Proteomics score: a potential biomarker for the prediction of prognosis in non-small cell lung cancer

Abstract

Background: Biomarkers based on quantitative genomics features are related to clinical prognosis in various cancer types. However, the association between proteomics and prognosis in non-small cell lung cancer (NSCLC) is unclear. Here, we developed a proteomics score for the prediction of prognosis in patients with NSCLC undergoing partial pneumonectomy. Methods: In total, 693 patients with NSCLC with reverse-phase protein array data from The Cancer Genome Atlas were randomly divided into discovery (n=346) and validation (n=347) cohorts. The least absolute shrinkage and selection operator algorithm (LASSO) was used to select the optimal features and build a proteomics score in the discovery set. Additionally, the performance of the proteomics nomogram was estimated using its calibration and time-dependent receiver operator characteristic (ROC) curves. Selection genomics were analyzed via bioinformation. Results: Using the LASSO model, we established a novel classifier based on 15 features. The proteomics score was significantly associated with overall survival (OS; both P Conclusions: The proposed proteomics score and nomogram showed excellent performance for the estimation of OS and DFS, which may help clinicians better identify patients with NSCLC who can benefit from surgery.