Abstract
Colorectal cancer (CRC) is the third leading cause of morbidity and mortality for malignant cancer globally. Accurate differentiation of colorectal lesions can provide information for precise treatment and minimize postoperative complications. Specific biomarkers for adenoma and carcinoma classification were compellingly needed. TCGA datasets were applied for identifying differential genes. Particularly, protease activated receptor 2 (PAR2) coded by F2RL1 gene displayed significant difference between clinical colorectal normal, adenoma and carcinoma tissues by IHC. Based on PAR2 targeting agonist, high affinity NIR probe PAR2-M was designed and validated both in vitro and in vivo, with membrane tag DiD as internal reference. In vivo fluorescence ratio of PAR2-M/DiD was absolutely higher in carcinoma than adenoma on CRC mouse models and confocal laser endomicroscopy (CLE) images exhibited their microstructures along with different PAR2 expression. The conclusive differentiation of adenoma and carcinoma was firmly supported by Convolutional Neural Networks (CNN) algorithm, with specificity of 95%.
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