Serum proteomics profiling identifies a signature for lung cancer early diagnosis

ABSTRACT: Lung cancer is the most common cause of death from cancer worldwide, largely due to frequent late diagnosis. Thus, there is the urgent need to develop new approaches to improve theearly detection of lung cancer, which would greatly affect patient survival. The quantitative protein expression profiles of microvesicles isolated from sera from 46 lung cancer patients and 41 high-risk non-cancer subjects were obtained using a mass spectrometry method based on library matching. We identified 33 differentially expressed proteins that allow discriminating the two groups. Based on serum protein expression profiles, we built a machine learning algorithm , which highlighted a decrease in the levels of Arysulfatase A (ARSA) as the most discriminating factor found in tumors. Our study identified a signature able to discriminate with high specificity and selectivity early lung cancer patients from high-risk healthy subjects. Among the differentially expressed proteins, ARSA emerged as the most promising biomarker for lung cancer early diagnosis.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood

SUBMITTER: Roberta Noberini  

LAB HEAD: Tiziana Bonaldi

PROVIDER: PXD034221 | Pride | 2023-02-13

REPOSITORIES: Pride