Isoform-level expression profiles provide better cancer signatures than gene-level expression profiles
A partir de données portant sur des lignées cellulaires, cette étude suggère qu'une signature basée sur l'expression des isoformes de protéines permet de distinguer cellules cancéreuses et cellules normales
BACKGROUND: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. We hypothesized that isoform-level expression profiles can better discriminate non-oncogenic and cancer cells than gene-level expression profiles.
METHODS:We 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.
RESULTS:Hierarchical 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, at the gene-level, this rate was much lower and was 75% at best. Statistical analyses between cancer and non-oncogenic samples revealed the existence of numerous genes having 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 that show differential expression at the isoform-level but not at gene-level.
CONCLUSIONS:In 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 that can be used to identify cancer cells from normal cells.
Genome Medicine , article en libre accès, 2012