Subclassification of lung squamous cell carcinoma
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE2088 | GEO | 2009/04/01
SECONDARY ACCESSION(S): PRJNA91781
REPOSITORIES: GEO
Similar Datasets
Project description:Two subclasses of lung squamous cell carcinoma with different gene expression profiles and prognosis identified by hierarchical clustering and validated by non-negative matrix factorization . BACKGROUND: Current clinical and histopathological criteria used to define lung squamous cell carcinomas (SCCs) are insufficient to predict clinical outcome. We attempted to make a clinically-useful classification based on gene expression profiling. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 48 surgically resected samples of lung SCC. 9 samples of lung adenocarcinoma and 30 of normal lung were also included to give a total of 87 samples analyzed. After gene filtering, the data were subjected to hierarchical clustering and consensus clustering with the non-negative matrix factorization (NMF) approach. FINDINGS: Initial analysis by hierarchical clustering allowed division of SCCs into two distinct subclasses. An additional independent round of hierarchical clustering and consensus clustering with the NMF approach provided a validation for the classification. Kaplan-Meier analysis with the log rank test pointed to a non-significant difference in survival (p=0.071) but the likelihood of survival to 6 years was significantly different between the two groups (40.5% vs 81.8%, p=0.014, Z-test). Biological process categories characteristic for each subclass were identified statistically and up-regulation of cell-proliferation related genes was evident in the subclass with a poor prognosis. In the subclass with the better survival, genes involved in differentiated intracellular functions, such as the MAPKKK cascade, ceramide metabolism, or regulation of transcription, were up-regulated. Keywords: repeat sample
2009-04-01 | E-GEOD-2088 | biostudies-arrayexpress
Project description:Background: It is still uncertain whether carcinoids of the lung and gastrointestinal (GI) tract have a common origin or whether they are closer in origin to carcinomas of the same organs. MicroRNA (miRNA) expression may clarify their nature and origin. Methods: First, to verify whether formalin-fixed paraffin-embedded (FFPE) samples retain the expression signature of the tissue, miRNA expression was compared between FFPE and frozen samples. Second, we selected surgically resected FFPE samples of pulmonary and GI carcinoids, as well as other types of tumors and normal tissues from each organ, and we compared the comprehensive expression patterns of miRNAs by microarray. These data were analyzed by hierarchical clustering and consensus clustering with non-negative matrix factorization (NMF). Results: The miRNA expression profiles of FFPE and frozen samples correlated quite well. In the first hierarchical clustering, most of the carcinoids formed one major cluster with loose subpartitioning into each organ type, while the second major cluster mainly comprised adenocarcinomas and normal tissues. The NMF approach largely supported hierarchical clustering. In the additional cluster analysis comparing carcinoids to small-cell lung carcinomas (SCLCs), carcinoids formed a distinct cluster, while SCLCs grouped together with pulmonary adenocarcinomas and normal lung tissues in another major cluster. Furthermore, we found some miRNAs that exhibited significant expression in carcinoids. Conclusion: Carcinoids had a characteristic pattern of miRNA expression, suggesting a common origin for pulmonary and GI carcinoids. The expression profiles were different in carcinoids and SCLCs, indicating distinct histogenesis of these neuroendocrine tumors.
2016-02-01 | GSE77380 | GEO
Project description:Background: Septic shock is a heterogeneous syndrome within which probably exist several biological subclasses. Discovery and identification of septic shock subclasses could provide the foundation for the design of more specifically targeted therapies. Herein we tested the hypothesis that pediatric septic shock subclasses can be discovered through genome-wide expression profiling. Methods: Genome-wide expression profiling was conducted using whole blood-derived RNA from 98 children with septic shock, followed by a series of bioinformatic approaches targeted at subclass discovery and characterization. Results: Three putative subclasses (subclasses A, B, and C) were initially identified based on an empiric, discovery-oriented expression filter and unsupervised hierarchical clustering. Statistical comparison of the 3 putative subclasses (ANOVA, Bonferonni correction, p < 0.05) identified 6,934 differentially regulated genes. K means clustering of these 6,934 genes generated 10 coordinately regulated gene clusters corresponding to multiple signaling and metabolic pathways, all of which were differentially regulated across the 3 subclasses. Leave one out cross validation procedures indentified 100 genes having the strongest predictive values for subclass identification. Forty-four of these 100 genes corresponded to signaling pathways relevant to the adaptive immune system and glucocorticoid receptor signaling, the majority of which were repressed in subclass A patients. Subclass A patients were also characterized by repression of genes corresponding to zinc-related biology. Phenotypic analyses revealed that subclass A patients were younger, had a higher illness severity, and a higher mortality rate than patients in subclasses B and C. Conclusions: Genome-wide expression profiling can identify pediatric septic shock subclasses having clinically relevant phenotypes. Expression data from 98 children with septic shock and 32 normal controls were generated using whole blood-derived RNA samples representing the first 24 hours of admission to the pediatric intensive care unit. The controls were used for normalization. Subsequently, we used the expression data to derive expression-based subclasses of patients using discovery oriented expression and statistical filters, followed by unsupervised hierarchical clustering.
2011-01-13 | E-GEOD-26440 | biostudies-arrayexpress
Project description:Background: Septic shock is a heterogeneous syndrome within which probably exist several biological subclasses. Discovery and identification of septic shock subclasses could provide the foundation for the design of more specifically targeted therapies. Herein we tested the hypothesis that pediatric septic shock subclasses can be discovered through genome-wide expression profiling. Methods: Genome-wide expression profiling was conducted using whole blood-derived RNA from 98 children with septic shock, followed by a series of bioinformatic approaches targeted at subclass discovery and characterization. Results: Three putative subclasses (subclasses A, B, and C) were initially identified based on an empiric, discovery-oriented expression filter and unsupervised hierarchical clustering. Statistical comparison of the 3 putative subclasses (ANOVA, Bonferonni correction, p < 0.05) identified 6,934 differentially regulated genes. K means clustering of these 6,934 genes generated 10 coordinately regulated gene clusters corresponding to multiple signaling and metabolic pathways, all of which were differentially regulated across the 3 subclasses. Leave one out cross validation procedures indentified 100 genes having the strongest predictive values for subclass identification. Forty-four of these 100 genes corresponded to signaling pathways relevant to the adaptive immune system and glucocorticoid receptor signaling, the majority of which were repressed in subclass A patients. Subclass A patients were also characterized by repression of genes corresponding to zinc-related biology. Phenotypic analyses revealed that subclass A patients were younger, had a higher illness severity, and a higher mortality rate than patients in subclasses B and C. Conclusions: Genome-wide expression profiling can identify pediatric septic shock subclasses having clinically relevant phenotypes.
2011-01-13 | GSE26440 | GEO
Project description:We report the identification of five subtypes of myelodysplastic syndromes (MDS) identified via unsupervised classification of DNA methylation (DNAm) profiles. Bisulfite padlock probe sequencing (BSPP) was used to measure DNAm of ~500,000 unique CpGs covering 140,749 non-overlapping regulatory regions across the genome in bone marrow DNA samples from 141 patients with MDS. Application of a non-negative matrix factorization (NMF) decomposition of DNAm profiles identified five consensus clusters described by five NMF components as the most stable grouping solution.
2019-09-30 | GSE129828 | GEO
Project description:Common approaches to gene signature discovery in single cell RNA-sequencing depend upon predefined structures like clustering or pseudo-temporal orderings, do not account for the sparsity of single cell data, or require prior normalization. We present single cell Hierarchical Poisson Factorization (scHPF), a Bayesian factorization method that adapts Hierarchical Poisson Factorization for de novo discovery of both continuous and discrete expression patterns in complex tissues. scHPF does not require prior normalization and outperforms other methods in benchmark datasets. Applied to single cell RNA-sequencing of the core and margin of a high-grade glioma, scHPF uncovers subtle regional expression biases within glioma subpopulations and an expression signature associated with inferior survival in glioblastoma.
2019-02-21 | GSE116621 | GEO
Project description:Methylation is closely involved in the development of various carcinomas. However, little datasets are available for small cell lung carcinoma (SCLC) due to the scarcity of fresh tumor samples. The aim of this study is to investigate the comprehensive genome-wide methylation profile of SCLC to predict the prognosis after surgical treatment. We investigated the high DNA methylated and low gene expression sites using 25 SCLC tumor tissues. First, we selected most differentially methylated CpG sites across the tumor tissues. Following hierarchical clustering (HC) and non-negative matrix factorization (NMF), gene ontology analysis was performed using DAVID software. Clustering of SCLC tumors led to the important identification of a CpG island methylator phenotype (CIMP) of SCLC, and showed that CIMP-high tumors had a significantly poorer prognosis (p = 0.001). Multivariate analysis revealed that postoperative chemotherapy, low neuroendocrine expression and the CIMP-low state were significantly good prognostic factors. Association analyses of methylation and gene expression provided 46 genes with significant correlation. Ontology studies to these genes showed that genes involved in extrinsic apoptosis pathway were suppressed, including TNFRSF1A, TNFRSF10A and TRADD, in CIMP-high tumors, prognosis of which was poorer. By comprehensive DNA methylation profiling, two distinct subgroups were identified to evoke a CIMP of SCLC as a useful marker for determination of treatment. Delineation of this phenotype may also be useful for the development of novel apoptosis-related chemotherapeutic agents for the treatment of an aggressive subtype of SCLC. Comprehensive genome-wide methylation analyses
2014-10-04 | E-GEOD-62021 | biostudies-arrayexpress
Project description:In order to identify key pathways associated with disease aggressiveness and therapeutics resistance in the most agrressive subset of PDAC, we analyzed gene expression profiling of tumor and adjacent non-tumor tissues from PDAC cases. Non-negative matrix factorization (NMF) clustering, using gene expression profile form PDAC tumors, revealed three patient subsets. A 142-gene signature specific to the subset with the worst patient survival, predicted prognosis and stratified patients with significantly different survival. Mechanistic and functional analyses of most aggressive subset revealed a HNF1B/Clusterin Axis negatively regulate pancreatic cancer progression and potentially be useful in designing novel strategies to attenuate disease progression. Affymetrix data from from these dataset were partially earlier submited by us as GEO accession#: GSE28735 and GSE 62452. The batch effect between the different sets of data was removed using Partek Genomic Suite and this normalized data was submitted to GEO in this submission.
2022-12-01 | GSE183795 | GEO
Project description:Purpose: The majority of patients with epithelial ovarian cancer (EOC) is diagnosed at advanced stage and has a poor prognosis. A proportion of these patients though will fare well, with a prognosis similar to patients with early stage disease while others die very quickly. Clinicopathological prognostic factors do not allow precise identification of these subgroups. Thus we have validated a molecular subclassification as prognostic factor in EOC. Experimental Design: One hundred ninety-four patients with EOC stage II to IV were characterized by whole-genome expression profiling of tumor tissues and classified using a published 112 gene-set, derived from a FIGO stage directed supervised classification approach. Results: The 194 tumor samples were classified into two subclasses of 95 (subclass 1) and 99 (subclass 2) tumors, grouping all 9 FIGO II tumors in subclass 1 (p=0.001). Subclass 2 (54% of advanced stage tumors) correlated significantly with peritoneal carcinomatosis and non-optimal debulking. Patients with subclass 2 tumors had a worse progression free survival (HR 1.67, p=0.005) by univariate analysis, but it was not an independent factor in multiple analysis. However, overall survival was impaired both, univariate (HR 3.68, p<0.001) and in models corrected for relevant clinicopathologic parameters (HR 3.13, p<0.001). Significance analysis of microarrays revealed 2,115 genes differentially expressed in both subclasses (FDR 5%). Conclusion: In this validation study we showed that in advanced-stage epithelial ovarian cancer two approximately equally large molecular subtypes exist, independent from classical clinocopathological parameters presenting with highly different whole genome expression profiles and an impressively different overall survival. Purpose: The majority of patients with epithelial ovarian cancer (EOC) is diagnosed at advanced stage and has a poor prognosis. A proportion of these patients though will fare well, with a prognosis similar to patients with early stage disease while others die very quickly. Clinicopathological prognostic factors do not allow precise identification of these subgroups. Thus we have validated a molecular subclassification as prognostic factor in EOC. Experimental Design: One hundred ninety-four patients with EOC stage II to IV were characterized by whole-genome expression profiling of tumor tissues and classified using a published 112 gene-set, derived from a FIGO stage directed supervised classification approach. Results: The 194 tumor samples were classified into two subclasses of 95 (subclass 1) and 99 (subclass 2) tumors, grouping all 9 FIGO II tumors in subclass 1 (p=0.001). Subclass 2 (54% of advanced stage tumors) correlated significantly with peritoneal carcinomatosis and non-optimal debulking. Patients with subclass 2 tumors had a worse progression free survival (HR 1.67, p=0.005) by univariate analysis, but it was not an independent factor in multiple analysis. However, overall survival was impaired both, univariate (HR 3.68, p<0.001) and in models corrected for relevant clinicopathologic parameters (HR 3.13, p<0.001). Significance analysis of microarrays revealed 2,115 genes differentially expressed in both subclasses (FDR 5%). Conclusion: In this validation study we showed that in advanced-stage epithelial ovarian cancer two approximately equally large molecular subtypes exist, independent from classical clinocopathological parameters presenting with highly different whole genome expression profiles and an impressively different overall survival. Targeted therapies in second line treatment gain more and more importance in managing recurrent or progressive carcinomas, particularly in ovarian cancer, a cancer entity characterized by a very high recurrence rate. One step ahead, it is necessary to define new therapeutic targets and to select patients who might benefit from these therapies already in first line settings. A robust molecular subclassification could provide both, an adequate patient selection and potential new targets. Notably, the validation of such a subclassification is of outstanding importance to obtain a reliable basis for a specific clinical decision and a rational for the expensive development of new targeted therapies. This work provides a comprehensive basis for both. The expression values of 204 epithelial ovarian cancer tissues are determined and used for the validation of a subclassification approach (Cancer Sci. 2009 Aug;100(8):1421-8.)
2014-01-01 | E-GEOD-49997 | biostudies-arrayexpress
Project description:Background: Septic shock heterogeneity has important implications for the conduct of clinical trials and individual patient management. We previously addressed this heterogeneity by indentifying 3 putative subclasses of children with septic shock based on a 100-gene expression signature corresponding to adaptive immunity and glucocorticoid receptor signaling. Herein we attempted to prospectively validate the existence of these gene expression-based subclasses in a validation cohort. Methods: Gene expression mosaics were generated from the 100 class-defining genes for 82 individual patients in the validation cohort. Patients were classified into 1 of 3 subclasses (“A”, “B”, or “C”) based on color and pattern similarity relative to reference mosaics generated from the original derivation cohort. Separate classifications were conducted by 21 individual clinicians and a computer-based algorithm. After subclassification the clinical database was mined for clinical phenotyping. Results: In the final consensus subclassification generated by clinicians, subclass A patients had a higher illness severity, as measured by illness severity scores and maximal organ failure, relative to subclasses B and C. The k coefficient across all possible inter-evaluator comparisons was 0.633. Similar observations were made based on the computer-generated subclassification. Patients in subclass A were also characterized by repression of a large number of genes having functional annotations related to zinc biology. Conclusions: We have validated the existence of subclasses of children with septic shock based on a biologically relevant, 100-gene expression signature. The subclasses can be indentified by clinicians without formal bioinformatics training, at a clinically relevant time point, and have clinically relevant phenotypic differences. Expression data from 82 children with septic shock and 21 normal controls were generated using whole blood-derived RNA samples representing the first 24 hours of admission to the pediatric intensive care unit. The controls were used for normalization. Subsequently, we used the expression data from 100 class defining genes to validate the existence of pediatric septic shock subclasses having phenotypic differences.
2011-01-13 | E-GEOD-26378 | biostudies-arrayexpress