Identification of regulatory SNPs associated with genetic modifications in lung adenocarcinoma

BACKGROUND: Although much research effort has been devoted to elucidating lung cancer, the molecular mechanism of tumorigenesis still remains unclear. A major challenge to improve the understanding of lung cancer is the difficulty of identifying reproducible differentially expressed genes across independent studies, due to their low consistency. To enhance the reproducibility of the findings, an integrated analysis was performed to identify regulatory SNPs. Thirty-two pairs of tumor and adjacent normal lung tissue specimens were analyzed using Affymetrix U133plus2.0, Affymetrix SNP 6.0, and Illumina Infinium Methylation microarrays. Copy number variations (CNVs) and methylation alterations were analyzed and paired t-tests were used to identify differentially expressed genes. RESULTS: A total of 505 differentially expressed genes were identified, and their dysregulated patterns moderately correlated with CNVs and methylation alterations based on the hierarchical clustering analysis. Subsequently, three statistical approaches were performed to explore regulatory SNPs, which revealed that the genotypes of 551 and 66 SNPs were associated with CNV and changes in methylation, respectively. Among them, downstream transcriptional dysregulation was observed in 9 SNPs for CNVs and 4 SNPs for methylation alterations. CONCLUSIONS: In summary, these identified SNPs concurrently showed the same direction of gene expression changes with genetic modifications, suggesting their pivotal roles in the genome for non-smoking women with lung adenocarcinoma. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13104-015-1053-8) contains supplementary material, which is available to authorized users

Finding the association of mRNA and miRNA using Next Generation Sequencing data of Kidney renal cell carcinoma

MicroRNAs (miRNAs) are a class of 22-nucleotide endogenous noncod- ing RNAs, plays important role in regulating target gene expression via repress- ing translation or promoting messenger RNAs (mRNA) degradation. Numerous re- searchers have found that miRNAs have serious effects on cancer. Therefore, study of mRNAs and miRNAs together through the integrated analysis of mRNA and miRNA expression profiling could help us in getting a deeper insight into the can- cer research. In this regards, High-Throughput Sequencing data of Kidney renal cell carcinoma is used here. The proposed method focuses on identifying mRNA- miRNA pair that has a signature in kidney tumor sample. For this analysis, Ran- dom Forests, Particle Swarm Optimization and Support Vector Machine classifier is used to have best sets of mRNAs-miRNA pairs. Additionally, the significance of selected mRNA-miRNA pairs is tested using gene ontology and pathway analysis tools. Moreover, the selected mRNA-miRNA pairs are searched based on changes in expression values of the used mRNA and miRNA dataset

Reduced expression of alpha-1,2-mannosidase I extends lifespan in Drosophila melanogaster and Caenorhabditis elegans

Exposure to sub-lethal levels of stress, or hormesis, was a means to induce longevity. By screening for mutations that enhance resistance to multiple stresses, we identified multiple alleles of alpha-1,2-mannosidase I (mas1) which, in addition to promoting stress resistance, also extended longevity. Longevity enhancement is also observed when mas1 expression is reduced via RNA interference in both Drosophila melanogaster and Caenorhabditis elegans. The screen also identified Edem1 (Edm1), a gene downstream of mas1, as a modulator of lifespan. As double mutants for both mas1 and Edm1 showed no additional longevity enhancement, it appeared that both mutations function within a common pathway to extend lifespan. Molecular analysis of these mutants revealed that the expression of BiP, a putative biomarker of dietary restriction (DR), is down-regulated in response to reductions in mas1 expression. These findings suggested that mutations in mas1 may extend longevity by modulating DR

Subgroup identification for treatment selection in biomarker adaptive design

BACKGROUND: Advances in molecular technology have shifted new drug development toward targeted therapy for treatments expected to benefit subpopulations of patients. Adaptive signature design (ASD) has been proposed to identify the most suitable target patient subgroup to enhance efficacy of treatment effect. There are two essential aspects in the development of biomarker adaptive designs: 1) an accurate classifier to identify the most appropriate treatment for patients, and 2) statistical tests to detect treatment effect in the relevant population and subpopulations. We propose utilization of classification methods to identity patient subgroups and present a statistical testing strategy to detect treatment effects. METHODS: The diagonal linear discriminant analysis (DLDA) is used to identify targeted and non-targeted subgroups. For binary endpoints, DLDA is directly applied to classify patient into two subgroups; for continuous endpoints, a two-step procedure involving model fitting and determination of a cutoff-point is used for subgroup classification. The proposed strategy includes tests for treatment effect in all patients and in a marker-positive subgroup, with a possible follow-up estimation of treatment effect in the marker-negative subgroup. The proposed method is compared to the ASD classification method using simulated datasets and two publically available cancer datasets. RESULTS: The DLDA-based classifier performs well in terms of sensitivity, specificity, positive and negative predictive values, and accuracy in the simulation data and the two cancer datasets, with superior accuracy compared to the ASD method. The subgroup testing strategy is shown to be useful in detecting treatment effect in terms of power and control of study-wise error. CONCLUSION: Accuracy of a classifier is essential for adaptive designs. A poor classifier not only assigns patients to inappropriate treatments, but also reduces the power of the test, resulting in incorrect conclusions. The proposed procedure provides an effective approach for subgroup identification and subgroup analysis

Identification of reproducible gene expression signatures in lung adenocarcinoma

Abstract Background Lung cancer is the leading cause of cancer-related death worldwide. Tremendous research efforts have been devoted to improving treatment procedures, but the average five-year overall survival rates are still less than 20%. Many biomarkers have been identified for predicting survival; challenges arise, however, in translating the findings into clinical practice due to their inconsistency and irreproducibility. In this study, we proposed an approach by identifying predictive genes through pathways. Results The microarrays from Shedden et al. were used as the training set, and the log-rank test was performed to select potential signature genes. We focused on 24 cancer-related pathways from 4 biological databases. A scoring scheme was developed by the Cox hazard regression model, and patients were divided into two groups based on the medians. Subsequently, their predictability and generalizability were evaluated by the 2-fold cross-validation and a resampling test in 4 independent datasets, respectively. A set of 16 genes related to apoptosis execution was demonstrated to have good predictability as well as generalizability in more than 700 lung adenocarcinoma patients and was reproducible in 4 independent datasets. This signature set was shown to have superior performances compared to 6 other published signatures. Furthermore, the corresponding risk scores derived from the set were found to associate with the efficacy of the anti-cancer drug ZD-6474 targeting EGFR. Conclusions In summary, we presented a new approach to identify reproducible survival predictors for lung adenocarcinoma, and the identified genes may serve as both prognostic and predictive biomarkers in the future. </jats:sec

Differential correlation analysis of glioblastoma reveals immune ceRNA interactions predictive of patient survival

BACKGROUND: Recent studies illuminated a novel role of microRNA (miRNA) in the competing endogenous RNA (ceRNA) interaction: two genes (ceRNAs) can achieve coexpression by competing for a pool of common targeting miRNAs. Individual biological investigations implied ceRNA interaction performs crucial oncogenic/tumor suppressive functions in glioblastoma multiforme (GBM). Yet, a systematic analysis has not been conducted to explore the functional landscape and prognostic significance of ceRNA interaction. RESULTS: Incorporating the knowledge that ceRNA interaction is highly condition-specific and modulated by the expressional abundance of miRNAs, we devised a ceRNA inference by differential correlation analysis to identify the miRNA-modulated ceRNA pairs. Analyzing sample-paired miRNA and gene expression profiles of GBM, our data showed that this alternative layer of gene interaction is essential in global information flow. Functional annotation analysis revealed its involvement in activated processes in brain, such as synaptic transmission, as well as critical tumor-associated functions. Notably, a systematic survival analysis suggested the strength of ceRNA-ceRNA interactions, rather than expressional abundance of individual ceRNAs, among three immune response genes (CCL22, IL2RB, and IRF4) is predictive of patient survival. The prognostic value was validated in two independent cohorts. CONCLUSIONS: This work addresses the lack of a comprehensive exploration into the functional and prognostic relevance of ceRNA interaction in GBM. The proposed efficient and reliable method revealed its significance in GBM-related functions and prognosis. The highlighted roles of ceRNA interaction provide a basis for further biological and clinical investigations. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12859-017-1557-4) contains supplementary material, which is available to authorized users

Assessment of point-of-care quantitative serum canine pancreatic lipase testing for diagnosing acute pancreatitis in dogs

IntroductionCurrent point-of-care testing for canine-specific pancreatic lipase (CPL) provides semi-quantitative measurements with binary results. Recently, a commercial point-of-care testing method (Vcheck CPL) that offers quantitative measurement of CPL has emerged. However, clinical studies on its value (or utility) are limited. Therefore, this study aimed to evaluate the clinical utility of this commercial point-of-care CPL in diagnosing dogs with suspected acute pancreatitis and to assess its correlation with a commercial semi-quantitative test and other clinicopathological variables.MethodsA prospective observational study included 33 dogs with suspected acute pancreatitis and 20 clinically healthy dogs. Serum Vcheck CPL and SNAP ® cPL were tested, and clinical consensus scores were determined by 5 internists. Eleven dogs with suspected acute pancreatitis underwent follow-up testing during hospitalization. The intra-class correlation coefficient (ICC) was used for statistical analysis to assess the agreement between assays and the internists’ consensus score.ResultsDogs with suspected acute pancreatitis had significantly higher serum Vcheck CPL (median: 843 μg/L, range: 77–2001, p &lt; 0.0001) than healthy control dogs (median: 94 μg/L, range: 49–294). By day 3 of hospitalization, serum Vcheck CPL had significantly decreased in dogs with suspected acute pancreatitis compared to day 1. The ICC score between the clinical consensus score, Vcheck CPL, and SNAP ® cPL was 0.75, indicating good agreement. Serum Vcheck CPL concentration was significantly correlated with serum concentrations of amylase, lipase, creatinine, ALP, and CRP.DiscussionThis study found good agreement between Vcheck CPL and SNAP ® cPL. This quantitative Vcheck CPL testing could serve as an adjunctive tool in diagnosing dogs with acute pancreatitis

Pembuatan Niosom Berbasis Maltodekstrin De 5-10 Dari Pati Singkong (Manihot Utilissima)

Niosomes are non ionic surfactant vesicles that have potential application in the delivery of hydrophobic or amphilic drugs. We developed proniosomes, a dry formulation using a maltodextrin as a carrier coated with non ionic surfactant, which can be used to produce niosomes within a minutes by addition of hot water followed by agitation. A novel method is reported here for rapid preparation of proniosomes with wide range of surfactant loading. Maltodextrin DE 5-10 was hidrolyzed from tapioca starch using Thermamyl L 120 da Novo at 85o C. The result from SEM analyses shown that proniosomes appear very similar to the maltodextrin, but the surface was more smooth. Niosome suspensions which was observed under the optical microscopy and particle size analyzer were evaluated as drug carrier using ibuprofen as a model. The result provide an indication of maltodextrin DE 5-10 from tapioca starch are potentialy carrier in the proniosome preparation which can be used for producing niosomes

Transcriptome Changes in Relation to Manic Episode

Bipolar disorder (BD) is highly heritable and well known for its recurrent manic and depressive episodes. The present study focused on manic episode in BD patients and aimed to investigate state-specific transcriptome alterations between acute episode and remission, including messenger RNAs (mRNAs), long noncoding RNAs (lncRNAs), and micro-RNAs (miRNAs), using microarray and RNA sequencing (RNA-Seq) platforms. BD patients were enrolled with clinical information, and peripheral blood samples collected at both acute and remission status spanning for at least 2 months were confirmed by follow-ups. Symptom severity was assessed by Young Mania Rating Scale. We enrolled six BD patients as the discovery samples and used the Affymetrix Human Transcriptome Array 2.0 to capture transcriptome data at the two time points. For replication, expression data from Gene Expression Omnibus that consisted of 11 BD patients were downloaded, and we performed a mega-analysis for microarray data of 17 patients. Moreover, we conducted RNA sequencing (RNA-Seq) in additional samples of 7 BD patients. To identify intraindividual differentially expressed genes (DEGs), we analyzed data using a linear model controlling for symptom severity. We found that noncoding genes were of majority among the top DEGs in microarray data. The expression fold change of coding genes among DEGs showed moderate to high correlations (∼0.5) across platforms. A number of lncRNAs and two miRNAs (MIR181B1 and MIR103A1) exhibited high levels of gene expression in the manic state. For coding genes, we reported that the taste function-related genes, including TAS2R5 and TAS2R3, may be mania state-specific markers. Additionally, four genes showed a nominal p-value of less than 0.05 in all our microarray data, mega-analysis, and RNA-Seq analysis. They were upregulated in the manic state and consisted of MS4A14, PYHIN1, UTRN, and DMXL2, and their gene expression patterns were further validated by quantitative real-time polymerase chain reaction (PCR) (qRT-PCR). We also performed weight gene coexpression network analysis to identify gene modules for manic episode. Genes in the mania-related modules were different from the susceptible loci of BD obtained from genome-wide association studies, and biological pathways in relation to these modules were mainly related to immune function, especially cytokine–cytokine receptor interaction. Results of the present study elucidated potential molecular targets and genomic networks that are involved in manic episode. Future studies are needed to further validate these biomarkers for their roles in the etiology of bipolar illness