Liu Huan-Jun, Guo Yuan-Ying, Li Du-Jun
Department of Hepatobiliary Surgery, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, 264000, China.
Department of Preventive Medicine, School of Public Health, Jilin University, Jilin 130000, China.
Pathol Res Pract. 2017 Apr;213(4):394-399. doi: 10.1016/j.prp.2016.09.017. Epub 2016 Sep 22.
The use of saliva as a diagnostic fluid enables non-invasive sampling and thus is a prospective sample for disease tests. This study fully utilized the information from the salivary transcriptome to characterize pancreatic cancer related genes and predict novel salivary biomarkers.
We calculated the enrichment scores of gene ontology (GO) and pathways annotated in Kyoto Encyclopedia of Genes and Genomes database (KEGG) for pancreatic cancer-related genes. Annotation of GO and KEGG pathway characterize the molecular features of genes. We employed Random Forest classification and incremental feature selection to identify the optimal features among them and predicted novel pancreatic cancer-related genes.
A total of 2175 gene ontology and 79 KEGG pathway terms were identified as the optimal features to identify pancreatic cancer-related genes. A total of 516 novel genes were predicted using these features. We discovered 29 novel biomarkers based on the expression of these 516 genes in saliva. Using our new biomarkers, we achieved a higher accuracy (92%) for the detection of pancreatic cancer. Another independent expression dataset confirmed that these novel biomarkers performed better than the previously described markers alone.
By analyzing the information of the salivary transcriptome, we predict pancreatic cancer-related genes and novel salivary gene markers for detection.
将唾液用作诊断液可实现非侵入性采样，因此是疾病检测的一种有前景的样本。本研究充分利用唾液转录组信息来表征胰腺癌相关基因并预测新型唾液生物标志物。
我们计算了基因本体论（GO）的富集分数以及在京都基因与基因组百科全书数据库（KEGG）中注释的与胰腺癌相关基因的通路。GO和KEGG通路注释表征了基因的分子特征。我们采用随机森林分类和增量特征选择来识别其中的最佳特征，并预测新型胰腺癌相关基因。
总共2175个基因本体论和79个KEGG通路术语被确定为识别胰腺癌相关基因的最佳特征。使用这些特征总共预测了516个新基因。我们基于这516个基因在唾液中的表达发现了29种新型生物标志物。使用我们的新生物标志物，我们在胰腺癌检测中实现了更高的准确率（92%）。另一个独立的表达数据集证实，这些新型生物标志物的表现优于单独使用先前描述的标志物。
通过分析唾液转录组信息，我们预测了胰腺癌相关基因以及用于检测的新型唾液基因标志物。
