Metabolic profiles of lung adenocarcinoma via peripheral blood and diagnostic model construction

Metabolomics-based search for lung cancer markers among patients with different smoking status

Tobacco smoking is the main etiological factor of lung cancer (LC), which can also cause metabolome disruption. This study aimed to investigate whether the observed metabolic shift in LC patients was also associated with their smoking status. Untargeted metabolomics profiling was applied for the initial screening of changes in serum metabolic profile between LC and chronic obstructive pulmonary disease (COPD) patients, selected as a non-cancer group. Differences in metabolite profiles between current and former smokers were also tested. Then, targeted metabolomics methods were applied to verify and validate the proposed LC biomarkers. For untargeted metabolomics, a single extraction-dual separation workflow was applied. The samples were analyzed using a liquid chromatograph-high resolution quadrupole time-of-flight mass spectrometer. Next, the selected metabolites were quantified using liquid chromatography-triple-quadrupole mass spectrometry. The acquired data confirmed that patients' stratification based on smoking status impacted the discriminating ability of the identified LC marker candidates. Analyzing a validation set of samples enabled us to determine if the putative LC markers were truly robust. It demonstrated significant differences in the case of four metabolites: allantoin, glutamic acid, succinic acid, and sphingosine-1-phosphate. Our research showed that studying the influence of strong environmental factors, such as tobacco smoking, should be considered in cancer marker research since it reduces the risk of false positives and improves understanding of the metabolite shifts in cancer patients.

Lipidomics reveals immune-related adverse events in NSCLC patients receiving immune checkpoint inhibitor

There is a lack of reliable biomarkers to predict and identify the risk of immune-related adverse events (irAEs) in non-small cell lung cancer (NSCLC) patients undergoing immune checkpoint inhibitor (ICI) treatment. This study aims to explore potential biomarkers using lipidomics to identify and predict the risk of irAEs in NSCLC patients receiving ICI treatment. This prospective study enrolled 94 NSCLC patients with IIIB/IV stage NSCLC who underwent first-line chemotherapy in combination with ICI treatment. The prediction cohort consisted of plasma samples collected from 60 patients before ICI treatment, and the occurrence of irAE was monitored within 6 months of initiating first-line ICI therapy. The validation cohort comprised 34 patients, with plasma samples obtained from 15 patients who did not develop irAE at 6 months of ICI treatment and plasma samples collected from 19 irAE patients at the onset of irAE. Through non-targeted lipidomics and semi-targeted lipid quantification analysis, we identify 11 differentially metabolized lipids and further screened these lipids with the area under the curve (AUC) > 0.7 to predict the occurrence of irAEs in NSCLC patients following ICI treatment. The results showed that the biomarker panel consisting of 9 lipids (LPC-18:2, PC-40:6, LPC-22:6, LPC-O-18:0, PS-38:0, PC-38:6, PC-37:6, PC-36:5,LPC-17:0) exhibited a good AUC of 0.859 in the prediction and 0.940 in the validation cohort phase of the receiver operating characteristic curve; The study utilizes plasma lipidomics to develop a rapid and effective prediction model for identifying irAEs in advanced NSCLC patients who treatment with first-line chemotherapy combined with immunotherapy.