Homo sapiens
Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: PXD000853
PROVIDER: PRJNA263446 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Cancer results from processes prone to selective pressure and dysregulation acting along the sequence-to-phenotype continuum DNA→RNA→Protein→Disease. However, the extent to which cancer is a manifestation of the proteome is unknown. Here we present an integrated omic map representing non-small cell lung carcinoma. Dysregulated proteins not previously implicated as cancer drivers are encoded throughout the genome including but not limited to regions of recurrent DNA amplification/deletion. Clustering reveals signatures composed of metabolism proteins particularly highly recapitulated between patient-matched primary and xenograft tumours. Interrogation of The Cancer Genome Atlas reveals cohorts of patients with lung and other cancers that have DNA alterations in genes encoding the signatures, and this was accompanied by differences in survival. The recognition of genome and proteome alterations as related products of selective pressure driving the disease phenotype may be a general approach to uncover and group together cryptic, polygenic disease drivers. Total RNAs from xenografts, primary tumor, and normal adjacent tissues were amplified by DASL kit and hybridized to Illumina HT12v4 chip
2014-10-09 | E-GEOD-62113 | biostudies-arrayexpress
Project description:Cancer results from processes prone to selective pressure and dysregulation acting along the sequence-to-phenotype continuum DNA→RNA→Protein→Disease. However, the extent to which cancer is a manifestation of the proteome is unknown. Here we present an integrated omic map representing non-small cell lung carcinoma. Dysregulated proteins not previously implicated as cancer drivers are encoded throughout the genome including but not limited to regions of recurrent DNA amplification/deletion. Clustering reveals signatures composed of metabolism proteins particularly highly recapitulated between patient-matched primary and xenograft tumours. Interrogation of The Cancer Genome Atlas reveals cohorts of patients with lung and other cancers that have DNA alterations in genes encoding the signatures, and this was accompanied by differences in survival. The recognition of genome and proteome alterations as related products of selective pressure driving the disease phenotype may be a general approach to uncover and group together cryptic, polygenic disease drivers.
2014-10-09 | GSE62113 | GEO
Project description:Background: Global gene expression profiling has been widely used in lung cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis and therapy response. So far, the value of these multi-gene signatures in clinical practice is unclear and the biological importance of individual genes is difficult to assess as the published signatures virtually do not overlap. Methods: Here we describe a novel single institute cohort, including 196 non-small lung cancer (NSCLC) cases with clinical information and long-term follow-up, which was used as a training set to screen for single genes with prognostic impact. The top 450 gene probe sets identified using a univariate Cox regression model (significance level p<0.01) were tested in a meta-analysis including five publicly available independent lung cancer cohorts (n=860). Results: The meta-analysis revealed that 17 probe sets were significantly associated with survival (p<0.0005) with a false discovery rate of 1%. The prognostic impact of one of these genes, the cell adhesion molecule 1 (CADM1), was confirmed by use of immunohistochemistry on a tissue microarray including 355 NSCLC samples. Low CADM1 protein expression was associated with shorter survival (p=0.028), with particular influence in the adenocarcinoma patient subgroup (p=0.002). Conclusions: We were able to validate single genes with independent prognostic impact using a novel NSCLC cohort together with a meta-analysis approach. CADM1 was identified as an immunohistochemical marker with a potential application in clinical diagnostics. Fresh frozen tissue of 196 consecutive NSCLC patients, operated between 1995 and 2005 were analyzed using Affymetrix microarrays HG-U133-Plus2. Clinical data were retrieved from the regional lung cancer registry.
2012-10-12 | E-GEOD-37745 | biostudies-arrayexpress
Project description:Background: Global gene expression profiling has been widely used in lung cancer research to identify clinically relevant molecular subtypes as well as to predict prognosis and therapy response. So far, the value of these multi-gene signatures in clinical practice is unclear and the biological importance of individual genes is difficult to assess as the published signatures virtually do not overlap. Methods: Here we describe a novel single institute cohort, including 196 non-small lung cancer (NSCLC) cases with clinical information and long-term follow-up, which was used as a training set to screen for single genes with prognostic impact. The top 450 gene probe sets identified using a univariate Cox regression model (significance level p<0.01) were tested in a meta-analysis including five publicly available independent lung cancer cohorts (n=860). Results: The meta-analysis revealed that 17 probe sets were significantly associated with survival (p<0.0005) with a false discovery rate of 1%. The prognostic impact of one of these genes, the cell adhesion molecule 1 (CADM1), was confirmed by use of immunohistochemistry on a tissue microarray including 355 NSCLC samples. Low CADM1 protein expression was associated with shorter survival (p=0.028), with particular influence in the adenocarcinoma patient subgroup (p=0.002). Conclusions: We were able to validate single genes with independent prognostic impact using a novel NSCLC cohort together with a meta-analysis approach. CADM1 was identified as an immunohistochemical marker with a potential application in clinical diagnostics.
2012-10-12 | GSE37745 | GEO
Project description:Non-small cell lung cancer (NSCLC) death rates exceed the next 3 prevalent cancers combined; however, most NSCLC tumors lack actionable mutations. Recent studies of NSCLC and other cancers revealed profound proteome remodelling with prognostic impact that is not fully predicted by DNA or RNA analyses. These revelations portend proteome-based cancer classification and treatment. This will require model systems that recapitulate tumor proteomes and phenotypes. A subset (~35%) of the most aggressive NSCLC can form a patient-derived xenograft (PDX). We generated 137 PDX models of aggressive NSCLC, which represent the histological, genome, transcriptome, and DNA methylation features and proteome remodelling of primary NSCLC. The models indicate 3 lung adenocarcinoma and 2 squamous cell carcinoma proteotypes that are associated with different patient outcomes, protein-phosphotyrosine profiles, candidate targets, and in adenocarcinoma, distinct stromal immune features. The PDX resource will foster proteome-directed stratification and development of new treatments for aggressive NSCLC.
2021-11-23 | GSE166998 | GEO
Project description:Non-small cell lung cancer (NSCLC) death rates exceed the next 3 prevalent cancers combined; however, most NSCLC tumors lack actionable mutations. Recent studies of NSCLC and other cancers revealed profound proteome remodelling with prognostic impact that is not fully predicted by DNA or RNA analyses. These revelations portend proteome-based cancer classification and treatment. This will require model systems that recapitulate tumor proteomes and phenotypes. A subset (~35%) of the most aggressive NSCLC can form a patient-derived xenograft (PDX). We generated 137 PDX models of aggressive NSCLC, which represent the histological, genome, transcriptome, and DNA methylation features and proteome remodelling of primary NSCLC. The models indicate 3 lung adenocarcinoma and 2 squamous cell carcinoma proteotypes that are associated with different patient outcomes, protein-phosphotyrosine profiles, candidate targets, and in adenocarcinoma, distinct stromal immune features. The PDX resource will foster proteome-directed stratification and development of new treatments for aggressive NSCLC.
2021-11-23 | GSE166997 | GEO
Project description:The tumor microenvironment strongly influences cancer development, progression and metastasis. The role of carcinoma-associated fibroblasts (CAFs) in these processes and their clinical impact has not been studied systematically in non-small cell lung carcinoma (NSCLC). We established primary cultures of CAFs and matched normal fibroblasts (NFs) from 15 resected NSCLC. We demonstrate that CAFs have greater ability than NFs to enhance the tumorigenicity of lung cancer cell lines. Microarray gene expression analysis of the 15 matched CAF and NF cell lines identified 46 differentially expressed genes, encoding for proteins that are significantly enriched for extracellular proteins regulated by the TGF-beta signaling pathway. We have identified a subset of 11 genes that formed a prognostic gene expression signature, which was validated in multiple independent NSCLC microarray datasets. Functional annotation using protein-protein interaction analyses of these and published cancer stroma-associated gene expression changes revealed prominent involvement of the focal adhesion and MAPK signalling pathways. Fourteen (30%) of the 46 genes also were differentially expressed in laser-capture micro-dissected corresponding primary tumor stroma compared to the matched normal lung. Six of these 14 genes could be induced by TGF-beta1 in NF. The results establish the prognostic impact of CAF-associated gene expression changes in NSCLC patients. This SuperSeries is composed of the following subset Series: GSE22862: Prognostic Gene Expression Signature of Carcinoma Associated Fibroblasts in Non-Small Cell Lung Cancer [expression profiling_CAFs] GSE22863: Prognostic Gene Expression Signature of Carcinoma Associated Fibroblasts in Non-Small Cell Lung Cancer [expression profiling_NSCLC stroma] GSE27284: Prognostic Gene Expression Signature of Carcinoma Associated Fibroblasts in Non-Small Cell Lung Cancer [methylation profiling] GSE27289: Prognostic Gene Expression Signature of Carcinoma Associated Fibroblasts in Non-Small Cell Lung Cancer [genome variation profiling]
2011-04-08 | E-GEOD-22874 | biostudies-arrayexpress
Project description:Through multidimensional genomic/protein multiomics analysis and clinical information integration of cancer tissue samples, a prognostic method for lung cancer, including non-small cell lung cancer (NSCLC), is developed and applied to precision medical care after discovering new drug targets.
2024-11-01 | PXD053903 | JPOST Repository
Project description:Through multidimensional genomic/protein multiomics analysis and clinical information integration of cancer tissue samples, a prognostic method for lung cancer, including non-small cell lung cancer (NSCLC), is developed and applied to precision medical care after discovering new drug targets.
2024-11-01 | PXD053921 | JPOST Repository
Project description:Through multidimensional genomic/protein multiomics analysis and clinical information integration of cancer tissue samples, a prognostic method for lung cancer, including non-small cell lung cancer (NSCLC), is developed and applied to precision medical care after discovering new drug targets.
2024-11-01 | PXD053969 | JPOST Repository