title: Survival analysis by penalized regression and matrix factorization.
abstract: Because every disease has its unique survival pattern, it is necessary to find a suitable model to simulate followups. DNA microarray is a useful technique to detect thousands of gene expressions at one time and is usually employed to classify different types of cancer. We propose combination methods of penalized regression models and nonnegative matrix factorization (NMF) for predicting survival. We tried L1- (lasso), L2- (ridge), and L1-L2 combined (elastic net) penalized regression for diffuse large B-cell lymphoma (DLBCL) patients' microarray data and found that L1-L2 combined method predicts survival best with the smallest logrank P value. Furthermore, 80% of selected genes have been reported to correlate with carcinogenesis or lymphoma. Through NMF we found that DLBCL patients can be divided into 4 groups clearly, and it implies that DLBCL may have 4 subtypes which have a little different survival patterns. Next we excluded some patients who were indicated hard to classify in NMF and executed three penalized regression models again. We found that the performance of survival prediction has been improved with lower logrank P values. Therefore, we conclude that after preselection of patients by NMF, penalized regression models can predict DLBCL patients' survival successfully.
authors.affiliation: Bioinformatics Center, Institute for Chemical Research, Kyoto University, Gokasho, Uji, Kyoto, Japan.
authors.forename: Yeuntyng
authors.initials: Y
authors.lastname: Lai
authors.forename: Morihiro
authors.initials: M
authors.lastname: Hayashida
authors.forename: Tatsuya
authors.initials: T
authors.lastname: Akutsu
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis*; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D006801:Humans; D015994:Incidence; D016403:Lymphoma, Large B-Cell, Diffuse / Q000378:metabolism* / Q000401:mortality*; D008297:Male; D008875:Middle Aged; D009363:Neoplasm Proteins / Q000032:analysis*; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D015995:Prevalence; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods; D016019:Survival Analysis; D015996:Survival Rate
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins