Feature selection combined with top-down and bottom-up strategies for survival analysis: A case of prognostic prediction in glioblastoma

Over the last decades, molecular signatures have attracted extensive attention in cancer research. However, most of the reported biomarkers show a weak distinguishing ability in predicting the survival risks of patients. Actually, univariate analysis is generally considered in regression analysis, which makes the existing statistical methods ineffective. Furthermore, there is too much human involvement in the ways of classifying patients with high and low risk. Last but not least, the participation of therapy after conservative surgery also makes the survival analysis more complex. In order to solve these problems, we propose a solid method of feature selection which combines top-down and bottom-up strategies. The top-down strategy is to randomly extract some genes each time and select candidate genes through cumulative voting. The bottom-up strategy is to fully enumerate the selected genes and to use a clustering algorithm to classify samples. We analyzed glioblastoma data from the Cancer Genome Atlas (TCGA) and got candidate signatures. The results of simulation data, as well as an independent test set the Chinese Glioma Genome Atlas (CGGA), verified the reliability of the method and validity of the selected features.

Automated summary

Over the last decades, molecular signatures have been extensively studied in cancer research, but most reported biomarkers have weak predictive ability for patient survival risks. Univariate analysis in regression analysis is generally considered ineffective, and the involvement of human classification methods and post-surgery therapy further complicates survival analysis. To address these issues, we propose a solid feature selection method combining top-down and bottom-up strategies, which has been validated through analysis of glioblastoma data and independent testing.