Ontology highlight
ABSTRACT:
PROVIDER: PRJNA701182 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:By using patient-derived xenografts (PDXs) as living surrogate of the clinical practice, we identify here induction of genes related to the IFN pathway as an early and specific predictor of tumor response to treatment. Gene expression profile of tumor cells after laser-capture microdissection of residual tumor foci to characterize the molecular changes occurring in residual tumor cells surviving chemotherapy
2015-12-31 | GSE58990 | GEO
Project description:By using patient-derived xenografts (PDXs) as living surrogate of the clinical practice, we identify here induction of genes related to the IFN pathway as an early and specific predictor of tumor response to treatment. Gene expression profile of tumor cells after laser-capture microdissection of residual tumor foci to characterize the molecular changes occurring in residual tumor cells surviving chemotherapy RNA was extracted from microdissected areas using the RNeasy Mini kit (Qiagen, Valencia, CA). This approach allowed isolating foci of human tumor cells from the murine stroma. Gene expression analysis was performed with Affymetrix Exon 1.0 ST microarrays. Hybridization, data normalization and statistical analysis were outsourced to GenoSplice Technology (Paris, France).
2015-12-31 | E-GEOD-58990 | biostudies-arrayexpress
Project description:BACKGROUND: Global gene expression analysis provides a comprehensive molecular characterization of non-small cell lung cancer. The aim of this study was to evaluate the feasibility of integrating expression profiling into routine clinical work-up by including minute bronchoscopic biopsies and develop a robust prognostic gene expression signature METHODS: Tissue samples from a series of 41 chemotherapy-naïve non-small cell lung cancer patients and 15 control patients with inflammatory lung diseases were obtained during routine clinical work-up and gene expression profiles were gained using a highly sensitive oligonucleotide array platform (Novachip ; 34'207 transcripts). Gene expression signatures were analyzed by correlation with histological and clinical parameters and validated on independent published datasets and immunohistochemistry. RESULTS: Tumor tissue classification based on the gene expression results was strongly dependent on the proportion of tumor cells present in the biopsies and showed an overall sensitivity of 80% and specificity of 89%. For prognostication we developed a metagene consisting of 13 genes, which was validated on 4 independent published datasets. The robustness of this metagene has been demonstrated by a virtual independence from tumor cells present in the biopsies. Furthermore, vascular endothelial growth factor-beta, one of the key prognostic genes was validated by immunohistochemistry on 508 independent tumor samples. CONCLUSIONS: The proposed strategy of integrating functional genomics into routine clinical work-up allows molecular tumor classification and prediction of survival in patients with non-small cell lung cancer of all stages and is suitable for an integration in the daily clinical practice. Keywords: Gene expression profiling for disease state analysis in lung cancer patients 56 lung biopsies, 4 different Phenotypes: NSCLC-squa., NSCLC-NOS, NSCLC-Adeno, Ctr.-Infl.
2008-04-10 | E-GEOD-11117 | biostudies-arrayexpress
Project description:To investigate the genes abnormally expressed in TOLF and their related biological processes and metabolic pathways through differential expression analysis and enrichment analysis by high-throughput sequencing of peripheral blood samples from TOLF patients and healthy people using human whole genome expression profile chip technology.To explore the mechanism of osteogenesis in OLF cells at the molecular level, and to provide molecular theoretical basis for the pathogenesis of OLF in clinical practice.
2022-05-01 | GSE181716 | GEO
Project description:BACKGROUND: Global gene expression analysis provides a comprehensive molecular characterization of non-small cell lung cancer. The aim of this study was to evaluate the feasibility of integrating expression profiling into routine clinical work-up by including minute bronchoscopic biopsies and develop a robust prognostic gene expression signature METHODS: Tissue samples from a series of 41 chemotherapy-naïve non-small cell lung cancer patients and 15 control patients with inflammatory lung diseases were obtained during routine clinical work-up and gene expression profiles were gained using a highly sensitive oligonucleotide array platform (Novachip ; 34'207 transcripts). Gene expression signatures were analyzed by correlation with histological and clinical parameters and validated on independent published datasets and immunohistochemistry. RESULTS: Tumor tissue classification based on the gene expression results was strongly dependent on the proportion of tumor cells present in the biopsies and showed an overall sensitivity of 80% and specificity of 89%. For prognostication we developed a metagene consisting of 13 genes, which was validated on 4 independent published datasets. The robustness of this metagene has been demonstrated by a virtual independence from tumor cells present in the biopsies. Furthermore, vascular endothelial growth factor-beta, one of the key prognostic genes was validated by immunohistochemistry on 508 independent tumor samples. CONCLUSIONS: The proposed strategy of integrating functional genomics into routine clinical work-up allows molecular tumor classification and prediction of survival in patients with non-small cell lung cancer of all stages and is suitable for an integration in the daily clinical practice. Keywords: Gene expression profiling for disease state analysis in lung cancer patients
2008-04-10 | GSE11117 | GEO
Project description:Our study provided a comprehensive mechanistic understanding of the neurotoxic effects induced by MA at multi-omics resolution, and offered valuable insights for enhancing the rational application of traditional Chinese medicine containing aconitum in clinical practice.
2024-07-03 | GSE261320 | GEO
Project description:The purpose of this study is to assess the feasibility of selecting personalized therapies for colon cancer patients who have failed standard treatments, using a new methodology based on the determination of a profile of chemosensitivity by comprehensive genetic expression analysis from tumor samples.
| 2133356 | ecrin-mdr-crc
Project description:Microarray gene expression (MAGE) signatures allow insights into the transcriptional processes of leukemias and may evolve as a molecular diagnostic test. Introduction of MAGE into clinical practice of leukemia diagnosis will require comprehensive assessment of variation due to the methodologies. For the first time, we present a study focused on analyzing the impact of different RNA preparation procedures on gene expression data for different subtypes of pediatric acute leukemias. Keywords: comparison of various total RNA preparation procedures
2007-05-31 | GSE7757 | GEO
Project description:The next generation of personalized medical treatment according to the type of personal genetic information are evolving rapidly. The genome analysis needs systematic infra and database based on personal genetic information. Therefore, a big data of genome-clinical information is important.
To determine the feasibility of the use of tumor’s molecular profiling and targeted therapies in the treatment of advanced cancer and to determine the clinical outcome(Response rate,PFS, duration of response and overall survival )of patients with advanced cancer, the investigators are going to take a tumor tissue of patients and process molecular profiling and receive molecular profile directed treatments.
| 2201468 | ecrin-mdr-crc
Project description:Technologies based on DNA microarrays have the potential to provide detailed information on genomic aberrations in tumor cells. In practice a major obstacle for quantitative detection of aberrations is the heterogeneity of clinical tumor tissue. Tumor tissue consists of a mixture of tumor cells and normal cells, including inflammatory and stromal cells, which may lead to a failure to detect aberrations in the tumor cells. Principal finding: Using SNP array data from 43 non-small cell lung cancer samples we have developed a bioinformatic algorithm that accurately models the fractions of normal and tumor cells in clinical tumor samples.
2009-08-11 | GSE16092 | GEO