Latent factor analysis facilitates modelling of oncogenic genes for colon adenocarcinoma.
Identification of oncogenic genes from a large sample number of genomic data is a challenge. In this study, a well-established latent factor model, Bayesian factor and regression model, are applied to predict unknown colon cancer related genes from colon adenocarcinoma genomic data. Four important latent factors were addressed by the latent factor model, focusing on characterisation of heterogeneity of expression patterns of specific oncogenic genes by using microarray data of 174 colon cancer patients. Based on the fact that variables included in the same latent factor have some common characteristics and known cancer related genes in Online Mendelian Inheritance in Man, the authors found that the four latent factors can be employed to predict unknown colon cancer related genes that were never reported in the literature. The authors validated 15 identified genes by checking their somatic mutations of the same patients from DNA sequencing data.
Changhe
C
Fu
Su
S
Deng
Qiqing
Q
Song
Ling
L
Jing
D000230:Adenocarcinoma / Q000235:genetics* / Q000473:pathology; D000465:Algorithms; D001499:Bayes Theorem; D003110:Colonic Neoplasms / Q000235:genetics* / Q000473:pathology; D019295:Computational Biology / Q000379:methods; D018076:DNA, Complementary / Q000378:metabolism; D005163:Factor Analysis, Statistical; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D015894:Genome, Human; D023281:Genomics; D006801:Humans; D009154:Mutation; D012044:Regression Analysis; D017422:Sequence Analysis, DNA
D018076:DNA, Complementary