PROBER: Segmentation and Differential Analysis Tool for Tiling Microarray Data

Thyroid cancer has become an increasingly common malignant tumor around the world, and its incidence is increasing year by year. In this study, mRNA microarray data of thyroid cancer patients from four periods were collected from the TCGA database. We performed a series of bioinformatics analyses on these mRNA expression profiles, including differential analysis, co-expression analysis, enrichment analysis, regulator prediction, and survival analysis. There were 13126, 10914, 13585, and 13241 differential genes in the four periods; 4822 differential genes were obtained by union and deduplication (p < 0.01). Weighted gene co-expression network analysis indicated a total of 21 functional disorder modules. In each module, PLD5, CHD4, ADGRA3, ITGA3, etc. were the key genes. Enrichment analysis showed that the dysfunctional module genes were mainly related to pre-replicative complex assembly, Cytokine–cytokine receptor interaction, and MAPK signaling pathway. We downloaded thyroid cancer-associated miRNA microarray data from the GEO database for differential analysis. Then, we crossed the predicted ncRNA with the differential miRNA to obtain thyroid cancer-associated regulatory factors. Finally, we found that miRNA-4665-3p regulates the core gene PLD5, and six regulators such as miRNA-3140-3p and miRNA-324-3p regulate the core gene CHD4. Survival analysis showed that both up-regulation of PLD5 expression and down-regulation of CHD4 expression accelerated patient death. According to the above analysis, we believe miRNA-4665-3p regulates the expression of PLD5 and affects the development of thyroid cancer. Its up-regulation promotes the death of patients.

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Online biomarker validation of survival-associated biomarkers in breast and ovarian cancer using microarray data of 3,862 patients.

Journal of Clinical Oncology ◽

10.1200/jco.2012.30.15_suppl.10571 ◽

2012 ◽

Vol 30 (15_suppl) ◽

pp. 10571-10571

Author(s):

Balazs Gyorffy ◽

Andras Lanczky ◽

Zoltan Szallasi

Keyword(s):

Breast Cancer ◽

Ovarian Cancer ◽

Cancer Patients ◽

Microarray Data ◽

Cancer Prognosis ◽

Analysis Tool ◽

Breast And Ovarian Cancer ◽

Kaplan Meier ◽

Biomarker Validation ◽

Prognostic Power

10571 Background: The pre-clinical validation of prognostic gene candidates in large independent patient cohorts is a pre-requisite for the development of robust biomarkers. Today, curated microarray cohorts combined with appropriate clinical data offer a cost effective tool to prescreen potential new biomarkers. In present study we expanded our online Kaplan-Meier plotter tool to assess the effect of genes on ovarian cancer prognosis. Methods: Gene expression data and survival information of breast and ovarian cancer patients were downloaded from GEO and TCGA. To analyze the prognostic value of the selected gene in the various cohorts the patients are divided into two groups according to the quantile expression of the gene. Filtering is implemented for stage, grade, and histology subtypes. Follow-up threshold is implemented to exclude long-term effects. A Kaplan-Meier survival plot is generated and significance is computed in the R statistical environment using Bioconductor packages. The combination of several probe sets can be employed to assess the mean of their expression as a multigene predictor of survival. Results: All together 1,390 ovarian cancer patients and 2,472 breast cancer patients were entered into the database. These groups can be compared using relapse free survival (n=2,324 in breast cancer and 1,090 in ovarian cancer) or overall survival (n=464 and n=1,287). We used this integrative data analysis tool to validate the prognostic power of 37 ovarian cancer associated biomarkers identified in the literature. Of these, CA125 (p=3.7e-5, HR=1.4), CDKN1B (p=5.4e-5, HR=1.4), KLK6 (p=0.002, HR=0.79), IFNG (p=0.004, HR=0.81), P16 (p=0.02, HR=0.66) and BIRC5 (p=0.00017, HR=0.75) were associated with survival. Conclusions: We set up a global online biomarker validation platform to mine all available microarray data to assess the prognostic power of 22,277 genes in 2,472 breast and 1,390 ovarian cancer patients. Registration-free online access at: http://www.kmplot.com/.

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A Pathway Analysis Tool for Analyzing Microarray Data of Species with Low Physiological Information

Advances in Bioinformatics ◽

10.1155/2008/719468 ◽

2008 ◽

Vol 2008 ◽

pp. 1-7 ◽

Cited By ~ 6

Author(s):

M. F. W. te Pas ◽

S. van Hemert ◽

B. Hulsegge ◽

A. J. W. Hoekman ◽

M. H. Pool ◽

...

Keyword(s):

Microarray Data ◽

Gene List ◽

Microarray Experiment ◽

Laboratory Animals ◽

Specific Gene ◽

Analysis Tool ◽

Pathway Information ◽

Pathway Analysis Tool ◽

Species Specific ◽

Specific Reactions

Pathway information provides insight into the biological processes underlying microarray data. Pathway information is widely available for humans and laboratory animals in databases through the internet, but less for other species, for example, livestock. Many software packages use species-specific gene IDs that cannot handle genomics data from other species. We developed a species-independent method to search pathways databases to analyse microarray data. Three PERL scripts were developed that use the names of the genes on the microarray. (1) Add synonyms of gene names by searching the Gene Ontology (GO) database. (2) Search the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database for pathway information using this GO-enriched gene list. (3) Combine the pathway data with the microarray data and visualize the results using color codes indicating regulation. To demonstrate the power of the method, we used a previously reported chicken microarray experiment investigating line-specific reactions to Salmonella infection as an example.

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GenRate: A Generative Model that Reveals Novel Transcripts in Genome-Tiling Microarray Data

Journal of Computational Biology ◽

10.1089/cmb.2006.13.200 ◽

2006 ◽

Vol 13 (2) ◽

pp. 200-214

Author(s):

Brendan J. Frey ◽

Quaid D. Morris ◽

Timothy R. Hughes

Keyword(s):

Microarray Data ◽

Generative Model ◽

Tiling Microarray ◽

Novel Transcripts

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An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients

Breast Cancer Research and Treatment ◽

10.1007/s10549-009-0674-9 ◽

2009 ◽

Vol 123 (3) ◽

pp. 725-731 ◽

Cited By ~ 1420

Author(s):

Balazs Györffy ◽

Andras Lanczky ◽

Aron C. Eklund ◽

Carsten Denkert ◽

Jan Budczies ◽

...

Keyword(s):

Breast Cancer ◽

Survival Analysis ◽

Microarray Data ◽

Breast Cancer Prognosis ◽

Cancer Prognosis ◽

Analysis Tool

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Copycat Layout: Network layout alignment via Cytoscape Automation

F1000Research ◽

10.12688/f1000research.15144.1 ◽

2018 ◽

Vol 7 ◽

pp. 822 ◽

Cited By ~ 1

Author(s):

Brett Settle ◽

David Otasek ◽

John H Morris ◽

Barry Demchak

Keyword(s):

The Other ◽

Analysis Tool ◽

Differential Analysis ◽

Network Comparison ◽

Homologous Genes ◽

Network Layout ◽

Node Attribute

The copycatLayout app is a network-based visual differential analysis tool that improves upon the existing layoutSaver app and is delivered pre-installed with Cytoscape, beginning with v3.6.0. LayoutSaver cloned a network layout by mapping node locations from one network to another based on node attribute values, but failed to clone view scale and location, and provided no means of identifying which nodes were successfully mapped between networks. Copycat addresses these issues and provides additional layout options. With the advent of Cytoscape Automation (packaged in Cytoscape v3.6.0), researchers can utilize the Copycat layout and its output in workflows written in their language of choice by using only a few simple REST calls. Copycat enables researchers to visually compare groups of homologous genes, generate network comparison images for publications, and quickly identify differences between similar networks at a glance without leaving their script. With a few extra REST calls, scripts can discover nodes present in one network but not in the other, which can feed into more complex analyses (e.g., modifying mismatched nodes based on new data, then re-running the layout to highlight additional network changes).

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Diarylheptanoids fromAlpinia officinarumCause Distinct but Overlapping Effects on the Translatome of B Lymphoblastoid Cells

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2014/204797 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 8

Author(s):

Tomohito Kakegawa ◽

Saeko Takase ◽

Eri Masubuchi ◽

Ken Yasukawa

Keyword(s):

Data Analysis ◽

Microarray Data ◽

Microarray Data Analysis ◽

Analysis Tool ◽

Messenger Rnas ◽

Proinflammatory Mediators ◽

Alpinia Officinarum ◽

Data Analysis Tool ◽

And Control ◽

Upregulated Genes

Diarylheptanoids (AO-0001, AO-0002, and AO-0003) isolated fromAlpinia officinaruminhibit proinflammatory mediators and exhibit cytotoxic and antiviral activity. However, the precise mechanisms of action of these diarylheptanoids are unknown as are their effects on expression of specific genes. Here, we used a translatome analysis to investigate the mechanisms and modes of action of these three diarylheptanoids. Polysome-associated messenger RNAs (mRNAs) were prepared from diarylheptanoids-treated and control cells from a human B lymphoblastoid cell line; these mRNA samples were then used for microarray analysis. Microarray Data Analysis Tool version 3.2 was used to analyze the microarray data analysis; this software uses pathway information of the WikiPathways for gene ontology analysis. Each of the diarylheptanoids caused upregulation or downregulation of the same 37 and 286 genes, respectively. Among the 37 upregulated genes, 16 were related to mRNA processing based on the WikiPathways analysis. Our findings provided new insights into the mode of action of diarylheptanoids fromA. officinarum.

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