Proteomics Characteristics Reveal the Risk of T1 Colorectal Cancer Metastasis to Lymph Nodes

Background: The presence of lymph node metastasis (LNM) affects treatment strategy decisions in T1NxM0 colorectal cancer (CRC), but the currently used clinicopathological-based risk stratification cannot predict LNM accurately. In this study, we established a classifier for predicting LNM in T1 CRC. Methods: We detected proteins in formalin-fixed paraffin-embedded (FFPE) tumor samples from 143 LNM-negative and 78 LNM-positive patients with T1 CRC and revealed changes in molecular and biological pathways by label-free LC-MS/MS. An effective prediction model was built and validated in a training cohort (N=132) and two validation cohorts (VC1, N=42; VC2, N=47) by machine learning. We further built a simplified classifier with 9 proteins. The expression patterns of 13 proteins were confirmed by immunohistochemistry, and the IHC score of 5 proteins were used to build a IHC predict model. Result: Patients with or without LNM have different molecular signatures. The 55-proteins prediction model achieved an impressive AUC of 1.00 in the training cohort, 0.96 in VC1 and 0.93 in VC2. The 9-protein classifier achieved an AUC of 0.824, and the calibration plot was excellent. We found that 5 biomarkers could predict LNM by the IHC score, with an AUC of 0.825. RHOT2 silence significantly enhanced migration and invasion of colon cancer cells. Conclusions: Our study explored the mechanism of metastasis in T1 CRC and can be used to facilitate the individualized prediction of LNM in patients with T1 CRC, which may provide a guidance for clinical practice in T1 CRC. ### Competing Interest Statement The authors have declared no competing interest.