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. 2023 Jan 13;24(2):1591.
doi: 10.3390/ijms24021591.

GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer

Elinor A Chapman  1   2 , James Baker  2   3 , Prashant Aggarwal  2   3 , David M Hughes  4 , Amara C Nwosu  5   6   7 , Mark T Boyd  2   3   8 , Catriona R Mayland  9   10 , Stephen Mason  10 , John Ellershaw  5   10 , Chris S Probert  2 , Séamus Coyle  8   11
Affiliations

GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer

Elinor A Chapman et al. Int J Mol Sci. .

Abstract

Predicting when a patient with advanced cancer is dying is a challenge and currently no prognostic test is available. We hypothesised that a dying process from cancer is associated with metabolic changes and specifically with changes in volatile organic compounds (VOCs). We analysed urine from patients with lung cancer in the last weeks of life by headspace gas chromatography mass spectrometry. Urine was acidified or alkalinised before analysis. VOC changes in the last weeks of life were identified using univariate, multivariate and linear regression analysis; 12 VOCs increased (11 from the acid dataset, 2 from the alkali dataset) and 25 VOCs decreased (23 from the acid dataset and 3 from the alkali dataset). A Cox Lasso prediction model using 8 VOCs predicted dying with an AUC of 0.77, 0.78 and 0.85 at 30, 20 and 10 days and stratified patients into a low (median 10 days), medium (median 50 days) or high risk of survival. Our data supports the hypothesis there are specific metabolic changes associated with the dying. The VOCs identified are potential biomarkers of dying in lung cancer and could be used as a tool to provide additional prognostic information to inform expert clinician judgement and subsequent decision making.

Keywords: GC-MS; SPME; VOCs; biomarkers; dying; lung cancer; palliative; urine; volatile.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
VOCs that increase towards death in the acid dataset. Jitter box plot graph for each metabolite identified as significant from univariate and linear regression analysis. The centre line is the median; box limits are upper and lower quartiles; whiskers are 1.5× interquartile range; points are individual observations. Y axis is VOC concentration on glog scale.
Figure 2
Figure 2
VOCs that decrease towards death in the acid dataset. Jitter box plot graph for each metabolite identified as significant from univariate and linear regression analysis. The centre line is the median; box limits are upper and lower quartiles; whiskers are 1.5× interquartile range; points are individual observations. Y axis is VOC concentration on glog scale.
Figure 3
Figure 3
Kaplan–Meier survival curves using the Cox Lasso regression model. It shows three groupings based on prediction of 21-day risk, representing high, medium and low risk of dying.
Figure 4
Figure 4
AUC for the Cox Lasso regression model. The blue line shows the mean and dotted line the median. The dark grey shows the confidence interval, and the light grey shows the minimum and maximum values.
Figure 5
Figure 5
VOCs that change towards death in the alkali dataset. Jitter box plot graph for each metabolite identified as significant from univariate and linear regression analysis. The centre line is the median; box limits are upper and lower quartiles; whiskers are 1.5× interquartile range; points are individual observations. Y axis is VOC concentration on glog scale.
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