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Background: Current diagnostic methods for colorectal cancer (CRC) are colonoscopy and sigmoidoscopy, which are invasive and complex procedures with possible complications. This study aimed to determine models for CRC identification that involve minimally invasive, affordable, portable, and accurate screening variables. Methods: This was a retrospective study that used data from electronic medical records of patients with CRC and healthy individuals between July 2017 and June 2018. Laboratory data, including liver enzymes, lipid profiles, complete blood counts, and tumor biomarkers, were extracted from the electronic medical records. Five machine learning models (logistic regression, random forest, k-nearest neighbors, support vector machine [SVM], and naïve Bayes) were used to identify CRC. The performances were evaluated using the areas under the curve (AUCs), sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV). Results: A total of 1164 electronic medical records (CRC patients: 582; healthy controls: 582) were included. The logistic regression model achieved the highest performance in identifying CRC (AUC: 0.865, sensitivity: 89.5%, specificity: 83.5%, PPV: 84.4%, NPV: 88.9%). The first four weighted features in the model were carcinoembryonic antigen (CEA), hemoglobin (HGB), lipoprotein (a) (Lp(a)), and high-density lipoprotein (HDL). A diagnostic model for CRC was established based on the four indicators, with an AUC of 0.849 (0.840-0.860) for identifying all CRC patients, and it performed best in discriminating patients with late colon cancer from healthy individuals with an AUC of 0.905 (0.889-0.929). Conclusions: The logistic regression model based on CEA, HGB, Lp(a), and HDL might be a powerful, noninvasive, and cost-effective method to identify CRC.

Inhibitor of β-catenin and T-cell factor (ICAT) was first found as a polypeptide that blocks β-catenin–TCF interaction. Abundant evidence has shown that ICAT has different functions in diverse cancers’ progression. Nevertheless, the roles it plays in colorectal cancer (CRC) have not been described. Here, we documented that ICAT expression was higher in CRC tissue than in the adjacent normal tissue and that prognosis was better in high-ICAT expression patients. The overexpression of ICAT inhibited CRC cell proliferation both in vitro and in vivo. Wnt pathway transcriptional activity was suppressed in the CRC cells with ICAT overexpression, where the CCND1 and MYC expression, which occurs downstream of the Wnt signaling pathway, was inhibited. Co-immunoprecipitation experiments showed that ICAT bound with β-catenin in stable overexpression cell lines; immunofluorescence showed the co-localization of ICAT and β-catenin in the cytoplasm. Overall, our study reveals that ICAT inhibits CRC cell proliferation by binding to cytoplasm-located β-catenin, and prevents its translocation, which results in Wnt signaling pathway inactivation. It may provide a scientific foundation for focusing on ICAT in treatments for CRC.