Authentication of differential gene expression in oral squamous cell carcinoma using machine learning applications.
Authentication of differential gene expression in oral squamous cell carcinoma using machine learning applications.
复制标题
DOI：
10.1186/s12903-021-01642-9
复制
发表时间：
2021-05-29
期刊：
影响因子：
2.9
通讯作者：
Park HR
中科院分区：
文献类型：
--
作者：
Pratama R;Hwang JJ;Lee JH;Song G;Park HR
最近，已经研究了基于遗传数据的肿瘤分类的可能性。然而，遗传数据集由于其庞大的规模和操作的复杂性而难以处理。在本研究中，我们使用口腔鳞状细胞癌（OSCC）基因集的基于成像的分类来检查机器学习应用程序的诊断性能。从癌症基因组图谱（TCGA）下载来自不同部位（包括口腔、非口腔头颈部、食管和宫颈区域）的SCC组织的RNA测序数据。通过卷积神经网络（CNN）和机器学习提取特征基因，并比较每种分析的性能。机器学习分析对OSCC肿瘤进行分类的能力非常出色。然而，该工具在区分来自相同类型组织的组织病理学上不同的癌症方面表现出比区分具有不同组织来源的相同组织病理学类型的癌症更差的性能，这表明差异基因表达模式是区分癌症类型的比组织病理学特征更重要的因素。基于CNN的诊断模型和使用RNA测序数据的可视化方法对于正确分类OSCC是有用的。分析显示，在各种类型的SCC，如KCNA 10，FOSL 2，PRDM 16的多组比较中差异表达的基因，从成对比较中提取的前导基因是FGF 20，DLC 1，和ZNF 705 D。在线版本包含补充材料，可通过10.1186/s12903-021-01642-9获得。
Recently, the possibility of tumour classification based on genetic data has been investigated. However, genetic datasets are difficult to handle because of their massive size and complexity of manipulation. In the present study, we examined the diagnostic performance of machine learning applications using imaging-based classifications of oral squamous cell carcinoma (OSCC) gene sets. RNA sequencing data from SCC tissues from various sites, including oral, non-oral head and neck, oesophageal, and cervical regions, were downloaded from The Cancer Genome Atlas (TCGA). The feature genes were extracted through a convolutional neural network (CNN) and machine learning, and the performance of each analysis was compared. The ability of the machine learning analysis to classify OSCC tumours was excellent. However, the tool exhibited poorer performance in discriminating histopathologically dissimilar cancers derived from the same type of tissue than in differentiating cancers of the same histopathologic type with different tissue origins, revealing that the differential gene expression pattern is a more important factor than the histopathologic features for differentiating cancer types. The CNN-based diagnostic model and the visualisation methods using RNA sequencing data were useful for correctly categorising OSCC. The analysis showed differentially expressed genes in multiwise comparisons of various types of SCCs, such as KCNA10, FOSL2, and PRDM16, and extracted leader genes from pairwise comparisons were FGF20, DLC1, and ZNF705D. The online version contains supplementary material available at 10.1186/s12903-021-01642-9.
登录
查看更多内容
影响因子：
13.8
作者：
Grewal, Jasleen K.;Tessier-Cloutier, Basile;Jones, Steven J. M.
通讯作者：
Jones, Steven J. M.
影响因子：
4.8
作者：
Lee, Dong Jin;Eun, Young-Gyu;Lee, Ju-Seog
通讯作者：
Lee, Ju-Seog
影响因子：
5.6
作者：
Wang, Jiaying;Wang, Yuanyong;Ma, Lei
通讯作者：
Ma, Lei
影响因子：
5
作者：
Postma, Emily L.;Verkooijen, Helena M.;van Hillegersberg, Richard
通讯作者：
van Hillegersberg, Richard
DOI：
10.1016/j.jormas.2017.05.004
发表时间：
2017-09-01
影响因子：
2.2
作者：
Foy, J. -P.;Saintigny, P.;Bertolus, C.
通讯作者：
Bertolus, C.