Homo sapiens
Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: phs000310.v1.p1
PROVIDER: PRJNA116195 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Current prostate cancer prognostic models are based on pre-treatment prostate-specific antigen (PSA) levels, biopsy Gleason score, and clinical staging but in practice are inadequate to accurately predict clinical disease progression. Hence, we sought to develop a molecular panel for prostate cancer progression by reasoning that molecular profiles might further improve current clinical models. We analyzed a Swedish Watchful Waiting cohort (1977â1999) with up to 30 years of clinical follow up using a novel method for gene expression profiling. This cDNA-mediated annealing, selection, ligation, and extension (DASL) method enabled the use of formalin-fixed paraffin-embedded transurethral resection of prostate (TURP) samples taken at the time of the initial diagnosis. We determined the expression profiles of 6100 genes for 281 men divided in two extreme groups: men who died of prostate cancer or developed metastases and men who survived more than 10 years without metastases (lethals and indolents, respectively). Several models using clinical and molecular features were evaluated for their ability to distinguish lethal from indolent cases. Surprisingly, none of the predictive models using molecular profiles significantly improved over models using clinical variables only. We reasoned that tumor sampling might preclude the identification of the dominant metastatic nodule. Additional computational analysis confirmed that molecular heterogeneity within both the lethal and indolent classes is widespread in prostate cancer as compared to other types of tumors. Thus the determination of the molecularly dominant tumor nodule may be limited by sampling at time of initial diagnosis, may not be present at time of initial diagnosis, or may occur as the disease progresses preventing the development of molecular biomarkers for prostate cancer progression. 281 cases from the population-based Swedish-Watchful Waiting cohort. The cohort consists of men with localized prostate cancer (clinical stage T1-T2, Mx, N0); Training set: first 186 samples; Validation cohort: remaining 95 cases from the same population.
2010-05-07 | E-GEOD-16560 | biostudies-arrayexpress
Project description:Integrative Molecular Concepts Modeling of Prostate Cancer Progression
| PRJNA97661 | ENA
Project description:Current prostate cancer prognostic models are based on pre-treatment prostate-specific antigen (PSA) levels, biopsy Gleason score, and clinical staging but in practice are inadequate to accurately predict clinical disease progression. Hence, we sought to develop a molecular panel for prostate cancer progression by reasoning that molecular profiles might further improve current clinical models. We analyzed a Swedish Watchful Waiting cohort (1977–1999) with up to 30 years of clinical follow up using a novel method for gene expression profiling. This cDNA-mediated annealing, selection, ligation, and extension (DASL) method enabled the use of formalin-fixed paraffin-embedded transurethral resection of prostate (TURP) samples taken at the time of the initial diagnosis. We determined the expression profiles of 6100 genes for 281 men divided in two extreme groups: men who died of prostate cancer or developed metastases and men who survived more than 10 years without metastases (lethals and indolents, respectively). Several models using clinical and molecular features were evaluated for their ability to distinguish lethal from indolent cases. Surprisingly, none of the predictive models using molecular profiles significantly improved over models using clinical variables only. We reasoned that tumor sampling might preclude the identification of the dominant metastatic nodule. Additional computational analysis confirmed that molecular heterogeneity within both the lethal and indolent classes is widespread in prostate cancer as compared to other types of tumors. Thus the determination of the molecularly dominant tumor nodule may be limited by sampling at time of initial diagnosis, may not be present at time of initial diagnosis, or may occur as the disease progresses preventing the development of molecular biomarkers for prostate cancer progression.
2010-03-05 | GSE16560 | GEO
Project description:Molecular Marker for predicting development of cancer in ulcerative colitis
| PRJNA93739 | ENA
Project description:Using laser capture microdissection to isolate over 100 specific cell populations, we report the profiling of prostate cancer progression from benign epithelium to metastatic disease. By analyzing these expression signatures in the context of over 15,000 “molecular concepts”, or sets of biologically connected genes, we generated an integrative model of prostate cancer progression. Keywords: disease state analysis
2006-12-17 | GSE6099 | GEO
Project description:Protein 4.1B suppresses prostate cancer progression and metastasis
| PRJNA100091 | ENA
Project description:Profiling of prostate tissues in prostate cancer progression
| PRJNA101627 | ENA
Project description:Optimizing molecular signatures for prostate cancer recurrence
| PRJNA134591 | ENA
Project description:In our investigations of the molecular pathways of prostate tumorigenesis in Nkx3.1; Pten mutant mice using gene expression profiling, we now find that the AP-1 transcription factors, c-Jun and c-Fos, are significantly up-regulated during cancer progression. Forced expression of c-Fos and c-Jun in prostate cancer cells results in increased tumorigenicity, activation of Erk MAP kinase, and enhanced survival in the absence of androgens, which are hallmarks of disease progression. In humans, Jun and Fos proteins are significantly up-regulated during prostate cancer progression and significantly correlated with activation of Erk MAP kinase. Most notably, expression of Jun is associated with disease recurrence independent of other currently used prognostic indicators. These analyses reveal a hitherto unappreciated role for AP-1 transcription factors in prostate cancer progression vis-à-vis Erk MAP kinase signaling, as well as the identification of a novel marker of disease recurrence, namely c-Jun. Keywords: Stages of Prostate Cancer
2008-06-27 | GSE11836 | GEO
Project description:Background Clinically useful molecular markers predicting the clinical course of patients diagnosed with non-muscle invasive bladder cancer are needed to improve treatment outcome. Methods We used custom designed oligonucleotide microarrays to validate four previously reported gene expression signatures for molecular diagnosis of disease stage and carcinoma in situ, and for predicting disease recurrence and progression. We analyzed tumors from 404 patients diagnosed with bladder cancer in hospitals in Denmark, Sweden, England, Spain and France. Molecular classifications were compared to pathological diagnosis and clinical outcome. The median follow-up time for the patients was 3.5 years. Results Classification of disease stage using a 52-gene classifier was found to be highly significantly correlated with pathological stage (P<0.001). Furthermore, the classifier added information regarding future disease progression of Ta or T1 tumors (P<0.001). The molecular 77-gene progression classifier was highly significantly correlated with progression free survival (P<0.001) and cancer specific survival (P=0.001). Furthermore, multivariate Cox´s regression analysis showed the progression classifier to be an independently significant variable associated with disease progression after adjustment for age, sex, stage, grade and treatment (hazard ratio 2.4, P=0.005). The diagnosis of carcinoma in situ (CIS) using a 68-gene classifier showed a highly significant correlation with histopathological CIS diagnosis (odds ratio 5.8, P<0.001) in multivariate logistic regression analysis. Conclusions We conclude that this multicenter validation study confirms the clinical utility of molecular classifiers to guide treatment decisions for patients initially diagnosed with non-muscle invasive bladder cancer. Keywords: Multi center validation study of gene expression signatures
2007-06-15 | GSE5479 | GEO