Significance Analysis Of Microarrays Algorithm Research Articles

Analysis of the autophagy gene expression profile of pancreatic cancer based on autophagy-related protein microtubule-associated protein 1A/1B-light chain 3.

BACKGROUNDPancreatic cancer is a highly invasive malignant tumor. Expression levels of the autophagy-related protein microtubule-associated protein 1A/1B-light chain 3 (LC3) and perineural invasion (PNI) are closely related to its occurrence and development. Our previous results showed that the high expression of LC3 was positively correlated with PNI in the patients with pancreatic cancer. In this study, we further searched for differential genes involved in autophagy of pancreatic cancer by gene expression profiling and analyzed their biological functions in pancreatic cancer, which provides a theoretical basis for elucidating the pathophysiological mechanism of autophagy in pancreatic cancer and PNI.AIMTo identify differentially expressed genes involved in pancreatic cancer autophagy and explore the pathogenesis at the molecular level.METHODSTwo sets of gene expression profiles of pancreatic cancer/normal tissue (GSE16515 and GSE15471) were collected from the Gene Expression Omnibus. Significance analysis of microarrays algorithm was used to screen differentially expressed genes related to pancreatic cancer. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to analyze the functional enrichment of the differentially expressed genes. Protein interaction data containing only differentially expressed genes was downloaded from String database and screened. Module mining was carried out by Cytoscape software and ClusterOne plug-in. The interaction relationship between the modules was analyzed and the pivot nodes between the functional modules were determined according to the information of the functional modules and the data of reliable protein interaction network.RESULTSBased on the above two data sets of pancreatic tissue total gene expression, 6098 and 12928 differentially expressed genes were obtained by analysis of genes with higher phenotypic correlation. After extracting the intersection of the two differential gene sets, 4870 genes were determined. GO analysis showed that 14 significant functional items including negative regulation of protein ubiquitination were closely related to autophagy. A total of 986 differentially expressed genes were enriched in these functional items. After eliminating the autophagy related genes of human cancer cells which had been defined, 347 differentially expressed genes were obtained. KEGG pathway analysis showed that the pathways hsa04144 and hsa04020 were related to autophagy. In addition, 65 clustering modules were screened after the protein interaction network was constructed based on String database, and module 32 contains the LC3 gene, which interacts with multiple autophagy-related genes. Moreover, ubiquitin C acts as a pivot node in functional modules to connect multiple modules related to pancreatic cancer and autophagy.CONCLUSIONThree hundred and forty-seven genes associated with autophagy in human pancreatic cancer were concentrated, and a key gene ubiquitin C which is closely related to the occurrence of PNI was determined, suggesting that LC3 may influence the PNI and prognosis of pancreatic cancer through ubiquitin C.

Molecular pathogenesis involved in human idiopathic pulmonary fibrosis based on an integrated microRNA‑mRNA interaction network.

Idiopathic pulmonary fibrosis (IPF) is considered to be an ailment of the lungs that cannot be cured, wherein the lung tissues are characterized by increased thickness and stiffness, and/or scars. Despite the fact that extensive success has been achieved regarding the molecular diagnostics and pathobiology, the basic pathogenesis associated with IPF has not yet been fully elucidated and requires further clarification. In the current research, the changes in microRNA (miRNA) and mRNA expression in IPF were investigated through an integrative network technique. The authentic miRNA and mRNA expression profiling datasets were downloaded from Gene Expression Omnibus, followed by identification of differentially expressed miRNAs and mRNAs with use of the Significance Analysis of Microarrays algorithm. Expansion of the molecular targets associated with miRNAs was performed with the use of CyTargetLinker in Cytoscape, which was succeeded by validation with the use of mRNA array expression profiling. The incorporated miRNA-mRNA network covered 27 genes, in addition to 22 miRNAs that were associated with IPF development. As revealed by the functional enrichment analysis, the cytokine-cytokine receptor interaction and glycine, serine and threonine metabolism signalling pathways were extensively associated with IPF development. Overall, the present incorporated network illustrated the key link between miRNA and genes in IPF; in particular, it was elucidated that miR-409-5p and has-miR-376c, together with their target genes (C-C motif chemokine ligand 20 and oncostatin M), are likely candidates involved in the promotion of IPF initiation and progression.

Application of network construction to estimate functional changes to insulin receptor substrates1 and2 in Huh7 cells following infection with the hepatitisC virus.

Hepatitis C virus (HCV) is closely associated with insulin resistance (IS), acting primarily by interfering with insulin signaling pathways, increasing cytokine-mediated (tumor necrosis factor α, interleukin 6) inflammatory responses and enhancing oxidative stress. In the insulin signaling pathways, the insulin receptor substrate (IRS) is one of the key regulatory factors. The present study constructed gene regulatory sub-networks specific for IRS1 and IRS2 in Huh7 cells and HCV-infected Huh7 (HCV-Huh7) cells using linear programming and a decomposition algorithm, and investigated the possible mechanisms underlying the function of IRS1/2 in HCV-induced IS in Huh7 cells. All data were obtained from GSE20948 of the Gene Expression Omnibus database from the National Center for Biotechnology Information. Genes which were significantly differentially expressed between Huh7 and HCV-Huh7 cells were analyzed using the significance analysis of microarray algorithm. The top 50 genes, including IRS1/2, were used as target genes to determine the gene regulatory networks and next the sub-networks of IRS1 and IRS2 in HCV-Huh7 and Huh7 cells using Gene Regulatory Network Inference Tool, an algorithm based on linear programming and the decomposition process. The IRS1/2 sub-networks were divided into upstream/downstream groups and activation/suppression clusters, and were further analyzed using Molecule Annotation System 3.0 and Database for Annotation, Visualization, and Integrated Discovery software, two online platforms for enrichment and clustering analysis and visualization. The results indicated that in Huh7 cells, the downstream network of IRS2 is more complex than that of IRS1, indicating that the insulin metabolism in Huh7 cells may be primarily mediated by IRS2. In HCV-Huh7 cells, the downstream pathway of IRS2 is blocked, suggesting that this may be the underlying mechanism in HCV infection that leads to insulin resistance. The present findings add a further dimension to the understanding of the pathological mechanisms of HCV infection-associated insulin resistance, and provide novel concepts for insulin resistance and glucose metabolism research.

Abstract C101: Circulating tumor necrosis factor receptor (TNFR) proteins in systemic disease outcome to combined-modality radiotherapy in rectal cancer

Abstract Introduction: In colorectal cancer, the impact of the tumor microenvironment with its immune effectors for disease outcome is increasingly acknowledged. Following curatively intended combined-modality radiotherapy in locally advanced rectal cancer (LARC), metastatic disease still remains a dominant cause of failure. Therefore, within the frame of a prospective LARC study of intensified neoadjuvant therapy (NCT00278694), and in light of intriguing recent findings of radiation effects on systemic antitumor immunity, we investigated whether treatment toxicity and survival outcome might be reflected by circulating immune factors. Experimental procedures: Using antibody array technology, the profiling of approximately 500 proteins was performed in serial serum samples collected during neoadjuvant chemotherapy (NACT; two cycles of the Nordic FLOX regimen) followed by long-course chemoradiotherapy (CRT) before final pelvic surgery. Array data was analyzed in the Significance Analysis of Microarrays algorithm, and functional associations among proteins were explored using the Search Tool for the Retrieval of Interacting Genes and Proteins software. Clinical endpoints were progression-free survival (PFS; median follow-up of 59 months for 85 patients) and treatment toxicities as assessed by Common Terminology Criteria for Adverse Events (CTCAE) scoring. Results: Analysis of the array data revealed significant changes in levels (relative to baseline) of a number of serum proteins during the neoadjuvant treatment course, among which osteoprotegerin (OPG; TNFRSF11B) showed the strongest increase. Single-parameter immunoassay OPG assessment demonstrated significant correlations between its baseline serum levels (range 13.6-177 pg/ml) and patients' age as well as common markers of systemic inflammation, such as erythrocyte sedimentation rate (ESR) and neutrophil-to-lymphocyte ratio (NLR). In univariate Cox regression analysis, high values of these variables were associated with adverse PFS (age-adjusted baseline OPG levels: hazard ratio (HR) 3.33, 95% confidence interval (CI) 1.24-8.94, p = 0.02; baseline ESR: HR 1.85, 95% CI 1.10-3.11, p = 0.02; post-NACT NLR: HR 1.89, 95% CI 1.08-3.31, p = 0.03). Of further note, patients' serum levels of RANK (TNFRSF11A) paralleled OPG alterations during the neoadjuvant therapy. Specifically, the CRT-induced changes in OPG and RANK were associated with PFS – the higher the increase (fold-change from NACT completion), the lower risk of a PFS event following definitive surgery (OPG: HR 0.27, 95% CI 0.09-0.87, p = 0.03; RANK: HR 0.13, 95% CI 0.02-0.76, p = 0.02). Correspondingly, by one-way analysis of variance, decline in serum levels during CRT (fold-change from NACT completion) was associated with adverse CTCAE grade diarrhea, the clinical correlate of treatment toxicity (OPG: p<0.01; RANK: p<0.01). Conclusions: In this LARC study, elevated levels of systemic inflammation markers on commencement of neoadjuvant therapy were associated with adverse PFS. On the contrary, the association between strong increase in circulating TNFR proteins at CRT completion and favorable outcome in a patient population treated with curative intent but with metastatic disease as the main PFS event suggests that systemic antitumor immunity may be invoked by radiation. Citation Format: Sebastian Meltzer, Erta Kalanxhi, Helga Helseth Hektoen, Svein Dueland, Kjersti Flatmark, Kathrine Røe Redalen, Anne Hansen Ree. Circulating tumor necrosis factor receptor (TNFR) proteins in systemic disease outcome to combined-modality radiotherapy in rectal cancer. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr C101.

Novel prognostic genes of diffuse large B-cell lymphoma revealed by survival analysis of gene expression data.

ObjectiveThis study aimed to identify prognostic genes for diffuse large B-cell lymphoma (DLBCL), using bioinformatic methods.MethodsFive gene expression data sets were downloaded from the Gene Expression Omnibus database. Significance analysis of microarrays algorithm was used to identify differentially expressed genes (DEGs) from two data sets. Functional enrichment analysis was performed for the DEGs with the Database for Annotation, Visualization and Integration Discovery (DAVID). Survival analysis was performed with the Kaplan–Meier method using function survfit from package survival of R for the other three data sets. Cox univariate regression analysis was used to further screen out prognostic genes.ResultsThirty-one common DEGs were identified in the two data sets, mainly enriched in the regulation of lymphocyte activation, immune response, and interleukin-mediated signaling pathway. Combined with 47 DLBCL-related genes acquired by literature retrieval, a total of 78 potential prognostic genes were obtained. Cases from the other three data sets were used in hierarchical clustering, and the 78 genes could cluster them into several subtypes with significant differences in survival curves. Cox univariate regression analysis revealed 45, 33, and eleven prognostic genes in the three data sets, respectively. Five common prognostic genes were revealed, including LCP2, TNFRSF9, FUT8, IRF4, and TLE1, among which LCP2, FUT8, and TLE1 were novel prognostic genes.ConclusionFive prognostic genes of DLBCL were identified in this study. They could not only be used for molecular subtyping of DLBCL but also be potential targets for treatment.

Expression analysis of the estrogen receptor target genes in renal cell carcinoma.

The aim of the present study was to investigate the differentially expressed genes (DEGs) and target genes of the estrogen receptor (ER) in renal cell carcinoma. The data (GSE12090) were downloaded from the gene expression omnibus database. Data underwent preprocessing using the affy package for Bioconductor software, then the DEGs were selected via the significance analysis of microarray algorithm within the siggenes package. Subsequently, the DEGs underwent functional and pathway enrichment analysis using Database for Annotation Visualization and Integrated Discovery software. Following data analysis, transcriptional regulatory networks between the DEGs and transcription factors were constructed. Finally, the ER target genes were subjected to gene ontology enrichment analysis. A total of 215 DEGs were identified between the chromophobe renal cell carcinoma samples and the oncocytoma samples, including 126 upregulated and 89 downregulated genes. Functional enrichment analysis indicated that 25% of the DEGs were significantly enriched in functions associated with the plasma membrane. Among those DEGs, 105 were regulated by the ER. Further regulatory network analysis indicated that the ER was mainly involved in the regulation of oncogenes and tumor suppressor genes, including protease serine 8, claudin 7 and Ras-related protein Rab-25. In the present study, the identified ER target genes were demonstrated to be closely associated with tumor development; this knowledge may improve the understanding of the ER regulatory mechanisms during tumor development and promote the discovery of predictive markers for renal cell carcinoma.

P0040 Expression analysis of oestrogen receptor target genes in renal cell carcinoma

<h3>Background</h3> The aim of this study was to explore differentially expressed genes (DEGs) and target genes of oestrogen receptors in renal cell carcinoma. <h3>Methods</h3> Data (GSE12090) were downloaded from the gene expression omnibus database. After data preprocessing using the affy package, DEGs were selected with the significance analysis of microarray algorithm within the siggenes package. DEGs were then subjected to function and pathway enrichment analysis by using DAVID (Database for Annotation Visualization and Integrated Discovery) software. Furthermore, the transcriptional regulatory networks between the DEGs and transcription factors were constructed. Finally, the oestrogen receptor target genes were subjected to Gene Ontology enrichment analysis. <h3>Findings</h3> A total of 215 DEGs were identified between the chromophobe renal cell carcinoma samples and the oncocytoma samples, including 126 up-regulated and 89 down-regulated genes. Function enrichment showed that 25% of the DEGs were significantly enriched into plasma membrane. Among those DEGs, 105 were regulated by oestrogen receptors. Further regulatory network analysis indicated that oestrogen receptors were mainly involved in the expression regulation of oncogenes and tumour suppressor genes, such as <i>PRSS8</i> (protease serine 8), <i>CLDN7</i> (claudin 7), and <i>RAB25</i> (Ras-related protein Rab-25). <h3>Interpretation</h3> In this study, the identified oestrogen receptor target genes were closely related to tumour development, which was helpful for understanding the regulation mechanism of oestrogen receptors during tumour development and for promoting the discovery of predictive markers in renal cell carcinoma.

GW24-e2497 Circulating MicroRNAs as Potential Biomarkers of Coagulation Dysfunction in Patients with Vulnerable Coronary Artery Disease

ObjectivesThe activation of coagulation and fibrinolysis plays a critical role in the incidence of coronary events. MicroRNAs (miRNAs) are small non-coding ribonucleic acids (RNAs) that negatively regulate gene expression on...

Inhibition of serine palmitoyltransferase delays the onset of radiation-induced pulmonary fibrosis through the negative regulation of sphingosine kinase-1 expression

The enforcement of sphingosine-1-phosphate (S1P) signaling network protects from radiation-induced pneumonitis. We now demonstrate that, in contrast to early postirradiation period, late postirradiation sphingosine kinase-1 (SphK1) and sphingoid base-1-phosphates are associated with radiation-induced pulmonary fibrosis (RIF). Using the mouse model, we demonstrate that RIF is characterized by a marked upregulation of S1P and dihydrosphingosine-1-phosphate (DHS1P) levels in the lung tissue and in circulation accompanied by increased lung SphK1 expression and activity. Inhibition of sphingolipid de novo biosynthesis by targeting serine palmitoyltransferase (SPT) with myriocin reduced radiation-induced pulmonary inflammation and delayed the onset of RIF as evidenced by increased animal lifespan and decreased expression of markers of fibrogenesis, such as collagen and α-smooth muscle actin (α-SMA), in the lung. Long-term inhibition of SPT also decreased radiation-induced SphK activity in the lung and the levels of S1P-DHS1P in the lung tissue and in circulation. In vitro, inhibition or silencing of serine palmitoyltransferase attenuated transforming growth factor-β1 (TGF-β)-induced upregulation of α-SMA through the negative regulation of SphK1 expression in normal human lung fibroblasts. These data demonstrate a novel role for SPT in regulating TGF-β signaling and fibrogenesis that is linked to the regulation of SphK1 expression and S1P-DHS1P formation.

Protein microarray analysis of serum factor-reactive autoantibodies reveals BAFF-binding autoantibodies in systemic lupus erythematosus (58.22)

Abstract Autoantibodies to cytokines and chemokines have been shown to influence immune pathways in patients afflicted with a range of immune pathology. We hypothesized that a multiplex platform capable of detecting antibodies to cytokines, chemokines and other immune factors would allow us to identify novel biomarkers of immune diseases and uncover new mechanisms of disease pathogenesis. We created a planar microarray containing a library of recombinant human proteins deposited on nitrocellulose-coated slides. We resolved binding of commercially available antibodies to cytokines and chemokines, and detected previously described cytokine reactivity in samples from patients with non-tuberculosis mycobacterium infection and autoimmune polyendocrine syndrome type 1. We further used the arrays to profile samples from three cohorts of systemic lupus erythematosus (SLE) patients. Among several cytokine targets, the significance analysis of microarrays algorithm identified that B-cell activating factor (BAFF)-binding autoantibodies correlated with subgroups of SLE patients. Reactive sera inhibited the activity of BAFF in a reporter cell assay, indicating that BAFF autoantibodies may influence the activity of this critical cytokine in SLE. Our results demonstrate that serum factor protein microarrays can be used to detect novel and potentially relevant autoreactivity to cytokines in human patient samples, and that BAFF-reactive autoantibodies may play a role in the pathophysiology of SLE.

A Weighted Principal Component Analysis and Its Application to Gene Expression Data

In this work, we introduce in the first part new developments in Principal Component Analysis (PCA) and in the second part a new method to select variables (genes in our application). Our focus is on problems where the values taken by each variable do not all have the same importance and where the data may be contaminated with noise and contain outliers, as is the case with microarray data. The usual PCA is not appropriate to deal with this kind of problems. In this context, we propose the use of a new correlation coefficient as an alternative to Pearson's. This leads to a so-called weighted PCA (WPCA). In order to illustrate the features of our WPCA and compare it with the usual PCA, we consider the problem of analyzing gene expression data sets. In the second part of this work, we propose a new PCA-based algorithm to iteratively select the most important genes in a microarray data set. We show that this algorithm produces better results when our WPCA is used instead of the usual PCA. Furthermore, by using Support Vector Machines, we show that it can compete with the Significance Analysis of Microarrays algorithm.

ParaSAM: a parallelized version of the significance analysis of microarrays algorithm

Motivation: Significance analysis of microarrays (SAM) is a widely used permutation-based approach to identifying differentially expressed genes in microarray datasets. While SAM is freely available as an Excel plug-in and as an R-package, analyses are often limited for large datasets due to very high memory requirements.Summary: We have developed a parallelized version of the SAM algorithm called ParaSAM to overcome the memory limitations. This high performance multithreaded application provides the scientific community with an easy and manageable client-server Windows application with graphical user interface and does not require programming experience to run. The parallel nature of the application comes from the use of web services to perform the permutations. Our results indicate that ParaSAM is not only faster than the serial version, but also can analyze extremely large datasets that cannot be performed using existing implementations.Availability:A web version open to the public is available at http://bioanalysis.genomics.mcg.edu/parasam. For local installations, both the windows and web implementations of ParaSAM are available for free at http://www.amdcc.org/bioinformatics/software/parasam.aspxContact: rmcindoe@mail.mcg.eduSupplementary information: Supplementary Data is available at Bioinformatics online.

Hepatic Gene Expression of Caucasian and African-American Patients with Obesity-Related Non-Alcoholic Fatty Liver Disease

There is increasing data suggesting that African Americans with NAFLD tend to have less progressive liver disease. The aim of this study is to assess differences in the hepatic gene expression of African-American and Caucasian patients with NAFLD who had undergone bariatric surgery. A total of 94 patients (81 NAFLD and 13 weight-matched controls with normal liver biopsy) were included. Of the entire cohort, 73 were Caucasians and 21 were African Americans. All patients were undergoing bariatric surgery. Two liver biopsies were obtained at the time of surgery. One biopsy was snap-frozen for gene expression and the other biopsy was stained for pathologic assessment. Liver biopsy confirmed that 24 patients from our cohort had NASH while 57 had only simple steatosis. Snap-frozen liver biopsy specimens of these patients were then used for the RNA extraction. cDNA probes were hybridized with customized microarray gene chips containing 5,220 relevant genes. Gene expression profiles were compared between groups using significance analysis of microarrays algorithm. In comparison to all Caucasian patients, African-American patients had over-expression of EPB41L1, IGF2, FAH, ACSL4, FUT4, CYP3A (q values < 10(-4)). In comparison to Caucasian NAFLD patients, African-American NAFLD patients showed over-expression of EPB41L1 and ACSL4 genes. Finally, in comparison to Caucasian NASH patients, African-American NASH patients showed over-expression of GSTM 2, GSTM4 and GSTM5 as well as FH and ASCL4 genes. Some genes highlighted by this analysis, particularly cytochrome CYP3A and glutathione transferases GSTM2, 4, 5, were previously implicated in the pathogenesis of NASH. African-American patients with biopsy-proven obesity-related NAFLD and NASH have a specific hepatic gene expression pattern that may explain their differences from Caucasian patients with NAFLD in developing progressive liver disease.

Differential expression of microRNAs in mouse liver under aberrant energy metabolic status

Despite years of effort, exact pathogenesis of nonalcoholic fatty liver disease (NAFLD) remains obscure. To gain an insight into the regulatory roles of microRNAs (miRNAs) in aberrant energy metabolic status and pathogenesis of NAFLD, we analyzed the expression of miRNAs in livers of ob/ob mice, streptozotocin (STZ)-induced type 1 diabetic mice, and normal C57BL/6 mice by miRNA microarray. Compared with normal C57BL/6 mice, ob/ob mice showed upregulation of eight miRNAs and downregulation of four miRNAs in fatty livers. Upregulation of miR-34a and downregulation of miR-122 was found in livers of STZ-induced diabetic mice. These results demonstrate that distinct miRNAs are strongly dysregulated in NAFLD and hyperglycemia. Comparison between miRNA expressions in livers of ob/ob mice and STZ-administered mice further revealed upregulation of four miRNAs and downregulation of two miRNAs in livers of ob/ob mice, indicating that these miRNAs may represent a molecular signature of NAFLD. A distinctive miRNA expression pattern was identified in ob/ob mouse liver, and hierarchical clustering of this pattern could clearly discriminate ob/ob mice from either normal C57BL/6 mice or STZ-administered mice. These findings suggest an important role of miRNAs in hepatic energy metabolism and implicate the participation of miRNAs in the pathophysiological processes of NAFLD.

Differential gene expression of peripheral blood mononuclear cells from rheumatoid arthritis patients may discriminate immunogenetic, pathogenic and treatment features

This study aimed to evaluate the association between the differential gene expression profiling of peripheral blood mononuclear cells of rheumatoid arthritis patients with their immunogenetic (human leucocyte antigen shared-epitope, HLA-SE), autoimmune response [anti-cyclic citrullinated peptide (CCP) antibodies], disease activity score (DAS-28) and treatment (disease-modifying antirheumatic drugs and tumour necrosis factor blocker) features. Total RNA samples were copied into Cy3-labelled complementary DNA probes, hybridized onto a glass slide microarray containing 4500 human IMAGE complementary DNA target sequences. The Cy3-monocolour microarray images from patients were quantified and normalized. Analysis of the data using the significance analysis of microarrays algorithm together with a Venn diagram allowed the identification of shared and of exclusively modulated genes, according to patient features. Thirteen genes were exclusively associated with the presence of HLA-SE alleles, whose major biological function was related to signal transduction, phosphorylation and apoptosis. Ninety-one genes were associated with disease activity, being involved in signal transduction, apoptosis, response to stress and DNA damage. One hundred and one genes were associated with the presence of anti-CCP antibodies, being involved in signal transduction, cell proliferation and apoptosis. Twenty-eight genes were associated with tumour necrosis factor blocker treatment, being involved in intracellular signalling cascade, phosphorylation and protein transport. Some of these genes had been previously associated with rheumatoid arthritis pathogenesis, whereas others were unveiled for future research.

Transcriptional changes in U343 MG-a glioblastoma cell line exposed to ionizing radiation

Glioblastoma multiforme (GBM) is a highly invasive and radioresistant brain tumor. Aiming to study how glioma cells respond to gamma-rays in terms of biological processes involved in cellular responses, we performed experiments at cellular context and gene expression analysis in U343-MG-a GBM cells irradiated with 1 Gy and collected at 6 h post-irradiation. The survival rate was approximately 61% for 1 Gy and was completely reduced at 16 Gy. By performing the microarray technique, 859 cDNA clones were analyzed. The Significance Analysis of Microarray algorithm indicated 196 significant expressed genes (false discovery rate (FDR) = 0.42%): 67 down-regulated and 97 up-regulated genes, which belong to several classes: metabolism, adhesion/cytoskeleton, signal transduction, cell cycle/apoptosis, membrane transport, DNA repair/DNA damage signaling, transcription factor, intracellular signaling, and RNA processing. Differential expression patterns of five selected genes (HSPA9B, INPP5A, PIP5K1A, FANCG, and TPP2) observed by the microarray analysis were further confirmed by the quantitative real time RT-PCR method, which demonstrated an up-regulation status of those genes. These results indicate a broad spectrum of biological processes (which may reflect the radio-resistance of U343 cells) that were altered in irradiated glioma cells, so as to guarantee cell survival.

The distinctive gastric fluid proteome in gastric cancer reveals a multi-biomarker diagnostic profile

BackgroundOverall gastric cancer survival remains poor mainly because there are no reliable methods for identifying highly curable early stage disease. Multi-protein profiling of gastric fluids, obtained from the anatomic site of pathology, could reveal diagnostic proteomic fingerprints.MethodsProtein profiles were generated from gastric fluid samples of 19 gastric cancer and 36 benign gastritides patients undergoing elective, clinically-indicated gastroscopy using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry on multiple ProteinChip arrays. Proteomic features were compared by significance analysis of microarray algorithm and two-way hierarchical clustering. A second blinded sample set (24 gastric cancers and 29 clinically benign gastritides) was used for validation.ResultsBy significance analysyis of microarray, 60 proteomic features were up-regulated and 46 were down-regulated in gastric cancer samples (p < 0.01). Multimarker clustering showed two distinctive proteomic profiles independent of age and ethnicity. Eighteen of 19 cancer samples clustered together (sensitivity 95%) while 27/36 of non-cancer samples clustered in a second group. Nine non-cancer samples that clustered with cancer samples included 5 pre-malignant lesions (1 adenomatous polyp and 4 intestinal metaplasia). Validation using a second sample set showed the sensitivity and specificity to be 88% and 93%, respectively. Positive predictive value of the combined data was 0.80. Selected peptide sequencing identified pepsinogen C and pepsin A activation peptide as significantly down-regulated and alpha-defensin as significantly up-regulated.ConclusionThis simple and reproducible multimarker proteomic assay could supplement clinical gastroscopic evaluation of symptomatic patients to enhance diagnostic accuracy for gastric cancer and pre-malignant lesions.

Gene Expression Signature in Advanced Colorectal Cancer Patients Select Drugs and Response for the Use of Leucovorin, Fluorouracil, and Irinotecan

In patients with advanced colorectal cancer, leucovorin, fluorouracil, and irinotecan (FOLFIRI) is considered as one of the reference first-line treatments. However, only about half of treated patients respond to this regimen, and there is no clinically useful marker that predicts response. A major clinical challenge is to identify the subset of patients who could benefit from this chemotherapy. We aimed to identify a gene expression profile in primary colon cancer tissue that could predict chemotherapy response. Tumor colon samples from 21 patients with advanced colorectal cancer were analyzed for gene expression profiling using Human Genome GeneChip arrays U133. At the end of the first-line treatment, the best observed response, according to WHO criteria, was used to define the responders and nonresponders. Discriminatory genes were first selected by the significance analysis of microarrays algorithm and the area under the receiver operating characteristic curve. A predictor classifier was then constructed using support vector machines. Finally, leave-one-out cross validation was used to estimate the performance and the accuracy of the output class prediction rule. We determined a set of 14 predictor genes of response to FOLFIRI. Nine of nine responders (100% specificity) and 11 of 12 nonresponders (92% sensitivity) were classified correctly, for an overall accuracy of 95%. After validation in an independent cohort of patients, our gene signature could be used as a decision tool to assist oncologists in selecting colorectal cancer patients who could benefit from FOLFIRI chemotherapy, both in the adjuvant and the first-line metastatic setting.

Antigen microarray profiling of autoantibodies in rheumatoid arthritis

Because rheumatoid arthritis (RA) is a heterogeneous autoimmune disease in terms of disease manifestations, clinical outcomes, and therapeutic responses, we developed and applied a novel antigen microarray technology to identify distinct serum antibody profiles in patients with RA. Synovial proteome microarrays, containing 225 peptides and proteins that represent candidate and control antigens, were developed. These arrays were used to profile autoantibodies in randomly selected sera from 2 different cohorts of patients: the Stanford Arthritis Center inception cohort, comprising 18 patients with established RA and 38 controls, and the Arthritis, Rheumatism, and Aging Medical Information System cohort, comprising 58 patients with a clinical diagnosis of RA of <6 months duration. Data were analyzed using the significance analysis of microarrays algorithm, the prediction analysis of microarrays algorithm, and Cluster software. Antigen microarrays demonstrated that autoreactive B cell responses targeting citrullinated epitopes were present in a subset of patients with early RA with features predictive of the development of severe RA. In contrast, autoimmune targeting of the native epitopes contained on synovial arrays, including several human cartilage gp39 peptides and type II collagen, were associated with features predictive of less severe RA. Proteomic analysis of autoantibody reactivities provides diagnostic information and allows stratification of patients with early RA into clinically relevant disease subsets.

Analysis of gene expression patterns in human postburn hypertrophic scars.

Hypertrophic scars cause cosmetic disfigurement and limited mobility in burn patients. To better understand the molecular pathophysiology of hypertrophic scar formation, microarray analyses were performed on normal skin and hypertrophic scars from four burn patients. Microarray analyses were determined in an effort to identify genes whose expression discriminated between normal skin and mature, hypertrophic scars. Surgical biopsies were obtained from two pediatric and two adult patients 6 to 15 months after burn injury. Total RNA was isolated from the samples and subjected to microarray analysis using the Affymetrix U95Av2 GeneChip. Results from this analysis revealed 31 probe sets representing genes that were consistently up-regulated at least two-fold in hypertrophic scar specimens from all four patients and four probe sets that were down-regulated. The significance analysis of microarrays algorithm also identified 35 probe sets whose increased expression resulted in the hierarchal clustering of the hypertrophic scar and normal tissue, seven of which were identical to the six genes identified by paired analyses. These six genes all displayed elevated levels of expression in the scar tissue. Proteins encoded by the genes identified included germline oligometric matrix protein, matrix metalloproteinase-16, collagen type 1alpha, pleiotrophin, and thrombospondin-4. Although the results presented here suggest that there may be unique patterns of gene expression in hypertrophic scars that may be important in the evaluation and treatment of hypertrophic scarring, the results must be confirmed with larger datasets.