The current gold standard for the diagnosis of lymph node metastasis is pathological examination of surgically resected lymph node specimens. However, lymph node metastases are different from the primary lesions, and the distribution of tumor cells is heterogeneous and more dispersed. Therefore, a single thin pathological section is difficult to obtain complete information, which may be misdiagnosed due to the failure to examine on the section containing tumor cells or the presence of micro-metastases.~The application of proteomics can obtain the overall information of the samples, including the remodeling of the microenvironment by the tumor metastases and the acclimation even before the metastasis, resulting in significant changes in the protein expression profiles of the lymph nodes, which are difficult to be completely presented in conventional pathological sections. This study aims to evaluate the accuracy of proteomics approaches on resected lymph node samples in evaluating lymph node metastasis status in cholangiocarcinoma patients, assisting in guiding precision medicine and making therapeutic decisions.~Positive controls of lymph node metastases and negative controls of normal lymph nodes were previously profiled using proteomic approaches. Machine-learning clustering method will be used to classify the newly examined lymph nodes.
This is a single-center, prospective, observational and exploratory clinical study. The object of this study is to evaluate the accuracy of proteomics approaches on resected lymph node samples in evaluating lymph node metastasis status in cholangiocarcinoma patients.