Purpose: Lung cancer is the most common cause of death from cancer worldwide, largely due to late diagnosis. Thus, there is an urgent need to develop new approaches to improve the detection of early-stage lung cancer, which would greatly improve patient survival.
Experimental design: The quantitative protein expression profiles of microvesicles isolated from the sera from 46 lung cancer patients and 41 high-risk non-cancer subjects were obtained using a mass spectrometry method based on a peptide library matching approach.
Results: We identified 33 differentially expressed proteins that allow discriminating the two groups. We also built a machine learning model based on serum protein expression profiles that can correctly classify the majority of lung cancer cases and that highlighted a decrease in the levels of Arysulfatase A (ARSA) as the most discriminating factor found in tumors.
Conclusions and clinical relevance: Our study identified a preliminary, non-invasive protein signature able to discriminate with high specificity and selectivity early-stage lung cancer patients from high-risk healthy subjects. These results provide the basis for future validation studies for the development of a non-invasive diagnostic tool for lung cancer.
Keywords: biomarker; early diagnosis; lung cancer; machine learning; mass spectrometry; serum.
© 2023 The Authors. Proteomics - Clinical Applications published by Wiley-VCH GmbH.