Expression data from lung adenocarcinoma
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Neumann J
PROVIDER: S-ECPF-GEOD-17475 | biostudies-other | 2010
REPOSITORIES: biostudies-other
Similar Datasets
Project description:The World Health Organization has subclassified adenocarcinoma based upon predominant cell morphology and growth pattern such as bronchioloalveolar carcinoma (BAC), adenocarcinoma with mixed subtypes (AC-mixed), and homogenously invasive tumors with a variety of histological patterns Pure invasive adenocarcinomas are often devoid of bronchioloalveolar morphology. The clinical importance of lung adenocarcinoma invasion is supported by several recent studies indicating that the risk of death in non-mucinous BAC is significantly lower than that of pure invasive tumors and in tumors with greater than 0.6 cm of fibrosis or linear invasion (J Thorac Oncol 6:244-285) To identify human tumor cell signatures associated with lung adenocarcinoma subtype and invasion, we performed microarray gene expression profiling of microdissected tumor cells noninvasive AC and AC-Mixed invasive tumors. 17 cases of noninvasive AC tumors and 23 cases of AC-mixed subtype invasive lung adenocarcinomas resected from 2002 to 2006 were examined (Columbia Lung Adenocarcinoma dataset)
| S-ECPF-GEOD-27716 | biostudies-other
Project description:Bcl-xL is an anti-apoptotic protein that is frequently found to be overexpressed in non-small cell lung cancer leading to an inhibition of apoptosis and poor prognosis. Recently, the role of miRNAs in regulating apoptosis and cell survival during tumorigenesis has become evident, with cancer cells showing perturbed expression of various miRNAs. We utilized miRNA microarrays to determine if miRNA dysregulation in bcl-xL silenced lung adenocarcinoma cells could be involved in regulating apoptotic behavior, and identified dysregulated miRNAs with putative targets involved in signal transduction pathways regulating apoptosis, cell proliferation and cell progression. Short interfering RNA-based transfection of A549 was carried out inducing a reduction in bcl-xL expression levels. 24 hours post-transfection total RNA was isolated using TRIzol reagent and hybridized onto Affymetrix GeneChip miRNA Arrays. A global miRNA expression profile was then established, which compared total RNA, extracted from siRNA-transfected and non-transfected A549 cells. All experiments were carried out with three independent biological replicates.
| S-ECPF-GEOD-47059 | biostudies-other
Project description:Affymetrix Human Genome U133A platform was used to obtain gene expression profiles of 28 pathologically and clinically well characterized adenocarcinomas of the lung. In addition, EGFR status was determined by fluorescent in situ hybridization and immunohistochemistry. The classification of the cases using a clustering algorithm applied to the expression data correlated significantly with the histopathological tumor grades. In contrast, no correlation of gene expression data and EGFR status could be detected.
2009-08-04 | GSE17475 | GEO
Project description:The major cause of cancer-related deaths in lung cancer patients is commonly attributed to its ability to metastasize to distant organs such as the bone, brain, liver and adrenal glands. The dysregulation in miRNA expression is believed to contribute toward the initiation and progression of metastasis through their capability to regulate target genes. In this study, two NSCLC sub-cell lines possessing opposing migration and invasion properties were established using serial transwell invasion assays, followed by miRNA microarray to obtain the overview of miRNA expression as well as identify important playes in metastasis. High and low invasive A549 sub-cell lines were established from heterogeneous parental population and selected through seven generations. Three replicates were used for RNA extraction and miRNA microarray.
| S-ECPF-GEOD-47056 | biostudies-other
Project description:Affymetrix Human Genome U133A platform was used to obtain gene expression profiles of 28 pathologically and clinically well characterized adenocarcinomas of the lung. In addition, EGFR status was determined by fluorescent in situ hybridization and immunohistochemistry. The classification of the cases using a clustering algorithm applied to the expression data correlated significantly with the histopathological tumor grades. In contrast, no correlation of gene expression data and EGFR status could be detected. In this study we evaluated the gene expression of 28 clinico-pathological well characterized lung adenocarcinomas.
2009-08-04 | E-GEOD-17475 | biostudies-arrayexpress
Project description:expression data from human lung adenocarcinoma cells A549
| PRJNA411770 | ENA
Project description:Expression profiles of 18,175 unique genes and three major genetic changes, p53, EGFR and K-ras, were investigated in 149 patients with non-small cell lung cancer (NSCLC), including 90 patients with adenocarcinomas (AD) to determine their relationships with various clinicopathologic features and Gene Ontology (GO) terms. Keywords: Disease state analysis Expression profiles in 149 patients with NSCLC, 9 patients with SCLC and 5 for normal lung tissue.
| S-ECPF-GEOD-11969 | biostudies-other
Project description:BackgroundDue to the high cost and low reproducibility of many microarray experiments, it is not surprising to find a limited number of patient samples in each study, and very few common identified marker genes among different studies involving patients with the same disease. Therefore, it is of great interest and challenge to merge data sets from multiple studies to increase the sample size, which may in turn increase the power of statistical inferences. In this study, we combined two lung cancer studies using microarray GeneChip, employed two gene shaving methods and a two-step survival test to identify genes with expression patterns that can distinguish diseased from normal samples, and to indicate patient survival, respectively.ResultsIn addition to common data transformation and normalization procedures, we applied a distribution transformation method to integrate the two data sets. Gene shaving (GS) methods based on Random Forests (RF) and Fisher's Linear Discrimination (FLD) were then applied separately to the joint data set for cancer gene selection. The two methods discovered 13 and 10 marker genes (5 in common), respectively, with expression patterns differentiating diseased from normal samples. Among these marker genes, 8 and 7 were found to be cancer-related in other published reports. Furthermore, based on these marker genes, the classifiers we built from one data set predicted the other data set with more than 98% accuracy. Using the univariate Cox proportional hazard regression model, the expression patterns of 36 genes were found to be significantly correlated with patient survival (p < 0.05). Twenty-six of these 36 genes were reported as survival-related genes from the literature, including 7 known tumor-suppressor genes and 9 oncogenes. Additional principal component regression analysis further reduced the gene list from 36 to 16.ConclusionThis study provided a valuable method of integrating microarray data sets with different origins, and new methods of selecting a minimum number of marker genes to aid in cancer diagnosis. After careful data integration, the classification method developed from one data set can be applied to the other with high prediction accuracy.
| S-EPMC476733 | biostudies-literature
Project description:Background: Sialic acid-binding immunoglobulin-type lectin (SIGLEC) family members are involved in regulating immune-cell activation, proliferation, and apoptosis, and they play an important role in tumor development. However, their expression and correlation with immune molecules in lung adenocarcinoma (LUAD) remain unclear. Methods: We utilized Gene Expression Profiling Interactive Analysis, Kaplan-Meier analysis, the limma package in R/Bioconductor, the University of California Santa Cruz Cancer Genome Browser, cBioPortal, STRING, Cytoscape, DAVID, and the Tumor Immune Estimation Resource for gene and protein profiling and analyses. Results: The results showed that SIGLEC10 and SIGLEC15 levels were upregulated in LUAD, whereas SIGLEC1, CD22 (SIGLEC2), CD33, myelin-associated glycoprotein (SIGLEC4), SIGLEC5, SIGLEC6, SIGLEC7, SIGLEC8, SIGLEC11, and SIGLEC14 levels were significantly downregulated, with their low expression associated with poor overall survival. Moreover, we observed high SIGLEC-mutation rates (22%) in LUAD patients, with SIGLEC functions determined as primarily involved in regulating the immune response, signal transduction, inflammatory response, and cell adhesion. Furthermore, we found that SIGLEC expression was significantly correlated with immune-cell infiltration, especially macrophages, neutrophils, and dendritic cells, and highly associated with immune molecules such as CD80, CD86, CD28, B-cell-activating factor, programmed cell death 1 ligand 2, and colony stimulating factor 1 receptor. Conclusion: These results provide insight into the potential molecular mechanism associated with SIGLEC-related development of LUAD, as well as clues for screening biomarkers and therapeutic targets.
| S-EPMC8008066 | biostudies-literature
Project description:The early symptoms of lung adenocarcinoma patients are inapparent, and the clinical diagnosis of lung adenocarcinoma is primarily through X-ray examination and pathological section examination, whereas the discovery of biomarkers points out another direction for the diagnosis of lung adenocarcinoma with the development of bioinformatics technology. However, it is not accurate and trustworthy to diagnose lung adenocarcinoma due to omics data with high-dimension and low-sample size (HDLSS) features or biomarkers produced by utilizing only single omics data. To address the above problems, the feature selection methods of biological analysis are used to reduce the dimension of gene expression data (GSE19188) and DNA methylation data (GSE139032, GSE49996). In addition, the Cartesian product method is used to expand the sample set and integrate gene expression data and DNA methylation data. The classification is built by using a deep neural network and is evaluated on K-fold cross validation. Moreover, gene ontology analysis and literature retrieving are used to analyze the biological relevance of selected genes, TCGA database is used for survival analysis of these potential genes through Kaplan-Meier estimates to discover the detailed molecular mechanism of lung adenocarcinoma. Survival analysis shows that COL5A2 and SERPINB5 are significant for identifying lung adenocarcinoma and are considered biomarkers of lung adenocarcinoma.
| S-EPMC9280023 | biostudies-literature