A Support Vector Machine Model Predicting the Risk of Duodenal Cancer in Patients with Familial Adenomatous Polyposis at the Transcript Levels
A Support Vector Machine Model Predicting the Risk of Duodenal Cancer in Patients with Familial Adenomatous Polyposis at the Transcript Levels
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支持向量机模型在转录水平上预测家族性腺瘤性息肉病患者十二指肠癌的风险
DOI：
10.1155/2020/5807295
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发表时间：
2020-06-16
影响因子：
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通讯作者：
Yang, Jun
中科院分区：
文献类型：
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作者：
Liu, Weiqing;Dong, Jian;Yang, Jun
目的家族性腺瘤性息肉病（FAP）是遗传性十二指肠癌的主要类型之一。FAP患者十二指肠癌风险的估计对于选择最佳治疗策略至关重要。方法从GEO数据库中检索与FAP相关的基因芯片数据集。通过FAP与正常样品以及FAP和十二指肠癌与正常样品鉴定差异表达的基因。此外，对这些差异表达基因进行功能富集分析。采用支持向量机（SVM）对癌症风险预测模型进行训练和验证。结果共筛选出196条FAP与正常对照差异表达基因。在FAP和十二指肠癌组织中共检测到177个表达相似的基因，这些基因主要集中在肿瘤通路和代谢相关通路中，提示FAP患者中这些基因可能与十二指肠癌的发生有关。其中，使用qRT-PCR，Cyclin D1、SDF-1、AXIN和TCF在FAP组织中显著上调。基于这177个基因，构建了一个支持向量机模型，用于预测FAP患者的癌症风险。经验证，该模型可以准确区分FAP患者的高风险与低风险的十二指肠癌。结论本研究提出了一种基于支持向量机的转录水平癌症风险预测模型。
Objective Familial adenomatous polyposis (FAP) is one major type of inherited duodenal cancer. The estimate of duodenal cancer risk in patients with FAP is critical for selecting the optimal treatment strategy. Methods Microarray datasets related with FAP were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes were identified by FAP vs. normal samples and FAP and duodenal cancer vs. normal samples. Furthermore, functional enrichment analyses of these differentially expressed genes were performed. A support vector machine (SVM) was performed to train and validate cancer risk prediction model. Results A total of 196 differentially expressed genes were identified between FAP compared with normal samples. 177 similarly expressed genes were identified both in FAP and duodenal cancer, which were mainly enriched in pathways in cancer and metabolic-related pathway, indicating that these genes in patients with FAP could contribute to duodenal cancer. Among them, Cyclin D1, SDF-1, AXIN, and TCF were significantly upregulated in FAP tissues using qRT-PCR. Based on the 177 genes, an SVM model was constructed for prediction of the risk of cancer in patients with FAP. After validation, the model can accurately distinguish FAP patients with high risk from those with low risk for duodenal cancer. Conclusion This study proposed a cancer risk prediction model based on an SVM at the transcript levels.