Developing Multiple Media Approach to Investigate Reproducible Characteristic VOCs of Lung Cancer Cells

Cellular volatile organic compound (VOC) detection is crucial for studying lung cancer biomarkers. However, the reported VOC biomarkers from the same cell line seem to be inconsistent across different research groups. It is possibly related to the variation in culture media, and the result obtained by a conventional single medium approach (SMA) depends on what medium is used in the cell experiment. This study proposes a multiple media approach (MMA) to investigate reproducible characteristic VOCs of lung cancer cells. Using solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) in combination with untargeted analysis, we conducted two independently repetitive experiments to compare lung cancer cells (A549) and normal lung cells (BEAS-2B) under three culture media conditions (RPMI 1640, DMEM, and Ham's F12). Both experiments indicated that, compared with 62-96 VOCs obtained by the SMA, only two VOCs (3-methyl-1-butanol and 2-methyl-1-butanol) were reproducibly achieved by the MMA. Moreover, their concentrations were significantly lower in lung cancer cells than in normal cells. Further targeted analysis confirmed the downregulation trend of both VOCs in subcutaneous and primary tumor tissues from the lung cancer model mouse. The present work demonstrated that the MMA cell experiment, just like the multicenter trials for cell lines, can facilitate the discovery of reproducible characteristic VOCs. This provides a cellular experimental basis and scientific evidence for lung cancer biomarker investigation and even breath biopsy technique development.

Authors

Institutions

Publication Details

Journal
Analytical Chemistry
Published
2024-12-18
DOI
https://doi.org/10.1021/acs.analchem.4c03894
Citations
4
Primary Topic
Advanced Chemical Sensor Technologies
Type
article
Field-Weighted Citation Impact
0.74

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Developing Multiple Media Approach to Investigate Reproducible Characteristic VOCs of Lung Cancer Cells

Yajing Chu, Yannan Chu, Dianlong Ge, Jijuan Zhou et al.
4 citations
Analytical Chemistry
Advanced Chemical Sensor Technologies
0.74
article

Developing Multiple Media Approach to Investigate Reproducible Characteristic VOCs of Lung Cancer Cells

Yajing Chu, Yannan Chu, Dianlong Ge, Jijuan Zhou, Yue Liu, Yan Lu, Xiangxue Zheng
article en
4 citations

Abstract

Cellular volatile organic compound (VOC) detection is crucial for studying lung cancer biomarkers. However, the reported VOC biomarkers from the same cell line seem to be inconsistent across different research groups. It is possibly related to the variation in culture media, and the result obtained by a conventional single medium approach (SMA) depends on what medium is used in the cell experiment. This study proposes a multiple media approach (MMA) to investigate reproducible characteristic VOCs of lung cancer cells. Using solid-phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS) in combination with untargeted analysis, we conducted two independently repetitive experiments to compare lung cancer cells (A549) and normal lung cells (BEAS-2B) under three culture media conditions (RPMI 1640, DMEM, and Ham's F12). Both experiments indicated that, compared with 62-96 VOCs obtained by the SMA, only two VOCs (3-methyl-1-butanol and 2-methyl-1-butanol) were reproducibly achieved by the MMA. Moreover, their concentrations were significantly lower in lung cancer cells than in normal cells. Further targeted analysis confirmed the downregulation trend of both VOCs in subcutaneous and primary tumor tissues from the lung cancer model mouse. The present work demonstrated that the MMA cell experiment, just like the multicenter trials for cell lines, can facilitate the discovery of reproducible characteristic VOCs. This provides a cellular experimental basis and scientific evidence for lung cancer biomarker investigation and even breath biopsy technique development.

Analytical ChemistryVol. 96(52)
University of Science and Technology of China (CN), Chinese Academy of Sciences (CN), Hefei Institutes of Physical Science (CN), Hefei Infectious Disease Hospital (CN)
National Natural Science Foundation of China, Hefei Institutes of Physical Science, Chinese Academy of Sciences
Zero hunger
Openalex Percentile: Top 31%
Advanced Chemical Sensor Technologies
0.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.