Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA234769 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The prognostic and predictive value of a five-microRNA-based classifier in breast cancer
| PRJNA248377 | ENA
Project description:The prognostic and predictive value of a six-microRNA-based classifier in stage II colon cancer
| PRJNA213381 | 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: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:Background: Predictive biomarkers for immune checkpoint inhibitors (ICIs) in non-small cell lung cancer (NSCLC) are limited. High-mannose glycans, enriched in tumors, can be selectively captured using OAA1 (recombinant OAA), a novel lectin. This study investigates whether plasma microRNAs (miRNAs) enriched by OAA1 serve as predictive markers of response to anti-PD-1 therapy. Methods: Pre-treatment plasma samples from 48 NSCLC patients treated with nivolumab were processed using OAA1 lectin columns. Levels of circulating miR-320a, miR-320b, and miR-3613-5p , which were identified as resistance associated miRNAs by microarray, were quantified with and without OAA1 enrichment. Associations with therapeutic response and overall survival were analyzed. Results: The three miRNAs were significantly upregulated in patients with stable or progressive disease compared to partial responders, but only after OAA1 enrichment. ROC and survival analyses showed improved predictive and prognostic power with OAA1-enriched miRNAs. For example, miR-3613-5p’s AUC improved from 0.837 to 0.897, and its hazard ratio increased from 3.386 to 7.815. Conclusion: OAA1-captured plasma miRNAs are associated with resistance to nivolumab and poor prognosis in NSCLC. This glycan-based enrichment strategy enhances the clinical value of circulating miRNAs and may complement tissue-based ICI biomarkers.
2026-04-01 | GSE310370 | GEO
Project description:Intratumoral heterogeneity (ITH) is a key driver of therapy resistance in glioblastoma (GBM). This study established a five-gene signature-based gITH classifier through multi-omics analysis, demonstrating robust prognostic predictive value. High-gITH tumors exhibited enhanced molecular complexity, with PDLIM4 identified as the central regulator showing strong correlations with stem-like properties and poor clinical outcomes. Functional validation confirmed that PDLIM4 knockdown suppressed ITH and tumor progression. Our work not only establishes a transcriptome-based quantification framework for GBM heterogeneity, but also reveals PDLIM4 as a promising therapeutic target, offering novel precision medicine strategies.
2026-03-27 | GSE325992 | GEO
Project description:Intratumoral heterogeneity (ITH) is a key driver of therapy resistance in glioblastoma (GBM). This study established a five-gene signature-based gITH classifier through multi-omics analysis, demonstrating robust prognostic predictive value. High-gITH tumors exhibited enhanced molecular complexity, with PDLIM4 identified as the central regulator showing strong correlations with stem-like properties and poor clinical outcomes. Functional validation confirmed that PDLIM4 knockdown suppressed ITH and tumor progression. Our work not only establishes a transcriptome-based quantification framework for GBM heterogeneity, but also reveals PDLIM4 as a promising therapeutic target, offering novel precision medicine strategies.
2026-04-02 | GSE326397 | GEO
Project description:Prognostic classifier based on genome-wide DNA methylation profiling in well differentiated thyroid tumors
| PRJNA381920 | ENA
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