Gene expression profiling of benign and matched tumour prostate tissue from patients diagnosed with prostate cacner (PCa)
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE246282 | GEO | 2023/10/31
REPOSITORIES: GEO
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Project description:Background. To develop a radiomics signature for predicting overall survival (OS) and progression-free survival (PFS) in patients with medulloblastoma (MB), and to investigate the incremental prognostic value and underlying biological pathways of the radiomics signature.Methods. A radiomics signature was constructed based on five magnetic resonance (MR) sequences (T1, T1c, T2, FLAIR and ADC) from a training cohort (n = 83) using LASSO-Cox model. Association between the signature and survival was evaluated on a testing cohort (n = 83). Incremental prognostic value of the signature beyond clinicomolecular factors was assessed with respect to calibration, discrimination, reclassification and clinical usefulness by a radiomics-clinicomolecular nomogram. Key pathways associated with the signature were identified. Prognostic value of pathway genes was assessed in a public TCGA cohort.Results. The radiomics signature was significantly associated with OS/PFS, independent from clinicomolecular factors. The radiomics-clinicomolecular nomogram predicted OS (C-index 0.762) and PFS (C-index 0.697) better than either the radiomics signature (C-index: OS: 0.649; PFS: 0.593) or the clinicomolecular nomogram (C-index: OS: 0.725; PFS: 0.691) only, with a better calibration and classification accuracy (net reclassification improvement: OS: 0.298, P = 0.022; PFS: 0.252, P = 0.026). Nine pathways were significantly correlated with the radiomics signature. Average expression value of pathway genes achieved significant risk stratification in public cohort (log-rank P = 0.016). Conclusion. This study demonstrated radiomics signature was an independent prognostic marker and offered incremental value over clinicomolecular factors in OS/PFS predictions for MB patients. The signature was associated with dysregulated pathways affecting patient prognosis in MB.
2020-06-01 | GSE151519 | GEO
Project description:Clinical manifestation of PCa is highly variable. Aggressive tumors require radical treatment, while clinically non-significant ones may be suitable for active surveillance. We have previously developed the prognostic ProstaTrend signature mainly on prostatectomy specimens by application of transcriptome‐wide microarray and RNA-sequencing (RNA-Seq) analyses. We used a cohort of 185 tumor specimens obtained from FFPE biopsies for RNA-Seq to facilitate the application of ProstaTrend at the beginning of routine PCa diagnostic. All patients of the FFPE biopsy cohort were treated by radical prostatectomy (RPx) and median follow-up for biochemical recurrence (BCR) was 9 years. Based on the transcriptome data of the FFPE biopsies, we filtered ProstaTrend for genes susceptible to FFPE-associated degradation via regression analysis. ProstaTrend was additionally restricted to genes with concordant prognostic effects in the RNA-Seq TCGA prostate adenocarcinoma (PRAD) cohort to ensure robust and broad applicability. The prognostic relevance of the refined Transcriptomic Risk Score (TRS) was analyzed by Kaplan-Meier curves, Cox-regression models in our FFPE-biopsy cohort and 9 other public datasets from PCa patients with BCR as primary endpoint. The TRS based on the revised ProstaTrend signature, which included 204 genes, was significantly associated with BCR in the FFPE biopsy cohort (Cox-regression p-value <0.001). The TRS retained prognostic relevance when adjusted for Gleason Score (GS). We confirmed a significant association with BCR in 9 independent cohorts with a total of 1109 PCa patients. Comparison of the prognostic performance of the TRS with 17 other prognostically relevant PCa panels revealed that the revised ProstaTrend was among the best-ranked panels.
2023-09-08 | GSE220095 | GEO
Project description:The clinical course of prostate cancer (PCa) is highly variable, demanding an individualized approach to therapy and robust prognostic markers for treatment decisions. We here present a random forest-based classification model to predict aggressive behaviour of PCa. DNA methylation changes between PCa cases with good or poor prognosis (discovery cohort with n=78) were used as input. The model was validated with data from two independent PCa cohorts from ICGC and TCGA. Ranking of cancer progression-related DNA methylation changes allowed selection of candidate genes for additional validation by immunohistochemistry. We identified loss of ZIC2 protein expression as a promising novel prognostic biomarker for PCa in >12,000 tissue micro-array tumors. The prognostic value of ZIC2 proved to be independent from established clinico-pathological variables including Gleason, stage, nodal stage and PSA. In summary, we have developed a PCa classification model which either directly or via expression analyses of the identified top ranked candidate genes might help in decision making related to the treatment of prostate cancer patients.
2019-10-29 | GSE127985 | GEO
Project description:Gleason grading is an important prognostic indicator for prostate adenocarcinoma and is crucial for patient treatment decisions. However, intermediate-risk patients diagnosed in Gleason Grade Groups (GG) 2 and GG3 can harbour either aggressive or non-aggressive disease, resulting in under- or over-treatment of a significant number of patients. Here, we performed proteomic, differential expression, machine learning, and survival analyses for 1,348 matched tumour and benign samples from 278 patients. Three proteins (F5, TMEM126B and EARS2) were identified as candidate biomarkers in patients with biochemical recurrence. Multivariate Cox regression yielded 18 proteins, from which a risk score was constructed to dichotomise prostate cancer patients into low- and high-risk groups. This 18-protein signature is prognostic for the risk of biochemical recurrence and completely independent of the intermediate GG. Our results suggest that markers generated by computational proteomic profiling have the potential for clinical applications including integration into prostate cancer management.
2023-11-30 | PXD041005 | Pride
Project description:PSA screening has led to enormous overtreatment of prostate cancer, due to the inability to distinguish potentially lethal disease at diagnosis. We reasoned that by identifying an mRNA signature of Gleason grade, the best predictor of prognosis, we could improve prediction of lethal disease among men with moderate Gleason 7 tumors, the most common grade, and most indeterminate in terms of prognosis. Using the complementary DNA (cDNA)–mediated annealing, selection, extension, and ligation assay, we measured the mRNA expression of 6,100 genes in prostate tumor tissue in the Swedish Watchful Waiting cohort (N=358) and Physicians’ Health Study (PHS; N=109). We developed an mRNA signature of Gleason comparing individuals with Gleason ≤6 to those with Gleason ≥8 tumors, and applied the model among Gleason 7 cases to discriminate lethal cases. We built a157-gene signature using the Swedish data that predicted Gleason with low misclassification (AUC=0.91); when this signature was tested in the PHS validation set, the discriminatory ability remained high (AUC=0.94). In men with Gleason 7 tumors, who were excluded from the model building, the signature significantly improved the prediction of lethal disease beyond knowing whether the Gleason score was 4+3 or 3+4 (p=0.006). Our expression signature and the genes identified may improve our understanding of the de-differentiation process of prostate tumors. Additionally, the signature may have clinical applications among men with Gleason 7, by further estimating their risk of lethal prostate cancer and thereby guiding therapy decisions to improve outcomes and reduce overtreatment.
2012-05-01 | GSE37663 | GEO
Project description:PSA screening has led to enormous overtreatment of prostate cancer, due to the inability to distinguish potentially lethal disease at diagnosis. We reasoned that by identifying an mRNA signature of Gleason grade, the best predictor of prognosis, we could improve prediction of lethal disease among men with moderate Gleason 7 tumors, the most common grade, and most indeterminate in terms of prognosis. Using the complementary DNA (cDNA)M-bM-^@M-^Smediated annealing, selection, extension, and ligation assay, we measured the mRNA expression of 6,100 genes in prostate tumor tissue in the Swedish Watchful Waiting cohort (N=358) and PhysiciansM-bM-^@M-^Y Health Study (PHS; N=109). We developed an mRNA signature of Gleason comparing individuals with Gleason M-bM-^IM-$6 to those with Gleason M-bM-^IM-%8 tumors, and applied the model among Gleason 7 cases to discriminate lethal cases. We built a157-gene signature using the Swedish data that predicted Gleason with low misclassification (AUC=0.91); when this signature was tested in the PHS validation set, the discriminatory ability remained high (AUC=0.94). In men with Gleason 7 tumors, who were excluded from the model building, the signature significantly improved the prediction of lethal disease beyond knowing whether the Gleason score was 4+3 or 3+4 (p=0.006). Our expression signature and the genes identified may improve our understanding of the de-differentiation process of prostate tumors. Additionally, the signature may have clinical applications among men with Gleason 7, by further estimating their risk of lethal prostate cancer and thereby guiding therapy decisions to improve outcomes and reduce overtreatment. 198 cases from the population-based Swedish-Watchful Waiting cohort. The cohort consists of men with localized prostate cancer (clinical stage T1-T2, Mx, N0); expression profiles from tumors with Gleason M-bM-^IM-%8 (N=89) were compared to those from tumors with Gleason M-bM-^IM-$6 (N=109)
2012-04-30 | E-GEOD-37663 | 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. 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 highly intra-tumoral heterogeneity and complex cell origination of PCa greatly limited the significance of traditional bulk RNA sequencing in finding better biomarker for disease stratification. In the context of these sequencing strategies, significant gene expression differences in specific cell population might be normalized or even shaded by genes in those less “important” but large in amount cells. Tissue specimens based single-cell RNA sequencing holds great promise for identification of novel stratification biomarkers. However, the preparation of enough viable cells for sequencing, in particular for prostate cancer (PCa), is very challenging when using this technique. In the current study, we, for the first time, employed single-cell RNA sequencing of PCa cells isolated from cancerous prostate tissues of two patients and identified fifteen cell groups including three luminal clusters with different expression profiles. One of these luminal clusters had the highest copy number variation level and marker genes mainly enriched in PCa-related metabolic process, thereafter, was considered as the malignant luminal cells in PCa. Further analysis indicates that the fifth subgroup of these cells highly expressed genes significantly correlated with PCa initiation and early development. In addition, we found this subgroup highly expressed PCa prognosis biomarkers such as AMACR and PCA3. These findings suggest that this cell subgroup might be a critical cell population for PCa diagnosis and stratification. Moreover, by further deciphering the gene expression of this subgroup, we identified another marker gene, HPN, with a 0.930 area under the curve (AUC) score distinguishing normal tissue from PCa lesion using the RNA-seq data from The Cancer Genome Atlas (TCGA). This finding was further validated by immunostaining of HPN in PCa tissue array. The expression of HPN in PCa with Gleason score > 6 is significantly higher than those in Gleason score = 6. Taken together, we provide a valuable resource for interpreting the tumor heterogeneity in PCa, and a novel candidate marker for PCa management.
2020-10-20 | GSE157703 | GEO
Project description:Expression levels of retinoic acid receptor gamma (NR1B3/RARG, encodes RARG), are commonly reduced in prostate cancer (PCa). Therefore we sought to establish the cellular and gene regulatory consequences of reduced RARG expression, and determine RARG regulatory mechanisms. RARG shRNA approaches in non-malignant (RWPE-1 and HPr1-AR) and malignant (LNCaP) prostate models revealed that reducing RARG levels, rather than adding exogenous retinoid ligand, had the greatest impact on prostate cell viability and gene expression. ChIP-Seq defined the RARG cistrome which was significantly enriched at active enhancers associated with AR binding sites. Reflecting a significant genomic role for RARG to regulate androgen signaling, RARG knockdown in HPr1-AR cells significantly regulated the magnitude of the AR transcriptome. RARG down-regulation was explained by increased miR-96 in PCa cell and mouse models, and TCGA PCa cohorts. Biochemical approaches confirmed that miR-96 directly regulated RARG expression and function. Capture of the miR-96 targetome by biotin-miR96 identified that RARG and a number of RARG interacting co-factors including TACC1 were all targeted by miR-96, and expression of these genes were prominently altered, positively and negatively, in the TCGA-PRAD cohort. Differential gene expression analyses between tumors in the TCGA-PRAD cohort with lower quartile expression levels of RARG and TACC1 and upper quartile miR-96, compared to the reverse, identified a gene network including several RARG target genes (e.g. SOX15) that significantly associated with worse disease free survival (hazard ratio 2.23, 95% CI 1.58 to 2.88, p=0.015). In summary, miR-96 targets a RARG network to govern AR signaling, PCa progression and disease outcome. ChIP-seq: RWPE-1-BAC-RARG-EGFP non-malignant, immortalized cell line infected with BAC-RARG-EGFP, +/- 10nM CD437 for 2 hours
2018-07-20 | GSE117179 | GEO
Project description:Expression levels of retinoic acid receptor gamma (NR1B3/RARG, encodes RARG), are commonly reduced in prostate cancer (PCa). Therefore we sought to establish the cellular and gene regulatory consequences of reduced RARG expression, and determine RARG regulatory mechanisms. RARG shRNA approaches in non-malignant (RWPE-1 and HPr1-AR) and malignant (LNCaP) prostate models revealed that reducing RARG levels, rather than adding exogenous retinoid ligand, had the greatest impact on prostate cell viability and gene expression. ChIP-Seq defined the RARG cistrome which was significantly enriched at active enhancers associated with AR binding sites. Reflecting a significant genomic role for RARG to regulate androgen signaling, RARG knockdown in HPr1-AR cells significantly regulated the magnitude of the AR transcriptome. RARG down-regulation was explained by increased miR-96 in PCa cell and mouse models, and TCGA PCa cohorts. Biochemical approaches confirmed that miR-96 directly regulated RARG expression and function. Capture of the miR-96 targetome by biotin-miR96 identified that RARG and a number of RARG interacting co-factors including TACC1 were all targeted by miR-96, and expression of these genes were prominently altered, positively and negatively, in the TCGA-PRAD cohort. Differential gene expression analyses between tumors in the TCGA-PRAD cohort with lower quartile expression levels of RARG and TACC1 and upper quartile miR-96, compared to the reverse, identified a gene network including several RARG target genes (e.g. SOX15) that significantly associated with worse disease free survival (hazard ratio 2.23, 95% CI 1.58 to 2.88, p=0.015). In summary, miR-96 targets a RARG network to govern AR signaling, PCa progression and disease outcome. RNA-seq: HPr1-AR non-malignant, immortalized cell line +/- shRARG, +/- 10nM DHT for 0,24,96 hours
2018-07-20 | GSE117110 | GEO