Chen Jin-Bor, Yang Huai-Shuo, Moi Sin-Hua, Chuang Li-Yeh, Yang Cheng-Hong
Division of Nephrology, Department of Internal Medicine, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung.
Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung.
Ther Adv Chronic Dis. 2021 Feb 15;12:2040622321992624. doi: 10.1177/2040622321992624. eCollection 2021.
Kidney renal clear cell carcinoma (KIRCC) is a highly heterogeneous and lethal cancer that can arise in patients with renal disease. DeepSurv combines a deep feed-forward neural network with a Cox proportional hazards function and could provide optimized survival results compared with convenient survival analysis.
This study used an improved DeepSurv algorithm to identify the candidate genes to be targeted for treatment on the basis of the overall mortality status of KIRCC subjects. All the somatic mutation missense variants of KIRCC subjects were abstracted from TCGA-KIRC database.
The improved DeepSurv model (95.1%) achieved greater balanced accuracy compared with the DeepSurv model (75%), and identified 610 high-risk variants associated with overall mortality. The results of gene differential expression analysis also indicated nine KIRCC mortality-risk-related pathways, namely the tRNA charging pathway, the D-myo-inositol-5-phosphate metabolism pathway, the DNA double-strand break repair by nonhomologous end-joining pathway, the superpathway of inositol phosphate compounds, the 3-phosphoinositide degradation pathway, the production of nitric oxide and reactive oxygen species in macrophages pathway, the synaptic long-term depression pathway, the sperm motility pathway, and the role of in hormone-like cytokine signaling pathway. The biological findings in this study indicate the KIRCC mortality-risk-related pathways were more likely to be associated with cancer cell growth, cancer cell differentiation, and immune response inhibition.
The results proved that the improved DeepSurv model effectively classified mortality-related high-risk variants and identified the candidate genes. In the context of KIRCC overall mortality, the proposed model effectively recognized mortality-related high-risk variants for KIRCC.
肾透明细胞癌（KIRCC）是一种高度异质性且致命的癌症，可发生于肾病患者。DeepSurv将深度前馈神经网络与Cox比例风险函数相结合，与便捷的生存分析相比，能提供优化的生存结果。
本研究使用改进的DeepSurv算法，根据KIRCC受试者的总体死亡状况，识别待靶向治疗的候选基因。KIRCC受试者的所有体细胞突变错义变体均从TCGA - KIRC数据库中提取。
改进后的DeepSurv模型（95.1%）与DeepSurv模型（75%）相比，实现了更高的平衡准确率，并识别出610个与总体死亡率相关的高风险变体。基因差异表达分析结果还表明了9条与KIRCC死亡风险相关的通路，即tRNA充电通路、D - 肌醇 - 5 - 磷酸代谢通路、非同源末端连接的DNA双链断裂修复通路、肌醇磷酸化合物的超级通路、3 - 磷酸肌醇降解通路、巨噬细胞中一氧化氮和活性氧的产生通路、突触长期抑制通路、精子运动通路以及在激素样细胞因子信号通路中的作用。本研究中的生物学发现表明，与KIRCC死亡风险相关的通路更可能与癌细胞生长、癌细胞分化和免疫反应抑制相关。
结果证明改进后的DeepSurv模型有效地对与死亡率相关的高风险变体进行了分类，并识别出了候选基因。在KIRCC总体死亡率的背景下，所提出的模型有效地识别出了KIRCC与死亡率相关的高风险变体。
