This study aimed to identify noninvasive protein markers capable of detecting the presence and prognosis of esophageal squamous-cell carcinoma (ESCC). Analyzing microarray expression data collected from 17-pair ESCC specimens, we identified one protein, matrix metalloproteinase-1 (MMP1), as a possibly useful marker. Plasma MMP1 was then measured by enzyme-linked immunosorbent assay (ELISA) in 210 ESCC patients and 197 healthy controls. ESCC patients had higher mean levels of MMP1 than controls (8.7 ± 7.5 vs. 6.7 ± 4.9 ng/mL, p < 0.0001). Using the highest quartile level (9.67 ng/mL) as cut-off, we found a 9.0-fold risk of ESCC in those with higher plasma MMP1 after adjusting for covariates (95% confidence interval = 2.2, 36.0). Heavy smokers and heavy drinkers with higher plasma MMP1 had 61.4- and 31.0 times the risk, respectively, than non-users with lower MMP1. In the survival analysis, compared to those with MMP1 ≤ 9.67 ng/mL, ESCC patients with MMP1 > 9.67 ng/mL had a 48% increase in the risk of ESCC death (adjusted hazard ratio = 1.48; 95% CI = 1.04–2.10). In conclusion, plasma MMP1 may serve as a noninvasive marker of detecting the presence and predicting the survival of ESCC.

The assessment of non-genotoxic hepatocarcinogenicity of chemicals is currently based on 2-year rodent bioassays. It is desirable to develop a fast and effective method to accelerate the identification of potential hepatocarcinogenicity of non-genotoxic chemicals. In this study, a novel method CPI is proposed to predict potential hepatocarcinogenicity of non-genotoxic chemicals. The CPI method is based on chemical-protein interactions and interpretable decision tree classifiers. The interpretable rules generated by the CPI method are analyzed to provide insights into the mechanism and biomarkers of non-genotoxic hepatocarcinogenicity. The CPI method with an independent test accuracy of 86% using only 1 protein biomarker outperforms the state-ofthe-art methods of gene expression profile-based toxicogenomics using 90 gene biomarkers. A protein ABCC3 was identified as a potential protein biomarker for further exploration. This study presents the potential application of CPI method for assessing non-genotoxic hepatocarcinogenicity of chemicals.

Chemical carcinogenicity is an important safety issue for the evaluation of drugs and environmental pollutants. The Ames test is useful for detecting genotoxic hepatocarcinogens. However, the assessment of Ames-negative hepatocarcinogens depends on 2-year rodent bioassays. Alternative methods are desirable for the efficient identification of Amesnegative hepatocarcinogens. This study proposed a decision tree-based method using chemical-chemical interaction information for predicting hepatocarcinogens. It performs much better than that using molecular descriptors with accuracies of 86% and 76% for validation and independent test, respectively. Four important interacting chemicals with interpretable decision rules were identified and analyzed. With the high prediction performances, the acquired decision rules based on chemicalchemical interactions provide a useful prediction method and better understanding of Ames-negative hepatocarcinogens.

No abstract