Abstract
BackgroundThe majority of mammalian genes generate multiple transcript variants and protein isoforms through alternative transcription and/or alternative splicing, and the dynamic changes at the transcript/isoform level between non-oncogenic and cancer cells remain largely unexplored. We hypothesized that isoform level expression profiles would be better than gene level expression profiles at discriminating between non-oncogenic and cancer cellsgene level.MethodsWe analyzed 160 Affymetrix exon-array datasets, comprising cell lines of non-oncogenic or oncogenic tissue origins. We obtained the transcript-level and gene level expression estimates, and used unsupervised and supervised clustering algorithms to study the profile similarity between the samples at both gene and isoform levels.ResultsHierarchical clustering, based on isoform level expressions, effectively grouped the non-oncogenic and oncogenic cell lines with a virtually perfect homogeneity-grouping rate (97.5%), regardless of the tissue origin of the cell lines. However, gene levelthis rate was much lower, being 75% at best based on the gene level expressions. Statistical analyses of the difference between cancer and non-oncogenic samples identified the existence of numerous genes with differentially expressed isoforms, which otherwise were not significant at the gene level. We also found that canonical pathways of protein ubiquitination, purine metabolism, and breast-cancer regulation by stathmin1 were significantly enriched among genes thatshow differential expression at isoform level but not at gene level.ConclusionsIn summary, cancer cell lines, regardless of their tissue of origin, can be effectively discriminated from non-cancer cell lines at isoform level, but not at gene level. This study suggests the existence of an isoform signature, rather than a gene signature, which could be used to distinguish cancer cells from normal cells.
Highlights
The majority of mammalian genes generate multiple transcript variants and protein isoforms through alternative transcription and/or alternative splicing, and the dynamic changes at the transcript/isoform level between non-oncogenic and cancer cells remain largely unexplored
Estimation of isoform level and gene level expression values from exon-array data The isoform level and gene level expression estimates were obtained by the Multi-Mapping Bayesian Gene eXpression (MMBGX) algorithm for Affymetrix whole-transcript arrays [28], based on the Ensembl database [29], which contains a total of 114,930 different transcript annotations that correspond to 35,612 different gene models
To test our hypothesis that the isoform level expression profiles are better than the gene level expression profiles at discriminating non-oncogenic and cancer cellsgene level, we performed unsupervised clustering of 160 samples
Summary
The majority of mammalian genes generate multiple transcript variants and protein isoforms through alternative transcription and/or alternative splicing, and the dynamic changes at the transcript/isoform level between non-oncogenic and cancer cells remain largely unexplored. Molecular profiling of gene expression, using microarrays, has shown that heterogeneity in outcome and survival of patients with cancer can be explained, in part, by genomic variation within the primary tumor. These technologies have helped identify many genetic and epigenetic modifications involved in the initiation. There is growing evidence linking aberrant use of alternative mRNA isoforms with cancer formation; several oncogenes and tumor-suppressor genes (for example, LEF1, TP63, TP73, HNF4A, RASSF1, and BCL2L1) are already known to have multiple promoters and alternative splice forms [10,11,12,13,14,15,16]. The prevalence of alternative splicing in cancer genomes has been discussed in the literature [18,19,20], and it has been shown that use of splice forms provides better classification of normal and cancerous prostate tissue, it is not clear whether the use of genome-wide isoform level geneexpression profiles can provide a better global discriminative signature for cancer and normal cells
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