An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules
An Ensemble Strategy to Predict Prognosis in Ovarian Cancer Based on Gene Modules
复制标题
基于基因模块预测卵巢癌预后的整体策略
DOI：
10.3389/fgene.2019.00366
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发表时间：
2019-04-24
影响因子：
3.7
通讯作者：
Zhang, Wen
中科院分区：
文献类型：
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作者：
Gao, Yi-Cheng;Zhou, Xiong-Hui;Zhang, Wen
由于癌症的高度异质性和复杂性，预测癌症患者的预后仍然是一个挑战。在这项工作中，我们使用聚类算法将患者划分为不同的亚型，以减少每个亚型中癌症患者的异质性。基于基因共表达网络可以揭示基因间的关系这一假设，网络中的某些社区可以影响癌症患者的预后，所有与癌症相关的社区都可以充分揭示癌症患者的预后。为了预测每个亚型癌症患者的预后，我们采用了基于相应亚型的基因共表达网络的集成分类器。使用TCGA（癌症基因组图谱）中卵巢癌患者的基因表达数据，确定了三种亚型。生存分析显示，不同亚型患者的生存风险不同。为每个亚型构建了三个集成分类器。留一法和独立验证表明，我们的方法优于控制和文献方法。此外，对各亚型的功能注释显示，有些群落与癌症相关。最后，我们发现目前的药物靶点可以部分支持我们的方法。
Due to the high heterogeneity and complexity of cancer, it is still a challenge to predict the prognosis of cancer patients. In this work, we used a clustering algorithm to divide patients into different subtypes in order to reduce the heterogeneity of the cancer patients in each subtype. Based on the hypothesis that the gene co-expression network may reveal relationships among genes, some communities in the network could influence the prognosis of cancer patients and all the prognosis-related communities could fully reveal the prognosis of cancer patients. To predict the prognosis for cancer patients in each subtype, we adopted an ensemble classifier based on the gene co-expression network of the corresponding subtype. Using the gene expression data of ovarian cancer patients in TCGA (The Cancer Genome Atlas), three subtypes were identified. Survival analysis showed that patients in different subtypes had different survival risks. Three ensemble classifiers were constructed for each subtype. Leave-one-out and independent validation showed that our method outperformed control and literature methods. Furthermore, the function annotation of the communities in each subtype showed that some communities were cancer-related. Finally, we found that the current drug targets can partially support our method.