Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA213381 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The prognostic and predictive value of a three-microRNA-based classifier in NSCLC
| PRJNA234769 | ENA
Project description:The prognostic and predictive value of a five-microRNA-based classifier in breast cancer
| PRJNA248377 | ENA
Project description:Background: Patients with early stage non-small cell lung carcinoma (NSCLC) may benefit from treatments based on more accurate prognosis. A 15-gene prognostic classifier for NSCLC was identified from mRNA expression profiling of tumor samples from the NCIC CTG JBR.10 trial. Here, we assessed its value in an independent set of cases. Methods: Expression profiling was performed on RNA from frozen, resected tumor tissues corresponding to 181 Stage I and II NSCLC cases collected at University Health Network (UHN181). Kaplan-Meier methodology was used to estimate three year overall survival probabilities and the prognostic effect of the classifier was assessed using log-rank testing. Cox proportional hazards model evaluated the signature's effect adjusting for clinical prognostic factors. Results: Expression data of the 15-gene classifier stratified UHN181 cases into high and low-risk subgroups with significantly different overall survival (HR=1.92, 95% CI: 1.15-3.23, p=0.012). Its strength as a prognostic classifier was superior to stage alone (HR=1.52, 95% CI: 0.90-2.55, p-value=0.11). In subgroup analysis, this classifier predicted survival in 127 Stage I patients (HR=2.17, 95% CI: 1.12-4.20, p=0.018) and the smaller subgroup of 48 Stage IA patients (HR=5.61, 95% CI: 1.19-26.45, p=0.014. The signature was prognostic for both adenocarcinoma and squamous cell carcinoma cases (HR= 1.76, p-value=0.058; HR= 4.19, p-value=0.045, respectively). Conclusions: The prognostic accuracy of a 15-gene classifier was validated in an independent cohort of 181 early stage NSCLC samples including Stage IA cases and in different NSCLC histologic subtypes.
2013-12-22 | GSE50081 | GEO
Project description:Background: Patients with early stage non-small cell lung carcinoma (NSCLC) may benefit from treatments based on more accurate prognosis. A 15-gene prognostic classifier for NSCLC was identified from mRNA expression profiling of tumor samples from the NCIC CTG JBR.10 trial. Here, we assessed its value in an independent set of cases. Methods: Expression profiling was performed on RNA from frozen, resected tumor tissues corresponding to 181 Stage I and II NSCLC cases collected at University Health Network (UHN181). Kaplan-Meier methodology was used to estimate three year overall survival probabilities and the prognostic effect of the classifier was assessed using log-rank testing. Cox proportional hazards model evaluated the signature's effect adjusting for clinical prognostic factors. Results: Expression data of the 15-gene classifier stratified UHN181 cases into high and low-risk subgroups with significantly different overall survival (HR=1.92, 95% CI: 1.15-3.23, p=0.012). Its strength as a prognostic classifier was superior to stage alone (HR=1.52, 95% CI: 0.90-2.55, p-value=0.11). In subgroup analysis, this classifier predicted survival in 127 Stage I patients (HR=2.17, 95% CI: 1.12-4.20, p=0.018) and the smaller subgroup of 48 Stage IA patients (HR=5.61, 95% CI: 1.19-26.45, p=0.014. The signature was prognostic for both adenocarcinoma and squamous cell carcinoma cases (HR= 1.76, p-value=0.058; HR= 4.19, p-value=0.045, respectively). Conclusions: The prognostic accuracy of a 15-gene classifier was validated in an independent cohort of 181 early stage NSCLC samples including Stage IA cases and in different NSCLC histologic subtypes. Expression profiling was performed on RNA from frozen, resected tumor tissues corresponding to 181 Stage I and II NSCLC cases collected at University Health Network (UHN181). !Series_contributor = Sandy,D,Der
2013-12-22 | E-GEOD-50081 | biostudies-arrayexpress
Project description:The purpose of this study was to establish a new prognostic model for stage II/III colon cancer. Using public DNA microarray data of colon cancer patients, we created an integrated prognostic model for classifying the patients into high- and low-risk groups based on the expression levels of 55 genes and the KRAS mutation status. For validation, we examined specimens from patients with stage II/III colon cancer who had undergone radical resection at our department, and successfully confirmed prognostic value of our model. We believe that our prognostic model may be clinically helpful to select patients for adjuvant chemotherapy.
2018-03-30 | GSE92921 | GEO
Project description:Medullary breast cancers (MBC) display a basal profile, but a favorable prognosis. We hypothesized that a previously published 368-gene expression signature associated with MBC might serve to define a prognostic classifier in basal cancers. We collected public gene expression and histoclinical data of 2145 invasive early breast adenocarcinomas. We developed a Support Vector Machine (SVM) classifier based on this 368-gene list in a learning set, and tested its predictive performances in an independent validation set. Then, we assessed its prognostic value and that of six prognostic signatures for disease-free survival (DFS) in the remaining 2034 samples. The SVM model accurately classified all MBC samples in the learning and validation sets. A total of 466 cases were basal across other sets. The SVM classifier separated them into two subgroups, subgroup 1 (resembling MBC) and subgroup 2 (not resembling MBC). Subgroup 1 exhibited 71% 5-year DFS, whereas subgroup 2 exhibited 50% (p=9.93E-05). The classifier outperformed the classical prognostic variables in multivariate analysis, conferring lesser risk for relapse in subgroup 1 (HR=0.52, p=3.9E-04). This prognostic value was specific to the basal subtype, in which none of the other prognostic signatures was informative.
2010-05-05 | GSE21653 | GEO
Project description:Purpose: The goal of this experiment was to identify differentially expressed miRNAs between colon cancer patients with and without metachronous metastases. Background: Colon cancer prognosis and treatment are currently based on a classification system still showing large heterogeneity in clinical outcome, especially in TNM-stages II-III. Prognostic biomarkers for metastasis risk are warranted, as development of distant recurrent disease mainly accounts for the high lethality rates of colon cancer. MicroRNAs have been proposed as potential biomarkers for cancer. Furthermore, a verified standard for normalization of the amount of input material in PCR-based relative quantification of miRNA expression is lacking. Methods: A selection of frozen tumor specimens from two independent patient cohorts with TNM-stage II-III microsatellite stable primary adenocarcinomas were used for laser capture microdissection. Next-generation sequencing was performed on small RNAs isolated from colorectal tumors from the Dutch cohort (N=50). Differential expression analysis, comparing in metastasized- and non-metastasized tumors, identified prognostic microRNAs. Validation was performed on colon tumors from the German cohort (N=43) using qPCR. Results: MiR-25-3p and miR-339-5p were identified and validated as independent prognostic markers and used to construct a multivariate nomogram for metastasis risk prediction. The nomogram showed good probability prediction in validation. Additionally, we recommend combination of miR-16-5p and miR-26a-5p as standard for normalization in qPCR of colon cancer tissue-derived microRNA expression. Conclusions: In this international study, we identified and validated an miRNA classifier in primary cancers, and propose a nomogram capable of predicting metastasis risk in microsatellite stable TNM-stage II-III colon cancer.
2014-11-08 | GSE63119 | GEO
Project description:Medullary breast cancers (MBC) display a basal profile, but a favorable prognosis. We hypothesized that a previously published 368-gene expression signature associated with MBC might serve to define a prognostic classifier in basal cancers. We collected public gene expression and histoclinical data of 2145 invasive early breast adenocarcinomas. We developed a Support Vector Machine (SVM) classifier based on this 368-gene list in a learning set, and tested its predictive performances in an independent validation set. Then, we assessed its prognostic value and that of six prognostic signatures for disease-free survival (DFS) in the remaining 2034 samples. The SVM model accurately classified all MBC samples in the learning and validation sets. A total of 466 cases were basal across other sets. The SVM classifier separated them into two subgroups, subgroup 1 (resembling MBC) and subgroup 2 (not resembling MBC). Subgroup 1 exhibited 71% 5-year DFS, whereas subgroup 2 exhibited 50% (p=9.93E-05). The classifier outperformed the classical prognostic variables in multivariate analysis, conferring lesser risk for relapse in subgroup 1 (HR=0.52, p=3.9E-04). This prognostic value was specific to the basal subtype, in which none of the other prognostic signatures was informative. The IPC series contained frozen tumor samples obtained from 266 early breast cancer patients who underwent initial surgery in our institution between 1992 and 2004. They included 227 cases previously reported {Finetti, 2008 #1758} and 39 additional cases, all similarly profiled using Affymetrix U133 Plus 2.0 human oligonucleotide microarrays as previously described {Finetti, 2008 #1758}. The study was approved by the IPC review board, and informed consent was available for each case. Gene expression data of 266 BCs were quantified by using whole-genome DNA microarrays (HG-U133 plus 2.0, Affymetrix).
2010-05-05 | E-GEOD-21653 | biostudies-arrayexpress
Project description:Intratumor heterogeneity fosters the evolution of the genome leading to metastatic progress and therapy resistance. Here, we investigate the relative contribution of genomic heterogeneity involving mutations and CNAs as prognostic and predictive determinants for disease recurrence in early-stage colon cancer. We combined targeted next-generation sequencing (NGS) and SNP arrays on a retrospective cohort of untreated stage II colon cancer patients to assess the association of genomic subclonality with time to recurrence (TTR).
2021-12-01 | GSE172190 | GEO
Project description:This study investigates the predictive and prognostic values of inflammatory markers and microRNA in stage IV colorectal cancer. The expression of inflammatory markers and microRNA in plasma will be correlated with tumor location, with dietary patterns and with survival during treatment.
| 2320890 | ecrin-mdr-crc