT-PCR method in order to determine mRNA expression levels of peptide growth factors VEGF, FGF2, TGFB1, EGF, and IGF1 in tissue specimens from 42 patients with PCa, 42 with BPH, and 10 normal prostate samples obtained post-mortem from young individuals, in order to examine their association with prostatic hyperplasia and neoplasia. Our results show that in PCa, growth factors VEGF, EGF and FGF2 are overexpressed, while TGFB1 and IGF1 have reduced mRNA levels. In BPH, transcript levels of FGF2 and EGF are normal, while VEGF, TGFB1 and IGF1 exhibit downregulation. Further statistical analysis revealed that PCa patients with high levels of PSA blood levels have decreased FGF2 expression (p=0.016). Additionally, cancer patients with low Gleason score (<7) have increased EGF (p=0.035) and IGF1 (p=0.031) mRNA levels. IGF1 levels are also elevated in tumors with TNM stages T1-T2 (p=0.030). In BPH, older patients have reduced EGF expression (p=0.018), while IGF1 is overexpressed in younger patients (p=0.041). Additionally, the coexpression pattern of the five studied growth factors differs significantly among normal, benign and malignant prostate. These results implicate VEGF, FGF2, TGFB1, EGF and IGF1 in the development of both PCa and BPH, rendering them potential targets for disease detection, monitoring and therapy.

BackgroundCurrent diagnosis and treatment of urinary bladder cancer (BC) has shown great progress with the utilization of microarrays.PurposeOur goal was to identify common differentially expressed (DE) genes among clinically relevant subclasses of BC using microarrays.Methodology/Principal FindingsBC 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/SignificanceThe identification of the common DE genes among BC samples of different histology can provide further insight into the discovery of new putative markers.

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Most miRNAs analyzed are down-regulated in bladder cancer. They may serve as candidate biomarkers for diagnostic and prognostic purposes in the future.

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These variables may enable identification of a subgroup of patients who will fail initial SWL. These patients may be candidates for endourologic alternatives as first-line treatment.

SPH presence should arouse suspicions concerning its etiology, since the most common cause is a renal tumor and approximately 50% of such tumors are malignant. CT scanning is a useful imaging modality for the initial evaluation of SPH, permitting identification of the underlying cause in most instances.

BackgroundUpper 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 FindingsmiRNA 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.ConclusionsWe 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.