Microbial signatures in prostate cancer
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ABSTRACT:
PROVIDER: PRJNA437665 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Investigationg microbiome specifically associated with prostate cancer compared to non-cancerous prostate controls.
2018-05-01 | GSE111648 | GEO
Project description:The derivation of molecular signatures indicative of disease status and predictive of subsequent behavior could facilitate the optimal choice of treatment for prostate cancer patients. In this study, we conducted a computational analysis of gene expression profile data obtained from 79 cases, 39 of which were classified as having disease recurrence, to investigate whether advanced computational algorithms can derive more accurate prognostic signatures for prostate cancer. At the 90% sensitivity level, a newly derived prognostic genetic signature achieved 85% specificity. This is the first reported genetic signature to outperform a clinically used postoperative nomogram. Furthermore, a hybrid prognostic signature derived by combination of the nomogram and gene expression data significantly outperformed both genetic and clinical signatures, and achieved a specificity of 95%. Our study demonstrates the feasibility of utilizing gene expression information for highly accurate prostate cancer prognosis beyond the current clinical systems, and shows that more advanced computational modeling of tissue-derived microarray data is warranted before clinical application of molecular signatures is considered. mRNA profiling was performed using 79 cases of prostate cancer of known disease recurrence status
2010-11-05 | E-GEOD-25136 | biostudies-arrayexpress
Project description:The derivation of molecular signatures indicative of disease status and predictive of subsequent behavior could facilitate the optimal choice of treatment for prostate cancer patients. In this study, we conducted a computational analysis of gene expression profile data obtained from 79 cases, 39 of which were classified as having disease recurrence, to investigate whether advanced computational algorithms can derive more accurate prognostic signatures for prostate cancer. At the 90% sensitivity level, a newly derived prognostic genetic signature achieved 85% specificity. This is the first reported genetic signature to outperform a clinically used postoperative nomogram. Furthermore, a hybrid prognostic signature derived by combination of the nomogram and gene expression data significantly outperformed both genetic and clinical signatures, and achieved a specificity of 95%. Our study demonstrates the feasibility of utilizing gene expression information for highly accurate prostate cancer prognosis beyond the current clinical systems, and shows that more advanced computational modeling of tissue-derived microarray data is warranted before clinical application of molecular signatures is considered.
2010-11-05 | GSE25136 | GEO
Project description:Identification of specific ceRNA signatures in prostate cancer (PCa)
| PRJNA591554 | ENA
Project description:To determine the altered microRNA expression signature in human prostate cancer compared to benign prostate tissue. To determine the altered mRNA expression signatures upon overexrpession miR-31 in prostate cancer cells.
2013-02-05 | GSE36802 | GEO
Project description:We performed a mass spectrometry-based proteomic analysis of normal and malignant prostate tissues from 22 men who underwent surgery for prostate cancer. Prostate cancer samples included Grade Groups (3 to 5), with 8 patients experiencing recurrence and 14 without evidence of recurrence with a mean of 6.8 years of follow-up.
2022-05-04 | PXD028118 | Pride
Project description:Primary tumor growth induces host tissue responses that are believed to support and promote tumor progression. Identification of the molecular characteristics of the tumor microenvironment and elucidation of its crosstalk with tumor cells may therefore be crucial for improving our understanding of the processes implicated in cancer progression, identifying potential therapeutic targets, and uncovering stromal gene expression signatures that may predict clinical outcome. A key issue to resolve, therefore, is whether the stromal response to tumor growth is largely a generic phenomenon, irrespective of the tumor type, or whether the response reflects tumor-specific properties. To address similarity or distinction of stromal gene expression changes during cancer progression, oligonucleotide-based Affymetrix microarray technology was used to compare the transcriptomes of laser-microdissected stromal cells derived from invasive human breast and prostate carcinoma. Invasive breast and prostate cancer-associated stroma was observed to display distinct transcriptomes, with a limited number of shared genes. Interestingly, both breast and prostate tumor-specific dysregulated stromal genes were observed to cluster breast and prostate cancer patients, respectively, into two distinct groups with statistically different clinical outcomes. By contrast, a gene signature that was common to the reactive stroma of both tumor types did not have survival predictive value. Univariate Cox analysis identified genes whose expression level was most strongly associated with patient survival. Taken together, these observations suggest that the tumor microenvironment displays distinct features according to the tumor type that provides survival-predictive value. 6 samples of stroma surrounding invasive breast primary tumors; 6 matched samples of normal stroma. 6 samples of stroma surrounding invasive prostate primary tumors; 6 matched samples of normal stroma.
2011-03-31 | E-GEOD-26910 | biostudies-arrayexpress
Project description:To determine the altered microRNA expression signature in human prostate cancer compared to benign prostate tissue. To determine the altered mRNA expression signatures upon overexrpession miR-31 in prostate cancer cells. Two condition experiments:1) Total RNA from 21 pairs of prostate cancers and matched benign prostate tissues were collected and processed for microRNA detection. 2) In LNCaP prostate cancer cells, miR-31 was overexpressed and compared to control miR-NC.
2013-02-05 | E-GEOD-36802 | biostudies-arrayexpress
Project description:Over the last two decades, tumor-derived RNA expression signatures have been developed for the two most commonly diagnosed tumors worldwide, namely prostate and breast tumors, in order to improve both outcome prediction and treatment decision-making. In this context, molecular signatures gained by main components of the tumor microenvironment, such as cancer-associated fibroblasts (CAFs), have been explored as prognostic and therapeutic tools. Nevertheless, a deeper understanding of the significance of CAFs-related gene signatures in breast and prostate cancers still remains to be disclosed. RNA sequencing technology (RNA-seq) was employed to profile and compare the transcriptome of CAFs isolated from patients affected by breast and prostate tumors. The differentially expressed genes (DEGs) characterizing breast and prostate CAFs were intersected with data from public datasets derived from bulk RNA-seq profiles of breast and prostate tumor patients. Pathway enrichment analyses allowed us to appreciate the biological significance of the DEGs. K-means clustering was applied to construct CAFs-related gene signatures specific for breast and prostate cancer and to stratify independent cohorts of patients into high and low gene expression clusters. Kaplan-Meier survival curves and log-rank tests were employed to predict differences in the outcome parameters of the clusters of patients. Decision-tree analysis was used to validate the clustering results and boosting calculations were then employed to improve the results obtained by the decision-tree algorithm. Data obtained in breast CAFs allowed us to assess a signature that includes 8 genes (ITGA11, THBS1, FN1, EMP1, ITGA2, FYN, SPP1, and EMP2) belonging to pro-metastatic signaling routes, such as the focal adhesion pathway. Survival analyses indicated that the cluster of breast cancer patients showing a high expression of the aforementioned genes displays worse clinical outcomes. Next, we identified a prostate CAFs-related signature that includes 11 genes (IL13RA2, GDF7, IL33, CXCL1, TNFRSF19, CXCL6, LIFR, CXCL5, IL7, TSLP, and TNFSF15) associated with immune responses. A low expression of these genes was predictive of poor survival rates in prostate cancer patients. The results obtained were significantly validated through a two-step approach, based on unsupervised (clustering) and supervised (classification) learning techniques, showing a high prediction accuracy (≥ 90%) in independent RNA-seq cohorts. We identified a huge heterogeneity in the transcriptional profile of CAFs derived from breast and prostate tumors. Of note, the two novel CAFs-related gene signatures might be considered as reliable prognostic indicators and valuable biomarkers for a better management of breast and prostate cancer patients.
2024-06-21 | GSE269968 | 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.
2019-10-09 | PXD013837 | Pride