Network-based survival analysis to discover target genes for developing cancer immunotherapies and predicting patient survival

ABSTRACT

Recently, cancer immunotherapies have been life-savers; however, only a fraction of treated patients have durable responses. Consequently, statistical methods that enable the discovery of target genes for developing new treatments and predicting the patient survival are of importance. This paper introduced a network-based survival analysis method and applied it to identify candidate genes as possible targets for developing new treatments. RNA-seq data from a mouse study was used to select differentially expressed genes, which were then translated to those in humans. We constructed a gene network and identified gene clusters using a training set of 310 human gliomas. Then we conducted gene set enrichment analysis to select the gene clusters with significant biological function. A penalized Cox model was built to identify a small set of candidate genes to predict survival. An independent set of 690 human glioma samples was used to evaluate a predictive accuracy of the survival model. The areas under time-dependent ROC curves in both the training and validation sets are more than 90%, indicating a strong association between selected genes and patient survival. Consequently, potential biomedical interventions targeting these genes might be able to alter their expressions and prolong patient survival.

Keywords:

• Multicellular gene network
• predictive models
• WGCNA
• survival models
• time-dependent ROCs and AUCs
• treatment resistance

Acknowledgments

The authors would like to thank Professor Jiguang Bao from Beijing Normal University and Professor Jian-Guo Sun from University of Missouri at Columbia for useful suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

X.H. is supported by China Scholarship Council (201806040074). Y.S. is supported by NIH/NCI grant P50CA225450 and P30CA016087. X.S. is supported by grants from the National Natural Science Foundation of China [grant numbers 11871070, 61503419], the Guangdong Basic and Applied Basic Research Foundation [grant number 2020B1515020047] and the Fundamental Research Funds for the Central Universities [grant number 20ykzd20].