title: Functional modules with disease discrimination abilities for various cancers.
abstract: Selecting differentially expressed genes (DEGs) is one of the most important tasks in microarray applications for studying multi-factor diseases including cancers. However, the small samples typically used in current microarray studies may only partially reflect the widely altered gene expressions in complex diseases, which would introduce low reproducibility of gene lists selected by statistical methods. Here, by analyzing seven cancer datasets, we showed that, in each cancer, a wide range of functional modules have altered gene expressions and thus have high disease classification abilities. The results also showed that seven modules are shared across diverse cancers, suggesting hints about the common mechanisms of cancers. Therefore, instead of relying on a few individual genes whose selection is hardly reproducible in current microarray experiments, we may use functional modules as functional signatures to study core mechanisms of cancers and build robust diagnostic classifiers.
authors.affiliation: Bioinformatics Centre, School of Life Science, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Chen
authors.initials: C
authors.lastname: Yao
authors.forename: Min
authors.initials: M
authors.lastname: Zhang
authors.forename: JinFeng
authors.initials: J
authors.lastname: Zou
authors.forename: HongDong
authors.initials: H
authors.lastname: Li
authors.forename: Dong
authors.initials: D
authors.lastname: Wang
authors.forename: Jing
authors.initials: J
authors.lastname: Zhu
authors.forename: Zheng
authors.initials: Z
authors.lastname: Guo
mesh_terms: D000465:Algorithms; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*