Transcriptomics biomarkers in prostate cancer
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ABSTRACT:
PROVIDER: PRJNA827124 | ENA |
REPOSITORIES: ENA
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Project description:Prostate cancer (PCa) is the number one cancer in men. It represents a challenge for its management due to its very high incidence but low risk of lethal cancer. Over-diagnosis and over-treatment are therefore two pitfalls. The PSA (Prostate Specific Antigen) assay used for early diagnosis and clinical or molecular prognostic factors are not sufficiently reliable to predict the evolution of the cancer and its lethal or non-lethal character. Although PCa is most often detected at a localised stage, there are almost 30% of metastatic or locally advanced forms for which treatments can slow down the evolution but cannot be curative. With the use of high-throughput technological tools such as transcriptomics , it is becoming possible to define molecular signatures and identify predictive biomarkers of tumour aggressiveness . Here, we have analyzed 137 samples.
2022-05-18 | GSE200879 | GEO
Project description:Discovery of lncRNA biomarkers of prostate cancer
| PRJNA474912 | ENA
Project description:This SuperSeries is composed of the following subset Series: GSE26022: [Gene Expression Training Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy GSE26242: [Gene Expression Validation Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy GSE26245: [miRNA Training Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy GSE26247: [miRNA Validation Set] Protein-coding and MicroRNA Biomarkers of Recurrence of Prostate Cancer Following Radical Prostatectomy Refer to individual Series
2011-06-01 | E-GEOD-26367 | biostudies-arrayexpress
Project description:Proteomic profiling of extracellular vesicles (EVs) represents a promising approach for early detection and therapeutic monitoring of diseases such as cancer, which was aimed for identifying novel biomarkers for prostate cancer diagnosis in this study. The focus of this study was to develop a robust data independent acquisition (DIA) using mass spectrometry to analyse urinary EV proteomics for prostate cancer and prostate inflammation screening. We combined three library-based analysis (direct-DIA, GPF-DIA, and fractionated DDA) to improve the stability and comprehensiveness of biomarkers. By applying this innovative DIA strategy in conjunction with stable automatic EVs extraction technology, we assessed the levels of urinary EV-associated proteins based on 40 samples consisting of 20 cases and 20 controls, where 18 EV proteins were identified to be differentiated in prostate cancer outcome, of which 3 (i.e., SERPINA3, LRG1, SCGB3A1) were shown to be consistently up regulated. We also observed 6 out of the 18 (33%) EV proteins that had been developed as drug targets, while some of them showed interactions. Moreover, the potential mechanistic pathways of significantly different EV proteins, were enriched in metabolic, immune, and inflammatory activities. These results showed consistent in an independent cohort consist of 20 participants. Based on random forest algorithm, we found that SERPINA3, LRG1, SCGB3A1 add predictable value in addition to age, prostate size, body mass index (BMI) and prostate-specific antigen (PSA). In summary, the current study revealed the EV proteomic landscape and biomarkers for prostate cancer, which has shown to provide promising insights of urine EV proteome in clinical implication.
2023-03-11 | PXD037506 | Pride
Project description:Prostate cancer (PCa) is one of the most prevalent cancer types in men worldwide. However, the main diagnostic tests available for PCa have limitations and need biopsy for histopathological confirmation of the disease. The prostate-specific antigen (PSA) is the main biomarker used for PCa early detection, but an elevated serum concentration is not cancer-specific. Therefore, there is a need for discovery of new non-invasive biomarkers that can accurately diagnose PCa. Here, we used trichloroacetic acid-induced protein precipitation and liquid chromatography-mass spectrometry to profile endogenous peptides in urine samples from patients with PCa (n = 33), benign prostatic hyperplasia (n = 25), and healthy individuals (n = 28). Receiver operating characteristic (ROC) curves were performed to evaluate the diagnostic performance of urinary peptides. In addition, proteasix tool was used for in silico prediction of protease cleavage sites. We found five urinary peptides derived from uromodulin significantly altered between the study groups, all of which were less abundant in the PCa group. In addition, urinary peptides outperformed PSA in discriminating between malignant and benign prostate conditions (AUC = 0.847), showing high sensitivity (81.82%) and specificity (88%). Overall, our study allowed the identification of urinary peptides with potential for use as non-invasive biomarkers in PCa diagnosis.
2022-10-25 | PXD037031 | Pride
Project description:The dysregulation of gene expression is an enabling hallmark of cancer. Computational analysis of transcriptomics data from human cancer specimens, complemented with exhaustive clinical annotation, provides an opportunity to identify core regulators of the tumorigenic process. Here we exploit well-annotated clinical datasets of prostate cancer for the discovery of transcriptional regulators relevant to prostate cancer. Following this rationale, we identify Microphthalmia-associated transcription factor (MITF) as a prostate tumor suppressor among a subset of transcription factors. Importantly, we further interrogate transcriptomics and clinical data to refine MITF perturbation-based empirical assays and unveil Crystallin Alpha B (CRYAB) as an unprecedented direct target of the transcription factor that is, at least in part, responsible for its tumor-suppressive activity in prostate cancer. This evidence was supported by the enhanced prognostic potential of a signature based on the concomitant alteration of MITF and CRYAB in prostate cancer patients. In sum, our study provides proof-of-concept evidence of the potential of the bioinformatics screen of publicly available cancer patient databases as discovery platforms, and demonstrates that the MITF-CRYAB axis controls prostate cancer biology.
2018-10-16 | GSE114345 | GEO
Project description:One of the challenges of current research in prostate cancer is to improve the differential non-invasive diagnosis of prostate cancer (PCa) and benign prostate hyperplasia (BPH). Extracellular vesicles (EV) are emerging structures with promising properties for intercellular communication. In addition, the characterization of EV in biofluids is an attractive source of non-invasive diagnostic, prognostic and predictive biomarkers. Here we show that urinary EV (uEV) from prostate cancer patients exhibit genuine and differential physical and biological properties. Importantly, transcriptomics characterization of uEVs led us to define the decreased abundance of Cadherin 3, type 1 (CDH3) transcript in uEV from PCa patients. Tissue and cell line analysis strongly suggested that the status of CDH3 in uEVs is a distal reflection of changes in the expression of this cadherin in the prostate tumor. Our results reveal that uEVs could represent a non-invasive tool to inform about the molecular alterations in prostate cancer. RNA extraction was performed in 10 ultracentrifuge-isolated-uEVs samples (4 BPH, 6 Pca)
2016-01-13 | E-GEOD-75441 | biostudies-arrayexpress
Project description:In the last decade, new high-throughput sequencing techniques have revealed the complexity of the human transcriptome, allowing the characterization of long non-coding (lnc)RNAs. Since their expression has been reported as very specific to tissue, developmental stage and pathological variations, some lncRNAs have been proposed as biomarkers for diagnosis as well as prognosis of tumors. In this project, we aim to build an exhaustive catalogue of long non-coding RNAs and isolate those which allow detection and risk assessement of prostate cancer. For this purpose we performed a high throughput total stranded RNA-sequencing of 24 samples (8 normal and 16 tumor tissues).
2019-11-12 | GSE115414 | GEO
Project description:Random forest-based modelling to detect novel biomarkers for prostate cancer progression
| PRJNA525995 | ENA
Project description:Intra-individual stability of the urine miRNA transcriptome was examined by investigating longitudinal changes over time in a cohort of patients with localized prostate cancer. Using training and validation cohorts, urinary miRNA biomarkers are characterized and validated their utility to identify aggressive prostate cancer.
2019-10-24 | GSE104431 | GEO