Discovery and Validation of Serum Autoantibodies Against Tumor-Associated Antigens as Biomarkers in Gastric Adenocarcinoma Based on the Focused Protein Arrays. Issue 1 (21st January 2021)

• Record Type: Journal Article
• Authors: Yang, Qian; Qin, Jiejie; Sun, Guiying; Qiu, Cuipeng; Jiang, Di; Ye, Hua; Wang, Xiao; Dai, Liping; Zhu, Jicun; Wang, Peng; Zhang, Jianying
• Is Part Of: Clinical and translational gastroenterology. Volume 12:Issue 1(2021)
• Page Start: e00284
• Publication Date: 2021-01-21
• Subjects: Stomach -- Diseases -- Periodicals; Intestines -- Diseases -- Periodicals; Gastroenterology; Gastrointestinal Diseases; Liver Diseases; Intestines -- Diseases; Stomach -- Diseases; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; Electronic journals; 616.33
• DOI: 10.14309/ctg.0000000000000284
• Languages: English
• ISSNs: 2155-384X

Abstract

Abstract : INTRODUCTION: Previous studies have demonstrated that autoantibodies against tumor-associated antigens (TAAs) in patients with cancer can be used as sensitive immunodiagnostic biomarkers for the detection of cancer. Most of these TAAs are involved in the tumorigenesis pathway. Cancer driver genes with intragenic mutations can promote tumorigenesis. This study aims to identify autoantibodies against TAAs encoded by cancer driver genes in sera as potential immunodiagnostic biomarkers for gastric adenocarcinoma (GAC). METHODS: Protein arrays based on cancer driver genes were customized for screening candidate TAAs in 100 GAC sera and 50 normal control (NC) sera. Autoantibodies against candidate TAAs were assessed by enzyme-linked immunosorbent assay in both training group (205 GAC sera and 205 NC sera) and independent validation group (126 GAC sera and 126 NC sera). Moreover, the immunodiagnostic models were respectively established and validated in the training group and validation group. RESULTS: A panel with 5 autoantibodies including anti-TP53, anti-COPB1, anti-GNAS, anti–serine/arginine-rich splicing factor 2, and anti-SMARCB1 was selected by the Fisher linear discriminant analysis model with an areas under receiver operating characteristic curve (AUC) of 0.928 (95% confidence interval [CI]: 0.888–0.967) in the training cohort and an AUC of 0.885 (95% CI: 0.852–0.918) in the validation cohort. Besides, the panel with 5 autoantibodies including anti-TP53, anti-COPB1, anti-GNAS, anti-PBRM1, and anti-ACVR1B which were selected by the binary logistic regression model showed an AUC of 0.885 (95% CI: 0.852–0.919) in the training cohort and 0.884 (95% CI: 0.842–0.925) in the validation cohort. DISCUSSION: Two panels which were selected in this study could boost the detection of anti-TAA autoantibodies in sera as biomarkers for the detection of GAC.