Bioinformatics and Computational Biology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
Department of Biostatistics, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
Asian Pac J Cancer Prev. 2021 Jun 1;22(6):1781-1787. doi: 10.31557/APJCP.2021.22.6.1781.
Comparison of gene expression algorithms may be beneficial for obtaining disease pattern or grouping patients based on the gene expression profile. The current study aimed to investigate whether the knowledge within these data is able to group the ovarian cancer patients with similar disease pattern.
Four different clustering methods were applied on 20 genes expression data of 37 women with ovarian cancer. All selected genes in this study had prominent roles in the control of the activity of the immune system, as well as the chemotaxis, angiogenesis, apoptosis, and etc. Comparison of different clustering methods such as K-means, Hierarchical, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Expectation-Maximization (EM) algorithm was the other aim of the present study. In addition, the percentage of correct prediction, Robustness-Performance Trade-off (RPT), and Silhouette criteria were used to evaluate the performance of clustering methods.
Six out of 20 genes (IFN-γ, Foxp3, IL-4, BCL-2, Oct4 and survivin) selected by the Laplacian score showed key roles in the development of ovarian cancer and their prognostic values were clinically and statistically confirmed. The results indicated proper capability of the expression pattern of these genes in grouping the patients with similar prognosis, i.e. patients alive after 5 years or dead (62.12%).
The results revealed the better performance for k-means and hierarchical clustering methods, and confirmed the fact that by using the expression profile of these genes, patients with similar behavior can be grouped in the same cluster with acceptable accuracy level. Certainly, the useful information from these data may contribute to the prediction of prognosis in ovarian cancer patients along with other features of patients.
.
比较基因表达算法可能有助于根据基因表达谱获得疾病模式或对患者进行分组。本研究旨在探讨这些数据中的知识是否能够将卵巢癌患者分为具有相似疾病模式的组。
对 37 名卵巢癌女性的 20 个基因表达数据应用了四种不同的聚类方法。本研究中选择的所有基因在免疫系统活性的控制、趋化作用、血管生成、细胞凋亡等方面都具有重要作用。本研究的另一个目的是比较不同的聚类方法，如 K-means、层次聚类、基于密度的应用空间聚类（DBSCAN）和期望最大化（EM）算法。此外，还使用了正确预测百分比、稳健性-性能权衡（RPT）和轮廓标准来评估聚类方法的性能。
拉普拉斯评分选择的 20 个基因中的 6 个（IFN-γ、Foxp3、IL-4、BCL-2、Oct4 和 survivin）在卵巢癌的发生发展中起关键作用，其预后价值在临床上和统计学上得到了证实。结果表明，这些基因表达模式在将具有相似预后的患者分组方面具有适当的能力，即 5 年后存活或死亡的患者（62.12%）。
结果表明 k-means 和层次聚类方法的性能更好，并证实了通过使用这些基因的表达谱，可以将具有相似行为的患者分组到相同的聚类中，具有可接受的准确度水平。当然，这些数据中的有用信息可能有助于预测卵巢癌患者的预后以及患者的其他特征。
