scholarly journals CMEP: a database for circulating microRNA expression profiling

2019 ◽  
Vol 35 (17) ◽  
pp. 3127-3132 ◽  
Author(s):  
Jian-Rong Li ◽  
Chun-Yip Tong ◽  
Tsai-Jung Sung ◽  
Ting-Yu Kang ◽  
Xianghong Jasmine Zhou ◽  
...  
Keyword(s):  
Feature Selection ◽  
Expression Profiling ◽  
Large Scale ◽  
Microrna Expression ◽  
Support Vector ◽  
Pathway Enrichment Analysis ◽  
Selection Function ◽  
Circulating Microrna ◽  
Circulating Mirnas ◽  
Circulating Mirna

Abstract Motivation In recent years, several experimental studies have revealed that the microRNAs (miRNAs) in serum, plasma, exosome and whole blood are dysregulated in various types of diseases, indicating that the circulating miRNAs may serve as potential noninvasive biomarkers for disease diagnosis and prognosis. However, no database has been constructed to integrate the large-scale circulating miRNA profiles, explore the functional pathways involved and predict the potential biomarkers using feature selection between the disease conditions. Although there have been several studies attempting to generate a circulating miRNA database, they have not yet integrated the large-scale circulating miRNA profiles or provided the biomarker-selection function using machine learning methods. Results To fill this gap, we constructed the Circulating MicroRNA Expression Profiling (CMEP) database for integrating, analyzing and visualizing the large-scale expression profiles of phenotype-specific circulating miRNAs. The CMEP database contains massive datasets that were manually curated from NCBI GEO and the exRNA Atlas, including 66 datasets, 228 subsets and 10 419 samples. The CMEP provides the differential expression circulating miRNAs analysis and the KEGG functional pathway enrichment analysis. Furthermore, to provide the function of noninvasive biomarker discovery, we implemented several feature-selection methods, including ridge regression, lasso regression, support vector machine and random forests. Finally, we implemented a user-friendly web interface to improve the user experience and to visualize the data and results of CMEP. Availability and implementation CMEP is accessible at http://syslab5.nchu.edu.tw/CMEP.

scholarly journals Analysis of Circulating microRNA Signatures and Preeclampsia Development

Cells ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1003
Author(s):  
Margarita L. Martinez-Fierro ◽  
Idalia Garza-Veloz
Keyword(s):  
Pregnant Women ◽  
Expression Profiling ◽  
Gene Prediction ◽  
Microrna Expression ◽  
Differentially Expressed ◽  
Control Group ◽  
Case Group ◽  
Circulating Microrna ◽  
Activated T Cells ◽  
Serum Microrna

microRNAs are important regulators of cell processes and have been proposed as potential preeclampsia biomarkers. We evaluated serum microRNA expression profiling to identify microRNAs involved in preeclampsia development. Serum microRNA expression profiling was evaluated at 12, 16, and 20 weeks of gestation (WG), and at the time of preeclampsia diagnosis. Two groups were evaluated using TaqMan low-density array plates: a control group with 18 normotensive pregnant women and a case group with 16 patients who developed preeclampsia during the follow-up period. Fifty-three circulating microRNAs were differentially expressed between groups (p < 0.05). Compared with controls, hsa-miR-628-3p showed the highest relative quantity values (at 12 WG = 7.7 and at 20 WG = 3.45) and the hsa-miRs -151a-3p and -573 remained differentially expressed from 16 to 20 WG (p < 0.05). Signaling pathways including cancer-related, axon guidance, Neurotrophin, GnRH, VEGF, and B/T cell receptor, were most commonly altered. Further target gene prediction revealed that nuclear factor of activated T-cells 5 gene was included among the transcriptional targets of preeclampsia-modulated microRNAs. Specific microRNAs including hsa-miRs -628-3p, -151a-3p, and -573 were differentially expressed in serum of pregnant women before they developed preeclampsia compared with controls and their participation in the preeclampsia development should be considered.

scholarly journals Genome-wide circulating microRNA expression profiling indicates biomarkers for epilepsy

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Jun Wang ◽  
Jin-Tai Yu ◽  
Lin Tan ◽  
Yan Tian ◽  
Jing Ma ◽  
...  
Keyword(s):  
Expression Profiling ◽  
Microrna Expression ◽  
Circulating Microrna ◽  
Genome Wide

scholarly journals Large-Scale microRNA Expression Profiling Identifies Putative Retinal miRNA-mRNA Signaling Pathways Underlying Form-Deprivation Myopia in Mice

PLoS ONE ◽  
2016 ◽  
Vol 11 (9) ◽  
pp. e0162541 ◽  
Author(s):  
Andrei V. Tkatchenko ◽  
Xiaoyan Luo ◽  
Tatiana V. Tkatchenko ◽  
Candida Vaz ◽  
Vivek M. Tanavde ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Expression Profiling ◽  
Large Scale ◽  
Microrna Expression

scholarly journals A COMPARATIVE STUDY ON GENE SELECTION METHODS FOR TISSUES CLASSIFICATION ON LARGE SCALE GENE EXPRESSION DATA

2016 ◽  
Vol 78 (5-10) ◽  
Author(s):  
Farzana Kabir Ahmad
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Gene Expression Data ◽  
Large Scale ◽  
Gene Selection ◽  
Support Vector ◽  
Breast Cancer Dataset ◽  
Expression Data ◽  
Selection Methods ◽  
Normal Tissues

Deoxyribonucleic acid (DNA) microarray technology is the recent invention that provided colossal opportunities to measure a large scale of gene expressions simultaneously. However, interpreting large scale of gene expression data remain a challenging issue due to their innate nature of “high dimensional low sample size”. Microarray data mainly involved thousands of genes, n in a very small size sample, p which complicates the data analysis process. For such a reason, feature selection methods also known as gene selection methods have become apparently need to select significant genes that present the maximum discriminative power between cancerous and normal tissues. Feature selection methods can be structured into three basic factions; a) filter methods; b) wrapper methods and c) embedded methods. Among these methods, filter gene selection methods provide easy way to calculate the informative genes and can simplify reduce the large scale microarray datasets. Although filter based gene selection techniques have been commonly used in analyzing microarray dataset, these techniques have been tested separately in different studies. Therefore, this study aims to investigate and compare the effectiveness of these four popular filter gene selection methods namely Signal-to-Noise ratio (SNR), Fisher Criterion (FC), Information Gain (IG) and t-Test in selecting informative genes that can distinguish cancer and normal tissues. In this experiment, common classifiers, Support Vector Machine (SVM) is used to train the selected genes. These gene selection methods are tested on three large scales of gene expression datasets, namely breast cancer dataset, colon dataset, and lung dataset. This study has discovered that IG and SNR are more suitable to be used with SVM. Furthermore, this study has shown SVM performance remained moderately unaffected unless a very small size of genes was selected.

scholarly journals microRNA expression profiling on individual breast cancer patients identifies novel panel of circulating microRNA for early detection

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Rimi Hamam ◽  
Arwa M. Ali ◽  
Khalid A. Alsaleh ◽  
Moustapha Kassem ◽  
Musaed Alfayez ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Early Detection ◽  
Cancer Patients ◽  
Expression Profiling ◽  
Microrna Expression ◽  
Circulating Microrna ◽  
Breast Cancer Patients

scholarly journals Optimization and Standardization of Circulating MicroRNA Detection for Clinical Application: The miR-Test Case

2016 ◽  
Vol 62 (5) ◽  
pp. 743-754 ◽  
Author(s):  
Matteo Jacopo Marzi ◽  
Francesca Montani ◽  
Rose Mary Carletti ◽  
Fabio Dezi ◽  
Elisa Dama ◽  
...  
Keyword(s):  
Early Detection ◽  
Large Scale ◽  
Rna Isolation ◽  
Clinical Samples ◽  
Circulating Microrna ◽  
Test Case ◽  
Circulating Mirnas ◽  
Serum Samples ◽  
Reverse Transcription Pcr ◽  
Cancer Early Detection

Abstract BACKGROUND The identification of circulating microRNAs (miRNAs) in the blood has been recently exploited for the development of minimally invasive tests for the early detection of cancer. Nevertheless, the clinical transferability of such tests is uncertain due to still-insufficient standardization and optimization of methods to detect circulating miRNAs in the clinical setting. METHODS We performed a series of tests to optimize the quantification of serum miRNAs that compose the miR-Test, a signature for lung cancer early detection, and systematically analyzed variables that could affect the performance of the test. We took advantage of a large-scale (&gt;1000 samples) validation study of the miR-Test that we recently published, to evaluate, in clinical samples, the effects of analytical and preanalytical variables on the quantification of circulating miRNAs and the clinical output of the signature (risk score). RESULTS We developed a streamlined and standardized pipeline for the processing of clinical serum samples that allows the isolation and analysis of circulating miRNAs by quantitative reverse-transcription PCR, with a throughput compatible with screening trials. The major source of analytical variation came from RNA isolation from serum, which could be corrected by use of external (spike-in) or endogenous miRNAs as a reference for normalization. We also introduced standard operating procedures and QC steps to check for unspecific fluctuations that arise from the lack of standardized criteria in the collection or handling of the samples (preanalytical factors). CONCLUSIONS We propose our methodology as a reference for the development of clinical-grade blood tests on the basis of miRNA detection.

scholarly journals Circulating microRNA expression profiling and bioinformatics analysis of dysregulated microRNAs of patients with coronary artery disease

Medicine ◽  
2018 ◽  
Vol 97 (27) ◽  
pp. e11428 ◽  
Author(s):  
Zhixiong Zhong ◽  
Jingyuan Hou ◽  
Qifeng Zhang ◽  
Wei Zhong ◽  
Bin Li ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Expression Profiling ◽  
Bioinformatics Analysis ◽  
Microrna Expression ◽  
Circulating Microrna ◽  
Artery Disease

Genome-wide circulating microRNA expression profiling reveals potential biomarkers for amyotrophic lateral sclerosis

2018 ◽  
Vol 64 ◽  
pp. 123-138 ◽  
Author(s):  
José Manuel Matamala ◽  
Raul Arias-Carrasco ◽  
Carolina Sanchez ◽  
Markus Uhrig ◽  
Leslie Bargsted ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Expression Profiling ◽  
Microrna Expression ◽  
Circulating Microrna ◽  
Genome Wide ◽  
Lateral Sclerosis ◽  
Potential Biomarkers
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