Integration of gene interaction information into a reweighted Lasso-Cox model for accurate survival prediction

Motivation

Accurately predicting the risk of cancer patients is a central challenge for clinical cancer research. For high-dimensional gene expression data, Cox proportional hazard model with the least absolute shrinkage and selection operator for variable selection (Lasso-Cox) is one of the most popular feature selection and risk prediction algorithms. However, the Lasso-Cox model treats all genes equally, ignoring the biological characteristics of the genes themselves. This often encounters the problem of poor prognostic performance on independent datasets.

Results

Here, we propose a Reweighted Lasso-Cox (RLasso-Cox) model to ameliorate this problem by integrating gene interaction information. It is based on the hypothesis that topologically important genes in the gene interaction network tend to have stable expression changes. We used random walk to evaluate the topological weight of genes, and then highlighted topologically important genes to improve the generalization ability of the RLasso-Cox model. Experiments on datasets of three cancer types showed that the RLasso-Cox model improves the prognostic accuracy and robustness compared with the Lasso-Cox model and several existing network-based methods. More importantly, the RLasso-Cox model has the advantage of identifying small gene sets with high prognostic performance on independent datasets, which may play an important role in identifying robust survival biomarkers for various cancer types.

Availability and implementation

http://bioconductor.org/packages/devel/bioc/html/RLassoCox.html

Supplementary information

Supplementary data are available at Bioinformatics online.

HiFreSP: A novel high-frequency sub-pathway mining approach to identify robust prognostic gene signatures

With the increasing awareness of heterogeneity in cancers, better prediction of cancer prognosis is much needed for more personalized treatment. Recently, extensive efforts have been made to explore the variations in gene expression for better prognosis. However, the prognostic gene signatures predicted by most existing methods have little robustness among different datasets of the same cancer. To improve the robustness of the gene signatures, we propose a novel high-frequency sub-pathways mining approach (HiFreSP), integrating a randomization strategy with gene interaction pathways. We identified a six-gene signature (CCND1, CSF3R, E2F2, JUP, RARA and TCF7) in esophageal squamous cell carcinoma (ESCC) by HiFreSP. This signature displayed a strong ability to predict the clinical outcome of ESCC patients in two independent datasets (log-rank test, P = 0.0045 and 0.0087). To further show the predictive performance of HiFreSP, we applied it to two other cancers: pancreatic adenocarcinoma and breast cancer. The identified signatures show high predictive power in all testing datasets of the two cancers. Furthermore, compared with the two popular prognosis signature predicting methods, the least absolute shrinkage and selection operator penalized Cox proportional hazards model and the random survival forest, HiFreSP showed better predictive accuracy and generalization across all testing datasets of the above three cancers. Lastly, we applied HiFreSP to 8137 patients involving 20 cancer types in the TCGA database and found high-frequency prognosis-associated pathways in many cancers. Taken together, HiFreSP shows higher prognostic capability and greater robustness, and the identified signatures provide clinical guidance for cancer prognosis. HiFreSP is freely available via GitHub: https://github.com/chunquanlipathway/HiFreSP.

Integration of gene interaction information into a reweighted random survival forest approach for accurate survival prediction and survival biomarker discovery

Accurately predicting patient risk and identifying survival biomarkers are two important tasks in survival analysis. For the emerging high-throughput gene expression data, random survival forest (RSF) is attracting more and more attention as it not only shows excellent performance on survival prediction problems with high-dimensional variables, but also is capable of identifying important variables according to variable importance automatically calculated within the algorithm. However, RSF still suffers from some problems such as limited predictive accuracy on independent datasets and limited biological interpretation of survival biomarkers. In this study, we integrated gene interaction information into a Reweighted RSF model (RRSF) to improve predictive accuracy and identify biologically meaningful survival markers. We applied RRSF to the prediction of patients with glioblastoma multiforme (GBM) and esophageal squamous cell carcinoma (ESCC). With a reconstructed global pathway network and an mRNA-lncRNA co-expression network as the prior gene interaction information, RRSF showed better overall predictive performance than RSF on three GBM and two ESCC datasets. In addition, RRSF identified a two-gene and three-lncRNA signature, which showed robust prognostic values and had high biological relevance to the development of GBM and ESCC, respectively.

Topologically inferring pathway activity for precise survival outcome prediction: breast cancer as a case

Accurately predicting the survival outcome of patients is of great importance in clinical cancer research. In the past decade, building survival prediction models based on gene expression data has received increasing interest. However, the existing methods are mainly based on individual gene signatures, which are known to have limited prediction accuracy on independent datasets and unclear biological relevance. Here, we propose a novel pathway-based survival prediction method called DRWPSurv in order to accurately predict survival outcome. DRWPSurv integrates gene expression profiles and prior gene interaction information to topologically infer survival associated pathway activities, and uses the pathway activities as features to construct Lasso-Cox model. It uses topological importance of genes evaluated by directed random walk to enhance the robustness of pathway activities and thereby improve the predictive performance. We applied DRWPSurv on three independent breast cancer datasets and compared the predictive performance with a traditional gene-based method and four pathway-based methods. Results showed that pathway-based methods obtained comparable or better predictive performance than the gene-based method, whereas DRWPSurv could predict survival outcome with better accuracy and robustness among the pathway-based methods. In addition, the risk pathways identified by DRWPSurv provide biologically informative models for breast cancer prognosis and treatment.

Prioritization of rheumatoid arthritis risk subpathways based on global immune subpathway interaction network and random walk strategy

The initiation and development of rheumatoid arthritis (RA) is closely related to mutual dysfunction of multiple pathways. Furthermore, some similar molecular mechanisms are shared between RA and other immune diseases. Therefore it is vital to reveal the molecular mechanism of RA through searching for subpathways of immune diseases and investigating the crosstalk effect among subpathways. Here we exploited an integrated approach combining both construction of a subpathway-subpathway interaction network and a random walk strategy to prioritize RA risk subpathways. Our research can be divided into three parts: (1) acquisition of risk genes and identification of risk subpathways of 85 immune diseases by using subpathway-lenient distance similarity (subpathway-LDS) method; (2) construction of a global immune subpathway interaction (GISI) network with subpathways identified by subpathway-LDS; (3) optimization of RA risk subpathways by random walk strategy based on GISI network. The results showed that our method could effectively identify RA risk subpathways, such as MAPK signaling pathway, prostate cancer pathway and chemokine signaling pathway. The integrated strategy considering crosstalk between immune subpathways significantly improved the effect of risk subpathway identification. With the development of GWAS, our method will provide insight into exploring molecular mechanisms of immune diseases and might be a promising approach for studying other diseases.