Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA189985 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:To identify potential markers for distinction of Small cell (SCLC) from large cell neuroendocrine (LCNEC) carcinoma, we have conducted the gene expression profiling on precisely isolated cancer cells and bronchial epithelial cells from these tumours and normal background lung. After statistical analysis and finding highly differentially expressed genes between these tumour groups, we performed qRT-PCR to delineate markers to test at protein level using immunohistochemistry. Markers showing statistical significance at qRT-PCR level were taken forward to stain tissue microarray sections of SCLC and LCNEC to further delineate markers. This resulted in identification of BAI3, CDX2, VIL1 and CD99 as potential candidates. These markers were then taken forward to stain whole sections of tumour sample available in house (University Hospitals Coventry and Warwickshire) which resulted in elimination of CD99. Thus, BAI3, CDX2 and VIL1 were taken further forward to stain tumour samples acquired externally in UK. Four Small cell lung carcinoma, four large cell neuroendocrine carcinoma and seven background lungs were laser microdissected and profiled on Agilent SurePrint G3 human gene expression 8x60K single channel array to identify potential markers for their classification and validated using qRT-PCR and immunohistochemistry.
2013-11-01 | E-GEOD-44447 | biostudies-arrayexpress
Project description:To identify potential markers for distinction of Small cell (SCLC) from large cell neuroendocrine (LCNEC) carcinoma, we have conducted the gene expression profiling on precisely isolated cancer cells and bronchial epithelial cells from these tumours and normal background lung. After statistical analysis and finding highly differentially expressed genes between these tumour groups, we performed qRT-PCR to delineate markers to test at protein level using immunohistochemistry. Markers showing statistical significance at qRT-PCR level were taken forward to stain tissue microarray sections of SCLC and LCNEC to further delineate markers. This resulted in identification of BAI3, CDX2, VIL1 and CD99 as potential candidates. These markers were then taken forward to stain whole sections of tumour sample available in house (University Hospitals Coventry and Warwickshire) which resulted in elimination of CD99. Thus, BAI3, CDX2 and VIL1 were taken further forward to stain tumour samples acquired externally in UK.
2013-11-01 | GSE44447 | GEO
Project description:Two prognostically significant subtypes of high-grade lung neuroendocrine tumors independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles. BACKGROUND: Classification of high-grade neuroendocrine tumors (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumors and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analyzed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumor samples. FINDINGS: Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analyzed clustered with this good-prognosis group. INTERPRETATION: Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterized markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumors, and provide important insights into their underlying biology. Keywords: other
2004-02-10 | E-GEOD-1037 | biostudies-arrayexpress
Project description:Two prognostically significant subtypes of high-grade lung neuroendocrine tumors independent of small-cell and large-cell neuroendocrine carcinomas identified by gene expression profiles. BACKGROUND: Classification of high-grade neuroendocrine tumors (HGNT) of the lung currently recognises large-cell neuroendocrine carcinoma (LCNEC) and small-cell lung carcinoma (SCLC) as distinct groups. However, a similarity in histology for these two carcinomas and uncertain clinical course have led to suggestions that a single HGNT classification would be more appropriate. Gene expression profiling, which can reproduce histopathological classification, and often defines new subclasses with prognostic significance, can be used to resolve HGNT classification. METHODS: We used cDNA microarrays with 40386 elements to analyze the gene expression profiles of 38 surgically resected samples of lung neuroendocrine tumors and 11 SCLC cell lines. Samples of large-cell carcinoma, adenocarcinoma, and normal lung were also included to give a total of 105 samples analyzed. The data were subjected to filtering to yield informative genes before unsupervised hierarchical clustering that identified relatedness of tumor samples. FINDINGS: Distinct groups for carcinoids, large-cell carcinoma, adenocarcinoma, and normal lung were readily identified. However, we were unable to distinguish LCNEC from SCLC by gene expression profiling. Three independent rounds of unsupervised hierarchical clustering consistently divided SCLC samples into two main groups with LCNEC samples largely integrated with these groups. Furthermore, patients in one of the groups identified by clustering had a significantly better clinical outcome than the other (83% vs 12% survived for 5 years; p=0.0094. None of the highly proliferative SCLC cell lines subsequently analyzed clustered with this good-prognosis group. INTERPRETATION: Our findings show that HGNT of the lung can be classified into two groups independent of SCLC and LCNEC. To this end, we have identified many genes, some of which encode well-characterized markers of cancer that distinguish the HGNT groups. These results have implications for the diagnosis, classification, and treatment of lung neuroendocrine tumors, and provide important insights into their underlying biology. Keywords: other
2004-02-11 | GSE1037 | GEO
Project description:Lung cancer is the worldwide leading cause of death from cancer. DNA methylation in gene promoter regions is a major mechanism of gene expression regulation that may promote tumorigenesis. Experimental Design Whole-genome DNA methylation analysis using 450K Illumina BeadArrays was performed on 12 normal lung tissues and 124 tumors including 83 adenocarcinomas, 23 squamous cell carcinomas (SqCC), one adenosquamous cancer, five large cell carcinomas, nine large cell neuroendocrine carcinomas (LCNEC), and three small cell carcinomas (SCLC). Complimentary gene expression analyses was performed on 117 of the 124 tumors using Illumina HT12 V4 arrays (reported here).
2014-10-05 | GSE60644 | GEO
Project description:Lung cancer is the worldwide leading cause of death from cancer. DNA methylation in gene promoter regions is a major mechanism of gene expression regulation that may promote tumorigenesis. Experimental Design Whole-genome DNA methylation analysis using 450K Illumina BeadArrays was performed on 12 normal lung tissues and 124 tumors including 83 adenocarcinomas, 23 squamous cell carcinomas (SqCC), one adenosquamous cancer, five large cell carcinomas, nine large cell neuroendocrine carcinomas (LCNEC), and three small cell carcinomas (SCLC). Complimentary gene expression analyses was performed on 117 of the 124 tumors using Illumina HT12 V4 arrays (reported here). Gene expression profiling of 117 lung carcinomas using Illumina HT-12 V4 microarrays.
2014-10-05 | E-GEOD-60644 | biostudies-arrayexpress
Project description:Lung cancer is the worldwide leading cause of death from cancer. DNA methylation in gene promoter regions is a major mechanism of gene expression regulation that may promote tumorigenesis. However, whether clinically relevant subgroups based on DNA methylation patterns exist in lung cancer is not well studied. We performed whole-genome methylation analysis using 450K Illumina BeadArrays on 124 tumors including 83 adenocarcinomas, 23 squamous cell carcinomas, one adenosquamous cancer, five large cell carcinomas, nine large cell neuroendocrine carcinomas (LCNEC), three small cell carcinomas (SCLC) and 12 normal lung tissues. Unsupervised class discovery was performed to identify DNA methylation subgroups with clinicopathological and molecular features. Subgroups were validated in two independent NSCLC cohorts. Unsupervised analysis identified five DNA methylation subgroups (epitypes). One epitype was distinctly associated with neuroendocrine tumors (LCNEC and SCLC). For adenocarcinoma, in both discovery and validation cohorts, remaining four epitypes were associated with differences in clinicopathological and molecular features, including global hypomethylation, promoter hypermethylation, copy number alterations, expression of proliferation-associated genes, association with unsupervised and supervised gene expression phenotypes, KRAS, TP53, KEAP1, SMARCA4, and STK11 mutations, smoking history, and patient outcome. Based on a multicohort approach we conducted a comprehensive survey of genome-wide DNA methylation in lung cancer, identifying a distinct neuroendocrine epitype and four adenocarcinoma epitypes associated with molecular and clinicopathological characteristics, and patient outcome. Our results bring further understanding of the epigenetic characteristics and molecular diversity in lung cancer generally and in adenocarcinoma specifically. Genome-wide DNA methylation analysis of 124 lung carcinomas and 12 normal lung tissues using Illumina Human Methylation 450K v1.0 Beadchips.
2014-10-05 | E-GEOD-56044 | biostudies-arrayexpress
Project description:Lung cancer is the worldwide leading cause of death from cancer. DNA methylation in gene promoter regions is a major mechanism of gene expression regulation that may promote tumorigenesis. However, whether clinically relevant subgroups based on DNA methylation patterns exist in lung cancer is not well studied. We performed whole-genome methylation analysis using 450K Illumina BeadArrays on 124 tumors including 83 adenocarcinomas, 23 squamous cell carcinomas, one adenosquamous cancer, five large cell carcinomas, nine large cell neuroendocrine carcinomas (LCNEC), three small cell carcinomas (SCLC) and 12 normal lung tissues. Unsupervised class discovery was performed to identify DNA methylation subgroups with clinicopathological and molecular features. Subgroups were validated in two independent NSCLC cohorts. Unsupervised analysis identified five DNA methylation subgroups (epitypes). One epitype was distinctly associated with neuroendocrine tumors (LCNEC and SCLC). For adenocarcinoma, in both discovery and validation cohorts, remaining four epitypes were associated with differences in clinicopathological and molecular features, including global hypomethylation, promoter hypermethylation, copy number alterations, expression of proliferation-associated genes, association with unsupervised and supervised gene expression phenotypes, KRAS, TP53, KEAP1, SMARCA4, and STK11 mutations, smoking history, and patient outcome. Based on a multicohort approach we conducted a comprehensive survey of genome-wide DNA methylation in lung cancer, identifying a distinct neuroendocrine epitype and four adenocarcinoma epitypes associated with molecular and clinicopathological characteristics, and patient outcome. Our results bring further understanding of the epigenetic characteristics and molecular diversity in lung cancer generally and in adenocarcinoma specifically.
2014-10-05 | GSE56044 | GEO
Project description:We analyzed chromatin accessibility, scATAC-seq and RNA-seq across neuroendocrine carcinomas from distinct anatomic origins including Merkel cell carcinomas, neuroendocrine prostate cancer, small cell lung cancer and gastrointestinal NECs.
2024-03-27 | GSE262624 | GEO
Project description:We analyzed chromatin accessibility, scATAC-seq and RNA-seq across neuroendocrine carcinomas from distinct anatomic origins including Merkel cell carcinomas, neuroendocrine prostate cancer, small cell lung cancer and gastrointestinal NECs.
2024-03-27 | GSE262623 | GEO