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

Abstract

BackgroundThe 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. MethodsWe 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. ResultPatients 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. ConclusionsOur 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.