Project description:Prostate cancer (PCa) is a common cancer and remains the second leading cause of cancer-associated mortality in men. To investigate the involvement of differentially expressing genes in PCa with deregulated pathways to allow earlier diagnosis of the disease, transcriptomic analyses of differential expression genes in Fine-Needle Aspiration (FNA) biopsies were for discrimination of PCa from benign prostatic hyperplasia (BPH). The RNA samples were extracted from four PCa biopsy samples and four BPH biopsy controls for microarray profiling and performed in Affymetrix Human U133 Plus 2 arrays for gene expression profiling analysis. Microarray data were analyzed using GeneSpring GX 10 (Agilent).On average, we detected expression of 47,000 transcripts.Under the criteria fold change > 2 or < 0.5, we obtained 1819 differential expressed genes(DEGs).Hierarchy cluster analysis also indicated that the 8 samples were distributed into two clusters, 4 PCa samples in one cluster and 4 BPH samples in another cluster.Then,qRT-PCR validation of the DEGs in PCa tissue and prostate cancer cells.The results revealed that grouping was reasonable and the data can be directly applied to further analysis.
Project description:Benign prostatic hyperplasia (BPH) is difficult to discriminate from prostate carcinoma (PCa) preoperatively. The non-invasive biomarkers are necessary to reduce the burden of biopsies and improve survival quality of patients. Previous research suggests that abnormal glycosylation of immunoglobulin gamma molecules (IgGs) may associated with immunological diseases and prostate diseases. Hence, characterizing intact N-glycopeptides of IgGs that correspond to N-glycan structure with specific site information enable better understanding of the molecular pathogenesis and finding out some novel signatures in preoperative discrimination of BPH from PCa. In this study, we profiled intact N-glycopeptides of purified IgGs from 51 PCa patients and 45 BPH patients by our developed N-glycoproteomic method using hydrophilic interaction liquid chromatography enrichment coupled with high resolution LC-MS/MS, and intact N-glycopeptides quantitative analysis was performed using pGlyco 2.0 and MaxQuant softwares. Our data provided plasma IgG subclass-specific and site-specific N-glycosylation quantitative information.
Project description:There is an unmet need for improved diagnostic testing and risk prediction for cases of prostate cancer (PCa) to improve care and reduce overtreatment of indolent disease. Here we have analysed the serum proteome and lipidome of 262 study participants by liquid chromatography-mass spectrometry, including participants with a diagnosis of PCa, benign prostatic hyperplasia (BPH), or otherwise healthy volunteers, with the aim of improving biomarker specificity. Although a two-class machine learning model was able to separate PCa from controls with sensitivity of 0.82 and specificity of 0.95, adding BPH resulted in a statistically significant decline in specificity for prostate cancer to 0.76, with half of BPH cases being misclassified by the model as PCa. A small number of biomarkers differentiating between BPH and prostate cancer were identified, including proteins in MAP Kinase pathways, as well as in lipids containing oleic acid; these may offer a route to greater specificity. These results highlight, however, that whilst there are opportunities for machine learning, these will only be achieved by use of appropriate training sets that include confounding comorbidities, especially when calculating the specificity of a test.
Project description:In this study we performed transcriptional profiling of transurethral resections of hormone resistant prostate cancer and compared it with benign prostatic hyperplasia (BPH), untreated localized prostate cancer and hormone sensitive prostate cancer. Keywords: time course; disease state analysis
Project description:miRNAs have been proven to be very useful biomarkers, readily detectable in body fluids, particularly urine may be a valuable source to identify changes in miRNA levels that contribute to better differentiate prostate cancer (PCa) from benign prostate hyperplasia (BPH) cases. In order to characterize microRNA expression in urine samples from PCa, we analyzed expression of 376 microRNAs in 9 samples of PCa and 9 of BPH. The Normalized Ct values were compared between PCa and BPH. Statistical comparisons were made using Mann-Whitney U test, considering two different distributions. We found statiscally differences n expression for 21 miRNAs (Fold change >2 and P value<0.05).
Project description:To further development of our gene expression signature for benign prostatic hyperplasia, we conducted expression profiles of BPH and normal samples.
Project description:In this study we performed transcriptional profiling of transurethral resections of hormone resistant prostate cancer and compared it with benign prostatic hyperplasia (BPH), untreated localized prostate cancer and hormone sensitive prostate cancer. Experiment Overall Design: Each sample (= Array) is derived from a different patient with the following subsequent disease states: 3x BPH, 7x localized prostate cancer samples, 2 horomone sensitive prostate cancer samples, one locally advanced prostate cancer, 4x hormone resistant prostate cancer early stage and 3x hormone resistant prostate cancer late stage. Experiment Overall Design: Platform was Affymetrix
Project description:Steroid 5α reductase 2 (SRD5A2) is the predominant enzyme responsible for prostatic development and growth. Despite the introduction of steroid 5α-reductase inhibitors (5ARI) for benign prostatic hyperplasia (BPH), the progression of LUTS is only slowed by 34% with 5ARI-response. Previous literature establishes link between obesity and BPH progression. We hypothesize that high fat diet is associated with prostate development.
Project description:To obtain a comprehensive view of the transcriptional programs in prostatic stromal cells of different histological/pathological origin, we profiled 18 adult human stromal cell cultures from normal transition zone (TZ), normal peripheral zone (PZ), benign prostatic hyperplasia (BPH), and prostate cancer (CA) using cDNA microarrays.
Project description:To identify the genes differently expressed in the epithelium and the stromal of Benign Prostatic Hyperplasia (BPH), we collect the epithelium and the stromal from the patients with benign prostatic hyperplasia by laser micro-dissection. And then, Affymetrix HG-U133_Plus_2 gene-chip was used to detect and compare the expression level of genes. To find which genes are most abundantly expressed in epithelium and stromal and what is the role of these genes in the pathogenesis of BPH. 8 prostate tissues were collected from patients undergone transurethral resection of the prostate (TURP) with informed consent. Each tissue was embedded in O.C.T and subsequently used for laser micro-dissection. The total RNA was isolated from each sample and equally mixed for gene-chip assay.