卵巢癌是一种早期诊断率低而致死率较高的恶性肿瘤,对其预后标志物的鉴定和生存率的预测仍是生存分析的重要任务。利用卵巢癌预后相关基因构建基因共表达网络,鉴定预后生物标志物并进行生存率的预测。首先,对TCGA(The cancer genome atlas)数据库下载的卵巢癌基因表达数据实施单因素回归分析,利用得到的747个预后相关基因构建卵巢癌预后加权基因共表达网络。其次,考虑网络的生物学意义,利用蛋白质相互作用(Protein-protein interaction,PPI)数据对共表达网络中的模块重新加权,并根据网络中基因的拓扑重要性对基因进行排序。最后,运用Cox比例风险回归对网络中的重要基因构建卵巢癌预后模型,鉴定了3个预后生物标志物。生存分析结果显示,这3个标志物能够显著区分不同预后的患者,较好地预测卵巢癌患者的预后情况。
Graphical representation of DNA sequences is a key component in studying biological problems. In order to gain new insights in DNA sequences, this paper combined the digitized methods of single-base, base pairs and coding in triplet bases with the times of base appearing, and then a novel 4D graphical representation method of DNA sequences was put forward. It was a one-to-one correspondence of the arbitrary DNA sequence and 4D graphical representation, that avoided causing non-unique 4D graphical representation and overlapping lines. The method could reflect the biological information features of DNA sequence more comprehensively and effectively without any losses. Based on the 4D graphical representation, we used the geometric center of 4D graphical representation as eigenvalue of DNA sequences analyses, which kept the original features of the data, and then established the Euclidean distances and included angles between vectors＇ ter- minal point for similarity analyses of the first extron of the beta-globulin gene among 11 species. Finally, we established the graph of systematic hierarchical cluster analysis of 11 species to observe more easily the relationship between species. A positive outcome was reached, and the results were in accord with biological taxonomy, which also supported the rationality and effectiveness of the novel 4D graphical representation.
