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Network-based survival analysis to discover target genes for developing cancer immunotherapies and predicting patient
Xinwei He1, Xiaoqiang Sun2, Yongzhao Shao1
1New York University, USA.
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 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 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 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.
Insights
This study developed a network-based survival analysis to identify genes for cancer immunotherapy. The method accurately predicts patient survival, offering new targets for treatment development.
Area of Science:
- Oncology
- Bioinformatics
- Genomics
Background:
- Cancer immunotherapies show promise but benefit only a subset of patients.
- Predicting patient survival and identifying novel therapeutic targets remain critical challenges.
Purpose of the Study:
- To develop and validate a network-based survival analysis method for identifying prognostic genes in cancer.
- To discover candidate genes for novel cancer treatment development and patient survival prediction.
Main Methods:
- Applied network-based survival analysis to RNA-seq data from mouse and human glioma studies.
- Constructed gene networks, identified clusters, and performed gene set enrichment analysis.
- Utilized a penalized Cox model for survival prediction and validated with independent patient cohorts.
Main Results:
- Identified significant gene clusters associated with biological functions relevant to glioma.
- Developed a survival prediction model with high accuracy (Area Under Curve > 90%) in both training and validation sets.
- Demonstrated a strong association between the selected candidate genes and patient survival outcomes.
Conclusions:
- The network-based survival analysis is a robust method for identifying prognostic biomarkers in cancer.
- The identified genes represent potential therapeutic targets for improving patient survival in glioma.
- This approach can guide the development of more effective cancer immunotherapies and personalized treatments.
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