Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA209040 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:DNA methylation profiling has become a powerful tool for neuro-oncology diagnostics. We investigated the value of using DNA methylation profiling to achieve molecular diagnosis in adult primary diffuse lower-grade gliomas according to WHO 2016 classification system of central nervous system tumors. We further evaluated the use of methylation profiling for improved molecular characterization of the tumors and identify prognostic differences beyond histological grade and molecular markers (IDH mutation and 1p/19q codeletion status).
2023-02-27 | GSE175877 | GEO
Project description:Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It presents little or no symptoms at the early stages, and typically unspecific symptoms at later stages. Of the OC subtypes, high-grade serous carcinoma (HGSC) is responsible for most OC deaths. However, very little is known about the metabolic course of this disease. In this longitudinal study, we investigated the temporal course of lipidome changes in a Dicer-Pten Double-Knockout (DKO) HGSC mouse model using machine and statistical learning approaches. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. In contrast, later stages were marked by more diverse lipids alterations, including fatty acids and their derivatives, triglycerides, ceramides, hexosylceramides, sphingomyelins, lysophosphatidylcholines, and phosphatidylinositols. These alterations provided evidence of perturbations in cell membrane stability, proliferation, and survival and candidates for early-stage and prognostic markers in humans.
2022-09-01 | ST002276 | MetabolomicsWorkbench
Project description:USP19 and RPL23 as candidate prognostic markers in advanced-stage high-grade serous ovarian carcinoma
| PRJNA697947 | ENA
Project description:Background: The prognostic value of histologic grade (HG) in invasive lobular carcinoma (ILC) remains uncertain, and most ILC tumors are graded as HG2. Genomic grade (GG) is a 97-gene signature that improves the prognostic value of HG. This study evaluates whether GG may overcome the limitations of HG in ILC. Methods: Gene expression data were generated from frozen tumor samples, and GG calculated according to the expression of 97 genes. The prognostic value of GG was assessed in a stratified Cox regression model for invasive disease-free survival (IDFS) and overall survival (OS).
2016-10-15 | GSE88770 | GEO
Project description:Ovarian cancer is one of the leading causes of death among patients with gynecological malignancies worldwide. To identify prognostic markers for ovarian cancer, we performed RNA-sequencing and analyzed the transcriptome data from 51 patients who received conventional therapies for high-grade serous ovarian carcinoma (HGSC). Patients with early-stage (I or II) HGSC exhibited higher immune gene expression than patients with advanced stage (III or IV) HGSC. To predict the prognosis of HGSC patients, we created machine learning-based models and identified RPL23 and USP19 as candidate prognostic markers. Specifically, patients with higher RPL23 mRNA levels had a worse prognosis and patients with higher USP19 mRNA levels had a better prognosis. This model was then used to analyze HGSC patient data hosted on The Cancer Genome Atlas; this exercise validated the prognostic abilities of these two genes with respect to patient survival. Taken together, the transcriptome profiles of RPL23 and USP19 determined using a machine-learning model could serve as prognostic markers in HGSC patients receiving conventional therapy.
2021-08-09 | GSE165808 | 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.
2013-12-22 | GSE50081 | GEO
Project description:There are no early detection biomarkers or prognostic markers for oral squamous cell carcinoma (OSCC) thus many are detected late, with unpredictable disease course. Using systems level analysis on shotgun proteomics with quantitative label-free ultra-definition mass spectrometry, our study aims to find novel proteins involved in both low and high grade OSCC tissue, and to further understand pathways involved in cancer development and progression, using proteomic analysis.
2022-03-02 | PXD009244 | Pride
Project description:Clear cell carcinoma (CCC), endometrioid carcinoma (EC), and serous carcinoma (SC) are the major histological subtypes of epithelial ovarian cancer (EOC), whose differences in carcinogenesis are still unclear. Here, we undertake comprehensive proteomic profiling of 80 CCC, 79 EC, 80 SC, and 30 control samples. Our analysis reveals the prognostic or diagnostic value of dysregulated proteins and phosphorylation sites in important pathways. Moreover, protein co-expression network not only provides comprehensive view of biological features of each histological subtype, but also indicate potential prognostic biomarkers and progression landmarks. Notably, EOC have strong inter-tumor heterogeneity, with significantly different clinical characteristics, proteomic patterns and signaling pathway disorders in CCC, EC, and SC. Finally, we infer MPP7 protein as potential therapeutic target for SC, whose biological functions are confirmed in SC cells. Our proteomic cohort provides valuable resources for understanding molecular mechanisms and developing treatment strategies of distinct histological subtypes.
2023-10-30 | PXD033741 | Pride
Project description:We aimed to find clinically relevant gene activities ruled by the signal transducer and activator of transcription 3 (STAT3) proteins in an ER(-) breast cancer population via network approach. STAT3 is negatively associated with both lymph nodal category and stage. MYC is a component of STAT3 network. MYC and STAT3 may co-regulate gene expressions for Warburg effect, stem cell like phenotype, cell proliferation and angiogenesis. We identified a STAT3 network in silico showing its ability in predicting its target gene expressions primarily for specific tumor subtype, tumor progression, treatment options and prognostic features. The aberrant expressions of MYC and STAT3 are enriched in triple negatives (TN). They promote histological grade, vascularity, metastasis and tumor anti-apoptotic activities. VEGFA, STAT3, FOXM1 and METAP2 are druggable targets. High levels of METAP2, MMP7, IGF2 and IGF2R are unfavorable prognostic factors. STAT3 is an inferred center regulator at early cancer development predominantly in TN. 91 specimens of primary infiltrating ductal carcinoma of breast (IDC) included triple negatives(48/91), ERBB2+(29/91),ER(-)PR(+)HER(-)(5/91), ER(-)PR(+)HER(+)(6/91) and ER(-) but HER(?)(3/91). Five specimens for metaplastic carcinoma of breast (MCB) were included. Seven non-tumor samples were surgically taken from breast tissue adjacent to some of 91 ER(-) IDC breast tumors as a control in this study.
2012-09-25 | E-GEOD-32641 | biostudies-arrayexpress
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