Proteomics-based Model for Predicting the Risk of Brain Metastasis in Patients with Resected Lung Adenocarcinoma carrying the EGFR Mutation

Abstract

Abstract Purpose By establishing a predictive model based on protein biomarkers, it is used to identify the high-risk population of The epidermal growth factor receptor(EGFR) mutated lung cancer patients who may experience brain metastasis after surgery, thereby reducing or delaying the occurrence of brain metastasis. Methods We conducted a retrospective study of BMs in the postoperative recurrent LUAD with EGFR mutation in the First Affiliated Hospital of Guangzhou Medical University. Tissue proteomic analysis was applied in the primary tumors of the resected LUAD in this study using liquid chromatography-mass spectrometry (LC-MS/MS). To identify potential markers to predict LUAD-BM, comparative analyses were processed on different groups to evaluate proteins associated with high-risk of BMs. Results A combination of three potential marker proteins were found to well distinguish distal metastasis (DM) and local recurrence (LR) of postoperative LUAD with EGFR mutation. GO analysis of significant changed proteins between BM and non-BM (NBM) indicates that lipid metabolism and cell cycle related pathways were involved in BMs of LUAD. And the enriched pathways correlated with BMs were found quite different in the comparison groups of postoperative adjuvant therapy, tyrosine kinase inhibitor (TKI) and chemotherapy groups. Finally, we developed a random forest algorithm model with eight proteins (RRS1, CPT1A, DNM1, SRCAP, MLYCD, PCID2, IMPAD1 and FILIP1), which showed excellent predictive value (AUC: 0.9401) of BM in patients with LUAD harboring EGFR mutation. Conclusions A predictive model, based on protein markers, was developed to precisely predict postoperative BM in operable LUAD carrying EGFR mutation.