Unsupervised analysis reveals two molecular subgroups of serous ovarian cancer with distinct gene expression profiles and survival.

ABSTRACT:

Purpose

Ovarian cancer is typically diagnosed at late stages, and thus, patients' prognosis is poor. Improvement in treatment outcomes depends, at least partly, on better understanding of ovarian cancer biology and finding new molecular markers and therapeutic targets.

Methods

An unsupervised method of data analysis, singular value decomposition, was applied to analyze microarray data from 101 ovarian cancer samples; then, selected genes were validated by quantitative PCR.

Results

We found that the major factor influencing gene expression in ovarian cancer was tumor histological type. The next major source of variability was traced to a set of genes mainly associated with extracellular matrix, cell motility, adhesion, and immunological response. Hierarchical clusterin

SUBMITTER: Lisowska KM 

PROVIDER: S-EPMC4869753 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature