Mouse Anti-Human AFP mnoclonal antibody, clone YB3

Hepatocellular carcinoma (HCC) and secondary liver tumors, such as colorectal cancer liver metastases are significant contributors to the overall burden of cancer-related morality. Current biomarkers, such as alpha-fetoprotein (AFP) for HCC, result in too many false negatives, necessitating noninvasive approaches with improved sensitivity. Volatile organic compounds (VOCs) detected in the breath of patients can provide valuable insight into disease processes and can differentiate patients by disease status. Here, we investigate whether 22 VOCs from the breath of 296 patients can distinguish those with no liver disease (n = 54), cirrhosis (n = 30), HCC (n = 112), pulmonary hypertension (n = 49), or colorectal cancer liver metastases (n = 51). This work extends previous studies by evaluating the ability for VOC signatures to differentiate multiple diseases in a large cohort of patients. Pairwise disease comparisons demonstrated that most of the VOCs tested are present in significantly different relative abundances (false discovery rate P < 0.1), indicating broad impacts on the breath metabolome across diseases. A predictive model developed using random forest machine learning and cross validation classified patients with 85% classification accuracy and 75% balanced accuracy. Importantly, the model detected HCC with 73% sensitivity compared with 53% for AFP in the same cohort. An added value of this approach is that influential VOCs in the predictive model may provide insight into disease etiology. Acetaldehyde and acetone, both of which have roles in tumor promotion, were considered important VOCs for differentiating disease groups in the predictive model and were increased in patients with cirrhosis and HCC compared to patients with no liver disease (false discovery rate P < 0.1). Conclusion: The use of machine learning and breath VOCs shows promise as an approach to develop improved, noninvasive screening tools for chronic liver disease and primary and secondary liver tumors.

The rapid detection of alpha-fetoprotein (AFP) in serum is of great significance in the early diagnosis of hepatocellular carcinoma (HCC). A novel six-channel microfluidic SERS chip integrated with Ag/Au nanocomposites (NCs) for rapid SERS identification of AFP in serum was proposed in this work. Automatic injection was realized by negative pressure formed by specially designed PDMS module on the SERS chip. The serum samples originating from 60 HCC patients and 60 healthy people were measured by SERS on the designed microchip under the optimized conditions. The SERS spectra of serum samples from normal people and patients were collected and analyzed by Principal Component Analysis (PCA) which separated the characteristic Raman peaks of AFP of the two groups into two distinct clusters. Linear Discriminate Analysis (LDA) based on the PCA generated features differentiated the patients' sera SERS spectra from the normal sera SERS spectra with a classification accuracy of 96.25% and a blind sample test accuracy of 95%. It is shown that the proposed microfludic SERS chip for AFP SERS test in serum can rapidly and efficiently identify HCC patients. It is of great research value and practical prospect in the fields of disease diagnosis and screening.