Cancer Informatics
Synopsis: An open access, peer reviewed electronic journal that covers the role of computational biology and bioinformatics in cancer treatment.
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An Integrative Analysis of microRNA and mRNA Expression - A Case Study
Authors: Li-Xuan Qin
Publication Date: 17 Jun 2008
Cancer Informatics 2008:6 369-379
Li-Xuan Qin
Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, New York, U.S.A.
Abstract
Background: MicroRNAs are believed to play an important role in gene expression regulation. They have been shown to be involved in cell cycle regulation and cancer. MicroRNA expression profiling became available owing to recent technology advancement. In some studies, both microRNA expression and mRNA expression are measured, which allows an integrated analysis of microRNA and mRNA expression.
Results: We demonstrated three aspects of an integrated analysis of microRNA and mRNA expression, through a case study of human cancer data. We showed that (1) microRNA expression efficiently sorts tumors from normal tissues regardless of tumor type, while gene expression does not; (2) many microRNAs are down-regulated in tumors and these microRNAs can be clustered in two ways: microRNAs similarly affected by cancer and microRNAs similarly interacting with genes; (3) taking let-7f as an example, targets genes can be identified and they can be clustered based on their relationship with let-7f expression.
Discussion: Our findings in this paper were made using novel applications of existing statistical methods: hierarchical clustering was applied with a new distance measure—the co-clustering frequency—to identify sample clusters that are stable; microRNA-gene correlation profiles were subject to hierarchical clustering to identify microRNAs that similarly interact with genes and hence are likely functionally related; the clustering of regression models method was applied to identify microRNAs similarly related to cancer while adjusting for tissue type and genes similarly related to microRNA while adjusting for disease status. These analytic methods are applicable to interrogate multiple types of -omics data in general.
Keywords: clustering, expression, microarray, microRNA
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The Metastasectomy and Timing of Pulmonary Metastases on the Outcome of Osteosarcoma Patients - 14/Sep/2009
Gromita: A Fully Integrated Graphical User Interface to Gromacs 4 - 07/Sep/2009
Gene Expression Based Leukemia Sub‑Classification Using Committee Neural Networks - 03/Sep/2009
Asymmetrical Weakness Associated with Central Nervous System Involvement in a Patient with Guillain-Barrè Syndrome - 03/Sep/2009
Modulation of the BRCA1 Protein and Induction of Apoptosis in Triple Negative Breast Cancer Cell Lines by the Polyphenolic Compound Curcumin - 02/Sep/2009
A Model for Protein Sequence Evolution Based on Selective Pressure for Protein Stability: Application to Hemoglobins - 27/Aug/2009
Growth Factor Receptors and Apoptosis Regulators: Signaling Pathways, Prognosis, Chemosensitivity and Treatment Outcomes of Breast Cancer - 17/Aug/2009
Molecular Imaging of Proteases in Cancer - 17/Aug/2009
A Commentary on “Risk Factors for Renal Cell Cancer in a Japanese Population” - 14/Aug/2009
Gastric Adenocarcinoma: Is Computed Tomography (CT) Useful in Preoperative Staging? - 12/Aug/2009
Novel Biomarkers for the Diagnosis of Urinary Tract Infection - A Systematic Review - 05/Aug/2009
On the Use of Bootstrapped Topologies in Coalescent-Based Bayesian McMc Inference: A Comparison of Estimation and Computational Efficiencies - 31/Jul/2009