Significance Analysis Of Microarrays Method Research Articles

Molecular insights into the development of hepatic metastases in colorectal cancer: a metastasis prediction study.

Colorectal cancer is presently the third most commonly diagnosed cancer in the United States. In this study, we identified molecular differences between hepatic and non-hepatic metastases in colorectal cancer and evaluated their prognostic significance. We downloaded primary data from the NCBI Gene Expression Omnibus (GSE6988, GSE62321, GSE50760, and GSE28722). To identify the molecular differences, we used the Significance Analysis of Microarray method. We selected nine prognostic genes (SYTL2, PTPLAD1, CDS1, RNF138, PIGR, WDR78, MYO7B, TSPAN3, and ATP5F1) with hepatic metastasis prediction score in colorectal cancer (hereafter referred to as LASSO Score). We confirmed the prognostic significance of the LASSO Score by using Kaplan-Meier survival analysis, multivariate analysis, the time-dependent area under the curve (AUC) of Uno's C-index, and the AUC of the receiver operating characteristic curve at 1-5 years. Survival analysis revealed that a high LASSO Score is associated with a poor prognosis in colorectal cancer patients with hepatic metastases (p = 0). Analysis of C-indices and AUC values from the receiver operating characteristic curve further supported this prediction by the LASSO Score. Multivariate analysis confirmed the prognostic significance of the LASSO Score (p = 1.13e-06). This study reveals the biological mechanisms underlying hepatic metastases in colorectal cancer and will help in developing targeted therapies for colorectal cancer.

Identification of differentially expressed genes in primary Sjögren's syndrome.

Primary Sjögren's syndrome (pSS) is a chronic systemic autoimmune disease that affects exocrine glands. To study the molecular mechanism and identify crucial genes/pathways in pSS pathogenesis, the microarray-based whole-genome gene expression profiles from salivary glands of patients with pSS and non-sicca controls were retrieved. After normalization and subsequent batch effect adjustment, significance analysis of microarrays method was applied to five available datasets, and 379 differentially expressed genes (DEGs) were identified. The 300 upregulated DEGs were enriched in Gene Ontology terms of immune and inflammatory responses, including antigen processing and presentation, interferon-mediated signaling pathway, and chemotaxis. Previously reported pSS-associated genes, including HLA-DRA, TAP2, PRDM1, and IFI16, were found to be significantly upregulated. The downregulated DEGs were enriched in pathways of salivary secretion, carbohydrate digestion and absorption, and starch and sucrose metabolism, implying dysfunction of salivary glands during pathogenesis. Next, a protein-protein interaction network was constructed, and B2M, an upregulated DEG, was shown to be a hub, suggesting its potential involvement in pSS development. In summary, we found the activation of pSS-associated genes in pathogenesis, and provide clues for salivary glands dysfunction. Experimental investigation on the identified DEGs in this study will deepen our understanding on pSS.

Bioinformatics analysis of gene expression data for the identification of critical genes in breast invasive carcinoma.

Gene expression data were analyzed in order to identify critical genes in breast invasive carcinoma (BRCA). Data from 1,073 BRCA samples and 99 normal samples were analyzed, which were obtained from The Cancer Genome Atlas. Differentially expressed genes (DEGs) were identified using the significance analysis of microarrays method and a functional enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery. Relevant microRNAs (miRNAs), transcription factors (TFs) and associated small molecule drugs were revealed by Fisher's exact test. Furthermore, protein-protein interaction (PPI) information was downloaded from the Human Protein Reference Database. Interactions with a Pearson's correlation coefficient >0.5 were identified and PPI networks were subsequently constructed. A survival analysis was also conducted according to the Kaplan-Meier method. Initially, the 1,073 BRCA samples were clustered into seven groups, and 5,394 DEGs that were identified in ≥4 groups were selected. These DEGs were involved in the cell cycle, ubiquitin-mediated proteolysis, oxidative phosphorylation and human immunodeficiency virus infection. In addition, TFs, including Sp1 transcription factor, DAN domain BMP antagonist family member 5, MYCN proto-oncogene, bHLH transcription factor and cAMP responsive element binding protein (CREB)1, were identified in the BRCA groups. Seven PPI networks were subsequently constructed and the top 10 hub genes were acquired, including RB transcriptional corepressor 1, inhibitor of nuclear factor (NF)-κB kinase subunit γ, NF-κB subunit 2, transporter 1, ATP binding cassette subfamily B member, CREB binding protein and proteasome subunit α3. A significant difference in survival was observed between the two combined groups (groups-2, −4 and −5 vs. groups-1, −3, −6 and −7). In conclusion, numerous critical genes were detected in BRCA, and relevant miRNAs, TFs and small molecule drugs were identified. These findings may advance understanding regarding the pathogenesis of BRCA.

Clarifying the molecular mechanism associated with carfilzomib resistance in human multiple myeloma using microarray gene expression profile and genetic interaction network.

Carfilzomib is a Food and Drug Administration-approved selective proteasome inhibitor for patients with multiple myeloma (MM). However, recent studies indicate that MM cells still develop resistance to carfilzomib, and the molecular mechanisms associated with carfilzomib resistance have not been studied in detail. In this study, to better understand its potential resistant effect and its underlying mechanisms in MM, microarray gene expression profile associated with carfilzomib-resistant KMS-11 and its parental cell line was downloaded from Gene Expression Omnibus database. Raw fluorescent signals were normalized and differently expressed genes were identified using Significance Analysis of Microarrays method. Genetic interaction network was expanded using String, a biomolecular interaction network JAVA platform. Meanwhile, molecular function, biological process and signaling pathway enrichment analysis were performed based on Gene Ontology and Kyoto Encyclopedia of Genes and Genomes. Totally, 27 upregulated and 36 downregulated genes were identified and a genetic interaction network associated with the resistant effect was expanded basing on String, which consisted of 100 nodes and 249 edges. In addition, signaling pathway enrichment analysis indicated that cytokine–cytokine receptor interaction, autophagy, ErbB signaling pathway, microRNAs in cancer and fatty acid metabolism pathways were aberrant in carfilzomib-resistant KMS-11 cells. Thus, in this study, we demonstrated that carfilzomib potentially conferred drug resistance to KMS-11 cells by cytokine–cytokine receptor interaction, autophagy, ErbB signaling pathway, microRNAs in cancer and fatty acid metabolism pathways, which may provide some potential molecular therapeutic targets for drug combination therapy against carfilzomib resistance.

Identification of characteristic gene modules of osteosarcoma using bioinformatics analysis indicates the possible molecular pathogenesis.

The aim of the present study was to investigate the possible pathogenesis of osteosarcoma using bioinformatics analysis to examine gene-gene interactions. A total of three datasets associated with osteosarcoma were downloaded from the Gene Expression Omnibus. The differentially expressed genes (DEGs) were identified using the significance analysis of microarrays method, which then were subjected to the Human Protein Reference Database to identify the protein-protein interaction (PPI) pairs and to construct a PPI network of the DEGs. Subsequent multilevel community analysis was applied to mine the modules in the network, followed by screening of the differential expression module using the GlobalAncova package. The genes in the differential expression modules were verified in the valid datasets. The verified genes underwent functional and pathway enrichment analysis. A total of 616 DEGs were selected to construct the PPI network, which included 5,808 osteosarcoma-specific interaction pairs and 8,012 normal-specific pairs. Tumor protein p53 (TP53), mitogen-activated protein kinase 1 (MAPK1) and estrogen receptor 1 (ESR1) were identified the most important osteosarcoma-associated genes, with the highest levels of topological properties. Neurogenic locus notch homolog protein 3 (NOTCH3) and caspase 1 (CASP1) were identified as the osteosarcoma-specific interaction pairs. Among all 23 mined modules, three were identified as differential expression modules, which were verified in the other two datasets. The genes in these modules were predominantly enriched in the FGFR, MAPK and Notch signaling pathways. Therefore, TP53, MAPK1, ESR1, NOTCH3 and CASP1 may be important in the development of osteosarcoma, and provides valuable clues to investigate the pathogenesis of osteosarcoma using the three differential expression modules.

Human brain arteriovenous malformation: an analysis of differential expressed genes

Much still remains unknown about the pathogenesis of brain arteriovenous malformations (AVMs). Previous studies have revealed the abnormal expression of various angiogenesis-related genes in AVMs. To further understand this disease, we sought to identify genes differently expressed in AVMs by means of the gene microarray technique. Nine AVMs specimen and nine samples of normal vessels are collected. Total RNA isolated from these specimen is hybridized with Oligonucleotide array and gene analysis was conducted. Analyzing data with the help of significance analysis of microarrays (SAM) and a free web-based molecular annotation system 3.0 (MAS 3.0). The SAM method identify 37 gene significantly up-regulated and 10 genes down-regulated in AVMs. Among those genes, VACN, SPARK and ARHGAP18 seem to play a facilitating role during the genesis of AVMs. Multiple pathways, as MAPK pathway, may also be involved.

Meta-Analysis of Microarray-Based Expression Profiles to Identify Differentially Expressed Genes in Intracranial Aneurysms

To gain comprehensive insight into the molecular mechanism of formation and rupture of intracranial aneurysms (IAs). All publicly accessible microarray-based whole-genome gene expression profiles on IAs were retrieved. The significance analysis of microarrays method was applied to identify differentially expressed genes (DEGs). Functional annotation was performed using gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. Expression of DEGs was examined using quantitative polymerase chain reaction. Six data sets of 3 microarray platforms were qualified and analyzed. Comparing expression profiles between aneurysmal wall and control vessels, 5232 significant DEGs were identified among 3 platforms, and MMP12 was shown to have the largest fold change of upregulation. In all 3 platforms, 46 DEGs were shared, and 1297 DEGs were commonly resolved in at least 2 microarray platforms. Among these 1297 concordant DEGs, the 512 upregulated genes were mainly enriched in inflammatory and immune response processes, whereas the 785 downregulated genes were primarily concentrated in smooth muscle cell contraction and development pathways. Comparison between expression profiles of ruptured and unruptured IAs revealed that a few angiogenic factors, including HIF1A, VEGFA, and ANGPTL4, were upregulated in ruptured aneurysms. Subsequently, the upregulation of MMP12, HIF1A, and VEGFA was partially confirmed using quantitative polymerase chain reaction among independent samples. Inflammation, immune response, and loss of contractile vascular smooth muscle cells could potentially contribute to the formation of IAs, whereas the role of angiogenesis and vascular remodeling in IA formation and rupture needs further exploration.

Retraction Note to: Analyzing time-series microarray data reveals key genes in spinal cord injury.

Although many scholars have utilized high-throughput microarrays to delineate gene expression patterns after spinal cord injury (SCI), no study has evaluated gene changes in raphe magnus (RM) and somatomotor cortex (SMTC), two areas in brain primarily affected by SCI. In present study, we aimed to analyze the differentially expressed genes (DEGs) of RM and SMTC between SCI model and sham injured control at 4, 24 h, 7, 14, 28 days, and 3 months using microarray dataset GSE2270 downloaded from gene expression omnibus and unpaired significance analysis of microarray method. Protein–protein interaction (PPI) network was constructed for DEGs at crucial time points and significant biological functions were enriched using DAVID. The results indicated that more DEGs were identified at 14 days in RM and at 4 h/3 months in SMTC after SCI. In the PPI network for DEGs at 14 days in RM, interleukin 6, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), FBJ murine osteosarcoma viral oncogene homolog (FOS), tumor necrosis factor, and nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor) were the top 5 hub genes; In the PPI network for DEGs at 3 months in SMTC, the top 5 hub genes were ubiquitin B, Ras‐related C3 botulinum toxin substrate 1 (rho family, small GTP binding protein Rac1), FOS, Janus kinase 2 and vascular endothelial growth factor A. Hedgehog and Wnt signaling pathways were the top 2 significant pathways in RM. These hub DEGs and pathways may be underlying therapeutic targets for SCI.

ANALYSIS OF MASS SPECTROMETRY DATA: SIGNIFICANCE ANALYSIS OF MICROARRAYS FOR SELDI-MS DATA IN PROTEOMICS

Mass Spectrometry (MS) has arguably become thecore technology in proteomics. MALDI and SELDI-TOFtechniques enable the study biological fluids, e.g. human blood.Analysis of these samples can lead to discover new biomarkerswhich can ease the diagnostic and prognostic of several diseases,e.g. various cancers. In this work, we focus on MS data fromSELDI-TOF experiments. We begin with a preprocessing step inorder to remove noises due to the acquisition process of the data.Then, we apply the differential analysis to a SELDI-MS data,using the Significance Analysis of Microarray (SAM) methodimplemented in Matlab. Results using the SAM method arecompared with those obtained by the conventional t -test andAnalysis Of Variance (ANOVA) in order to evaluate its efficacyand its performance. As a result, we demonstrate that the SAMmethod can be adapted for effective significance analysis ofSELDI-MS data. It is deemed powerful and provides betterresults that totes. An easy-to-use application is developed withMatlab for mass spectrometry data analysis from raw spectra todifferential analysis, including the SAM method.

RETRACTED ARTICLE: Analyzing time-series microarray data reveals key genes in spinal cord injury

Although many scholars have utilized high-throughput microarrays to delineate gene expression patterns after spinal cord injury (SCI), no study has evaluated gene changes in raphe magnus (RM) and somatomotor cortex (SMTC), two areas in brain primarily affected by SCI. In present study, we aimed to analyze the differentially expressed genes (DEGs) of RM and SMTC between SCI model and sham injured control at 4, 24 h, 7, 14, 28 days, and 3 months using microarray dataset GSE2270 downloaded from gene expression omnibus and unpaired significance analysis of microarray method. Protein-protein interaction (PPI) network was constructed for DEGs at crucial time points and significant biological functions were enriched using DAVID. The results indicated that more DEGs were identified at 14 days in RM and at 4 h/3 months in SMTC after SCI. In the PPI network for DEGs at 14 days in RM, interleukin 6, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), FBJ murine osteosarcoma viral oncogene homolog (FOS), tumor necrosis factor, and nuclear receptor subfamily 3, group C, member 1 (glucocorticoid receptor) were the top 5 hub genes; In the PPI network for DEGs at 3 months in SMTC, the top 5 hub genes were ubiquitin B, Ras-related C3 botulinum toxin substrate 1 (rho family, small GTP binding protein Rac1), FOS, Janus kinase 2 and vascular endothelial growth factor A. Hedgehog and Wnt signaling pathways were the top 2 significant pathways in RM. These hub DEGs and pathways may be underlying therapeutic targets for SCI.

Identification and validation co-differentially expressed genes with NAFLD and insulin resistance

Insulin resistance is a major feature and pathogenic factor of nonalcoholic fatty liver disease (NAFLD). Theoretically, genetic variation in candidate genes related to insulin resistance may contribute to the pathogenesis of NAFLD. The purpose of this study was to identify potentially pathogenic genes involved in NAFLD and insulin resistance that have not yet been discovered. This study yielded five important discoveries. 1. A total of 21 co-differentially expressed genes in both the NAFLD and insulin-resistance groups were identified from the pool containing thousands of genes via the significance analysis of microarraysmethod. 2. MAP kinase-interacting serine/threonine kinase 2 (Mknk2) was the unique gene to be identified that is involved in the insulin signaling pathway and Mitogen Activated Protein Kinase signaling pathway according to the Kyoto Encyclopedia of Genes and Genomes database. 3. Mknk2 mRNA and protein expression were dose-dependently up-regulated by palmitic acid (PA) in mouse primary hepatocytes. 4. Western blotting analysis and quantitative real-time PCR confirmed that Mknk2 affected the expression of acetyl-CoA carboxylases-1 and fatty acid synthase. 5. The inhibition of Mknk2 alleviated PA-induced insulin resistance, whereas the overexpression of Mknk2 resulted in the aggravation of insulin resistance in PA-treated hepatocytes. Therefore, we predict that MKNK2 may be a key protein related to NAFLD and insulin resistance.

Epidemiology and Candidate Genes of Non-alcoholic Fatty Liver Disease

Non-alcoholic fatty liver disease (NAFLD) is a chronic condition characterized by fat accumulation combined with low-grade inflammation in the liver. A large body of clinical and experimental data shows that an increased flux of free fatty acids from increased visceral adipose tissue can lead to NAFLD and insulin resistance. Thus, patients with obesity, insulin resistance, and dyslipidemia are at the greatest risk for NAFLD. Many people believe NAFLD is one of the phenotypes of metabolic syndrome or insulin resistance. In the United States, NAFLD prevalence was reported to be approximately 34% in the adult population. In previous studies performed in Korea, NAFLD prevalence varied from 17.9% to 27.4%, depending on the study populations and diagnostic methods. Prevalence of NAFLD confirmed by liver biopsy was 51.4% in living liver donors in Korea. Recent data showed that NAFLD prevalence was 27.3% (38.3% in men and 12.6% in women) among 161,891 Seoul and Gyeonggi-province residents receiving a health examination from January 2009 to December 2010. Insulin resistance is a major feature and pathogenic factor of NAFLD. Theoretically, genetic variation in candidate genes related to insulin resistance may contribute to the pathogenesis of NAFLD. A recent genetic study using the significance analysis of microarrays method reported that 21 candidate genes, from a pool containing thousands of genes, were involved in NAFLD and insulin resistance. Comprehensive investigation of common genes that are potentially pathogenic in the development of NAFLD is warranted. (J Korean Diabetes 2014;15:71-75)

Assessing Differential Expression in Two-Color Microarrays: A Resampling-Based Empirical Bayes Approach

Microarrays are widely used for examining differential gene expression, identifying single nucleotide polymorphisms, and detecting methylation loci. Multiple testing methods in microarray data analysis aim at controlling both Type I and Type II error rates; however, real microarray data do not always fit their distribution assumptions. Smyth's ubiquitous parametric method, for example, inadequately accommodates violations of normality assumptions, resulting in inflated Type I error rates. The Significance Analysis of Microarrays, another widely used microarray data analysis method, is based on a permutation test and is robust to non-normally distributed data; however, the Significance Analysis of Microarrays method fold change criteria are problematic, and can critically alter the conclusion of a study, as a result of compositional changes of the control data set in the analysis. We propose a novel approach, combining resampling with empirical Bayes methods: the Resampling-based empirical Bayes Methods. This approach not only reduces false discovery rates for non-normally distributed microarray data, but it is also impervious to fold change threshold since no control data set selection is needed. Through simulation studies, sensitivities, specificities, total rejections, and false discovery rates are compared across the Smyth's parametric method, the Significance Analysis of Microarrays, and the Resampling-based empirical Bayes Methods. Differences in false discovery rates controls between each approach are illustrated through a preterm delivery methylation study. The results show that the Resampling-based empirical Bayes Methods offer significantly higher specificity and lower false discovery rates compared to Smyth's parametric method when data are not normally distributed. The Resampling-based empirical Bayes Methods also offers higher statistical power than the Significance Analysis of Microarrays method when the proportion of significantly differentially expressed genes is large for both normally and non-normally distributed data. Finally, the Resampling-based empirical Bayes Methods are generalizable to next generation sequencing RNA-seq data analysis.

Activation of T and NK Cells Following Lenalidomide Therapy in Patients with Follicular Lymphoma.

Abstract 2766 Background:Lenalidomide plus rituximab therapy is a highly effective and well-tolerated therapy in patients (pts) with follicular lymphoma (FL). In a Phase II trial, this combination induced a complete remission rate of 87% in pts with advanced stage untreated FL (Fowler et al, Ann Oncol, 2011; 22; suppl 4:137). A randomized Phase III trial was recently initiated to compare this combination with current standard of care therapies in pts with FL. Although lenalidomide is known to be an immunomodulatory drug with effects on a variety of immune cells in vitro, its effects have not been well studied in vivo in humans. Understanding the in vivo effects of lenalidomide could lead to novel combination strategies to enhance the efficacy and improve clinical outcome in FL and other malignancies. Methods:Pts received lenalidomide 20 mg/day on days 1–21 of each 28-day cycle and rituximab was given at 375 mg/m2on day 1 of each cycle. Peripheral blood mononuclear cells (PBMC) were phenotyped by multiparametric flow cytometry at baseline, on cycle 2 day 15 (C2D15), and at the end of cycle 6. In addition, peripheral blood (PB) samples were collected in PAXgene Blood RNA tubes at baseline and on C2D15 for whole genome gene expression profiling (GEP). Results:Immunophenotyping of baseline and end of cycle 6 PBMC (n=17) showed that the percentages and absolute numbers of CD3+, CD4+, CD8+, TCRgd, and Foxp3+ regulatory T cells; and NK, NKT, and myeloid dendritic cells were not significantly different between the two time points. However, a significant increase in CD4+CD45RO+ (p<0.01) and CD8+CD45RO+ (p=0.04) memory T cells was observed post-therapy. Further characterization of CD4+ T cells showed a significant increase in central memory T cells (p<0.001) and a decrease in naïve (p<0.01) and terminally differentiated (p<0.01) T cells, but no change in effector memory T cells. The increase in CD8+ central memory T cells was marginally significant (p=0.06). Plasmacytoid dendritic cells (PDC) were also significantly increased (p=0.02). In contrast, no such changes in T cell subsets or PDC were observed in FL pts (n=9) treated with 6 cycles of R-CHOP chemotherapy that received equal number of rituximab doses and analyzed at similar time points (baseline and end of cycle 6).To understand lenalidomide-induced changes on a molecular level, we compared GEP data at C2D15 vs. baseline for 7 pairs of PB samples. The paired significance analysis of microarrays method, based on Student’s t test, identified 1,748 differentially expressed genes (DEG; 713 up, 1035 down), without a fold-change threshold, in C2D15 samples vs. baseline. Results were influenced by rituximab-induced depletion of B cells in C2D15 samples, but there were many changes that suggested altered PBMC physiology. Noteworthy up-regulated genes (>1.5 fold) included genes associated with T and NK cell activation including BATF, CCR2, CD1B, CD2, CD160, CTLA4, CXCR3, ICOS, and LAG3; and CD163 and CD209, phagocytic receptors expressed on monocytes/macrophages. Down-regulated genes (>1.5 fold) included CXCR5, which mediates B cell migration into follicles; and IL1B and TNFSF13B (BAFF), which are produced by activated macrophages and induce B cell proliferation. Gene set enrichment analysis of all GEP results, and Ingenuity Pathway Analysis of DEGs, indicated up regulation of multiple pathways and processes including ribosomal and mitochondrial components involved in translation and oxidative phosphorylation, CTLA4 signaling in cytotoxic T cells, and differentiation and signaling by ICOS and CD28 in T helper cells. We confirmed up regulation of CTLA4, ICOS, and LAG3 at the protein level in C2D15 PBMC by flow cytometry. Furthermore, treatment of PBMC derived from untreated FL pts with lenalidomide in vitro resulted in up regulation of these molecules in T and/or NK cells consistent with our in vivo results. Conclusions:In FL pts, lenalidomide induced multiple changes in the immune system including increases in PDC and memory T cell subsets, activation of T and NK cells, and down-regulation of certain genes mediating B cell migration and proliferation. These results provide insights into the mechanism of action of lenalidomide and suggest that it can be combined with other immunostimulatory agents such as therapeutic vaccines, adoptive T cell therapy strategies, and immune checkpoint inhibitors to further enhance its efficacy in FL and other malignancies. Disclosures:Fowler:Celgene: Research Funding. Heise:Celgene Corporation: Employment, Equity Ownership. Lacerte:Celgene: Honoraria. Samaniego:Celgene: Research Funding. Neelapu:Celgene Corporation: Research Funding.

DbDEMC: a database of differentially expressed miRNAs in human cancers

BackgroundMicroRNAs (miRNAs) are small noncoding RNAs about 22 nt long that negatively regulate gene expression at the post-transcriptional level. Their key effects on various biological processes, e.g., embryonic development, cell division, differentiation and apoptosis, are widely recognized. Evidence suggests that aberrant expression of miRNAs may contribute to many types of human diseases, including cancer. Here we present a database of differentially expressed miRNAs in human cancers (dbDEMC), to explore aberrantly expressed miRNAs among different cancers.ResultsWe collected the miRNA expression profiles of 14 cancer types, curated from 48 microarray data sets in peer-reviewed publications. The Significance Analysis of Microarrays method was used to retrieve the miRNAs that have dramatically different expression levels in cancers when compared to normal tissues. This database provides statistical results for differentially expressed miRNAs in each data set. A total of 607 differentially expressed miRNAs (590 mature miRNAs and 17 precursor miRNAs) were obtained in the current version of dbDEMC. Furthermore, low-throughput data from the same literature were also included in the database for validation. An easy-to-use web interface was designed for users. Annotations about each miRNA can be queried through miRNA ID or miRBase accession numbers, or can be browsed by different cancer types.ConclusionsThis database is expected to be a valuable source for identification of cancer-related miRNAs, thereby helping with the improvement of classification, diagnosis and treatment of human cancers. All the information is freely available through http://159.226.118.44/dbDEMC/index.html.

Genetic Susceptibility Loci in Rheumatoid Arthritis Establish Transcriptional Regulatory Networks with Other Genes

Linkage studies have identified the human leukocyte antigen (HLA)-DRB1 as a putative rheumatoid arthritis (RA) susceptibility locus (SL). Nevertheless, it was estimated that its contribution was partial, suggesting that other non-HLA genes may play a role in RA susceptibility. To test this hypothesis, we conducted microarray transcription profiling of peripheral blood mononuclear cells in 15 RA patients and analyzed the data, using bioinformatics programs (significance analysis of microarrays method and GeneNetwork), which allowed us to determine the differentially expressed genes and to reconstruct transcriptional networks. The patients were grouped according to disease features or treatment with tumor necrosis factor blocker. Transcriptional networks that were reconstructed allowed us to identify the interactions occurring between RA SL and other genes, for example, HLA-DRB1 interacting with FNDC3A (fibronectin type III domain containing 3A). Given that fibronectin fragments can stimulate mediators of matrix and cartilage destruction in RA, this interaction is of special interest and may contribute to a clearer understanding of the functional role of HLA-DRB1 in RA pathogenesis.

Gene Expression Profile (GEP) of Chronic Myeloid Leukemia (CML) Patients at Diagnosis: Two Distinguished Subgroups of CML Patients Identified, Based on a Molecular Signature, Irrespective of Their Sokal Risk Score

CML is a clonal myeloproliferative disease which typically presents in chronic phase (CP), in which malignant progenitor cells proliferate rapidly but retain much of their ability to differentiate, with the disease later evolving to accelerated phase/blast crisis. Even after the introduction of imatinib, the calculation of the Sokal and the Euro prognostic scores has remained essential in clinical practice, since allow to stratify CML patients at different evolutive risk at diagnosis, guiding therapeutic decisions. More recently, numerous research efforts are ongoing to gain a better understanding about the intrinsic heterogeneity of CML, in order to identify a novel molecular signature which might characterize patients (pts) with different prognosis, and propensity to respond to treatmentIn the present study we adopted a GEP strategy in an attempt to identify genes and pathways, able to predict and/or elucidate the disease course of CP-CML pts at the time of diagnosis. To this aim, highly enriched CD34+ cells from peripheral blood obtained at diagnosis from pts with untreated Ph+ CML in CP were used throughout the study. Overall, 28 pts were included in the present analysis. They were diagnosed from August 2006 to June 2008; front-line treatment was either imatinib (12 pts) or nilotinib (16 pts).GEP was performed using the Affymetrix HG133 Plus microarray platform as per manufacturer's recommendation. Raw data was normalized using the RMA algorithm and filtered. Unsupervised analysis was performed by Multidimensional Scaling and hierarchical clustering. Top differential genes were selected by significance analysis of microarrays method (SAM), setting the FDR threshold 0.01. Genes associated with Sokal risk score were searched by various methods (Limma, ANOVA, SAM, ROC analysis, EB-arrays). Analyses were performed using R and Bioconductor, BRB array tools and MeV. To analyse the data, we have first stratified our cohort of pts according to their Sokal risk, into 3 groups (high, intermediate and low risk) of 10, 13 and 5 pts, respectively. We did not identify any significant gene signature uniquely associated with Sokal risk score, even by various supervised techniques. Neither was it possible to obtain a signature from the most extreme phenotypes, i.e. by excluding intermediate-risk pts. Interestingly, however, unsupervised analysis of the whole gene expression data set clearly identified two subgroups of pts (we have named them A and B), which included 15 and 13 pts, respectively. Those two subgroups of pts show a differential expression of a list of at least 461 probe sets, which represents the most significantly up and down regulated probes, without any false positive. Both a hierarchical clustering of these probe sets, as well as a multidimensional scaling plot showed a clear demarcation between the two subgroups of pts, and the Sokal risk score did not co-vary along with their gene expression profile. In the group A of pts, of the 461 probe sets, 317 resulted up-regulated and 144 down-regulated. Genes up-regulated are mainly involved in regulation of transcription and/or gene expression (40/317 probe sets code for zinc finger protein), whereas down-modulated genes are involved in cell differentiation, cell death and cell cycle regulation.Of interest, several genes and functional classes showed the same deregulated expression as previously described during progression to blast crisis of CML (Radich et al. PNAS 2006). Moreover, 6 probe sets (EREG, FOS, IL8, WT1, HSPA1A and DKFZp761P0423) that are part of the Radich “top ten” list of genes most significantly associated with progression in CML, are differentially expressed in our two subgroups.Overall, our data suggests the existence of two biologically distinct subgroups of CML, irrespective of Sokal score, which could be identifiable at diagnosis. Those findings further support a hypothesis that different clinical behaviours of the disease and response to treatment could be associated with different gene expression profiles of individual pts. Our plan is to follow up the pts which we have analyzed in this study and to investigate if those molecular signatures, identified through GEP could be correlated with treatment responses.

An Investigation on Performance of Significance Analysis of Microarray (SAM) for the Comparisons of Several Treatments with one Control in the Presence of Small‐variance Genes

One of multiple testing problems in drug finding experiments is the comparison of several treatments with one control. In this paper we discuss a particular situation of such an experiment, i.e., a microarray setting, where the many-to-one comparisons need to be addressed for thousands of genes simultaneously. For a gene-specific analysis, Dunnett's single step procedure is considered within gene tests, while the FDR controlling procedures such as Significance Analysis of Microarrays (SAM) and Benjamini and Hochberg (BH) False Discovery Rate (FDR) adjustment are applied to control the error rate across genes. The method is applied to a microarray experiment with four treatment groups (three microarrays in each group) and 16,998 genes. Simulation studies are conducted to investigate the performance of the SAM method and the BH-FDR procedure with regard to controlling the FDR, and to investigate the effect of small-variance genes on the FDR in the SAM procedure.

Significance analysis of microarray for relative quantitation of LC/MS data in proteomics

BackgroundAlthough fold change is a commonly used criterion in quantitative proteomics for differentiating regulated proteins, it does not provide an estimation of false positive and false negative rates that is often desirable in a large-scale quantitative proteomic analysis. We explore the possibility of applying the Significance Analysis of Microarray (SAM) method (PNAS 98:5116-5121) to a differential proteomics problem of two samples with replicates. The quantitative proteomic analysis was carried out with nanoliquid chromatography/linear iron trap-Fourier transform mass spectrometry. The biological sample model included two Mycobacterium smegmatis unlabeled cell cultures grown at pH 5 and pH 7. The objective was to compare the protein relative abundance between the two unlabeled cell cultures, with an emphasis on significance analysis of protein differential expression using the SAM method. Results using the SAM method are compared with those obtained by fold change and the conventional t-test.ResultsWe have applied the SAM method to solve the two-sample significance analysis problem in liquid chromatography/mass spectrometry (LC/MS) based quantitative proteomics. We grew the pH5 and pH7 unlabelled cell cultures in triplicate resulting in 6 biological replicates. Each biological replicate was mixed with a common 15N-labeled reference culture cells for normalization prior to SDS/PAGE fractionation and LC/MS analysis. For each biological replicate, one center SDS/PAGE gel fraction was selected for triplicate LC/MS analysis. There were 121 proteins quantified in at least 5 of the 6 biological replicates. Of these 121 proteins, 106 were significant in differential expression by the t-test (p < 0.05) based on peptide-level replicates, 54 were significant in differential expression by SAM with Δ = 0.68 cutoff and false positive rate at 5%, and 29 were significant in differential expression by the t-test (p < 0.05) based on protein-level replicates. The results indicate that SAM appears to overcome the false positives one encounters using the peptide-based t-test while allowing for identification of a greater number of differentially expressed proteins than the protein-based t-test.ConclusionWe demonstrate that the SAM method can be adapted for effective significance analysis of proteomic data. It provides much richer information about the protein differential expression profiles and is particularly useful in the estimation of false discovery rates and miss rates.

Transcriptional Profiles of the Human Pathogenic Fungus Paracoccidioides brasiliensis in Mycelium and Yeast Cells

Paracoccidioides brasiliensis is the causative agent of paracoccidioidomycosis, a disease that affects 10 million individuals in Latin America. This report depicts the results of the analysis of 6,022 assembled groups from mycelium and yeast phase expressed sequence tags, covering about 80% of the estimated genome of this dimorphic, thermo-regulated fungus. The data provide a comprehensive view of the fungal metabolism, including overexpressed transcripts, stage-specific genes, and also those that are up- or down-regulated as assessed by in silico electronic subtraction and cDNA microarrays. Also, a significant differential expression pattern in mycelium and yeast cells was detected, which was confirmed by Northern blot analysis, providing insights into differential metabolic adaptations. The overall transcriptome analysis provided information about sequences related to the cell cycle, stress response, drug resistance, and signal transduction pathways of the pathogen. Novel P. brasiliensis genes have been identified, probably corresponding to proteins that should be addressed as virulence factor candidates and potential new drug targets.