Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA120363 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Full title: Predictive Gene Signatures as Strong Prognostic Indicators of the Effectiveness of Bacillus Calmetteâ??Guérin (BCG) Immunotherapy in Primary pT1 Bladder Cancers Intravesical BCG immunotherapy is effective in prevention of recurrence and progression in many cases of non-muscle invasive bladder cancer, but many patients fail to respond. This study identified predictive gene signatures in primary pT1 bladder cancer with BCG immunotherapy. Fourty-Eight patients with primary pT1 bladder cancer treated with BCG immunotherapy were used. Microarray gene expression analysis of the 48 primary bladder cancers was carried out. Predictive gene signatures were individually selected based on the recurrence and progression status. Among 43,148 unique genes, 424 and 287 candidate predictive genes that could predict recurrence and progression, respectively, were selected. Time to recurrence or progression was shorter for patients with poor-predictive gene signatures than good-predictive gene signatures (log-rank test, p <0.001, respectively). Validation of predictive gene signatures with RT-PCR was nearly identical to those of microarray (log-rank test, p <0.05, respectively). In multivariate regression analysis, predictive gene signatures were the only independent predictors of recurrence (HR 3.38, p = 0.048) or progression (HR 10.49, p = 0.048) in validation cohorts. Predictive gene signatures have strong diagnostic value for determining the response to intravesical BCG immunotherapy in primary pT1 bladder cancer. Keywords: Gene expression, Bladder cancer, BCG Total RNA, extracted from the fresh frozen tissues of 48 pT1 bladder cancers, was isolated by TRIzol reagent (Life Technologies, NY, USA), according to the manufacturer's protocol. The quality and integrity of the RNA were confirmed by agarose gel electrophoresis and ethidium bromide staining, followed by visual examination under ultraviolet light. Five-hundred nanograms of total RNA were used for labeling hybridization according to the manufacturerâ??s protocol (Illumina HumanWG-6 BeadChip, version 2, Illumina, Inc., San Diego, CA, USA). Arrays were scanned with an Illumina Bead Array Reader confocal scanner (BeadStation 500GXDW; Illumina, Inc.) according to the manufacturer's instructions. Initial microarray gene expression data were obtained using Bead Studio software with gene expression analysis module (version 3.1.3, Illumina, Inc.).
2010-04-07 | E-GEOD-19423 | biostudies-arrayexpress
Project description:Full title: Predictive Gene Signatures as Strong Prognostic Indicators of the Effectiveness of Bacillus Calmette–Guérin (BCG) Immunotherapy in Primary pT1 Bladder Cancers Intravesical BCG immunotherapy is effective in prevention of recurrence and progression in many cases of non-muscle invasive bladder cancer, but many patients fail to respond. This study identified predictive gene signatures in primary pT1 bladder cancer with BCG immunotherapy. Fourty-Eight patients with primary pT1 bladder cancer treated with BCG immunotherapy were used. Microarray gene expression analysis of the 48 primary bladder cancers was carried out. Predictive gene signatures were individually selected based on the recurrence and progression status. Among 43,148 unique genes, 424 and 287 candidate predictive genes that could predict recurrence and progression, respectively, were selected. Time to recurrence or progression was shorter for patients with poor-predictive gene signatures than good-predictive gene signatures (log-rank test, p <0.001, respectively). Validation of predictive gene signatures with RT-PCR was nearly identical to those of microarray (log-rank test, p <0.05, respectively). In multivariate regression analysis, predictive gene signatures were the only independent predictors of recurrence (HR 3.38, p = 0.048) or progression (HR 10.49, p = 0.048) in validation cohorts. Predictive gene signatures have strong diagnostic value for determining the response to intravesical BCG immunotherapy in primary pT1 bladder cancer. Keywords: Gene expression, Bladder cancer, BCG
2010-03-26 | GSE19423 | GEO
Project description:Although recent advances in high-throughput technology and data-driven approach have provided many insights into non-muscle invasive bladder cancer (NMIBC), previous studies are still limited in their ability to predict the clinical behavior of NMIBC including response to intravesical therapy. We aim to develop a prognostic index (PI) consisting of a small gene group that predicts the NMIBC progression and response to intravesical bacillus calmette-guérin (BCG) therapy. We analyzed progression-associated genes using Cox regression analysis and validated their predictive values using a fully connected neural network (FNN) algorithm. By applying a pathway enrichment analysis to these genes, a PI system consisting of small core genes for NMIBC progression was developed. Gene expression profiling in NMIBC patients identified a prognostic gene set for predicting NMIBC progression in multiple patient cohorts. Pathway enrichment analysis revealed a 23-gene signature. We incorporated these genes into the PI system, which was a significant prognostic indicator of NMIBC progression. The PI system was shown to be an independent risk factor by a multivariate analysis and subset stratification according to stage and grade. The subset analysis also revealed that the PI system could identify patients who would benefit from BCG immunotherapy. The 23-gene-based PI represents a promising diagnostic tool for identifying high-risk NMIBC patients who would display different clinical behaviors and response to BCG immunotherapy.
2024-02-01 | GSE224248 | GEO
Project description:Chronic lymphocytic leukemia (CLL) is a heterogeneous malignancy, characterized by a variable clinical course. While clinical and laboratory parameters are increasingly being used to refine prognosis, they do not accurately predict response to commonly used therapy. We used gene expression profiling to generate and further refine prognostic and predictive markers. Genomic signatures that reflect progressive disease and responses to chemotherapy or chemo-immunotherapy were created using cancer cell lines and patient leukemia samples. We validated these signatures using independent clinical data from four separate cohorts representing a total of 301 CLL patients. A prognostic genomic signature created from patient leukemic cell gene expression data coupled with clinical parameters could statistically differentiate patients with stable or progressive disease in the training dataset. The progression signature was then validated in two independent datasets, demonstrating a capacity to accurately identify patients at risk for progressive disease. In addition, two distinct genomic signatures that predict response to chlorambucil or pentostatin, cyclophosphamide, and rituximab were also generated and were shown to accurately distinguish responding and non-responding CLL patients. Microarray analysis of CLL patientsâ lymphocytes can be used to refine prognosis and predict response to different therapies. These results have direct implications for standard and investigational therapeutics in CLL patients. Experiment Overall Design: For the predictive genomic signature or response to pentostatin, cyclophosphamide, and rituximab, 20 CLL leukemia samples were used in the training set, and 20 CLL leukemia samples were used in the validation set
2009-11-11 | E-GEOD-10137 | biostudies-arrayexpress
Project description:Strong Time Dependence of the 76-Gene Prognostic Signature
| PRJNA97873 | ENA
Project description:Chronic lymphocytic leukemia (CLL) is a heterogeneous malignancy, characterized by a variable clinical course. While clinical and laboratory parameters are increasingly being used to refine prognosis, they do not accurately predict response to commonly used therapy. We used gene expression profiling to generate and further refine prognostic and predictive markers. Genomic signatures that reflect progressive disease and responses to chemotherapy or chemo-immunotherapy were created using cancer cell lines and patient leukemia samples. We validated these signatures using independent clinical data from four separate cohorts representing a total of 301 CLL patients. A prognostic genomic signature created from patient leukemic cell gene expression data coupled with clinical parameters could statistically differentiate patients with stable or progressive disease in the training dataset. The progression signature was then validated in two independent datasets, demonstrating a capacity to accurately identify patients at risk for progressive disease. In addition, two distinct genomic signatures that predict response to chlorambucil or pentostatin, cyclophosphamide, and rituximab were also generated and were shown to accurately distinguish responding and non-responding CLL patients. Microarray analysis of CLL patientsâ lymphocytes can be used to refine prognosis and predict response to different therapies. These results have direct implications for standard and investigational therapeutics in CLL patients. Experiment Overall Design: For the prognostic genomic signature, 68 CLL leukemia samples were used (36 from patients with stable disease and 32 from patients with progressive disease).
2009-11-11 | E-GEOD-10138 | biostudies-arrayexpress
Project description:Improved risk stratification and predictive biomarkers of treatment response are needed for non–muscle-invasive bladder cancer (NMIBC). Here we assessed the clinical utility of targeted RNA and DNA molecular profiling in NMIBC. We performed RNA-based profiling by NanoString nCounter on non–muscle-invasive bladder cancer (NMIBC) clinical specimens and found that a novel expression signature of an inflamed tumor microenvironment (TME), but not molecular subtyping, was associated with improved recurrence-free survival after bacillus Calmette-Guérin (BCG) immunotherapy. We further demonstrated that immune checkpoint gene expression was not associated with higher recurrence rates after BCG.
2021-10-05 | GSE185264 | GEO
Project description:Identification of prognostic gene signatures in AML
| PRJNA154793 | ENA
Project description:Glioblastomas (GBM), the most common and lethal type of primary brain tumor in adult, currently lack effective treatment and prognostic indicators. Hypoxia，a pathology promoting tumor progression in most solid tumors, has been reported to exhibit a negative correlation with GBM prognosis. Hypoxia related tumor-associated macrophages (hTAMs), the major cellular component of hypoxic regions, have not being systematically studied. We characterized the transcriptomic identity of hTAMs at the single-cell level, simultaneously discovering signatures indicative of poor outcomes. Immunofluorescent staining and spatially resolved transcriptomics revealed pronounced disparities regarding the morphology and distribution of TAMs between hypoxic and non-hypoxic regions of GBMs. We identified ARL4C and HSPA5 as prognostic indicators, exhibiting spatial patterns congruent with hypoxic regions and predicting worse outcomes, verified by qPCR and cell culture. Intercellular communication analysis using single-cell-omics revealed that hypoxia related GBM tumor cells (hGBM cells) exploit the TIMP1/LRP1 ligand-receptor axis to modulate hTAMs, resulting in a worse prognosis. Our study has offered clinically prognostic indicators, along with potentially therapeutic targets for GBMs.
2024-01-02 | GSE251908 | GEO
Project description:CHAC1 mRNA expression is a strong prognostic biomarker in breast and ovarian cancer
| PRJNA128077 | ENA