scholarly journals Expression imbalance map: a new visualization method for detection of mRNA expression imbalance regions

2003 ◽  
Vol 13 (1) ◽  
pp. 31-46 ◽  
Author(s):  
Makoto Kano ◽  
Kunihiro Nishimura ◽  
Shumpei Ishikawa ◽  
Shuichi Tsutsumi ◽  
Koichi Hirota ◽  
...  
Keyword(s):  
Mrna Expression ◽  
Expression Profiles ◽  
Genomic Structure ◽  
Comparative Genomic ◽  
Visualization Method ◽  
Expression Data ◽  
Microarray Expression ◽  
Insight Into ◽  
Arbitrary Selection ◽  
Selection Of

We describe the development of a new visualization method, called the expression imbalance map (EIM), for detecting mRNA expression imbalance regions, reflecting genomic losses and gains at a much higher resolution than conventional technologies such as comparative genomic hybridization (CGH). Simple spatial mapping of the microarray expression profiles on chromosomal location provides little information about genomic structure, because mRNA expression levels do not completely reflect genomic copy number and some microarray probes would be of low quality. The EIM, which does not employ arbitrary selection of thresholds in conjunction with hypergeometric distribution-based algorithm, has a high tolerance of these complex factors. The EIM could detect regionally underexpressed or overexpressed genes (called, here, an expression imbalance region) in lung cancer specimens from their gene expression data of oligonucleotide microarray. Many known as well as potential loci with frequent genomic losses or gains were detected as expression imbalance regions by the EIM. Therefore, the EIM should provide the user with further insight into genomic structure through mRNA expression.

scholarly journals Unique MicroRNA and mRNA Interactions in EGFR-Mutated Lung Adenocarcinoma

2018 ◽  
Vol 7 (11) ◽  
pp. 419 ◽  
Author(s):  
Sophia Subat ◽  
Kentaro Inamura ◽  
Hironori Ninomiya ◽  
Hiroko Nagano ◽  
Sakae Okumura ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Mrna Expression ◽  
Regulatory Network ◽  
Egfr Mutation ◽  
Expression Profiles ◽  
Integrative Analysis ◽  
Future Research ◽  
Mutation Status ◽  
Egfr Mutated ◽  
Insight Into

The EGFR gene was one of the first molecules to be selected for targeted gene therapy. EGFR-mutated lung adenocarcinoma, which is responsive to EGFR inhibitors, is characterized by a distinct oncogenic pathway in which unique microRNA (miRNA)–mRNA interactions have been observed. However, little information is available about the miRNA–mRNA regulatory network involved. Both miRNA and mRNA expression profiles were investigated using microarrays in 155 surgically resected specimens of lung adenocarcinoma with a known EGFR mutation status (52 mutated and 103 wild-type cases). An integrative analysis of the data was performed to identify the unique miRNA–mRNA regulatory network in EGFR-mutated lung adenocarcinoma. Expression profiling of miRNAs and mRNAs yielded characteristic miRNA/mRNA signatures (19 miRNAs/431 mRNAs) in EGFR-mutated lung adenocarcinoma. Five of the 19 miRNAs were previously listed as EGFR-mutation-specific miRNAs (i.e., miR-532-3p, miR-500a-3p, miR-224-5p, miR-502-3p, and miR-532-5p). An integrative analysis of miRNA and mRNA expression revealed a refined list of putative miRNA–mRNA interactions, of which 63 were potentially involved in EGFR-mutated tumors. Network structural analysis provided a comprehensive view of the complex miRNA–mRNA interactions in EGFR-mutated lung adenocarcinoma, including DUSP4 and MUC4 axes. Overall, this observational study provides insight into the unique miRNA–mRNA regulatory network present in EGFR-mutated tumors. Our findings, if validated, would inform future research examining the interplay of miRNAs and mRNAs in EGFR-mutated lung adenocarcinoma.

scholarly journals Modular network inference between miRNA–mRNA expression profiles using weighted co-expression network analysis

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nisar Wani ◽  
Debmalya Barh ◽  
Khalid Raza
Keyword(s):  
Network Analysis ◽  
Mrna Expression ◽  
Mirna Expression ◽  
Regulatory Networks ◽  
Network Inference ◽  
Expression Profiles ◽  
Interaction Network ◽  
Pathway Enrichment Analysis ◽  
Expression Data ◽  
Cancer Data

Abstract Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer.

scholarly journals Molecular Comparison of Imatinib-Naïve and Resistant Gastrointestinal Stromal Tumors: Differentially Expressed microRNAs and mRNAs

Cancers ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 882 ◽  
Author(s):  
Amirnasr ◽  
Gits ◽  
van Kuijk ◽  
Smid ◽  
Vriends ◽  
...  
Keyword(s):  
Mrna Expression ◽  
Cellular Proliferation ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Imatinib Resistance ◽  
Network Analyses ◽  
Biochemical Pathways ◽  
Imatinib Resistant ◽  
Differentially Expressed Mirnas ◽  
Insight Into

Despite the success of imatinib in advanced gastrointestinal stromal tumor (GIST) patients, 50% of the patients experience resistance within two years of treatment underscoring the need to get better insight into the mechanisms conferring imatinib resistance. Here the microRNA and mRNA expression profiles in primary (imatinib-naïve) and imatinib-resistant GIST were examined. Fifty-three GIST samples harboring primary KIT mutations (exon 9; n = 11/exon 11; n = 41/exon 17; n = 1) and comprising imatinib-naïve (IM-n) (n = 33) and imatinib-resistant (IM-r) (n = 20) tumors, were analyzed. The microRNA expression profiles were determined and from a subset (IM-n, n = 14; IM-r, n = 15) the mRNA expression profile was established. Ingenuity pathway analyses were used to unravel biochemical pathways and gene networks in IM-r GIST. Thirty-five differentially expressed miRNAs between IM-n and IM-r GIST samples were identified. Additionally, miRNAs distinguished IM-r samples with and without secondary KIT mutations. Furthermore 352 aberrantly expressed genes were found in IM-r samples. Pathway and network analyses revealed an association of differentially expressed genes with cell cycle progression and cellular proliferation, thereby implicating genes and pathways involved in imatinib resistance in GIST. Differentially expressed miRNAs and mRNAs between IM-n and IM-r GIST were identified. Bioinformatic analyses provided insight into the genes and biochemical pathways involved in imatinib-resistance and highlighted key genes that may be putative treatment targets.

miRNA and mRNA Expression Profiles Reveal Insight into Chitosan-Mediated Regulation of Plant Growth

2018 ◽  
Vol 66 (15) ◽  
pp. 3810-3822 ◽  
Author(s):  
Xiaoqian Zhang ◽  
Kecheng Li ◽  
Ronge Xing ◽  
Song Liu ◽  
Xiaolin Chen ◽  
...  
Keyword(s):  
Plant Growth ◽  
Mrna Expression ◽  
Expression Profiles ◽  
Insight Into

scholarly journals Magellan: A Web Based System for the Integrated Analysis of Heterogeneous Biological Data and Annotations; Application to DNA Copy Number and Expression Data in Ovarian Cancer

2006 ◽  
Vol 2 ◽  
pp. 117693510600200 ◽  
Author(s):  
Chris B. Kingsley ◽  
Wen-Lin Kuo ◽  
Daniel Polikoff ◽  
Andy Berchuck ◽  
Joe W. Gray ◽  
...  
Keyword(s):  
Mrna Expression ◽  
Analytical Methods ◽  
Patient Survival ◽  
Biological Data ◽  
Integrated Analysis ◽  
Comparative Genomic ◽  
Expression Data ◽  
Data Set ◽  
Web Based ◽  
Web Based System

Recent advances in high throughput biological methods allow researchers to generate enormous amounts of data from a single experiment. In order to extract meaningful conclusions from this tidal wave of data, it will be necessary to develop analytical methods of sufficient power and utility. It is particularly important that biologists themselves be able to perform many of these analyses, such that their background knowledge of the experimental system under study can be used to interpret results and direct further inquiries. We have developed a web-based system, Magellan, which allows the upload, storage, and analysis of multivariate data and textual or numerical annotations. Data and annotations are treated as abstract entities, to maximize the different types of information the system can store and analyze. Annotations can be used in analyses/visualizations, as a means of subsetting data to reduce dimensionality, or as a means of projecting variables from one data type or data set to another. Analytical methods are deployed within Magellan such that new functionalities can be added in a straightforward fashion. Using Magellan, we performed an integrated analysis of genome-wide comparative genomic hybridization (CGH), mRNA expression, and clinical data from ovarian tumors. Analyses included the use of permutation-based methods to identify genes whose mRNA expression levels correlated with patient survival, a nearest neighbor classifier to predict patient survival from CGH data, and curated annotations such as genomic position and derived annotations such as statistical computations to explore the quantitative relationship between CGH and mRNA expression data.

scholarly journals Identification of Subtype Specific miRNA-mRNA Functional Regulatory Modules in Matched miRNA-mRNA Expression Data: Multiple Myeloma as a Case

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Yunpeng Zhang ◽  
Wei Liu ◽  
Yanjun Xu ◽  
Chunquan Li ◽  
Yingying Wang ◽  
...  
Keyword(s):  
Multiple Myeloma ◽  
Mrna Expression ◽  
Regulatory Networks ◽  
Mirna Target ◽  
Expression Profiles ◽  
Biological Data ◽  
Expression Data ◽  
Target Information ◽  
Mrna Expression Data ◽  
Regulatory Modules

Identification of miRNA-mRNA modules is an important step to elucidate their combinatorial effect on the pathogenesis and mechanisms underlying complex diseases. Current identification methods primarily are based upon miRNA-target information and matched miRNA and mRNA expression profiles. However, for heterogeneous diseases, the miRNA-mRNA regulatory mechanisms may differ between subtypes, leading to differences in clinical behavior. In order to explore the pathogenesis of each subtype, it is important to identify subtype specific miRNA-mRNA modules. In this study, we integrated the Ping-Pong algorithm and multiobjective genetic algorithm to identify subtype specific miRNA-mRNA functional regulatory modules (MFRMs) through integrative analysis of three biological data sets: GO biological processes, miRNA target information, and matched miRNA and mRNA expression data. We applied our method on a heterogeneous disease, multiple myeloma (MM), to identify MM subtype specific MFRMs. The constructed miRNA-mRNA regulatory networks provide modular outlook at subtype specific miRNA-mRNA interactions. Furthermore, clustering analysis demonstrated that heterogeneous MFRMs were able to separate corresponding MM subtypes. These subtype specific MFRMs may aid in the further elucidation of the pathogenesis of each subtype and may serve to guide MM subtype diagnosis and treatment.

scholarly journals Hybrid-Controlled Neurofuzzy Networks Analysis Resulting in Genetic Regulatory Networks Reconstruction

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Roozbeh Manshaei ◽  
Pooya Sobhe Bidari ◽  
Mahdi Aliyari Shoorehdeli ◽  
Amir Feizi ◽  
Tahmineh Lohrasebi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Optimal Number ◽  
Genetic Regulatory Networks ◽  
Expression Data ◽  
Time Series Gene Expression ◽  
Neurofuzzy Networks ◽  
Microarray Expression

Reverse engineering of gene regulatory networks (GRNs) is the process of estimating genetic interactions of a cellular system from gene expression data. In this paper, we propose a novel hybrid systematic algorithm based on neurofuzzy network for reconstructing GRNs from observational gene expression data when only a medium-small number of measurements are available. The approach uses fuzzy logic to transform gene expression values into qualitative descriptors that can be evaluated by using a set of defined rules. The algorithm uses neurofuzzy network to model genes effects on other genes followed by four stages of decision making to extract gene interactions. One of the main features of the proposed algorithm is that an optimal number of fuzzy rules can be easily and rapidly extracted without overparameterizing. Data analysis and simulation are conducted on microarray expression profiles of S. cerevisiae cell cycle and demonstrate that the proposed algorithm not only selects the patterns of the time series gene expression data accurately, but also provides models with better reconstruction accuracy when compared with four published algorithms: DBNs, VBEM, time delay ARACNE, and PF subjected to LASSO. The accuracy of the proposed approach is evaluated in terms of recall and F-score for the network reconstruction task.

scholarly journals Comparison of lncRNA and mRNA expression in mouse brains infected by a wild-type and a lab-attenuated Rabies lyssavirus

2020 ◽  
Author(s):  
Baokun Sui ◽  
Dong Chen ◽  
Wei Liu ◽  
Bin Tian ◽  
Lei Lv ◽  
...  
Keyword(s):  
Mrna Expression ◽  
Clinical Symptoms ◽  
Expression Profiles ◽  
Differentially Expressed ◽  
Wild Type ◽  
Fresh Insight ◽  
Neuron Injury ◽  
Insight Into ◽  
Generation Sequencing

Rabies is a lethal disease caused by Rabies lyssavirus, commonly known as rabies virus (RABV), and results in nearly 100 % death once clinical symptoms occur in human and animals. Long non-coding RNAs (lncRNAs) have been reported to be associated with viral infection. But the role of lncRNAs involved in RABV infection is still elusive. In this study, we performed global transcriptome analysis of both of lncRNA and mRNA expression profiles in wild-type (WT) and lab-attenuated RABV-infected mouse brains by using next-generation sequencing. The differentially expressed lncRNAs and mRNAs were analysed by using the edgeR package. We identified 1422 differentially expressed lncRNAs and 4475 differentially expressed mRNAs by comparing WT and lab-attenuated RABV-infected brains. Then we predicted the enriched biological pathways by the Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) database based on the differentially expressed lncRNAs and mRNAs. Our analysis revealed the relationships between lncRNAs and RABV-infection-associated immune response and ion transport-related pathways, which provide a fresh insight into the potential role of lncRNA in immune evasion and neuron injury induced by WT RABV.

scholarly journals A machine learning-based clinical tool for diagnosing myopathy using multi-cohort microarray expression profiles

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Andrew Tran ◽  
Chris J. Walsh ◽  
Jane Batt ◽  
Claudia C. dos Santos ◽  
Pingzhao Hu
Keyword(s):  
Machine Learning ◽  
Expression Profiles ◽  
Systemic Disease ◽  
Gene Signature ◽  
Muscle Disease ◽  
Support Vector ◽  
Expression Data ◽  
Clinical Tool ◽  
Disease Subtypes ◽  
Microarray Expression

Abstract Background Myopathies are a heterogenous collection of disorders characterized by dysfunction of skeletal muscle. In practice, myopathies are frequently encountered by physicians and precise diagnosis remains a challenge in primary care. Molecular expression profiles show promise for disease diagnosis in various pathologies. We propose a novel machine learning-based clinical tool for predicting muscle disease subtypes using multi-cohort microarray expression data. Materials and methods Muscle tissue samples originating from 1260 patients with muscle weakness. Data was curated from 42 independent cohorts with expression profiles in public microarray gene expression repositories, which represent a broad range of patient ages and peripheral muscles. Cohorts were categorized into five muscle disease subtypes: immobility, inflammatory myopathies, intensive care unit acquired weakness (ICUAW), congenital, and chronic systemic disease. The data contains expression data on 34,099 genes. Data augmentation techniques were used to address class imbalances in the muscle disease subtypes. Support vector machine (SVM) models were trained on two-thirds of the 1260 samples based on the top selected gene signature using analysis of variance (ANOVA). The model was validated in the remaining samples using area under the receiver operator curve (AUC). Gene enrichment analysis was used to identify enriched biological functions in the gene signature. Results The AUC ranges from 0.611 to 0.649 in the observed imbalanced data. Overall, using the augmented data, chronic systemic disease was the best predicted class with AUC 0.872 (95% confidence interval (CI): 0.824–0.920). The least discriminated classes were ICUAW with AUC 0.777 (95% CI: 0.668–0.887) and immobility with AUC 0.789 (95% CI: 0.716–0.861). Disease-specific gene set enrichment results showed that the gene signature was enriched in biological processes including neural precursor cell proliferation for ICUAW and aerobic respiration for congenital (false discovery rate q-value < 0.001). Conclusion Our results present a well-performing molecular classification tool with the selected gene markers for muscle disease classification. In practice, this tool addresses an important gap in the literature on myopathies and presents a potentially useful clinical tool for muscle disease subtype diagnosis.

scholarly journals Integrated analysis of tobacco miRNA and mRNA expression profiles under PVY infection provids insight into tobacco-PVY interactions

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Yushuang Guo ◽  
Meng-ao Jia ◽  
Yumei Yang ◽  
Linlin Zhan ◽  
Xiaofei Cheng ◽  
...  
Keyword(s):  
Mrna Expression ◽  
Expression Profiles ◽  
Integrated Analysis ◽  
Insight Into
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