Genome-wide analysis of gene expression patterns in human prostate cancer (PCa)
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE69223 | GEO | 2018/04/22
REPOSITORIES: GEO
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Project description:As proteins represent cell function, proteomic analysis of prostate tissues may elucidate the mechanisms of prostate cancer (PCa) progression. To that end, formalin-fixed paraffin-embedded (FFPE) tissues are valuable resources, but associated with large analytical difficulties due to protein cross-linking. Our aim is to characterize tissue protein changes associated with castration resistant PCa. This study focused at the development of a reliable protocol for the proteomic analysis of FFPE tissues which was followed by a pilot study using FFPE PCa clinical samples to investigate whether the optimized protocol can provide biologically relevant data in the context of PCa. Archival FFPE mouse tissues were processed using seven protein extraction protocols including combinations of homogenization means (beads, sonication, boiling) and buffers (Tris-HCl and Urea-Thiourea). The proteomics output was evaluated by SDS electrophoresis and high resolution LC-MS/MS analysis. FFPE tissues (3 sections, 15������������������m each per sample) from 10 patients with PCa corresponding to tumor (GS=6 or GS���������������������������8) and adjacent benign regions were processed with the optimized protocol. Extracted proteins were analyzed by GeLC-MS/MS followed by statistical and bioinformatics analysis using Cytsoscape and Open Targets tool. A combination of cell lysis by Tris-HCl, SDS and DTE with bead homogenization, sonication and boiling provided most efficient protein extraction from FFPE tissues based on protein identifications and reproducibility. Comparison between the FFPE and matched FF tissues showed a substantial overlap in protein identifications (502 common out of 1,216 IDs commonly identified in all FF samples and 530 IDs commonly identified in all FFPE samples) with a strong correlation in relative abundances (rs=0.846, p<.001). Proteins significantly deregulated between PCa GS���������������������������8 and PCa GS=6 represented extracellular matrix organisation, phosphorylation and gluconeogenesis pathways, while proteins deregulated between cancerous tissues and benign counterparts, reflected increased translation, peptide synthesis and protein metabolism in the former, as expected. Further disease association analysis using the Open Targets platform showed that 60% of the differentially expressed proteins in cancer versus normal adjacent were significantly associated with PCa. These results support the relevance of the proteomic findings in the context of PCa and the reliability of the optimized protocol for proteomics analysis of FFPE material. A large scale study including 160 clinical FFPE prostate cancer and benign tissues to define protein changes associated with aggressive PCa, is planned.
2019-11-15 | PXD015981 | Pride
Project description:Prostate cancer (PCa) causes significant mortality among men worldwide. Galectin-4 plays an important roles in regulatiog cancer metastasis and high level of galectin-4 correlated with poor survival in subpopulation of PCa patients. To further understand the galectin-4-mediated signature and develop effective treatment, we froced expressed galectin-4 in 22Rv1 and LNCaP cells, and downregulated galectin-4 in 22Rv1-M4 cells. Next, cDNA microarray were applyed to study galectin-4-mediated gene expression and cancer progression.
2018-06-30 | GSE100301 | GEO
Project description:Copy number variations (CNVs) in the human genome have been linked to various carcinomas including prostate cancer (PCa). This study was conducted to identify CNVs in high grade PCa. We performed a pilot genome-wide CNV analysis in 36 subjects (18 high grade PCa and 18 benign prostatic hyperplasia) using array comparative genomic hybridization (aCGH) technique. Array results were validated using PCR-based copy number counting method. A total of 339 CNV regions were found to be unique to PCa subjects in this cohort (P < 0.05). Data segregation and filtering revealed six putative CNV loci associated with susceptibility to PCa. Of these, four were rare (1q21.3, 15q15, 3q27.2 and 7p12.1) and one was a novel copy number gain (12q23.1), harbouring genes such as the ARNT, THBS1, SLC5A8 and DDC which are crucial in the p53 and cancer pathways. Another CNV was a loss at 8p11.21 which contains the SFRP1 gene from the Wnt signalling pathway, known for its interaction with androgen receptors as reported for urological malignancy. Cross comparison analysis with genes already known to be associated with PCa revealed significant CNVs involved in crucial biological processes that elicit cancer pathogenesis via cytokine production, disease progression through endothelial cell proliferation and xenobiotic metabolism. In conclusion, these findings suggest that the CNV regions identified could provide an insight into the development of high grade PCa.
2018-12-18 | GSE79402 | GEO
Project description:An integrated transcriptome-wide gene expression analysis, including differential gene expression analysis and weighted gene co-expression network analysis (WGCNA), was carried out based on transcriptomics data from a series of nine matched, histologically confirmed PCa and benign samples using the Affymetrix Clariom D Human array. The analysis identified a set of potential gene markers highly associated with tumor status (malignant vs. benign). We then used these genes to establish a minimal progression-free survival (PFS)-associated gene signature (GS) (PCBP1, PABPN1, PTPRF, DANCR, and MYC) using the least absolute shrinkage and selection operator (LASSO) and stepwise multivariate Cox regression analyses from The Cancer Genome Atlas prostate adenocarcinoma (TCGA-PRAD) dataset. Our signature was able to predict PFS over 1, 3, and 5 years in the TCGA-PRAD dataset, with an area under the curve (AUC) of 0.64–0.78, and our signature remained as a prognostic factor independent of age, Gleason score, and pathological T and N stages. A nomogram combining the signature and Gleason score demonstrated improved predictive capability for PFS (AUC: 0.71–0.85) and was superior to the Cambridge Prognostic Group (CPG) model alone and some conventionally used clinicopathological factors in predicting PFS. In conclusion, we have identified and validated a novel five-gene signature and established a nomogram that effectively predicted PFS in patients with PCa. These findings may improve current prognostic tools for PFS and contribute to clinical decision-making in PCa treatment.
2023-10-31 | GSE246282 | GEO
Project description:Mounting evidence indicates that long non-coding RNAs (lncRNAs) play vital roles in tumorigenesis and progression of cancers. However, the functions and regulatory mechanisms of lncRNAs in prostate cancer (PCa) are still largely unknown. In this study, we discovered a PCa-specific lncRNA, PRCAT71, significantly highly expressed in metastatic and primary PCa compared to benign prostate tissues. Silencing PRCAT71 inhibited cancerous properties of PCa cells, as well as androgen receptor (AR) signaling. Mechanismly, PRCAT71 acts as a scaffold to recruit KH-type splicing regulatory protein (KHSRP) to AR mRNA and stabilize AR mRNA. Furthermore, PRCAT71 is transcriptionally regulated by AR, thus forming a positive regulatory loop between AR and PRCAT71 in PCa. Our findings demonstrate that the PRCAT71-KHSRP-AR axis is a promising therapeutic target to treat PCa.
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2018-06-06 | GSE95726 | GEO
Project description:We profiled genome-wide gene expression of human prostate benign and malignant tissue to identify potential biomarkers and immunotherapy targets. We stratified malignant specimens according to their TMPRSS2:ERG gene fusion status. Radical prostatectomy tissue samples were obtained from the Hershey Foundation Prostate Cancer Serum and Tumor Bank at our institution. Morphologic diagnosis was done by a pathologist. OCT blocks containing >30% of PCa tissue (with Gleason score of 6 or 7) were selected for RNA purification. A biopsy punch was used to select the PCa tissues from the OCT sample blocks. Benign or PCa tissues were homogenized using a TissueLyser (Qiagen) at 28 Hz for 5 min. Total RNA was isolated using Trizol reagent. RNA was quantified by NanoDrop ND-1000 spectrophotometer, and quality was evaluated with Agilent RNA 6000 NanoChip and the 2100 Bioanalyzer, with 28S/18S ratios and RIN determined by 2100 Expert software.
2014-03-18 | E-GEOD-55945 | biostudies-arrayexpress
Project description:Prognostic biomarkers are useful to screen patients with clinically localized prostate cancer (PCa) who are at high risk of metastatic progression. The tumor transcriptome can be used to evaluate the aggressiveness of PCa and predict adverse patient outcomes. Genomewide gene expression levels were measured in primary tumor samples of 503 patients in a population‐based cohort.
2019-12-06 | GSE141551 | GEO
Project description:Prostate cancer (PCa) remains a prevalent and deadly disease. The histology-based Gleason score (GS) of PCa tissue biopsy is the most accurate predictor of disease aggressiveness and an important measure to guide decision-making with respect to treatment strategies and patient management. However, inherent variability associated with PCa tumour sampling and the subjective determination of the GS are still key challenges precluding accurate diagnostication and prognostication. Thus, novel molecular signatures are urgently needed to distinguish between indolent and aggressive forms of PCa for better patient management and outcomes. Herein, we have used label-free LC-MS/MS-based proteomics to profile the proteome of 50 PCa tissues spanning five GS-based PCa grades (n = 10 per group) relative to five tissues from individuals with benign prostatic hyperplasia (BPH). Over 2,000 proteins were consistently identified albeit at different levels between and within the patient groups, revealing biological processes associated with specific grades. Excitingly, a panel of 11 prostate-derived proteins including IGKV3D-20, RNASET2, TACC2, ANXA7, LMOD1, PRCP, GYG1, NDUFV1, H1FX, APOBEC3C, CTSZ displayed the potential to accurately stratify patients displaying low and high GS. This is the first study to characterise the prostate tissue proteome signatures of the five PCa grades relative to BPH. We report a panel of proteins that accurately can distinguish low and high GS PCa tissues. These promising proteins can be further explored as candidate biomarkers for PCa aggressiveness.
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