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 diverse clinical outcomes of prostate cancer have led to the development of gene signature assays predicting disease progression. Improved prostate cancer progression biomarkers are needed as current RNA biomarker tests have varying success for high-risk prostate cancer. Interest grows in universal gene signatures for invasive carcinoma progression. Early breast and prostate cancers share characteristics, including hormone dependence and BRCA1/2 mutations. Given the similarities in the pathobiology of breast and prostate cancer, we utilized the NanoString BC360 panel, comprising the validated PAM50 classifier and pathway-specific signatures associated with general tumor progression as well as breast cancer-specific classifiers. This retrospective cohort of primary prostate cancers (n=53) was stratified according to biochemical recurrence status and the CAPRA-S to identify genes related to high-risk disease
2024-10-03 | GSE244631 | 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:The high heterogeneity and low percentage of neuroendocrine cells in prostate cancer limit the utility of traditional bulk RNA sequencing and even single-cell RNA sequencing to find better biomarkers for early diagnosis and stratification. Re-clustering of specific cell-type holds great promise for identification of intra-cell-type heterogeneity. However, this has not yet been used in studying neuroendocrine prostate cancer heterogeneity. Neuroendocrine cluster(s) were individually identified in each castration-resistant prostate cancer specimen and combined for trajectory analysis. Three neuroendocrine states were identified. Neuroendocrine state 2 with the highest AR score was considered the initial starting state of neuroendocrine transdifferentiation. State 1 and state 3 with distinct high neuroendocrine scores and marker genes enriched in N-Myc and REST target genes, respectively, were considered as two different types of neuroendocrine differentiated cancer cells. These two states contained distinct groups of prostate cancer biomarkers and a strong distinguishing ability of normal versus cancerous prostate across different pathological grading was found in the N-Myc-associated state. Our data highlight the central role of N-Myc and REST in mediating lineage plasticity and classifying neuroendocrine phenotypes.
2025-08-28 | GSE306622 | GEO
Project description:Comprehensive methylome sequencing reveals prognostic epigenetic biomarkers for prostate cancer mortality
| PRJNA667048 | ENA
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