Gene expression profiling of benign and matched tumour prostate tissue from patients diagnosed with prostate cacner (PCa)
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PROVIDER: PRJNA1032440 | ENA |
REPOSITORIES: ENA
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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: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:To investigate the mechanisms of PCa progression, we performed expression profiling of human prostate cancer and benign tissues.
2018-04-22 | GSE69223 | GEO
Project description:Prostate cancer (PCa) is the most common malignant tumour, with high incidence rates and heterogeneity in men around the world. It is becoming an increasing burden on the health care system due to lack of tests for precise detection of disease, risk assessment and response to treatments. We used SWATH-MS as a discovery proteomics tool to identify markers in the serum of newly diagnosed PCa patients and healthy controls to facilitate the improved diagnosis. Furthermore, identification and expression of proteins was also determined in patients undergone radiotherapy and prostatectomy for monitoring of the clinical response to respective treatment options. Thus, this study provides the basis for a proteomic signature to improve diagnostics of patients with localised prostate cancer.
2023-03-11 | PXD025484 | Pride
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: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.
2024-01-26 | PXD046530 | Pride
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.
2019-10-09 | PXD013837 | Pride
Project description:Spatial transcriptomic profiles of prostate tissue obtained from a patient diagnosed with benign prostatic hyperplasia (BPH).
| PRJNA1012689 | ENA
Project description:The study aim was to identify novel serum markers of prostate cancer diagnosis compared to benign prostate hyperplasia and healthy controls
2016-10-04 | PXD004575 | Pride
Project description:We assessed differential gene expression in tissue specimens of PCa and BPH patients who underwent radical prostatectomy by next-generation sequencing (HiSeq 2000). We stratified PCa patients according to seven clinical risk groups based on Gleason Score (GS), the presence of regional lymph node metastases (pN) and the occurrence of death of disease (DoD): (i) very low risk (group V: GS<7, pN0), (ii) low risk (group L: GS=7, pN0), (iii) medium risk (group M: GS<=7, pN1), (iv) high risk survivors without lymph node infiltration (group H-s: GS>7, pN0), (v) high risk non-survivors without lymph node infiltration (group H-d: GS>7, pN0, DoD), (vi) high risk survivors with lymph node infiltration (group H+s: GS>7, pN1), (vii) high risk non-survivors with lymph node infiltration (group H+d: GS>7, pN1, DoD). For high risk groups adjacent tumor-free prostate tissue samples were obtained (groups H+sf, H-sf, H+df, H+df). The control group contained patients with benign prostate hyperplasia (group C). Information on the course of the disease, survival of the patients and the cause of death were obtained from the general practitioners or treating urologists or from records of the regional tumor registry. Clinicopathological parameters were obtained by routine histopathological examination of the surgical specimens. Serum levels of the prostate-specific antigen (PSA) were determined preoperatively (Abbott, Wiesbaden, Germany). Frozen tissue samples were embedded in Tissue-Tek OCT-compound (Sakura Finetek GmbH, Staufen im Breisgau, Germany) and fixed on metal indenters by freezing. Cryo-sections were prepared using a cryo-microtome (Leica Biosystems, Nußloch, Germany) equipped with a microtome blade C35 cooled to -28 °C. For every tissue three stacks of consecutive cryo-sections (10 µm) were generated, each bordered/flanked by HE-stained cryo-sections (4 µm), which were evaluated by a pathologist with regard to tumor and stroma cell content. PCa tissue stacks had to be flanked on either side by sections containing at least 50 % tumor cells to be used for further analyses. Sections of the tissue stacks were immediately transferred to 1 ml Qiazol (Qiagen, Hilden, Germany) and stored at -80 °C until RNA isolation.
2020-06-17 | GSE134073 | GEO