title: Molecular classification of prostate adenocarcinoma by the integrated somatic mutation profiles and molecular network.
abstract: Prostate cancer is one of the most common cancers in men and a leading cause of cancer death worldwide, displaying a broad range of heterogeneity in terms of clinical and molecular behavior. Increasing evidence suggests that classifying prostate cancers into distinct molecular subtypes is critical to exploring the potential molecular variation underlying this heterogeneity and to better treat this cancer. In this study, the somatic mutation profiles of prostate cancer were downloaded from the TCGA database and used as the source nodes of the random walk with restart algorithm (RWRA) for generating smoothed mutation profiles in the STRING network. The smoothed mutation profiles were selected as the input matrix of the Graph-regularized Nonnegative Matrix Factorization (GNMF) for classifying patients into distinct molecular subtypes. The results were associated with most of the clinical and pathological outcomes. In addition, some bioinformatics analyses were performed for the robust subtyping, and good results were obtained. These results indicated that prostate cancers can be usefully classified according to their mutation profiles, and we hope that these subtypes will help improve the treatment stratification of this cancer in the future.
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China. yanglei_hmu@163.com.
authors.forename: Lei
authors.initials: L
authors.lastname: Yang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
authors.forename: Shiyuan
authors.initials: S
authors.lastname: Wang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
authors.forename: Meng
authors.initials: M
authors.lastname: Zhou
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
authors.forename: Xiaowen
authors.initials: X
authors.lastname: Chen
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Jiang
authors.affiliation: The Key Laboratory of Mammalian Reproductive Biology and Biotechnology of the Ministry of Education, Inner Mongolia University, Hohhot, 010021, China. yczuo@imu.edu.cn.
authors.forename: Yongchun
authors.initials: Y
authors.lastname: Zuo
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China. lvyingli_hmu@163.com.
authors.forename: Yingli
authors.initials: Y
authors.lastname: Lv
mesh_terms: D000230:Adenocarcinoma / Q000235:genetics / Q000378:metabolism / Q000473:pathology; D000328:Adult; D000368:Aged; D014408:Biomarkers, Tumor; D019295:Computational Biology / Q000379:methods; D004252:DNA Mutational Analysis; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006801:Humans; D053208:Kaplan-Meier Estimate; D008297:Male; D008875:Middle Aged; D009154:Mutation; D060787:Neoplasm Grading; D009362:Neoplasm Metastasis; D009367:Neoplasm Staging; D011379:Prognosis; D011471:Prostatic Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000401:mortality / Q000473:pathology
chemical_list: D014408:Biomarkers, Tumor