Han Li, Cheng Mei-Hong, Zhang Min, Cheng Kai
Department of Ophthalmology, Yidu Central Hospital of Weifang, Qingzhou 262500, Shandong Province, China.
Department of Ophthalmology, Jinan Maternity and Child Care Hospital, Jinan 250001, Shandong Province, China.
J Cancer Res Ther. 2018;14(Supplement):S22-S27. doi: 10.4103/0973-1482.180678.
The objective of this paper was to investigate key genes in retinoblastoma using a novel method which is mainly based on five kinds of genes, differentially expressed genes (DEGs), differential pathway genes (DPGs), seed genes (common genes between DEGs and DPGs), hub genes and informative genes (common genes of hub genes and DEGs), and support vector machines (SVM) model.
In the proposed method, the first step was to identify five types of significant genes. DEGs were identified using linear models for microarray data (Limma) package (The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia). DPGs were originated from differential pathways based on attract method. Hub genes of mutual information network which is constructed by the context likelihood of relatedness algorithm were obtained according to topological degree centrality analysis. For the second step, SVM model was implemented to assess the classification performance of DEGs, DPGs, seed genes, hub genes, and informative genes, depending on its induces the area under the receiver operating characteristics curve (AUC), true negative rate (TNR), true positive rate (TPR) and the Matthews coefficient correlation classification (MCC).
We detected 479 DEGs, 747 DPGs, 29 seed genes, 34 hub genes, and 7 informative genes in total for retinoblastoma. The classification performance of informative genes was the best of all with AUC = 1.00, TNR = 1.00, TPR = 1.00, and MCC = 1.00, hence they were considered to key genes which included EPARS1, FN1, HLA-DPA1, HLA-DPB1, HLA-DRA, CFI, and transforming growth factor, beta receptor II.
We have successfully identified seven key genes, which might be potential biomarkers for detection and therapy of retinoblastoma for current and future study.
本文的目的是使用一种新方法研究视网膜母细胞瘤中的关键基因，该方法主要基于五种基因，即差异表达基因（DEGs）、差异通路基因（DPGs）、种子基因（DEGs和DPGs之间的共同基因）、枢纽基因和信息基因（枢纽基因和DEGs的共同基因），以及支持向量机（SVM）模型。
在所提出的方法中，第一步是识别五种类型的显著基因。使用微阵列数据线性模型（Limma）软件包（澳大利亚墨尔本沃尔特和伊丽莎·霍尔医学研究所）识别DEGs。DPGs源自基于吸引方法的差异通路。根据拓扑度中心性分析，获得由相关性算法的上下文似然性构建的互信息网络的枢纽基因。第二步，根据SVM模型诱导的受试者工作特征曲线下面积（AUC）、真阴性率（TNR）、真阳性率（TPR）和马修斯系数相关分类（MCC），评估DEGs、DPGs、种子基因、枢纽基因和信息基因的分类性能。
我们总共检测到视网膜母细胞瘤中有479个DEGs、747个DPGs、29个种子基因、34个枢纽基因和7个信息基因。信息基因的分类性能最佳，AUC = 1.00，TNR = 1.00，TPR = 1.00，MCC = 1.00，因此它们被认为是关键基因，包括EPARS1、FN1、HLA - DPA1、HLA - DPB1、HLA - DRA、CFI和转化生长因子β受体II。
我们成功鉴定出七个关键基因，它们可能是当前和未来视网膜母细胞瘤检测和治疗的潜在生物标志物。
