The prediction of lymph node metastasis using clinic-pathological data and molecular information from endometrial cancers lacks accuracy and is therefore currently not routinely used in patient management. Consequently, although only a small percentage of patients with endometrial cancers suffer from metastasis, the majority undergo radical surgery including removal of pelvic lymph nodes. Upon analysis of publically available data and published research, we compiled a list of 60 proteins having the potential to display differential abundance between primary endometrial cancers with versus those without lymph node metastasis. Using data dependent acquisition LC-ESI-MS/MS we were able to detect 23 of these proteins in endometrial cancers, and using data independent LC-ESI-MS/MS the differential abundance of five of those proteins was observed. The localization of the differentially expressed proteins, was visualized using peptide MALDI MSI in whole tissue sections as well as tissue microarrays of 43 patients. The proteins identified were further validated by immunohistochemistry. Our data indicate that annexin A2 protein level is upregulated, whereas annexin A1 and α actinin 4 expression are downregulated in tumours with lymph node metastasis compared to those without lymphatic spread. Moreover, our analysis confirmed the potential of these markers, to be included in a statistical model for prediction of lymph node metastasis. The predictive model using highly ranked m/z values identified by MALDI MSI showed significantly higher predictive accuracy than the model using immunohistochemistry data. In summary, using publicly available data and complementary proteomics approaches, we were able to improve the prediction model for lymph node metastasis in EC.
Keywords: Annexin A2; Endometrial cancer; Label free LC-MS/MS; MALDI MSI; Metastasis; α actinin 4.
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