A New Approach to Deriving Prognostic Gene Pairs From Cancer Patient-Specific Gene Correlation Networks.

Many of the known prognostic gene signatures for cancer are individual genes or combination of genes, found by the analysis of microarray data. However, many of the known cancer signatures are less predictive than random gene expression signatures, and such random signatures are significantly associated with proliferation genes. With the availability of RNA-seq gene expression data for thousands of human cancer patients, we have analyzed RNA-seq and clinical data of cancer patients and constructed gene correlation networks specific to individual cancer patients. From the patient-specific gene correlation networks, we derived prognostic gene pairs for three types of cancer. In this paper, we propose a new method for inferring prognostic gene pairs from patient-specific gene correlation networks. The main difference of our method from previous ones includes (1) it is focused on finding prognostic gene pairs rather than prognostic genes, (2) it can identify prognostic gene pairs from RNA-seq data even when no significant prognostic genes exist, and (3) prognostic gene pairs can serve as robust prognostic biomarkers in the sense that most prognostic gene pairs show little association with proliferation genes, the major boosting factor of the predictive power of random gene signatures. Evaluation of our method with extensive data of three types of cancer (liver cancer, pancreatic cancer, and stomach cancer) showed that our approach is general and that gene pairs can serve as more reliable prognostic signatures for cancer than genes. Analysis of patient-specific gene networks suggests that prognosis of individual cancer patients is affected by the existence of prognostic gene pairs in the patient-specific network and by the size of the patient-specific network. Although preliminary, our approach will be useful for finding gene pairs to predict survival time of patients and to tailor treatments to individual characteristics. The program for dynamically constructing patient-specific gene networks and for finding prognostic gene pairs is available at http://bclab.inha.ac.kr/LPS.