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Five miRNAs Considered as Molecular Targets for Predicting Esophageal Cancer.

Jia-ying Zhao | Fei Wang | Yi Li | Xing-bo Zhang | Lei Yang | Wei Wang | Hao Xu | Da-zhong Liu | Lin-you Zhang
Medical science monitor : international medical journal of experimental and clinical research | 2015

Esophageal cancer (EC) is one of the most aggressive malignant gastrointestinal tumors; however the traditional therapies for EC are not effective enough. Great improvements are needed to explore new and valid treatments for EC. We aimed to screen the differentially expressed miRNAs (DEMs) in esophageal cancer and explore the pathogenesis of esophageal cancer along with functions and pathways of the target genes.

Pubmed ID: 26498375

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PlantCyc (tool)

RRID:SCR_002110

Multi species reference database. Comprehensive plant biochemical pathway database, containing curated information from literature and computational analyses about genes, enzymes, compounds, reactions, and pathways involved in primary and secondary metabolism.

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KEGG (tool)

RRID:SCR_012773

Integrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.

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TargetScan (tool)

RRID:SCR_010845

Web tool to predict biological targets of miRNAs by searching for presence of conserved 8mer, 7mer and 6mer sites that match seed region of each miRNA. Nonconserved sites are also predicted and sites with mismatches in seed region that are compensated by conserved 3' pairing. Used to search for predicted microRNA targets in mammals.

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LIMMA (tool)

RRID:SCR_010943

Software package for the analysis of gene expression microarray data, especially the use of linear models for analyzing designed experiments and the assessment of differential expression.

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