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.

中文翻译：

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从癌症患者特异性基因相关网络推导预后基因对的新方法

许多已知的癌症预后基因特征是通过微阵列数据分析发现的单个基因或基因组合。然而，许多已知的癌症特征不如随机基因表达特征具有预测性，并且这种随机特征与增殖基因显着相关。随着数千名人类癌症患者的 RNA-seq 基因表达数据的可用性，我们分析了癌症患者的 RNA-seq 和临床数据，并构建了针对个体癌症患者的基因相关网络。从患者特异性基因相关网络中，我们得出了三种癌症的预后基因对。在本文中，我们提出了一种从患者特异性基因相关网络中推断预后基因对的新方法。我们的方法与以前的方法的主要区别包括（1）它专注于寻找预后基因对而不是预后基因，（2）即使不存在显着的预后基因，它也可以从 RNA-seq 数据中识别预后基因对，以及（ 3）预后基因对可以作为强有力的预后生物标志物，因为大多数预后基因对与增殖基因几乎没有关联，增殖基因是随机基因特征预测能力的主要促进因素。使用三种癌症（肝癌、胰腺癌和胃癌）的大量数据对我们的方法进行评估表明，我们的方法是通用的，并且基因对可以作为比基因更可靠的癌症预后特征。对患者特异性基因网络的分析表明，个体癌症患者的预后受患者特异性网络中预后基因对的存在和患者特异性网络大小的影响。虽然是初步的，但我们的方法将有助于寻找基因对以预测患者的存活时间并根据个体特征定制治疗。用于动态构建患者特异性基因网络和寻找预后基因对的程序可在http://bclab.inha.ac.kr/LPS.