BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide. Autoantibodies to tumor-associated proteins in the serum profile, as new biomarkers, may improve the early detection of HCC. METHODS: In this study, we interrogated a HCC cDNA T7 phage library for tumor-associated proteins using biopan enrichment techniques with HCC patient and normal sera. The enrichment of tumor-associated proteins after biopanning was tested using plaque assay and immunochemical detection. The putative tumor-associated phage clones were collected for PCR and sequencing analysis. Identities of those selected sequences were revealed through the sequence BLAST program. The identified phage-expressed proteins were then used to develop phage protein ELISA to measure matching autoantibodies using 70 HCC patients, 50 chronic hepatitis patients, and 70 normal serum samples. The logistic regression model and leave-one-out validation were used to evaluate predictive accuracies with a single marker as well as with combined markers. RESULTS: Twenty-six phage-displayed proteins have sequence identity with known or putative tumor-associated proteins. Immunochemical reactivity of patient sera with phage-expressed proteins showed that the autoantibodies to phage-expressed protein CENPF, DDX3, HSPA4, HSPA5, VIM, LMNB1, and TP53 had statistical significance in HCC patients. Measurements of the seven autoantibodies combined in a logistic regression model showed that combined measurements of these autoantibodies was more predictive of disease than any single antibody alone, underscoring the importance of identifying multiple potential markers. CONCLUSION: Autoantibody in the serum profiling is a promising approach for early detection and diagnosis of HCC. The panel of autoantibodies appears preferable to achieve superior accuracy rather than an autoantibody alone, and may have significant relevance to tumor biology, novel drug development, and immunotherapies.
BACKGROUND:Hepatocellular carcinoma (HCC) is one of the most common tumors worldwide. Autoantibodies to tumor-associated proteins in the serum profile, as new biomarkers, may improve the early detection of HCC. METHODS: In this study, we interrogated a HCC cDNA T7 phage library for tumor-associated proteins using biopan enrichment techniques with HCC patient and normal sera. The enrichment of tumor-associated proteins after biopanning was tested using plaque assay and immunochemical detection. The putative tumor-associated phage clones were collected for PCR and sequencing analysis. Identities of those selected sequences were revealed through the sequence BLAST program. The identified phage-expressed proteins were then used to develop phage protein ELISA to measure matching autoantibodies using 70 HCC patients, 50 chronic hepatitispatients, and 70 normal serum samples. The logistic regression model and leave-one-out validation were used to evaluate predictive accuracies with a single marker as well as with combined markers. RESULTS: Twenty-six phage-displayed proteins have sequence identity with known or putative tumor-associated proteins. Immunochemical reactivity of patient sera with phage-expressed proteins showed that the autoantibodies to phage-expressed protein CENPF, DDX3, HSPA4, HSPA5, VIM, LMNB1, and TP53 had statistical significance in HCC patients. Measurements of the seven autoantibodies combined in a logistic regression model showed that combined measurements of these autoantibodies was more predictive of disease than any single antibody alone, underscoring the importance of identifying multiple potential markers. CONCLUSION: Autoantibody in the serum profiling is a promising approach for early detection and diagnosis of HCC. The panel of autoantibodies appears preferable to achieve superior accuracy rather than an autoantibody alone, and may have significant relevance to tumor biology, novel drug development, and immunotherapies.
Authors: Brooke L Fridley; Ryan Abo; Xiang-Lin Tan; Gregory D Jenkins; Anthony Batzler; Ann M Moyer; Joanna M Biernacka; Liewei Wang Journal: OMICS Date: 2013-11-07
Authors: Catrin H Middleton; William Irving; John F R Robertson; Andrea Murray; Celine B Parsy-Kowalska; Isabel K Macdonald; Jane McElveen; Jared Allen; Graham F Healey; Brian J Thomson; Stephen J Ryder; Stefan Holdenrieder; Caroline J Chapman Journal: PLoS One Date: 2014-08-05 Impact factor: 3.240