Biofluid Metabolic Profiling for Lung Cancer Screening via Reactive Matrix-Assisted Laser Desorption Ionization Mass Spectrometry

Abstract

Lung cancer (LC) has the highest mortality rate among various cancer diseases. Developing an early screening method for LC with high classification accuracy is essential. Herein, 2-hydrazinoquinoline (2-HQ) is utilized as a dual-mode reactive matrix for metabolic fingerprint analysis and LC screening via matrix-assisted laser desorption ionization mass spectrometry (MALDI−MS). Metabolites in both positive mode and negative mode can be detected using 2-HQ as the matrix, and derivative analysis of aldehyde and ketone compounds can be achieved simultaneously. Hundreds of serum and urine samples from LC patients and healthy volunteers were analyzed. Combined with machine learning, LC patients and healthy volunteers were successfully distinguished with a high area under the curve value (0.996 for blind serum samples and 0.938 for urine). The MS signal was identified for metabolic profiling, and dysregulated metabolites of the LC group were analyzed. The above results showed that this method has great potential for rapid screening of LC.

Desorption Separation Ionization Mass Spectrometry (DSI-MS) for Rapid Analysis of COVID-19

“…Over the past decade, with the rapid development of artificial intelligence, machine learning (ML) and deep learning have been widely applied in the fields of mass spectrometry and clinical detection due to their advantages of high-throughput, time-saving, and laborsaving data processing. 20,21 In this work, we used the innovative desorption separation ionization (DSI) device in conjunction with an LTQ Orbitrap XL mass spectrometer to facilitate the efficient detection and screening of COVID-19 urine samples for the first time. In order to improve the detection sensitivity of carbonyl compounds in urine, derivatization reagent N,N-dimethylethylenediamine (DMED) was used and the heating circular table of the DSI device can precisely meet the temperature conditions of the derivatization reaction.…”

“…Of note, APGD-MS has been extensively utilized for detecting solid, liquid, and gas samples and has great potential for rapid screening of disease samples. ,− With the consistent implementation of MS technology in the clinical domain, these studies have demonstrated the potential of MS technology to screen and diagnose diseases in the clinical field. Over the past decade, with the rapid development of artificial intelligence, machine learning (ML) and deep learning have been widely applied in the fields of mass spectrometry and clinical detection due to their advantages of high-throughput, time-saving, and labor-saving data processing. , …”