MicroRNA expression differentiates histology and predicts survival of lung cancer
Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: GSE15201
PROVIDER: PRJNA114959 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Abstract. Background. Treatment for lung adenocarcinoma (AD) and squamous cell carcinoma (SQ) is similar and shows comparable poor efficacy. We investigated whether microRNA (miR) expression profiles can differentiate histological subtypes, predict survival, and suggest histology-specific targets for treatment. Methods. We analyzed miR expression in 165 AD and 125 SQ tissue samples from the Environmental And Genetics in Lung cancer Etiology (EAGLE) study using a custom oligo array with 440 human mature antisense miRs. We compared miR expression profiles using t-tests and F-tests and accounted for multiple testing using global permutation tests. We categorized miR-targeted genes using Gene Ontology. We assessed the association of miR expression with clinical outcome using log-rank tests, Cox proportional hazards and survival risk prediction models, accounting for demographic and tumor characteristics. Findings. MiR expression profiles strongly differed between AD and SQ (global p<0.0001), particularly in the early stages. All members of the let-7 family and other oncosuppressor miRs were down-regulated in SQ, while oncomiRs like miR-21 were up-regulated in AD. The miRs with the strongest expression differences are located on chromosome loci most often altered in lung cancer (e.g., 3p21-22). Major findings were confirmed by QRT-PCR in EAGLE samples and in an independent cohort. In SQ, low expression of miRs down-regulated in the histology comparison was associated with 1.2 to 3.6-fold increased mortality risk. A 5-miR signature significantly predicted survival for SQ. Interpretation. We identified a miR expression profile that strongly differentiated AD from SQ and had prognostic implications. These findings may lead to histology-based therapeutic approaches.
2010-03-11 | GSE15201 | GEO
Project description:PURPOSE:The molecular drivers that determine histology in lung cancer are largely unknown. We investigated whether microRNA (miR) expression profiles can differentiate histologic subtypes and predict survival for non-small cell lung cancer. EXPERIMENTAL DESIGN:We analyzed miR expression in 165 adenocarcinoma and 125 squamous cell carcinoma (SQ) tissue samples from the Environment And Genetics in Lung cancer Etiology (EAGLE) study using a custom oligo array with 440 human mature antisense miRs. We compared miR expression profiles using t tests and F tests and accounted for multiple testing using global permutation tests. We assessed the association of miR expression with tobacco smoking using Spearman correlation coefficients and linear regression models, and with clinical outcome using log-rank tests, Cox proportional hazards, and survival risk prediction models, accounting for demographic and tumor characteristics. RESULTS:MiR expression profiles strongly differed between adenocarcinoma and SQ (P(global) < 0.0001), particularly in the early stages, and included miRs located on chromosome loci most often altered in lung cancer (e.g., 3p21-22). Most miRs, including all members of the let-7 family, were downregulated in SQ. Major findings were confirmed by quantitative real time-polymerase chain reaction (qRT-PCR) in EAGLE samples and in an independent set of lung cancer cases. In SQ, the low expression of miRs that are downregulated in the histology comparison was associated with 1.2- to 3.6-fold increased mortality risk. A five-miR signature significantly predicted survival for SQ. CONCLUSIONS:We identified a miR expression profile that strongly differentiated adenocarcinoma from SQ and had prognostic implications. These findings may lead to histology-based therapeutic approaches.
| S-EPMC3163170 | biostudies-literature
Project description:Migrated from 1.6 id: 1015897590491013 GEDP id: 760 In current clinical practice, histology-based grading of diffuse infiltrative gliomas is the best predictor of patient survival time. Yet histology provides little insight into the underlying biology of gliomas and is limited in its ability to identify and guide new molecularly targeted therapies. We have performed large-scale gene expression analysis using the Affymetrix HG U133 oligonucleotide arrays on 85 diffuse infiltrating gliomas of all histologic types to assess whether a gene expression-based, histology-independent classifier is predictive of survival and to determine whether gene expression signatures provide insight into the biology of gliomas. We found that gene expression-based grouping of tumors is a more powerful survival predictor than histologic grade or age. The poor prognosis samples could be grouped into three different poor prognosis groups, each with distinct molecular signatures. We further describe a list of 44 genes whose expression patterns reliably classify gliomas into previously unrecognized biological and prognostic groups: these genes are outstanding candidates for use in histology-independent classification of high-grade gliomas. The ability of the large scale and 44 gene set expression signatures to group tumors into strong survival groups was validated with an additional external and independent data set from another institution composed of 50 additional gliomas. This demonstrates that large-scale gene expression analysis and subset analysis of gliomas reveals unrecognized heterogeneity of tumors and is efficient at selecting prognosis-related gene expression differences which are able to be applied across institutions. nelso-00262 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG-U133A, HG-U133B Organism: Homo sapiens (ncbitax) Material Types: total RNA, synthetic_RNA, organism_part, whole_organism Disease States: Glioma, Glioblastoma, Oligodendroglial Tumor, astrocytomas
2016-06-14 | E-GEOD-83294 | biostudies-arrayexpress
Project description:Migrated from 1.6 id: 1015897590491013 GEDP id: 760 In current clinical practice, histology-based grading of diffuse infiltrative gliomas is the best predictor of patient survival time. Yet histology provides little insight into the underlying biology of gliomas and is limited in its ability to identify and guide new molecularly targeted therapies. We have performed large-scale gene expression analysis using the Affymetrix HG U133 oligonucleotide arrays on 85 diffuse infiltrating gliomas of all histologic types to assess whether a gene expression-based, histology-independent classifier is predictive of survival and to determine whether gene expression signatures provide insight into the biology of gliomas. We found that gene expression-based grouping of tumors is a more powerful survival predictor than histologic grade or age. The poor prognosis samples could be grouped into three different poor prognosis groups, each with distinct molecular signatures. We further describe a list of 44 genes whose expression patterns reliably classify gliomas into previously unrecognized biological and prognostic groups: these genes are outstanding candidates for use in histology-independent classification of high-grade gliomas. The ability of the large scale and 44 gene set expression signatures to group tumors into strong survival groups was validated with an additional external and independent data set from another institution composed of 50 additional gliomas. This demonstrates that large-scale gene expression analysis and subset analysis of gliomas reveals unrecognized heterogeneity of tumors and is efficient at selecting prognosis-related gene expression differences which are able to be applied across institutions.
2016-06-14 | GSE83294 | GEO
Project description:Histological classification of gliomas guides treatment decisions. Because of the high interobserver variability, we aimed to improve classification by performing gene expression profiling on a large cohort of glioma samples of all histological subtypes and grades. The seven identified intrinsic molecular subtypes are different from histological subgroups and correlate better to patient survival. Our data indicate that distinct molecular subgroups clearly benefit from treatment. Specific genetic changes (EGFR amplification, IDH1 mutation, 1p/19q LOH) segregate in -and may drive- the distinct molecular subgroups. Our findings were validated on three large independent sample cohorts (TCGA, REMBRANDT, and GSE12907). We provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histology. 276 glioma samples of all histology, 8 control samples
2010-04-26 | E-GEOD-16011 | biostudies-arrayexpress
Project description:We show that the microRNA transcriptome undergoes a global state transition during the initiation and progression of acute myeloid leukemia, and accurately predicts time to disease development.
2021-09-08 | GSE173785 | GEO
Project description:Survival rates of cancer patients vary widely within and between malignancies. While genetic aberrations are at the root of all cancers, individual genomic features cannot explain these distinct disease outcomes. In contrast, intra-tumour heterogeneity (ITH) has the potential to elucidate pan-cancer survival rates and the biology that drives cancer prognosis1,2. Unfortunately, a comprehensive and effective framework to measure ITH across cancers is missing3. Here, we introduce a scalable measure of chromosomal copy number heterogeneity that predicts patient survival across cancers. We show that the level of ITH can be derived from a single-sample copy number profile. Using gene-expression data we demonstrate that ongoing chromosomal instability underlies the observed heterogeneity. Analysing 11,534 primary cancer samples from 35 different malignancies, we find that copy number heterogeneity can be accurately deduced and predicts cancer survival across tissues of origin and stages of disease. Our results provide a unifying molecular explanation for the different survival rates observed between cancer types.
| EGAS00001004702 | EGA
Project description:Purpose:The Vimentin gene plays a pivotal role in epithelial-to-mesenchymal transition (EMT) and is known to be over-expressed in the prognostically poor basal-like breast cancer subtype. Recent studies have reported Vimentin DNA methylation in association with poor clinical outcomes in other solid tumors, but not in breast cancer. We therefore quantified Vimentin DNA methylation in breast tumors and matched normal pairs in association with gene expression and survival in a cohort of 83 breast cancer patients. Materials and Methods:Vimentin methylation was quantified in 14 breast cell lines, 83 breast tumors, and 57 matched normal pairs using MALDI-TOF mass spectrometry. Gene expression data via qRT-PCR in cell lines, and oligo microarray data from breast tissues was correlated with percent methylation in the Vimentin promoter. A threshold of 20 percent average methylation was set for bivariate and multivariate tests of association between methylation and tumor subtype, tumor histopathology, and survival. Results:Vimentin was differentially methylated in luminal breast cancer cell lines, and in luminal A, luminal B and HER2+ breast tumor subtypes, but was rare in basal-like cell lines and tumors. Increased methylation was strongly correlated with decreased mRNA expression in cell lines, and had a moderate inverse correlation in breast tumors. Importantly, Vimentin methylation predicted poor overall survival independent of race, subtype, stage, nodal status or estrogen receptor positivity. Conclusion:Vimentin methylation predicts overall survival in breast cancer patients and holds promise as a prognostic biomarker for guiding treatment and prophylaxis. reference x sample
2013-02-19 | E-GEOD-35629 | biostudies-arrayexpress
Project description:Histological classification of gliomas guides treatment decisions. Because of the high interobserver variability, we aimed to improve classification by performing gene expression profiling on a large cohort of glioma samples of all histological subtypes and grades. The seven identified intrinsic molecular subtypes are different from histological subgroups and correlate better to patient survival. Our data indicate that distinct molecular subgroups clearly benefit from treatment. Specific genetic changes (EGFR amplification, IDH1 mutation, 1p/19q LOH) segregate in -and may drive- the distinct molecular subgroups. Our findings were validated on three large independent sample cohorts (TCGA, REMBRANDT, and GSE12907). We provide compelling evidence that expression profiling is a more accurate and objective method to classify gliomas than histology.
2010-04-26 | GSE16011 | GEO
Project description:ABI1-based 7-gene expression signature predicts breast cancer metastasis and survival
| PRJNA670946 | ENA