Sequential analysis of transcript expression patterns improves survival prediction in multiple cancers.

ABSTRACT:

Background

Long-term survival in numerous cancers often correlates with specific whole transcriptome profiles or the expression patterns of smaller numbers of transcripts. In some instances, these are better predictors of survival than are standard classification methods such as clinical stage or hormone receptor status in breast cancer. Here, we have used the method of "t-distributed stochastic neighbor embedding" (t-SNE) to show that, collectively, the expression patterns of small numbers of functionally-related transcripts from fifteen cancer pathways correlate with long-term survival in the vast majority of tumor types from The Cancer Genome Atlas (TCGA). We then ask whether the sequential application of t-SNE using the transcripts from a second pathway improves predictive capa

SUBMITTER: Mandel J 

PROVIDER: S-EPMC7140376 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature