Isoform level expression profiles provide better cancer signatures than gene level expression profiles.

ABSTRACT:

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 would be better than gene level expression profiles at discriminating between non-oncogenic and cancer cellsgene level.

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 i

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC3706752 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature