Salivary glycopatterns as potential biomarkers for diagnosis of gastric cancer

// Jian Shu 1, * , Hanjie Yu 1, * , Xiaojie Li 2, * , Dandan Zhang 1 , Xiawei Liu 1 , Haoqi Du 1 , Jiaxu Zhang 1 , Zhao Yang 1 , Hailong Xie 3 , Zheng Li 1 1 Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China 2 Department of Pothology. First People`s Hospital of Chenzhou, Chenzhou, China 3 Institute of Cancer Research, University of South China, Hengyang, China * These authors contributed equally to this work Correspondence to: Zheng Li, email: zhengli@nwu.edu.cn Hailong Xie, email: xhl0078@sina.com.cn Keywords: salivary glycopatterns, gastric cancer, biomarker, diagnostic models Received: January 17, 2017      Accepted: February 28, 2017      Published: March 10, 2017 ABSTRACT Gastric cancer (GC) is still an extremely severe health issue with high mortality due to the lacking of effective biomarkers. In this study, we aimed to investigate the alterations of salivary protein glycosylation related to GC and assess the possibility of salivary glycopatterns as potential biomarkers for the diagnosis of GC. Firstly, 94 patients with GC ( n = 64) and atrophic gastritis (AG) ( n = 30), as well as 30 age- and sex-matched healthy volunteers (HV) were enrolled in the test group to probe the difference of salivary glycopatterns using lectin microarrays, the results were validated by saliva microarrays and lectin blotting analysis. Then, the diagnostic model of GC (Model GC) and AG (Model AG) were constructed based on 15 candidate lectins which exhibited significant alterations of salivary glycopattern by logistic stepwise regression. Finally, two diagnostic models were assessed in the validation group including HV ( n = 30) and patients with GC ( n = 23) and AG ( n = 24) and achieved high diagnostic power (Model GC (AUC: 0.89, sensitivity: 0.96 and specificity: 0.80), Model AG (AUC: 0.83, sensitivity: 0.92 and specificity: 0.72)). This study provides pivotal information to distinguish HV, AG and GC based on precise alterations in salivary glycopatterns, which have great potential to be biomarkers for diagnosis of GC.