Kourou Konstantina, Papaloukas Costas, Fotiadis Dimitrios I
IEEE J Biomed Health Inform. 2017 Mar;21(2):320-327. doi: 10.1109/JBHI.2016.2636448. Epub 2016 Dec 7.
Oral squamous cell carcinoma has been characterized as a complex disease which involves dynamic genomic changes at the molecular level. These changes indicate the worth to explore the interactions of the molecules and especially of differentially expressed genes that contribute to cancer progression. Moreover, based on this knowledge the identification of differentially expressed genes and related molecular pathways is of great importance. In the present study, we exploit differentially expressed genes in order to further perform pathway enrichment analysis. According to our results we found significant pathways in which the disease associated genes have been identified as strongly enriched. Furthermore, based on the results of the pathway enrichment analysis we propose a methodology for predicting oral cancer recurrence using dynamic Bayesian networks. The methodology takes into consideration time series gene expression data in order to predict a disease recurrence. Subsequently, we are able to conjecture about the causal interactions between genes in consecutive time intervals. Concerning the performance of the predictive models, the overall accuracy of the algorithm is 81.8% and the area under the ROC curve 89.2% regarding the knowledge from the overrepresented pre-NOTCH Expression and processing pathway.
口腔鳞状细胞癌已被表征为一种复杂疾病，其在分子水平上涉及动态基因组变化。这些变化表明探索分子间相互作用，特别是有助于癌症进展的差异表达基因间相互作用的价值。此外，基于这些知识，鉴定差异表达基因和相关分子途径非常重要。在本研究中，我们利用差异表达基因进一步进行通路富集分析。根据我们的结果，我们发现了一些重要通路，其中与疾病相关的基因被确定为高度富集。此外，基于通路富集分析的结果，我们提出了一种使用动态贝叶斯网络预测口腔癌复发的方法。该方法考虑时间序列基因表达数据以预测疾病复发。随后，我们能够推测连续时间间隔内基因之间的因果相互作用。关于预测模型的性能，该算法的总体准确率为81.8%，关于过度表达的NOTCH前体表达和加工通路的知识，ROC曲线下面积为89.2%。
