DNA Microarray-based Gene Expression Research Articles

DNA Microarray-Based Global Gene Expression Profiling in Human Amniotic Epithelial Cells Predicts the Potential of Microalgae-Derived Squalene for the Nervous System and Metabolic Health

In recent years, perinatal stem cells, such as human amniotic epithelial cells (hAECs), have attracted increasing interest as a novel tool of stem cell-based high-throughput drug screening. In the present study, we investigated the bioactivities of squalene (SQ) derived from ethanol extract (99.5%) of a microalgae Aurantiochytrium Sp. (EEA-SQ) in hAECs using whole-genome DNA microarray analysis. Tissue enrichment analysis showed that the brain was the most significantly enriched tissue by the differentially expressed genes (DEGs) between EEA-SQ-treated and control hAECs. Further gene set enrichment analysis and tissue-specific functional analysis revealed biological functions related to nervous system development, neurogenesis, and neurotransmitter modulation. Several adipose tissue-specific genes and functions were also enriched. Gene-disease association analysis showed nervous system-, metabolic-, and immune-related diseases were enriched. Altogether, our study suggests the potential health benefits of microalgae-derived SQ and we would further encourage investigation in EEA-SQ and its derivatives as potential therapeutics for nervous system- and metabolism-related diseases.

Application of TruGraf v1: A Novel Molecular Biomarker for Managing Kidney Transplant Recipients With Stable Renal Function

TruGraf v1 is a laboratory-developed DNA microarray-based gene expression blood test to enable proactive noninvasive serial assessment of kidney transplant recipients with stable renal function. It has been previously validated in patients identified as Transplant eXcellence (TX: stable serum creatinine, normal biopsy results, indicative of immune quiescence), and not-TX (renal dysfunction and/or rejection on biopsy results). TruGraf v1 is intended for use in subjects with stable renal function to measure the immune status as an alternative to invasive, expensive, and risky surveillance biopsies. Materials and MethodsIn this study, simultaneous blood tests and clinical assessments were performed in 192 patients from 7 transplant centers to evaluate TruGraf v1. The molecular testing laboratory was blinded to renal function and biopsy results. ResultsOverall, TruGraf v1 accuracy (concordance between TruGraf v1 result and clinical and/or histologic assessment) was 74% (142/192), and a result of TX was accurate in 116 of 125 (93%). The negative predictive value for TruGraf v1 was 90%, with a sensitivity 74% and specificity of 73%. Results did not significantly differ in patients with a biopsy-confirmed diagnosis vs those without a biopsy. ConclusionsTruGraf v1 can potentially support a clinical decision enabling unnecessary surveillance biopsies with high confidence, making it an invaluable addition to the transplant physician's tool kit for managing patients. TruGraf v1 testing can potentially avoid painful and risky invasive biopsies, reduce health care costs, and enable frequent assessment of patients with stable renal function to confirm the presence of immune quiescence in the peripheral blood.

Cancer Classification from DNA Microarray Data using mRMR and Artificial Neural Network

Cancer is the uncontrolled growth of abnormal cells in the body and is a major death cause now a days. Cancer may arise anywhere in the human body, and it names are remarked as body parts such as colon cancer, lung cancer, breast cancer. It is notable that cancer treatment is much easier in the initial stage rather than it outbreaks. DNA microarray based gene expression profiling has become efficient technique for cancer identification in early stage and a number of studies are available in this regard. Existing methods used different feature selection methods (e.g., wrapper and filter approaches) to select relevant genes and then employed distinct classifiers (e.g., artificial neural network, Naive Bayes, Decision Tree, Support Vector Machine) to identify cancer. This study considered information theoretic based minimum Redundancy Maximum Relevance (mRMR)method to select important genes and then employed artificial neural network (ANN) for cancer classification. Proposed mRMR-ANN method has been tested on a suite of benchmark data sets of various cancer. Experimental results revealed the proposed method as an effective method for cancer classification when performance compared with several related exiting methods.

Analytical validation of a prognostic prostate cancer gene expression assay using formalin fixed paraffin embedded tissue

BackgroundThere is a clear need for assays that can predict the risk of metastatic prostate cancer following curative procedures. Importantly these assays must be analytically robust in order to provide quality data for important clinical decisions. DNA microarray based gene expression assays measure several analytes simultaneously and can present specific challenges to analytical validation. This study describes the analytical validation of one such assay designed to predict metastatic recurrence in prostate cancer using primary formalin fixed paraffin embedded tumour material.MethodsAccuracy was evaluated with a method comparison study between the assay development platform (Prostate Disease Specific Array) and an alternative platform (Xcel™ microarray) using 50 formalin-fixed, paraffin-embedded prostate cancer patient samples. An additional 70 samples were used to establish the assay reportable range. Determination of assay precision and sensitivity was performed on multiple technical replicates of three prostate cancer samples across multiple variables (operators, days, runs, reagent lots, and equipment) and RNA/cDNA inputs respectively using the appropriate linear mixed model.ResultsThe overall agreement between the development and alternative platform was 94.7% (95% confidence interval, 86.9–98.5%). The reportable range was determined to be 0.150 to 1.107 for core needle biopsy samples and − 0.214 to 0.844 for radical prostatectomy samples. From the precision study, the standard deviations for assay repeatability and reproducibility were 0.032 and 0.040 respectively. The sensitivity study demonstrated that a total RNA input and cDNA input of 50 ng and 3.5 μg respectively was conservative.ConclusionThe Metastatic Assay was found to be highly reproducible and precise. In conclusion the studies demonstrated an acceptable analytical performance for the assay and support its potential use in the clinic.

Application of RNA-seq Derived Diagnostic Algorithms of T-cell Mediated Kidney Rejection (TCMR) to Publicly Available DNA Microarray-based Gene Expression Datasets

Application of RNA-seq Derived Diagnostic Algorithms of T-cell Mediated Kidney Rejection (TCMR) to Publicly Available DNA Microarray-based Gene Expression Datasets

Differential and directional estrogenic signaling pathways induced by enterolignans and their precursors.

Mammalian lignans or enterolignans are metabolites of plant lignans, an important category of phytochemicals. Although they are known to be associated with estrogenic activity, cell signaling pathways leading to specific cell functions, and especially the differences among lignans, have not been explored. We examined the estrogenic activity of enterolignans and their precursor plant lignans and cell signaling pathways for some cell functions, cell cycle and chemokine secretion. We used DNA microarray-based gene expression profiling in human breast cancer MCF-7 cells to examine the similarities, as well as the differences, among enterolignans, enterolactone and enterodiol, and their precursors, matairesinol, pinoresinol and sesamin. The profiles showed moderate to high levels of correlation (R values: 0.44 to 0.81) with that of estrogen (17β-estradiol or E2). Significant correlations were observed among lignans (R values: 0.77 to 0.97), and the correlations were higher for cell functions related to enzymes, signaling, proliferation and transport. All the enterolignans/precursors examined showed activation of the Erk1/2 and PI3K/Akt pathways, indicating the involvement of rapid signaling through the non-genomic estrogen signaling pathway. However, when their effects on specific cell functions, cell cycle progression and chemokine (MCP-1) secretion were examined, positive effects were observed only for enterolactone, suggesting that signals are given in certain directions at a position closer to cell functions. We hypothesized that, while estrogen signaling is initiated by the enterolignans/precursors examined, their signals are differentially and directionally modulated later in the pathways, resulting in the differences at the cell function level.

Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers

Artificial neural networks (ANNs) can be used to develop a technique to classify lymph node negative breast cancer that is prone to distant metastases based on gene expression signatures. The neural network used is a multilayered feed forward network that employs back propagation algorithm. Once trained with DNA microarray-based gene expression profiles of genes that were predictive of distant metastasis recurrence of lymph node negative breast cancer, the ANNs became capable of correctly classifying all samples and recognizing the genes most appropriate to the classification. To test the ability of the trained ANN models in recognizing lymph node negative breast cancer, we analyzed additional idle samples that were not used beforehand for the training procedure and obtained the correctly classified result in the validation set. For more substantial result, bootstrapping of training and testing dataset was performed as external validation. This study illustrates the potential application of ANN for breast tumor diagnosis and the identification of candidate targets in patients for therapy.

Fluorescence-based bioassays for the detection and evaluation of food materials.

We summarize here the recent progress in fluorescence-based bioassays for the detection and evaluation of food materials by focusing on fluorescent dyes used in bioassays and applications of these assays for food safety, quality and efficacy. Fluorescent dyes have been used in various bioassays, such as biosensing, cell assay, energy transfer-based assay, probing, protein/immunological assay and microarray/biochip assay. Among the arrays used in microarray/biochip assay, fluorescence-based microarrays/biochips, such as antibody/protein microarrays, bead/suspension arrays, capillary/sensor arrays, DNA microarrays/polymerase chain reaction (PCR)-based arrays, glycan/lectin arrays, immunoassay/enzyme-linked immunosorbent assay (ELISA)-based arrays, microfluidic chips and tissue arrays, have been developed and used for the assessment of allergy/poisoning/toxicity, contamination and efficacy/mechanism, and quality control/safety. DNA microarray assays have been used widely for food safety and quality as well as searches for active components. DNA microarray-based gene expression profiling may be useful for such purposes due to its advantages in the evaluation of pathway-based intracellular signaling in response to food materials.

DNA microarray-based gene expression profiling of estrogenic chemicals.

We summarize updated information about DNA microarray-based gene expression profiling by focusing on its application to estrogenic chemicals. First, estrogenic chemicals, including natural/industrial estrogens and phytoestrogens, and the methods for detection and evaluation of estrogenic chemicals were overviewed along with a comprehensive list of estrogenic chemicals of natural or industrial origin. Second, gene expression profiling of chemicals using a focused microarray containing estrogen-responsive genes is summarized. Third, silent estrogens, a new type of estrogenic chemicals characterized by their estrogenic gene expression profiles without growth stimulative or inhibitory effects, have been identified so far exclusively by DNA microarray assay. Lastly, the prospect of a microarray assay is discussed, including issues such as commercialization, future directions of applications and quality control methods.

Human osteosarcoma cells respond to sorafenib chemotherapy by downregulation of the tumor progression factors S100A4, CXCR4 and the oncogene FOS

Osteosarcoma is a rare but aggressive bone neoplasm in humans, which is commonly treated with surgery, classical chemotherapy and radiation. Sorafenib, an inhibitor of a number of kinases targeting the Raf/MEK/ERK pathway, is a promising new chemotherapeutic agent in human medicine that has been approved since 2006 for the therapy of renal cell carcinoma and since 2007 for the treatment of hepatocellular carcinoma. Here, we studied the antimetastatic potential of 4 µM of this multikinase inhibitor in a human osteosarcoma cell line. DNA microarray-based gene expression profiling detected 297 and 232 genes upregulated or downregulated at a threshold of >2-fold expression alteration (P<0.05) in the sorafenib-treated cells. Three genes (CXCR4, FOS and S100A4) that are involved in tumor progression were chosen for validation by quantitative PCR (qPCR) and protein expression analysis. The decrease in RNA expression detected by microarray profiling was confirmed by qPCR for all three genes (P<0.01). On the protein level, sorafenib-induced reduction of S100A4 was verified both by western blotting and immunohistochemistry. For CXCR4 and c-Fos, a reduced protein expression was shown by immunohistochemistry, for c-Fos also by immunoblotting. We conclude that sorafenib could serve as a potent chemotherapeutical agent by which to inhibit the metastatic progression of osteosarcomas.

Gene selection for cancer tumor detection using a novel memetic algorithm with a multi-view fitness function

Cancer is one of the key research topics in the medical field. An accurate detection of different cancer tumor types has great value in providing better treatment facilities and risk minimization for patients. Recently, DNA microarray-based gene expression profiles have been employed to correlate the clinical behavior of cancers with the differential gene expression levels in cancerous and benign tumors. An accurate classifier with linguistic interpretability using a small number of relevant genes is beneficial to microarray data analysis and development of inexpensive diagnostic tests. Several well-known and frequently used techniques for designing classifiers from microarray data, such as a support vector machine, neural networks, k-nearest neighbor, and logistic regression model, suffer from low comprehensibility. This paper proposes a new memetic algorithm which is capable of extracting interpretable and accurate fuzzy if–then rules from cancer data. This paper is the first proposal of memetic algorithms with the Multi-View fitness function approach. The new presented Multi-View fitness function considers two kinds of evaluating procedures. The first procedure, which is located in the main evolutionary structure of the algorithm, evaluates each single fuzzy if–then rule according to the specified rule quality (the evaluating procedure does not consider other rules). However, the second procedure determines the quality of each fuzzy rule according to the whole fuzzy rule set performance. In comparison to classic memetic algorithms, these kinds of memetic algorithms enhance the rule discovery process significantly.

Late Stage Cathepsin C, CXCL13 and Ki-67 Overexpression Correlate with Regional Neuropathology in a BSE Transgenic Murine Model

A DNA microarray-based gene expression analysis study was performed with bovine spongiform encephalopathy (BSE) transgenic mice. Several genes were found to be overexpressed including the lysosomal enzyme cathepsin C, the chemokine CXCL13 and a number of genes related to cellular proliferation. The brains from terminal stage, BSE inoculated, 'bovinized', transgenic mice were subjected to immunohistochemistry with antibodies against these two proteins and Ki-67, a cell proliferation marker, to assess the biological relevance of the gene expression changes. Differential expression of cathepsin C and CXCL13 proteins and increased expression of Ki-67 was observed. These changes were localized to areas of deposition of PrP(res) and spongiform change and to areas showing an astroglial and microglial response. These findings suggest that these proteins are involved in the mechanisms leading to the establishment of transmissible spongiform encephalopathy.

DNA microarray-based gene expression profiling in diagnosis, assessing prognosis and predicting response to therapy in colorectal cancer

Colorectal cancer is the most common cancer of the gastrointestinal tract. It is considered as a biological model of a certain type of cancerogenesis process in which progression from an early to late stage adenoma and cancer is accompanied by distinct genetic alterations. Clinical and pathological parameters commonly used in clinical practice are often insufficient to determine groups of patients suitable for personalized treatment. Moreover, reliable molecular markers with high prognostic value have not yet been determined. Molecular studies using DNA-based microarrays have identified numerous genes involved in cell proliferation and differentiation during the process of cancerogenesis. Assessment of the genetic profile of colorectal cancer using the microarray technique might be a useful tool in determining the groups of patients with different clinical outcomes who would benefit from additional personalized treatment. The main objective of this study was to present the current state of knowledge on the practical application of gene profiling techniques using microarrays for determining diagnosis, prognosis and response to treatment in colorectal cancer.

Wavelet-based feature extraction for DNA microarray classification

Complementary DNA (cDNA) microarray-based tumor gene expression profiles have been successfully used for cancer diagnosis. The main difficulty in processing cDNA microarrays is the ultra-high dimensionality of the microarrays. In this paper, we approach the dimensionality reduction using a novel wavelet-based approach that extracts classification features through microarray-block processing, thresholding, and averaging of approximation coefficients. The proposed cancer detection system presents the extracted features to a support vector machine SVM for classification (tumor or non-tumor). To show the robustness of the proposed system, its performance is tested on two public cancer microarray databases.

Worsening of Obesity and Metabolic Status Yields Similar Molecular Adaptations in Human Subcutaneous and Visceral Adipose Tissue: Decreased Metabolism and Increased Immune Response

Context: It is not known whether biological differences reported between sc adipose tissue (SAT) and visceral adipose tissue (VAT) depots underlie the pathogenicity of visceral fat. Objective: We compared SAT and VAT gene expression according to obesity, visceral fat accumulation, insulin resistance, and presence of the metabolic syndrome. Design: Subjects were assigned into four groups (lean, overweight, obese, and obese with metabolic syndrome). Setting: Subjects were recruited at a university hospital. Patients: Thirty-two women were included. Main Outcome Measures: Anthropometric measurements, euglycemic-hyperinsulinemic clamps, blood analyses, and computed tomography scans were performed, and paired samples of SAT and VAT were obtained for DNA microarray-based gene expression profiling. Results: Considering the two fat depots together, 1125 genes were more and 1025 genes were less expressed in lean compared with metabolic syndrome subjects. Functional annotation clustering showed, from lean to metabolic syndrome subjects, progressive down-regulation of metabolic pathways including branched-chain amino acid, fatty acid, carbohydrate, and mitochondrial energy metabolism and up-regulation of immune response genes involved in toll-like receptor, TNF, nuclear factor-κB, and apoptosis pathways. Metabolism and immune response genes showed an opposite correlation with fat mass, fat distribution, or insulin resistance indices. These associations were similar in SAT and VAT, although about 1000 genes showed differential expression between SAT and VAT. Conclusions: The increase in adiposity and the worsening of metabolic status are associated with a coordinated down-regulation of metabolism-related and up-regulation of immune response-related gene expression. Molecular adaptations in SAT prove as discriminating as those in VAT.

Gene expression profiling of gliomas: merging genomic and histopathological classification for personalised therapy

The development of DNA microarray technologies over the past decade has revolutionised translational cancer research. These technologies were originally hailed as more objective, comprehensive replacements for traditional histopathological cancer classification systems, based on microscopic morphology. Although DNA microarray-based gene expression profiling (GEP) remains unlikely in the near term to completely replace morphological classification of primary brain tumours, specifically the diffuse gliomas, GEP has confirmed that significant molecular heterogeneity exists within the various morphologically defined gliomas, particularly glioblastoma (GBM). Herein, we provide a 10-year progress report on human glioma GEP, with focus on development of clinical diagnostic tests to identify molecular subtypes, uniquely responsive to adjuvant therapies. Such progress may lead to a more precise classification system that accurately reflects the cellular, genetic, and molecular basis of gliomagenesis, a prerequisite for identifying subsets uniquely responsive to specific adjuvant therapies, and ultimately in achieving individualised clinical care of glioma patients.

Worsening of Obesity and Metabolic Status Yields Similar Molecular Adaptations in Human Subcutaneous and Visceral Adipose Tissue: Decreased Metabolism and Increased Immune Response

It is not known whether biological differences reported between sc adipose tissue (SAT) and visceral adipose tissue (VAT) depots underlie the pathogenicity of visceral fat. We compared SAT and VAT gene expression according to obesity, visceral fat accumulation, insulin resistance, and presence of the metabolic syndrome. Subjects were assigned into four groups (lean, overweight, obese, and obese with metabolic syndrome). Subjects were recruited at a university hospital. Thirty-two women were included. Anthropometric measurements, euglycemic-hyperinsulinemic clamps, blood analyses, and computed tomography scans were performed, and paired samples of SAT and VAT were obtained for DNA microarray-based gene expression profiling. Considering the two fat depots together, 1125 genes were more and 1025 genes were less expressed in lean compared with metabolic syndrome subjects. Functional annotation clustering showed, from lean to metabolic syndrome subjects, progressive down-regulation of metabolic pathways including branched-chain amino acid, fatty acid, carbohydrate, and mitochondrial energy metabolism and up-regulation of immune response genes involved in toll-like receptor, TNF, nuclear factor-κB, and apoptosis pathways. Metabolism and immune response genes showed an opposite correlation with fat mass, fat distribution, or insulin resistance indices. These associations were similar in SAT and VAT, although about 1000 genes showed differential expression between SAT and VAT. The increase in adiposity and the worsening of metabolic status are associated with a coordinated down-regulation of metabolism-related and up-regulation of immune response-related gene expression. Molecular adaptations in SAT prove as discriminating as those in VAT.

GeneMesh: a web-based microarray analysis tool for relating differentially expressed genes to MeSH terms

BackgroundAn important objective of DNA microarray-based gene expression experimentation is determining inter-relationships that exist between differentially expressed genes and biological processes, molecular functions, cellular components, signaling pathways, physiologic processes and diseases.ResultsHere we describe GeneMesh, a web-based program that facilitates analysis of DNA microarray gene expression data. GeneMesh relates genes in a query set to categories available in the Medical Subject Headings (MeSH) hierarchical index. The interface enables hypothesis driven relational analysis to a specific MeSH subcategory (e.g., Cardiovascular System, Genetic Processes, Immune System Diseases etc.) or unbiased relational analysis to broader MeSH categories (e.g., Anatomy, Biological Sciences, Disease etc.). Genes found associated with a given MeSH category are dynamically linked to facilitate tabular and graphical depiction of Entrez Gene information, Gene Ontology information, KEGG metabolic pathway diagrams and intermolecular interaction information. Expression intensity values of groups of genes that cluster in relation to a given MeSH category, gene ontology or pathway can be displayed as heat maps of Z score-normalized values. GeneMesh operates on gene expression data derived from a number of commercial microarray platforms including Affymetrix, Agilent and Illumina.ConclusionsGeneMesh is a versatile web-based tool for testing and developing new hypotheses through relating genes in a query set (e.g., differentially expressed genes from a DNA microarray experiment) to descriptors making up the hierarchical structure of the National Library of Medicine controlled vocabulary thesaurus, MeSH. The system further enhances the discovery process by providing links between sets of genes associated with a given MeSH category to a rich set of html linked tabular and graphic information including Entrez Gene summaries, gene ontologies, intermolecular interactions, overlays of genes onto KEGG pathway diagrams and heatmaps of expression intensity values. GeneMesh is freely available online at http://proteogenomics.musc.edu/genemesh/.

Metabolite stress and tolerance in the production of biofuels and chemicals: Gene‐expression‐based systems analysis of butanol, butyrate, and acetate stresses in the anaerobe Clostridium acetobutylicum

Metabolite accumulation has pleiotropic, toxic, or beneficial effects on cell physiology, but such effects are not well understood at the molecular level. Cells respond and adapt to metabolite stress by mechanisms largely unexplored, especially in the context of multiple and simultaneous stresses. Solventogenic and related clostridia have an inherent advantage for production of biofuels and chemicals directly from cellulosic material and other complex carbohydrates, but issues of product/metabolite tolerance and related culture productivities remain. Using DNA microarray-based gene expression analysis, the transcriptional-stress responses of Clostridium acetobutylicum to fermentation acids acetate and butyrate and the solvent product butanol were analyzed and compared in the context of cell physiology. Ontological analysis demonstrated that stress by all three metabolites resulted in upregulation of genes related to post-translational modifications and chaperone activity, and downregulation of the translation-machinery genes. Motility genes were downregulated by acetate-stress only. The general metabolite stress included upregulation of numerous stress genes (dnaK, groES, groEL, hsp90, hsp18, clpC, and htrA), the solventogenic operon aad-ctfA-ctfB, and other solventogenic genes. Acetate stress downregulated expression of the butyryl-CoA- and butyrate-formation genes, while butyrate stress downregulated expression of acetate-formation genes. Pyrimidine-biosynthesis genes were downregulated by most stresses, but purine-biosynthesis genes were upregulated by acetate and butyrate, possibly for thiamine and histidine biosynthesis. Methionine-biosynthesis genes were upregulated by acetate stress, indicating a possibly conserved stress response mechanism also observed in Escherichia coli. Nitrogen-fixation gene expression was upregulated by acetate stress. Butyrate stress upregulated many iron-metabolism genes, riboflavin-biosynthesis genes, and several genes related to cellular repair from oxidative stress, such as perR and superoxide dismutases. Butanol stress upregulated the glycerol metabolism genes glpA and glpF. Surprisingly, metabolite stress had no apparent effect on the expression of the sporulation-cascade genes. It is argued that the list of upregulated genes in response to the three metabolite stresses includes several genes whose overexpression would likely impart tolerance, thus making the information generated in this study, a valuable source for the development of tolerant recombinant strains.

Gene Expression Analysis of Independent Data Sets Identifies HBG1 to Be Associated with Outcome in Cytogenetically Normal AML.

Abstract 2613Poster Board II-589Cytogenetically normal acute myeloid leukemia (CN-AML) represents a biologically and clinically heterogeneous group. During recent years novel molecular markers like FLT3, CEBPA and NPM1 gene mutations as well as deregulated expression of single genes such as EVI1, MN1, ERG and BAALC have been identified that provide important prognostic information in CN-AML. Furthermore, DNA microarray-based gene expression profiling (GEP) has been shown to capture the molecular heterogeneity of leukemia and has been applied to build clinical outcome predictors in CN-AML.In this study, we wanted to assess whether GEP based outcome prediction using novel biostatistical approaches applied to large gene expression data sets could refine previous findings. First we profiled gene expression in a large set of clinically well annotated CN-AML (entered on a multicenter trial for patients <60 years, AMLSG 07-04, n=154 cases) using Affymetrix Human Genome U133plus2.0 microarrays. Then, we applied L1-penalized Cox proportional hazards regression to develop a sparse prognostic model for overall survival. Using this algorithm we were able to define a gene expression signature correlated with outcome in CN-AML. Interestingly, our model resulted in a signature that only comprised one probe set corresponding to the HBG1 gene (hemoglobin, gamma A). While quantitative RT-PCR validated correct measurement of HBG1 expression (correlation r=0.96, n=15), we next evaluated our finding in independent cohorts of CN-AML cases. We applied our “HBG1 gene signature” to previously published CN-AML data (Metzeler et al. Blood 2008) comprising 163 patient samples on Affymetrix U133A/B (data set I) and 79 patient samples on Affymetrix U133plus2.0 microarrays (data set II). Univariate Cox PH regression showed that our gene expression predictor was highly significant for overall survival in both data sets (P=0.002, HR 1.71, 95%CI 1.23–2.39; and P<0.001, HR 3.03, 95%CI 1.84–5.02, respectively). Adjusted for age, NPM1, and FLT3-ITD mutational status HBG1 retained its prognostic relevance in multivariate analysis (P=0.007 and P<0.001, respectively).As HBG1 expression might be used as novel prognostic marker in AML, the role of HBG1 expression in AML remains to be determined. HBG1 expression might represent a surrogate marker indicating the activation of distinct oncoproteins such as MYB, which recently has been shown to transcriptionally repress the level of gamma-globulin. On the other hand HBG1 expression has been shown to be induced upon exposure to azacytidine. Thus, HBG1 expression could also reflect underlying epigenetic profiles of prognostic relevance. While studying HBG1 expression in larger CN-AML cohorts as well as in the context of other molecular markers might help to further determine the prognostic impact of HBG1, integrative analyses combining GEP with genomics and epigenomics data sets will provide novel insights into the mechanisms underlying HBG1 expression in CN-AML. Disclosures:No relevant conflicts of interest to declare.