title: Cancer progression analysis based on ordinal relationship of cancer stages and co-expression network modularity.
abstract: A comprehensive understanding of cancer progression may shed light on genetic and molecular mechanisms of oncogenesis, and provide important information for effective diagnosis and prognosis. We propose a multicategory logit model to identify genes that show significant correlations across multiple cancer stages. We have applied the approach on a Prostate Cancer (PCA) progression data and obtained a set of genes that show consistent trends across multiple stages. Further analysis based on multiple evidences demonstrates that our candidate list includes not only some well-known prostate-cancer-related genes, but also novel genes that have been confirmed very recently.
authors.affiliation: Department of Electrical Engineering and Computer Science, Case Western Reserve University, 10900 Euclid Avenue, Olin 514, Cleveland, OH 44106, USA. yoon.pyon@case.edu
authors.forename: Yoon Soo
authors.initials: YS
authors.lastname: Pyon
authors.forename: Xin
authors.initials: X
authors.lastname: Li
authors.forename: Jing
authors.initials: J
authors.lastname: Li
mesh_terms: D018450:Disease Progression; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D023281:Genomics / Q000379:methods*; D006801:Humans; D008297:Male; D009367:Neoplasm Staging; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000473:pathology; D011471:Prostatic Neoplasms / Q000235:genetics / Q000473:pathology