Combination detection of IgG- and IgA-related autoantibodies for the early diagnosis of gastric cancer

BackgroundAutoantibodies against tumor-associated antigens (TAAs) are promising noninvasive cancer biomarkers due to their specificity and stability. Gastric cancer (GC) diagnosis often requires invasive procedures, emphasizing the need for reliable blood-based biomarkers.ObjectiveThis study assessed whether serum IgG and IgA autoantibodies, individually or combined, could serve as noninvasive biomarkers for gastric cancer.Experimental designWe analyzed 27 autoantibodies in serum from 265 healthy controls, 296 GC patients, and 195 gastritis patients using protein microarray. Autoantibody levels and the IgG/IgA ratio were calculated. Diagnostic accuracy was evaluated using receiver operating characteristic (ROC) curves.ResultsWe identified 24 differentially expressed autoantibodies (DEAs) for IgA and 17 for IgG between GC patients and controls. In distinguishing GC from gastritis, 20 DEAs for IgA and 23 for IgG were significant. The IgG/IgA ratio of MIP1 beta had the highest diagnostic performance between atrophic gastritis and GC, while MMP7 was the most effective between chronic gastritis and GC. The gbm model with 14 autoantibodies had the highest Youden's index for GC versus controls, and a 13-autoantibody model performed best for GC versus all gastritis.ConclusionsSpecific panels of autoantibodies could serve as noninvasive diagnostic tools for distinguishing GC from controls and gastritis.

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Publication Details

Journal
Cancer Biomarkers
Published
2025-10-01
DOI
https://doi.org/10.1177/18758592251363414
Primary Topic
Monoclonal and Polyclonal Antibodies Research
Type
article
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article

Combination detection of IgG- and IgA-related autoantibodies for the early diagnosis of gastric cancer

Wei Zhu, Xiaomao Yin, Xianzhu Zhou, Jianmin Fang et al.
Cancer Biomarkers
Monoclonal and Polyclonal Antibodies Research
article

Combination detection of IgG- and IgA-related autoantibodies for the early diagnosis of gastric cancer

Wei Zhu, Xiaomao Yin, Xianzhu Zhou, Jianmin Fang, Ruo‐Pan Huang, Yanlin Wang, Tao Wang, Yiqi Du, Hua Dong, Shuhong Luo, C Fu, Yuxin Wang
article en

Abstract

BackgroundAutoantibodies against tumor-associated antigens (TAAs) are promising noninvasive cancer biomarkers due to their specificity and stability. Gastric cancer (GC) diagnosis often requires invasive procedures, emphasizing the need for reliable blood-based biomarkers.ObjectiveThis study assessed whether serum IgG and IgA autoantibodies, individually or combined, could serve as noninvasive biomarkers for gastric cancer.Experimental designWe analyzed 27 autoantibodies in serum from 265 healthy controls, 296 GC patients, and 195 gastritis patients using protein microarray. Autoantibody levels and the IgG/IgA ratio were calculated. Diagnostic accuracy was evaluated using receiver operating characteristic (ROC) curves.ResultsWe identified 24 differentially expressed autoantibodies (DEAs) for IgA and 17 for IgG between GC patients and controls. In distinguishing GC from gastritis, 20 DEAs for IgA and 23 for IgG were significant. The IgG/IgA ratio of MIP1 beta had the highest diagnostic performance between atrophic gastritis and GC, while MMP7 was the most effective between chronic gastritis and GC. The gbm model with 14 autoantibodies had the highest Youden's index for GC versus controls, and a 13-autoantibody model performed best for GC versus all gastritis.ConclusionsSpecific panels of autoantibodies could serve as noninvasive diagnostic tools for distinguishing GC from controls and gastritis.

Cancer BiomarkersVol. 42(10)
Second Military Medical University (CN), Guangzhou Center for Disease Control and Prevention (CN), Biocure (United States) (US), Changhai Hospital (CN), RayBiotech (China) (CN), Shanghai Tenth People's Hospital (CN), National Research Center for Rehabilitation Technical Aids (CN), Twelfth Guangzhou City People's Hospital (CN), South China University of Technology (CN)
Openalex Percentile: Top 59%
Monoclonal and Polyclonal Antibodies Research
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