Significance Analysis Of Microarrays Research Articles

Smoking and drinking influence the advancing of ischemic stroke disease by targeting PTGS2 and TNFAIP3.

In the present study, we explored the influence of cigarette smoking and alcohol drinking on gene expression level and related functions and pathways on the development of ischemic stroke (IS) disease. The gene expression profile of E-GEOD-22255 was obtained from 20 IS samples (7 patients without smoking or drinking history and 13 patients with smoking or drinking history) and 20 controls (9 normal controls without smoking or drinking history and 11 controls with smoking or drinking history). The correlation degree between gene expression and grouping were measured by significance analysis of microarray (SAM). Smoking or drinking-related DEGs were screened. GO functional and KEGG pathway enrichment analyses were processed. Based on the KEGG database, a pathway relationship network was constructed. DEGs in significant functions and pathways were inserted and regarded as key DEGs. Gene co-expression network was constructed based on the expression value of key genes. In total, 319 IS-related DEGs, which were induced by smoking and drinking, were screened and enriched in various functions and pathways, including inflammatory response, nuclear factor-κB (NF-κB) signaling pathway and influenza A. Pathway relationship network was constructed with 44 nodes and the hub node was the MAPK signaling pathway. After merging, 87 key DEGs were obtained. The gene co-expression network with 43 node edges was constructed and the hub node was prostaglandin-endoperoxide synthase 2. In IS patients, smoking and drinking may induce different expression of many genes, including PTGS2, TNFAIP3, ZFP36 and NFKBIZ. In addition, these genes participated in various pathways, such as inflammatory response.

A proteomics analysis of cell signaling pathways in colon cancer.

608 Background: Colorectal cancer is the second most common cancer in women and third most common cancer in men. Major signaling pathways are known to be deregulated in cancer, however, there are only a few studies addressing activation status of more than one or two pathways in colorectal cancer in comparison with the normal tissue. The present study aims to compare the expression pattern of proteins associated with cell signaling in paired tumor and non-tumor samples of patients with colon cancer, as well as to define the cluster of proteins to differentiate patients with non-metastatic (Dukes’ grade B) and metastatic (Dukes’ grade C&D) colon cancer. Methods: Frozen tumor and non-tumor samples were collected after tumor resection from 19 patients with colon cancer. The "Panorama" (Sigma-Aldrich) Antibody Microarray-Cell Signaling kits were used for the analyses. The expression ratios of paired tumor/non-tumor samples were calculated for the each protein. We employed R packages ‘samr’, ‘gplots’, ‘supclust’ ( pelora, wilma algorithms), ‘glmnet’ for the differential expression analysis, supervised clustering and penalized logistic regression, respectively. Results: Significance analysis of microarrays revealed 9 up-regulated proteins, including protein kinase C gamma, c-Myc, MDM2, bCOP, p14ARF, p57kip2, GRB2, APP, pan cytokeratin, and 1 down-regulated protein (GAP1) in tumoral mucosa. Pan cytokeratin and amyloid precursor protein were up-regulated in tumor vs. non-tumor tissue, and were selected in the predictive cluster to discriminate colon cancer type. Higher levels of S-100b and phospho-Tau-pSer199/202 were confirmed as the predictors of non-metastatic colon cancer by all of employed regression/clustering methods. Conclusions: Deregulated proteins in colon cancer are involved in oncogenic signal transduction, cell cycle control, and regulation of cytoskeleton formation/transport. Further studies are needed to validate potential protein markers of colon cancer development and metastatic progression.

Signaling Pathway Impact Analysis through Significance Analysis of Microarray Gene Expression Data

The aim of signaling pathway impact analysis is to identify significant pathways which are related to a disease. Identification of significant pathways is possible by finding differentiallyexpressed genes, the level of differential expression, as well as the effect of interaction with other genes in the pathway. In this study, we have suggested an approach to improve the outputof signaling pathway impact analysis by identifying differentially expressed genes using Significance Analysis of Microarray (SAM) gene selection method.

Abstract A22: Stromal cell signature in luminal breast cancer associated with response to neoadjuvant chemotherapy

Abstract Background: In locally advanced breast cancer, neoadjuvant chemotherapy may reduce tumor dimension and allow breast-conserving surgeries. However, it is not yet possible to predict which patients will benefit from neoadjuvant chemotherapy, as some patients may not experience tumor reduction, especially in luminal tumor samples. Tumor behavior reflects an intense crosstalk between malignant and stromal cells; however, little importance has been placed on the influence of the stromal cell in tumor response to chemotherapy. Objective: Our aim was to evaluate whether a stromal cell transcriptional signature might be associated with response to neoadjuvant anthracycline and taxane, in locally advanced luminal breast cancer, using a direct approach of stromal cell selection. Methodology: Twenty-nine patients diagnosed with locally advanced luminal breast cancer received neoadjuvant chemotherapy (doxorubicin and cyclophosphamide followed by paclitaxel). Breast cancer slices were submitted to laser-capture microdissection (LCM) to select stromal cells. Total RNA from stromal enriched cells were isolated using Arcturus PicoPure RNA isolation (Life technologies) according to the manufacturer's protocol. Linearly amplified two-round RiboAmp HSPlus2-round and Low Input Quick Amp (Agilent Technologies) protocols were adapted to optimize the amplification for the small numbers of cells produced by LCM. The labelled cRNAs were hybridized onto the Sure Print G3 (8×60K, Agilent Technologies). After having washed the slides, the arrays were scanned with the Agilent Bundle Model B Microarray Scanner System (Agilent Technologies). Scanned image files were visually inspected for artifacts, and the fluorescence intensities were extracted and preprocessed with Agilent Feature Extraction software (v10.7.1) and to normalize the results geneSpring GX12.1 software (Agilent Technologies) was used. Expression levels were compared using MultiExperiment Viewer (MeV) software, applying significance analysis of microarrays (SAM). Gene set enrichment analysis (GSEA) was used to identify whether predefined gene sets might associate with gene expression differences between phenotypes (http://software.broadinstitute.org/gsea/index.jsp). This methodology makes it possible to detect situations where all genes, in a predefined set, change in a small but coordinated way. Results: In estrogen receptor (ER) positive tumors, GSEA (FDR < 0.1) indicated that tumor down-staging was correlated with one KEGG gene set: antigen processing antigen presentation (comprehending CD74, CD8A, CD8B, and 9 molecules of HLA complex, among others) and with four Gene Ontology (GO) gene sets (biologic process), including one related with immune response: regulation of T cell activation (comprehending genes such as ZAP70, LCK, CD24, CD47, CD3E, among others). Gene sets associated with tumor non-down-staging (GSEA FDR < 0.25) were KEGG gene set: extracellular cell matrix (ECM) receptor interaction (including 5 types of integrins, 6 collagens, 4 laminins, fibronectin 1, and trombospondin 1), as well as seven GO gene sets (biologic process): cell adhesion; cell recognition; embryonic development; (cellular component): cortical cytoskeleton; cell projection part; cell surface; cytosolic part. Conclusion: A stromal signature associated with immune response in locally advanced luminal breast cancer may be associated with tumor down-staging following neoadjuvant chemotherapy. Financial Support: FAPESP 09/100088-7. Citation Format: Maria Lucia H. Katayama, Rene A da Costa Vieira, Rosimeire A. Roela, Victor P. Andrade, Luiz Guilherme C. A. de Lima, Giselly Encinas, Ligia M. Kerr, Simone Maistro, M. Mitzi Brentani, Maria A. A. Koike Folgueira. Stromal cell signature in luminal breast cancer associated with response to neoadjuvant chemotherapy [abstract]. In: Proceedings of the AACR International Conference held in cooperation with the Latin American Cooperative Oncology Group (LACOG) on Translational Cancer Medicine; May 4-6, 2017; São Paulo, Brazil. Philadelphia (PA): AACR; Clin Cancer Res 2018;24(1_Suppl):Abstract nr A22.

Pathway Analysis of Gene Expression of E14 Versus E18 Fetal Fibroblasts.

Objective: Fetuses early in gestation heal skin wounds without forming scars. The biological mechanisms behind this process are largely unknown. Fibroblasts, however, are cells known to be intimately involved in wound healing and scar formation. We examined fibroblasts in different stages of development to characterize differences in gene expression that may result in the switch from regenerative wound repair to repair with scarring. Approach: Fibroblasts were isolated and cultured from the back skin of BALB/c wild-type mouse fetuses at embryonic day (E)14 and E18 (n = 10). The fibroblast total RNA was extracted, and microarray analysis was conducted using chips containing 42,000 genes. Significance analysis of microarrays was performed to identify genes with greater than twofold expression difference and a false discovery rate of less than two. Identified genes subsequently underwent enrichment analysis to detect differentially expressed pathways. Results: Two hundred seventy-five genes were differentially expressed between E14 and E18 in fetal fibroblasts. Thirty genes were significantly downregulated and 245 genes were significantly upregulated at E18 compared with E14. Ingenuity pathway analysis identified the top 20 signaling pathways differentially activated in fetal fibroblasts between the E18 and E14 time points. Innovation: To our knowledge, this work represents the first instance where differentially expressed genes and signaling pathways between fetal fibroblasts at E14 and E18 have been studied. Conclusion: The genes and pathways identified here potentially underlie the mechanism behind the transition from fetal wound healing via regeneration to wound healing by repair, and may prove to be key targets for future therapeutics.

Gene Expression Patterns of Cutaneous Squamous Cell Carcinoma and Actinic Keratosis: Biomarkers Screening for Skin Disease Diagnosis.

BackgroundActinic keratosis (AK) was an intraepidermal tumor which caused by ultraviolet irradiation-induced skin damage.ObjectiveThe aim was to screen biomarkers for development of skin disease by comparing the gene expression profiles between cutaneous squamous cell carcinoma (CSCC) and AK.MethodsGSE45216 with 30 cutaneous squamous cell carcinoma patients and 10 actinic keratosis patients were downloaded and significance analysis of microarrays was processed to screen differently expressed genes (DEGs). Fisher's exact test was processed for DEGs enrichment. Pathway relationship network systematically reflected the signal conduction and synergism between enriched pathways based on Kyoto Encyclopedia of Genes and Genomes database. Gene co-expression network was constructed according to gene expression data. Quantitative real-time-PCR was used to verify screened biomarkers.ResultsTotal 410 DEGs were screened and enriched into various functions, such as signal transduction and negative regulation of apoptotic process. They also participated into cytokine-cytokine receptor interaction and focal adhesion. The pathway relationship network was constructed with 27 nodes. Hub nodes with higher degree of this network were mitogen-activated protein kinase signaling pathway and apoptosis. The gene co-expression network was constructed with 39 nodes. Thereinto, hub node was ELOVL fatty acid elongase. The expression levels of ELOVL4 and HPGD were significantly higher in CSCC samples than that in AK samples, while the expression levels of INHBA and LAMC2 in CSCC samples were significantly lower than that in AK samples.ConclusionThese screened genes, including ELOVL4, HPGD, INHBA and LAMC2, played important roles in transformation from AK to CSCC.

The peripheral blood transcriptome identifies dysregulation of inflammatory response genes in polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is a common endocrine disorder in women, resulting in ovulation failure and other metabolic problems. However, the underlying mechanisms of it remain largely uncertain due to the complexity of clinical manifestations. This systemic disorder is involved in endocrine, metabolism, immune system and many organs, and few studies have explored peripheral blood transcriptome in patients with PCOS. We performed gene expression profiling of peripheral blood from 8 PCOS patients and eight healthy women with microarray. The significance analysis of microarray (SAM) software was employed to screen the differentially expressed genes (DEGs) and gene ontology (GO) was used for functional enrichment analysis. In total, 181 DEGs with fold-changes >2.0 and q-values <0.05 were identified between the two groups. Among them, 149 were up-regulated and 32 down-regulated in PCOS. Unsupervised clustering of expressed genes could readily differentiate PCOS from control. More importantly, inflammatory response pathway including 14 dysregulated genes was highly enriched in PCOS. Furthermore, 10 DEGs were validated using quantitative reverse-transcription PCR (qRT-PCR) assays. Our study provides independent evidence for the involvement of systemic inflammatory response in PCOS and it may facilitate a greater understanding of this complex disease.

The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer

Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM) method was applied to detect the significant genes in rectal and lung cancers to showcase the utility of imputation approaches in preserving significant genes. To determine the impact of different imputation methods on the identification of important genes, the chi-squared test was used to compare the proportions of overlaps between significant genes detected from original data and those detected from the imputed datasets. Additionally, the significant genes are tested for their enrichment in important pathways, using the ConsensusPathDB. Our results showed that almost all the significant genes and pathways of the original dataset can be detected in all imputed datasets, indicating that there is no significant difference in the performance of various imputation methods tested. The source code and selected datasets are available on http://profiles.bs.ipm.ir/softwares/imputation_methods/.

The screening and analysis of protein signatures and signaling associated with chemoresistance based on Protein Pathway Array technology in gastric cancer.

The present study was aimed to identify proteins associated with signaling pathways involved in chemoresistance, and establish a predictive model for chemoresistance in gastric cancer patients after radical surgery. A total of 140clinically-stagedIII gastric cancer samples from patients after D2radical gastrectomy were enrolled in the present study. Protein Pathway Array (PPA) and 286antibodies were used to assess the protein expression in tumor tissues of patients. The Significance Analysis of Microarray (SAM) software and clustering and discriminant analysis were used to identify differentially expressed proteins between chemosensitive and chemoresistant subsets, and a predictive model for chemoresistance was established using the independent predictive factors. The Ingenuity Pathway Analysis (IPA) software was also used to investigate the relationship between proteins and the signaling transduction network. A total of 23proteins were differentially expressed between 67chemosensitive and 73chemoresitant tumor tissues. Six proteins including PLK1 and DACH1 were independent risk factors for chemoresistance. A predictive model for chemoresistance by these proteins was established, and the accuracy, the sensitivity, and the specificity of this modal was 89.3, 90.3 and 88.2%, respectively. In addition, the present study revealed that differentially expressed proteins were closely related to cellular activity, DNA methylation and DNA damage and repair, and also involved in the ERK/MAPK, Wnt/β-catenin, PI3K/AKT, apoptosis and p53 signaling pathways. In conclusion, the predictive model established by PPA may be an effective detection system for predicting the chemosensitivity of gastric cancer patients after D2 gastrectomy.

Modifying SAMseq to account for asymmetry in the distribution of effect sizes when identifying differentially expressed genes.

RNA-Seq is a developing technology for generating gene expression data by directly sequencing mRNA molecules in a sample. RNA-Seq data consist of counts of reads recorded to a particular gene that are often used to identify differentially expressed (DE) genes. A common statistical method used to analyze RNA-Seq data is Significance Analysis of Microarray with emphasis on RNA-Seq data (SAMseq). SAMseq is a nonparametric method that uses a resampling technique to account for differences in sequencing depths when identifying DE genes. We propose a modification of this method that takes into account asymmetry in the distribution of the effect sizes by taking into account the sign of the test statistics. Through simulation studies, we showthat the proposed method, comparedwith the traditional SAMseqmethod and other existing methods provides better power for identifying truly DE genes or more sufficiently controls FDR in most settings where asymmetry is present. We illustrate the use of the proposed method by analyzing an RNA-Seq data set containing C57BL/6J (B6) and DBA/2J (D2) mouse strains samples.

Predicting pathogenic genes for primary myelofibrosis based on a system‑network approach.

The aim of the present study was to predict pathogenic genes for primary myelofibrosis (PMF) using a system-network approach by combining protein-protein interaction (PPI) network and gene expression data with known pathogenic genes. PMF gene expression profiles, known pathogenic genes and protein-protein interactions were obtained. Using these data, differentially expressed genes (DEGs) were identified between PMF and normal conditions using significance analysis of microarrays, and seed genes were determined based on the intersection of known pathogenic genes and the PMF gene expression profile. A new network was constructed using the seed genes and their adjacent DEGs within the PPI network. Subsequently, a pathogenic network was extracted from the new network, and contained genes that interacted with at least two seed genes, and the candidate pathogenic genes were predicted based on the cohesion with seed genes. Cluster analysis was performed to mine the pathogenic modules from the pathogenic network, and functional analysis was performed to identify the putative biological processes of the candidate pathogenic genes. Results from the present study identified 845 DEGs between PMF and normal conditions, and 45 seed genes in PMF were screened. Subsequently, a pathogenic network comprising 103 nodes and 265 interactions was constructed, and 4 pathogenic modules (modules A-D) were mined from the pathogenic network. There were nine candidate pathogenic genes contained within Module A and four potential pathogenic genes, including E1A-binding protein p300, RAS-like proto-oncogene A, von Willebrand factor and RAF-1 proto-oncogene, serine/threonine kinase, were identified that may be involved in the same biological process with the seed genes. This study predicted 10 candidate pathogenic genes and several signaling pathways that may be related to the pathogenesis of PMF using a system-network approach. These predictions may shed light on the PMF pathogenesis and may provide guidelines for future experimental verification.

A rank-based algorithm of differential expression analysis for small cell line data with statistical control.

To detect differentially expressed genes (DEGs) in small-scale cell line experiments, usually with only two or three technical replicates for each state, the commonly used statistical methods such as significance analysis of microarrays (SAM), limma and RankProd (RP) lack statistical power, while the fold change method lacks any statistical control. In this study, we demonstrated that the within-sample relative expression orderings (REOs) of gene pairs were highly stable among technical replicates of a cell line but often widely disrupted after certain treatments such like gene knockdown, gene transfection and drug treatment. Based on this finding, we customized the RankComp algorithm, previously designed for individualized differential expression analysis through REO comparison, to identify DEGs with certain statistical control for small-scale cell line data. In both simulated and real data, the new algorithm, named CellComp, exhibited high precision with much higher sensitivity than the original RankComp, SAM, limma and RP methods. Therefore, CellComp provides an efficient tool for analyzing small-scale cell line data.

A unique gene expression signature is significantly differentially expressed in tumor-positive or tumor-negative sentinel lymph nodes in patients with melanoma.

The purpose of this study was to learn whether molecular characterization through gene expression profiling of node-positive and node-negative sentinel lymph nodes (SLNs) in patients with clinical stage I and II melanoma may improve the understanding of mechanisms of metastasis and identify gene signatures for SLNs/SLNs that correlate with diagnosis or clinical outcome. Gene expression profiling was performed on SLN biopsies of 48 (24 SLN and 24 SLN) patients (T3a/b-T4a/b) who underwent staging of SLNs using transcriptome profiling analysis on 5 μm sections of fresh SLNs. U133A 2.0 Affymetrix gene chips were used. Significance analysis of microarrays was used to test the association between gene expression level and SLN status. Genes with fold change more than 1.5 and q value less than 0.05 were considered differentially expressed. Pathway analysis was performed using Ingenuity Pathway Analysis. The Benjamini and Hochberg method was used to adjust for multiple testing in pathway analysis. We identified 89 probe sets that were significantly differentially expressed (1.5-27-fold; q<0.05). Upon performing the pathway analysis, it was found that 25 genes were common among the most significant and biologically relevant canonical pathways. The molecules and pathways that achieved differential expression of highest statistical significance were notably related to melanoma and its microenvironment and to signaling pathways implicated in immunosuppression and development of cancer. A 25-gene signature is significantly differentially expressed between SLN and SLN and is related to melanoma oncogenesis and immunosuppression. The identified expression profile provides a signature of melanoma nodal involvement. These findings warrant further investigation into the mechanisms of metastasis, melanoma metastasis diagnosis, and prediction of outcome.

Cesarean Section, Formula Feeding, and Infant Antibiotic Exposure: Separate and Combined Impacts on Gut Microbial Changes in Later Infancy.

Established during infancy, our complex gut microbial community is shaped by medical interventions and societal preferences, such as cesarean section, formula feeding, and antibiotic use. We undertook this study to apply the significance analysis of microarrays (SAM) method to quantify changes in gut microbial composition during later infancy following the most common birth and postnatal exposures affecting infant gut microbial composition. Gut microbiota of 166 full-term infants in the Canadian Healthy Infant Longitudinal Development birth cohort were profiled using 16S high-throughput gene sequencing. Infants were placed into groups according to mutually exclusive combinations of birth mode (vaginal/cesarean birth), breastfeeding status (yes/no), and antibiotic use (yes/no) by 3 months of age. Based on repeated permutations of data and adjustment for the false discovery rate, the SAM statistic identified statistically significant changes in gut microbial abundance between 3 months and 1 year of age within each infant group. We observed well-known patterns of microbial phyla succession in later infancy (declining Proteobacteria; increasing Firmicutes and Bacteroidetes) following vaginal birth, breastfeeding, and no antibiotic exposure. Genus Lactobacillus, Roseburia, and Faecalibacterium species appeared in the top 10 increases to microbial abundance in these infants. Deviations from this pattern were evident among infants with other perinatal co-exposures; notably, the largest number of microbial species with unchanged abundance was seen in gut microbiota following early cessation of breastfeeding in infants. With and without antibiotic exposure, the absence of a breast milk diet by 3 months of age following vaginal birth yielded a higher proportion of unchanged abundance of Bacteroidaceae and Enterobacteriaceae in later infancy, and a higher ratio of unchanged Enterobacteriaceae to Alcaligenaceae microbiota. Gut microbiota of infants born vaginally and exclusively formula fed became less enriched with family Veillonellaceae and Clostridiaceae, showed unchanging levels of Ruminococcaceae, and exhibited a greater decline in the Rikenellaceae/Bacteroidaceae ratio compared to their breastfed, vaginally delivered counterparts. These changes were also evident in cesarean-delivered infants to a lesser extent. The clinical relevance of these trajectories of microbial change is that they culminate in taxon-specific abundances in the gut microbiota of later infancy, which we and others have observed to be associated with food sensitization.

A novel approach using metabolomics coupled with hematological and biochemical parameters to explain the enriching-blood effect and mechanism of unprocessed Angelica sinensis and its 4 kinds of processed products

Ethnopharmacological relevanceAngelica sinensis (AS), root of Angelica sinensis (Oliv.) Diels, an important kind of Chinese traditional herbal medicine, has been used for women to enrich the blood for thousands of years. It is mainly distributed in Gansu province of China. According to Traditional Chinese medicine usage, unprocessed AS (UAS) and its 4 kinds of processed products (ASs) are all used to treat different diseases or syndromes. The difference among the enriching-blood effects of ASs is unclear. And their exact mechanisms of enriching the blood are not fully understood. Aim of the studyIn this study, our aim is to compare the enriching-blood effect and explain the related mechanism of ASs, to lay the foundation for the blood deficiency diagnosis and the rational use of ASs in the clinic. Materials and methodsASs were used to intervene the blood deficiency syndrome model mice induced by acetyl phenylhydrazine (APH) and cyclophosphamide (CTX). A novel approach using metabolomics coupled with hematological and biochemical parameters to explain the enriching-blood effect and mechanism of ASs was established. The blood routine examination, ATPase, glucose-6-phosphate dehydrogenase, methemoglobin, glutathion peroxidase, glutathione reductase, and erythropoietin were measured. Two biofluids (plasma and urine) obtained from mice were analyzed with GC-MS. Distinct changes in metabolite patterns of the two biofluids after mice were induced by APH and CTX, and mice were intervened with ASs were analyzed using partial least squares-discriminant analysis. Potential biomarkers were found using a novel method including variable importance in the projection (VIP) >1.0, volcano plot analysis, and significance analysis of microarray. ResultsThe results of hematological, biochemical parameters and the integrated metabolomics all showed the blood deficiency syndrome model was built successfully, ASs exhibited different degree of enriching-blood effect, and AS pached with alcohol (AAS) exhibited the best enriching-blood effect. 16 metabolites in the plasma and 8 metabolites in the urine were considered as the potential biomarkers. These metabolites were involved in 7 metabolic pathways which were concerned with the different enriching-blood effect mechanisms of ASs. The correlation analysis results confirmed L-Valine (plasma), Linoleic acid (urine), L-Aspartic acid (urine) and Cholesterol (urine) were strong positive or negative associated with biochemical indicators. ConclusionsThe enriching-blood effects of ASs are different. The pathological mechanisms of blood deficiency syndrome and the enriching-blood effect mechanism of ASs are involved in 7 metabolic pathways. L-Valine (plasma), Linoleic acid (urine), L-Aspartic acid (urine), Cholesterol (urine) are four important biomarkers being related to the enriching-blood effect of ASs. The combination of VIP, volcano plot analysis and significance analysis of microarray is suitable for screening biomarkers in metabolomics study. They can lay the foundation for clinical practice.

Radiomic features predict Ki-67 expression level and survival in lower grade gliomas.

To investigate the radiomic features associated with Ki-67 expression in lower grade gliomas and assess the prognostic values of these features. Patients with lower grade gliomas (n = 117) were randomly assigned into the training (n = 78) and validation (n = 39) sets. A total of 431 radiological features were extracted from each patient. Differential radiological features between the low and high Ki-67 expression groups were screened by significance analysis of microarrays. Then, generalized linear analysis was performed to select features that could predict the Ki-67 expression level. Predictive efficiencies were further evaluated in the validation set. Cox regression analysis was performed to investigate the prognostic values of Ki-67 expression level and Ki-67-related radiological features. A group of nine radiological features were screened for prediction of Ki-67 expression status; these achieved accuracies of 83.3% and 88.6% (areas under the curves, 0.91 and 0.93) in the training and validation sets, respectively. Of these features, only spherical disproportion (SD) was found to be a prognostic factor. Patients in the high SD group exhibited worse outcomes in the whole cohort (overall survival, p < 0.0001; progression-free survival, p < 0.0001). Ki-67 expression level and SD were independent prognostic factors in the multivariate Cox regression analysis. This study identified a radiomic signature for prediction of Ki-67 expression level as well as a prognostic radiological feature in patients with lower grade gliomas.

Transcriptome profiling of antiviral immune and dietary fatty acid dependent responses of Atlantic salmon macrophage-like cells

BackgroundDue to the limited availability and high cost of fish oil in the face of increasing aquaculture production, there is a need to reduce usage of fish oil in aquafeeds without compromising farm fish health. Therefore, the present study was conducted to determine if different levels of vegetable and fish oils can alter antiviral responses of salmon macrophage-like cells (MLCs). Atlantic salmon (Salmo salar) were fed diets containing 7.4% (FO7) or 5.1% (FO5) fish oil. These diets were designed to be relatively low in EPA + DHA (i.e. FO7: 1.41% and FO5: 1%), but near the requirement level, and resulting in comparable growth. Vegetable oil (i.e. rapeseed oil) was used to balance fish oil in experimental diets. After a 16-week feeding trial, MLCs isolated from fish in these dietary groups were stimulated by a viral mimic (dsRNA: pIC) for 6 h (qPCR assay) and 24 h (microarray and qPCR assays).ResultsThe fatty acid composition of head kidney leukocytes varied between the two dietary groups (e.g. higher 20:5n-3 in the FO7 group). Following microarray assays using a 44K salmonid platform, Rank Products (RP) analysis showed 14 and 54 differentially expressed probes (DEP) (PFP < 0.05) between the two diets in control and pIC groups (FO5 vs. FO7), respectively. Nonetheless, Significance Analysis of Microarrays (SAM, FDR < 0.05) identified only one DEP between pIC groups of the two diets. Moreover, we identified a large number (i.e. 890 DEP in FO7 and 1128 DEP in FO5 overlapping between SAM and RP) of pIC-responsive transcripts, and several of them were involved in TLR−/RLR-dependent and cytokine-mediated pathways. The microarray results were validated as significantly differentially expressed by qPCR assays for 2 out of 9 diet-responsive transcripts and for all of the 35 selected pIC-responsive transcripts.ConclusionFatty acid-binding protein adipocyte (fabp4) and proteasome subunit beta type-8 (psmb8) were significantly up- and down-regulated, respectively, in the MLCs of fish fed the diet with a lower level of fish oil, suggesting that they are important diet-responsive, immune-related biomarkers for future studies. Although the different levels of dietary fish and vegetable oils involved in this study affected the expression of some transcripts, the immune-related pathways and functions activated by the antiviral response of salmon MLCs in both groups were comparable overall. Moreover, the qPCR revealed transcripts responding early to pIC (e.g. lgp2, map3k8, socs1, dusp5 and cflar) and time-responsive transcripts (e.g. scarb1-a, csf1r, traf5a, cd80 and ctsf) in salmon MLCs. The present study provides a comprehensive picture of the putative molecular pathways (e.g. RLR-, TLR-, MAPK- and IFN-associated pathways) activated by the antiviral response of salmon MLCs.

Disease-stabilizing treatment based on all-trans retinoic acid and valproic acid in acute myeloid leukemia \u2013 identification of responders by gene expression profiling of pretreatment leukemic cells

BackgroundAcute myeloid leukemia (AML) is an aggressive malignancy only cured by intensive therapy. However, many elderly and unfit patients cannot receive such treatment due to an unacceptable risk of treatment-related morbidity and mortality. Disease-stabilizing therapy is then the only possible strategy, one alternative being treatment based on all-trans retinoic acid (ATRA) combined with the histone deacetylase inhibitor valproic acid and possibly low-toxicity conventional chemotherapy.MethodsPrimary AML cells were derived from 43 patients included in two clinical studies of treatment based on ATRA, valproic acid and theophyllamine; low toxicity chemotherapy (low-dose cytarabine, hydroxyurea, 6-mercaptopurin) was also allowed. Pretreatment leukemic cells were analyzed by mutation profiling of 54 genes frequently mutated in myeloid malignancies and by global gene expression profiling before and during in vivo treatment.ResultsPatients were classified as responders and non-responders to the treatment, however response to treatment showed no significant associations with karyotype or mutational profiles. Significance analysis of microarray (SAM) showed that responders and non-responders significantly differed with regard to the expression of 179 different genes. The differentially expressed genes encoding proteins with a known function were further classified based on the PANTHER (protein annotation through evolutionary relationship) classification system. The identified genes encoded proteins that are involved in several important biological functions, but a main subset of the genes were important for transcriptional regulation. These pretherapy differences in gene expression were largely maintained during treatment. Our analyses of primary AML cells during in vivo treatment suggest that ATRA modulates HOX activity (i.e. decreased expression of HOXA3, HOXA4 and HOXA5 and their regulator PBX3), but altered function of DNA methyl transferase 3A (DNMT3A) and G-protein coupled receptor signaling may also contribute to the effect of the overall treatment.ConclusionsResponders and non-responders to AML stabilizing treatment based on ATRA and valproic acid differ in the pretreatment transcriptional regulation of their leukemic cells, and these differences may be important for the clinical effect of this treatment.Trial registrationsClinicalTrials.gov no. NCT00175812; EudraCT no. 2004–001663-22, registered September 9, 2005 and ClinicalTrials.gov no. NCT00995332; EudraCT no. 2007–2007–001995-36, registered October 14, 2009.

Robust gene selection methods using weighting schemes for microarray data analysis

BackgroundA common task in microarray data analysis is to identify informative genes that are differentially expressed between two different states. Owing to the high-dimensional nature of microarray data, identification of significant genes has been essential in analyzing the data. However, the performances of many gene selection techniques are highly dependent on the experimental conditions, such as the presence of measurement error or a limited number of sample replicates.ResultsWe have proposed new filter-based gene selection techniques, by applying a simple modification to significance analysis of microarrays (SAM). To prove the effectiveness of the proposed method, we considered a series of synthetic datasets with different noise levels and sample sizes along with two real datasets. The following findings were made. First, our proposed methods outperform conventional methods for all simulation set-ups. In particular, our methods are much better when the given data are noisy and sample size is small. They showed relatively robust performance regardless of noise level and sample size, whereas the performance of SAM became significantly worse as the noise level became high or sample size decreased. When sufficient sample replicates were available, SAM and our methods showed similar performance. Finally, our proposed methods are competitive with traditional methods in classification tasks for microarrays.ConclusionsThe results of simulation study and real data analysis have demonstrated that our proposed methods are effective for detecting significant genes and classification tasks, especially when the given data are noisy or have few sample replicates. By employing weighting schemes, we can obtain robust and reliable results for microarray data analysis.

OR40 Applying single-cell mass cytometry to investigate the immune system of highly sensitized patients who undergo intravenous immunoglobulin desensitization treatment

Aim To identify potential immune markers for predicting and monitoring IVIG desensitization by CyTOF technology. Methods CyTOF: A panel of 37 antibodies against phenotypic and functional immune makers were labelled with metals and used to define 27 cell populations by Single-cell mass cytometry. Applying Cytobank ( http://www.cytobank.org ) (Chen and Kotecha, 2014), visualizing cellular features of interest across all the cells in a dataset were manually gated and clustered cell population according to the proper cell markers. SEM (Significance Analysis of Microarrays) paired algorithms was used for the data statistical analysis. Results Two highly sensitized patient frozen PBMC samples were tested in vitro and in vivo by CyTOF. The expression of HLA-class I (HLA-I) decreased in all cell populations after incubating patient PMBC (in vitro sample) with 2.5%IVIG for 16 h. Excepting B cells, the expression of HLA-I also decreased after 15 courses of IVIG treatment (in vivo sample). IVIG treatment increased inhibitory Fc receptor CD32IIb expression significantly on basophils, myeloid dendritic cells (DC), and all B cells (especially memory B cells) in vivo; and increased CD56 and CD25 expressions on NK cells and Treg cells respectively. Conclusions IVIG treatment significantly increases inhibitory markers expression on APC cells and activated Treg cells in desensitization patient. Inhibitory Fc receptor CD32IIb, CD56, CD25, and HLA-I could be useful immune marker for predicting and monitoring IVIG efficacy in desensitization.