Tissue-specific gene expression templates for accurate molecular characterization of the normal physiological states of multiple human tissues with implication in development and cancer studies

Pei-Ing Hwang,Chin-Di Wang,Shinsheng Yuan,Bai-Ling Lin,Guani Wu,Huan-Bin Wu,Ker-Chau Li,Cheng-Tao Chen

doi:10.1186/1471-2164-12-439

Abstract

BackgroundTo elucidate the molecular complications in many complex diseases, we argue for the priority to construct a model representing the normal physiological state of a cell/tissue.ResultsBy analyzing three independent microarray datasets on normal human tissues, we established a quantitative molecular model GET, which consists of 24 tissue-specific Gene Expression Templates constructed from a set of 56 genes, for predicting 24 distinct tissue types under disease-free condition. 99.2% correctness was reached when a large-scale validation was performed on 61 new datasets to test the tissue-prediction power of GET. Network analysis based on molecular interactions suggests a potential role of these 56 genes in tissue differentiation and carcinogenesis.Applying GET to transcriptomic datasets produced from tissue development studies the results correlated well with developmental stages. Cancerous tissues and cell lines yielded significantly lower correlation with GET than the normal tissues. GET distinguished melanoma from normal skin tissue or benign skin tumor with 96% sensitivity and 89% specificity.ConclusionsThese results strongly suggest that a normal tissue or cell may uphold its normal functioning and morphology by maintaining specific chemical stoichiometry among genes. The state of stoichiometry can be depicted by a compact set of representative genes such as the 56 genes obtained here. A significant deviation from normal stoichiometry may result in malfunction or abnormal growth of the cells.

Highlights

To elucidate the molecular complications in many complex diseases, we argue for the priority to construct a model representing the normal physiological state of a cell/tissue
By re-analyzing some of the transcriptomic datasets produced from normal human tissues in the Gene Expression Omnibus (GEO), we identified a set of 56 genes whose transcript profiles are endowed with strong tissue-specific properties for 24 different tissue types under the disease-free condition
While searching KEGG for the pathways mapped by the 56 genes, we found focal adhesion, ECMreceptor interaction, and PPAR signaling pathway overrepresented. (Additional file 1: Table S2) We conducted hierarchical clustering with the 56 genes from the 24 tissue types shared by our three training datasets

Summary

Introduction

To elucidate the molecular complications in many complex diseases, we argue for the priority to construct a model representing the normal physiological state of a cell/tissue It has been well-recognized that within a cell, genes participate in cascades of biochemical events (pathways), and the pathways themselves cross-talk with each other as a delicate and intriguing network system. By re-analyzing some of the transcriptomic datasets produced from normal human tissues in the Gene Expression Omnibus (GEO), we identified a set of 56 genes whose transcript profiles are endowed with strong tissue-specific properties for 24 different tissue types under the disease-free condition. Our results suggest that homeostasis among various molecules in a cell/tissue may play a key role in maintaining its normal functioning and the homeostasis state can be characterized by the 56 genes

Methods

Results

Conclusion