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
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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.
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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: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.
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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.
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Project description:The present study screened potential genes related to lung adenocarcinoma, with the aim of further understanding disease pathogenesis. The GSE2514 dataset including 20 lung adenocarcinoma and 19 adjacent normal tissue samples from 10 patients with lung adenocarcinoma aged 45-73 years was downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) between the two groups were screened using the t-test. Potential gene functions were predicted using functional and pathway enrichment analysis, and protein-protein interaction (PPI) networks obtained from the STRING database were constructed with Cytoscape. Module analysis of PPI networks was performed through MCODE in Cytoscape. In total, 535 upregulated and 465 downregulated DEGs were identified. These included ATP5D, UQCRC2, UQCR11 and genes encoding nicotinamide adenine dinucleotide (NADH), which are mainly associated with mitochondrial ATP synthesis coupled electron transport, and which were enriched in the oxidative phosphorylation pathway. Other DEGs were associated with DNA replication (PRIM1, MCM3, and RNASEH2A), cell surface receptor-linked signal transduction and the enzyme-linked receptor protein signaling pathway (MAPK1, STAT3, RAF1, and JAK1), and regulation of the cytoskeleton and phosphatidylinositol signaling system (PIP5K1B, PIP5K1C, and PIP4K2B). Our findings suggest that DEGs encoding subunits of NADH, PRIM1, MCM3, MAPK1, STAT3, RAF1, and JAK1 might be associated with the development of lung adenocarcinoma.
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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:expression data from human lung adenocarcinoma cells A549
| PRJNA411770 | ENA