Human Proteome Microarray identifies autoantibodies to tumor-associated antigens as serological biomarkers for the diagnosis of hepatocellular carcinoma

The identification of the high-efficiency and non-invasive biomarkers for hepatocellular carcinoma (HCC) detection is urgently needed. This study aims to screen out potential autoantibodies to tumor-associated antigens (TAAbs) and to assess their diagnostic value for HCC. Fifteen potential TAAbs were screened out from the Human Proteome Microarray by 30 HCC sera and 22 normal control sera, of which eight passed multiple-stage validations by ELISA with a total of 1625 human serum samples from normal controls (NCs) and patients with HCC, liver cirrhosis, chronic hepatitis B, gastric cancer, esophageal cancer, and colorectal cancer. Finally, an immunodiagnostic model including six TAAbs (RAD23A, CAST, RUNX1T1, PAIP1, SARS, PRKCZ) was constructed by logistic regression, and yielded the area under curve (AUC) of 0.835 and 0.788 in training and validation sets, respectively. The serial serum samples from HCC model mice were tested to explore the change in TAAbs during HCC formation, and an increasing level of autoantibodies was observed. In conclusion, the panel of six TAAbs can provide potential value for HCC detection, and the strategy to identify novel serological biomarkers can also provide new clues in understanding immunodiagnostic biomarkers.

Automated summary

This study aimed to identify potential autoantibodies to tumor-associated antigens (TAAbs) for hepatocellular carcinoma (HCC) detection. Eight TAAbs were validated and used to construct an immunodiagnostic model with an AUC of 0.835 and 0.788 in the training and validation sets, respectively. The study also observed an increase in TAAbs levels during HCC formation in mouse serum samples. In conclusion, the panel of six TAAbs shows potential value for HCC detection and provides new insights into immunodiagnostic biomarkers.