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• W2892719209 abstract "Colorectal cancer is a major public health issue, with incidences continuing to rise owing to the growing and aging world population. Current screening strategies for colorectal cancer diagnosis suffer from various limitations, including invasiveness and poor uptake. Consequently, there is an unmet clinical need for a minimally invasive, sensitive, and specific method for detecting the presence of colorectal cancer and pre-malignant lesions.An indirect enzyme-linked immunosorbent assay was used to measure the primary (IgM) and secondary (IgG) adaptive humoral immune responses to a panel of previously identified cancer antigens in the sera of normal and adenoma samples, and sera from patients with colorectal cancer.An optimal panel of 7 biomarkers capable of identifying patients with colorectal cancer as distinct from both normal and adenoma samples is identified. The cumulative sensitivity and specificity of the assay are 70.8% and 86.5%, respectively. The positive and negative predictive values of the cohort are 77.3% and 82.1%. This assay was not able to accurately discriminate between normal and adenoma samples. Patients whose serum was positive for the presence of anti-ICLN IgM autoantibodies had a significantly poorer 5-year survival than patients whose serum was negative (P = .004).This study describes a novel minimally invasive enzyme-linked immunosorbent assay-based method, capable of identifying patients with colorectal cancer as distinct from both normal and adenoma samples. Patients are likely to be far more amenable to a blood-based test such as the one described herein, rather than a fecal-based test, likely leading to increased patient uptake." @default.
• W2892719209 created "2018-10-05" @default.
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• W2892719209 date "2019-03-01" @default.
• W2892719209 modified "2023-10-16" @default.
• W2892719209 title "Measurement of the IgM and IgG Autoantibody Immune Responses in Human Serum has High Predictive Value for the Presence of Colorectal Cancer" @default.
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• W2892719209 doi "https://doi.org/10.1016/j.clcc.2018.09.009" @default.
• W2892719209 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30366678" @default.
• W2892719209 hasPublicationYear "2019" @default.
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