Abstract
Background: Type 2 diabetes (T2D) is a common multi-factorial disease that is primarily accounted to ineffective insulin action in lowering blood glucose level and later escalates to impaired insulin secretion by pancreatic β cells. Deregulation in insulin signaling to its target organs is attributed to this disease phenotype. Various genome-wide microarray studies from multiple insulin responsive tissues have been conducted in past but due to inherent noise in microarray data and heterogeneity in disease etiology; reproduction of prioritized pathways/genes is very low across various studies.
Objective: In this study, we aim to identify consensus signaling and metabolic pathways through system level meta-analysis of multiple expression-sets to elucidate T2D pathobiology. Method: We used ‘R’, an open source statistical environment, which is routinely used for Microarray data analysis particularly using special sets of packages available at Bioconductor. We primarily focused on gene-set analysis methods to elucidate various aspects of T2D. Result: Literature-based evidences have shown the success of our approach in exploring various known aspects of diabetes pathophysiology. Conclusion: Our study stressed the need to develop novel bioinformatics workflows to advance our understanding further in insulin signaling.Keywords: Type 2 Diabetes, Insulin-signaling, Microarray, Meta-analysis, Bioconductor, Gene-set analysis.
Current Genomics
Title:System Level Meta-analysis of Microarray Datasets for Elucidation of Diabetes Mellitus Pathobiology
Volume: 18 Issue: 3
Author(s): Aditya Saxena*, Kumar Sachin and Ashok Kumar Bhatia
Affiliation:
- Department of Biotechnology, Institute of Applied Sciences & Humanities, GLA University, Mathura P.O. Box: 281 406, Mathura,India
Keywords: Type 2 Diabetes, Insulin-signaling, Microarray, Meta-analysis, Bioconductor, Gene-set analysis.
Abstract: Background: Type 2 diabetes (T2D) is a common multi-factorial disease that is primarily accounted to ineffective insulin action in lowering blood glucose level and later escalates to impaired insulin secretion by pancreatic β cells. Deregulation in insulin signaling to its target organs is attributed to this disease phenotype. Various genome-wide microarray studies from multiple insulin responsive tissues have been conducted in past but due to inherent noise in microarray data and heterogeneity in disease etiology; reproduction of prioritized pathways/genes is very low across various studies.
Objective: In this study, we aim to identify consensus signaling and metabolic pathways through system level meta-analysis of multiple expression-sets to elucidate T2D pathobiology. Method: We used ‘R’, an open source statistical environment, which is routinely used for Microarray data analysis particularly using special sets of packages available at Bioconductor. We primarily focused on gene-set analysis methods to elucidate various aspects of T2D. Result: Literature-based evidences have shown the success of our approach in exploring various known aspects of diabetes pathophysiology. Conclusion: Our study stressed the need to develop novel bioinformatics workflows to advance our understanding further in insulin signaling.Export Options
About this article
Cite this article as:
Saxena Aditya*, Sachin Kumar and Bhatia Kumar Ashok, System Level Meta-analysis of Microarray Datasets for Elucidation of Diabetes Mellitus Pathobiology, Current Genomics 2017; 18 (3) . https://dx.doi.org/10.2174/1389202918666170105093339
DOI https://dx.doi.org/10.2174/1389202918666170105093339 |
Print ISSN 1389-2029 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5488 |
Call for Papers in Thematic Issues
Advanced Computational Algorithms and Artificial Intelligence in Clinical Pharmacogenomics
In the era of personalized medicine, understanding the relationship between genetics and drug response is crucial. This issue delves into innovative methodologies, leveraging deep computational analysis and artificial intelligence, to enhance the field of Clinical Pharmacogenomics. The interdisciplinary approach harnesses the power of advanced high-throughput genotyping technologies, sophisticated computational analysis, ...read more
Applications of Single-cell Sequencing Technology in Reproductive Medicine
Single cell sequencing (SCS) technology utilizes individual cells' genetic material to sequence their genome, transcriptome, and epigenetics at the molecular level. It offers insights into cell heterogeneity and enables the study of limited biological materials. Since its recognition as a valuable technique in 2011, single cell sequencing has yielded numerous ...read more
Big Data in Cancer Research
Cancer is a significant threat to human life and health, remaining a highly aggressive killer. It is a leading cause of death worldwide and represents a crucial medical issue for humanity. However, in the past decade, the effectiveness of new synthetic anticancer agents has not matched the current clinical speculation. ...read more
Current Genomics in Cardiovascular Research
Cardiovascular diseases are the main cause of death in the world, in recent years we have had important advances in the interaction between cardiovascular disease and genomics. In this Research Topic, we intend for researchers to present their results with a focus on basic, translational and clinical investigations associated with ...read more
Related Journals
- Author Guidelines
- Graphical Abstracts
- Fabricating and Stating False Information
- Research Misconduct
- Post Publication Discussions and Corrections
- Publishing Ethics and Rectitude
- Increase Visibility of Your Article
- Archiving Policies
- Peer Review Workflow
- Order Your Article Before Print
- Promote Your Article
- Manuscript Transfer Facility
- Editorial Policies
- Allegations from Whistleblowers
- Announcements
Related Articles
-
Tailoring Antiplatelet Therapy: A Step Toward Individualized Therapy to Improve Clinical Outcome?
Current Pharmaceutical Design Asymptomatic Primary Hyperparathyroidism: Management and Implications
Recent Patents on Endocrine, Metabolic & Immune Drug Discovery Survey of Mortality Due to Influenza A in North of Iran, 2015-2016
Current Respiratory Medicine Reviews Optimizing Conventional Medical Therapies in Inflammatory Bowel Disease in 2014
Current Drug Targets Nailfold Capillaroscopy Within and Beyond the Scope of Connective Tissue Diseases
Current Rheumatology Reviews Ginkgo biloba Extract in Vascular Protection: Molecular Mechanisms and Clinical Applications
Current Vascular Pharmacology Genetic Studies of Type 2 Diabetes in South Asians: A Systematic Overview
Current Diabetes Reviews The Woman’s Heart: Insights into New Potential Targeted Therapy
Current Medicinal Chemistry Role of Inflammation in Diabetic Nephropathy
Current Diabetes Reviews Phytochemicals to Prevent Inflammation and Allergy
Recent Patents on Inflammation & Allergy Drug Discovery Effect of Diabetes on The Blood Brain Barrier
Current Pharmaceutical Design Imaging Subclinical Atherosclerosis: Where Do We Stand?
Current Cardiology Reviews Metabolomics and the Diagnosis of Human Diseases -A Guide to the Markers and Pathophysiological Pathways Affected
Current Medicinal Chemistry Use of Insulin and Insulin Analogs and Risk of Cancer — Systematic Review and Meta-Analysis of Observational Studies
Current Drug Safety Sirtuins: Developing Innovative Treatments for Aged-Related Memory Loss and Alzheimer’s Disease
Current Neurovascular Research Novel Peptides under Development for the Treatment of Type 1 and Type 2 Diabetes Mellitus
Current Drug Targets - Immune, Endocrine & Metabolic Disorders Antidiabetics: Structural Diversity of Molecules with a Common Aim
Current Medicinal Chemistry Why and How We Should Treat Elderly Patients with Hypertension?
Current Vascular Pharmacology Lipid Management and Peripheral Arterial Disease
Current Drug Targets Mesenchymal Stem Cell Therapy in Intracerebral Haemorrhagic Stroke
Current Medicinal Chemistry