Abstract
Detecting gene sets that serve as biomarkers for differentiating patient survival groups may help diagnose diseases robustly and develop multi-gene targeted therapies. However, due to the exponential growth of search space imposed by gene combinations, the performance of existing methods is still far from satisfactory. In this study, we developed a new method called BISG (BIclustering based Survival-related Gene sets detection) based on a rectified factor network (RFN) model, which allows efficiently biclustering gene subsets. By correlating genes in each significant bicluster with patient survival outcomes using a log-rank test and multi-sampling strategy, multiple survival-related gene sets can be detected. We applied BISG on three different cancer types, and the resulting gene sets were tested as biomarkers for survival analyses. Secondly, we systematically analyzed 12 different cancer datasets. Our analysis shows that the genes in all the survival-related gene sets are mainly from five gene families: microRNA protein coding host genes, zinc fingers C2H2-type, solute carriers, CD (cluster of differentiation) molecules, and ankyrin repeat domain containing genes. Moreover, we found that they are mainly enriched in heme metabolism, apoptosis, hypoxia and inflammatory response-related pathways. We compared BISG with two other methods, GSAS and IPSOV. Results show that BISG can better differentiate patient survival groups in different datasets. The identified biomarkers suggested by our study provide useful hypotheses for further investigation. BISG is publicly available with open source at https://github.com/LingtaoSu/BISG.
Highlights
Identifying biomarker genes for survival risk prediction allows earlier detection of mortality risk and design of individualized therapy (Wang and Liu, 2018)
The survival curves of patients with and without top-ranked four most significant biclusters for each of the three cancer types are shown in Supplementary Figure S3, where the bicluster gene sets identified by our methods can well separate patients into two different survival groups
To test whether biomarker gene sets detected by BISG with datasets from cBioPortal database can differentiate patients into different survival groups with new independent datasets, we collected three microarray datasets GSE16011, GSE3494, and GSE11969, as well as their corresponding sample survival information (Table 2) from GEO as independent test datasets to confirm the biomarkers detected in gliomas, breast cancer and lung adenocarcinoma, respectively
Summary
Identifying biomarker genes for survival risk prediction allows earlier detection of mortality risk and design of individualized therapy (Wang and Liu, 2018). Functionally related genes often exhibit a similar pattern in only a subset of samples or under specific experimental conditions (Padilha and Campello, 2017). This problem can be solved by biclustering, which can be used to detect latent row and column groups of different response patterns (Zhang et al, 2017; Saelens et al, 2018). The rectified factor network (RFN) model (Clevert et al, 2015) was introduced, which aims at finding a sparse, non-negative representation of the input, and extracting the covariance structure of the data. By utilizing the projected Newton and projected gradient update strategies during optimization, RFN can efficiently carry out biclustering with high accuracy
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