Du Gang, Zhang Wenqian, Zhang Zhen, Zeng Meizhai, Wang Yuxia
Department of Laboratory Medicine, Zhujiang Hospital of Southern Medical University, Guangzhou, Guangdong 510000, P.R. China.
Department of Reproductive Medicine, The First Affiliated Hospital, Jinan University, Guangzhou, Guangdong 510000, P.R. China.
Oncol Lett. 2019 Jul;18(1):699-705. doi: 10.3892/ol.2019.10389. Epub 2019 May 21.
Endometrial carcinoma (EC) is a malignant neoplasm of the endometrial epithelium, which may be diagnosed by pathological investigations. The aim of the current study was to identify new markers for the diagnosis of EC using machine learning. The association between human T cell lymphotropic virus type 1 (HTLV-1) infection and endometrial cancer risk have not been widely reported. It remains ambiguous whether HTLV-1 infection is associated with several types of cancer. The present study investigated the association between HTLV-1 infection-associated genes and EC risk. RNA sequencing uterine cancer expression data were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were identified between normal and matched tumor samples. A total of 41 genes were selected by an overlap between HTLV-1 infection pathway-associated genes and the DEGs. Two-way hierarchical clustering analysis (HCA) and a support vector machine (SVM) classifier were constructed using the 41 genes. The accuracy of the candidate genes in risk-stratifying the samples was 100%. The accuracy of the proposed SVM model was 100%. In addition, the classification power of the SVM model was validated using a merged dataset (TCGA and the Genotype-Tissue Expression project). This predictive feature achieved reliable outcomes with risk-stratifying samples of almost 99% in two-way HCA, and an accuracy yield of 98% of the SVM classifier. In conclusion, the 41 genes identified in the current study may be implicated in the development of EC and may be of prognostic value for the disease. The results obtained the current study suggest that HTLV-1 may be potentially associated with EC and highlight potential disease mechanisms.
子宫内膜癌（EC）是子宫内膜上皮的恶性肿瘤，可通过病理检查诊断。本研究的目的是使用机器学习识别用于诊断EC的新标志物。人类嗜T细胞病毒1型（HTLV-1）感染与子宫内膜癌风险之间的关联尚未得到广泛报道。HTLV-1感染是否与几种癌症相关仍不明确。本研究调查了HTLV-1感染相关基因与EC风险之间的关联。从癌症基因组图谱（TCGA）数据库下载了RNA测序子宫癌表达数据。在正常样本和匹配的肿瘤样本之间鉴定出差异表达基因（DEG）。通过HTLV-1感染途径相关基因与DEG之间的重叠选择了总共41个基因。使用这41个基因构建了双向层次聚类分析（HCA）和支持向量机（SVM）分类器。候选基因对样本进行风险分层的准确率为100%。所提出的SVM模型的准确率为100%。此外，使用合并数据集（TCGA和基因型-组织表达项目）验证了SVM模型的分类能力。这种预测特征在双向HCA中对样本进行风险分层时获得了近99%的可靠结果，SVM分类器的准确率为98%。总之，本研究中鉴定出的41个基因可能与EC的发生有关，可能对该疾病具有预后价值。本研究获得的结果表明HTLV-1可能与EC潜在相关，并突出了潜在的疾病机制。
