Abstract
Introduction
Previous studies have explored prediction value of serum metabolites in neoadjuvant chemoradiation therapy (NCRT) response for rectal cancer. To date, limited literature is available for serum metabolome changes dynamically through NCRT.
Objectives
This study aimed to explore temporal change pattern of serum metabolites during NCRT, and potential metabolic biomarkers to predict the pathological response to NCRT in locally advanced rectal cancer (LARC) patients.
Methods
Based on dynamic UHPLC-QTOF-MS untargeted metabolomics design, this study included 106 LARC patients treated with NCRT. Biological samples of the enrolled patients were collected in five consecutive time-points. Untargeted metabolomics was used to profile serum metabolic signatures from LARC patients. Then, we used fuzzy C-means clustering (FCM) to explore temporal change patterns in metabolites cluster and identify monotonously changing metabolites during NCRT. Repeated measure analysis of variance (RM-ANOVA) and multilevel partial least-squares discriminant analysis (ML-PLS-DA) were performed to select metabolic biomarkers. Finally, a panel of dynamic differential metabolites was used to build logistic regression prediction models.
Results
Metabolite profiles showed a clearly tendency of separation between different follow-up panels. We identified two clusters of 155 serum metabolites with monotonously changing patterns during NCRT (74 decreased metabolites and 81 increased metabolites). Using RM-ANOVA and ML-PLS-DA, 8 metabolites (L-Norleucine, Betaine, Hypoxanthine, Acetylcholine, 1-Hexadecanoyl-sn-glycero-3-phosphocholine, Glycerophosphocholine, Alpha-ketoisovaleric acid, N-Acetyl-L-alanine) were further identified as dynamic differential biomarkers for predicting NCRT sensitivity. The area under the ROC curve (AUC) of prediction model combined with the baseline measurement was 0.54 (95%CI = 0.43 ~ 0.65). By incorporating the variability indexes of 8 dynamic differential metabolites, the prediction model showed better discrimination performance than baseline measurement, with AUC = 0.67 (95%CI 0.57 ~ 0.77), 0.64 (0.53 ~ 0.75), 0.60 (0.50 ~ 0.71), and 0.56 (0.45 ~ 0.67) for the variability index of difference, linear slope, ratio, and standard deviation, respectively.
Conclusion
This study identified eight metabolites as dynamic differential biomarkers to discriminate NCRT-sensitive and resistant patients. The changes of metabolite level during NCRT show better performance in predicting NCRT sensitivity. These findings highlight the clinical significance of metabolites variabilities in metabolomics analysis.
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Data availability
Metabolomics dataset was available at https://github.com/jiali1021/LARC-dynamic-metabolomics. Other datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Abbreviations
- TRG:
-
Tumor regression grade
- RSD:
-
Relative standard deviation
- FCM:
-
Fuzzy C-means clustering
- CoA:
-
Coenzyme A
- Ach :
-
Acetylcholine
- 5-FU :
-
5-Fluorouracil
- VIP:
-
Variable importance in projection
- AUC:
-
Area under the receiver operating characteristic curve
- LARC:
-
Locally advanced rectal cancer
- NCRT:
-
Preoperative neoadjuvant chemoradiation therapy
- UHPLC-QTOF-MS:
-
Ultra-high performance liquid chromatography to quadrupole time-of-flight mass spectrometry
- MSCA:
-
Multilevel simultaneous component analysis
- RM-ANOVA:
-
Repeated measure analysis of variance
- ML-PLS-DA:
-
Multilevel partial least-squares discriminant analysis
- KEGG :
-
Kyoto encyclopedia of genes and genomes
- SMPDB:
-
The small molecule pathway database
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Acknowledgements
This study is a joint effort of many investigators and staff members whose contribution is gratefully acknowledged. We appreciate the enrolled adult volunteers for the provision of data.
Funding
This study was supported by grants from Natural Science Foundation of Shanghai (19ZR1410600 [J. Zhu]), Key Research foundation of Zhejiang (2022C03015 [J. Zhu]), National Natural Science Foundation of China (82003554, [H. Jia]), National Natural Science Foundation of China (81973147, 82222064, [T. Zhang]), National Nature Science Foundation of China ([82173613, [Z. Wu]), Scientific Project of Shanghai Municipal Health Commission (201940151, [H. Jia]). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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JL., HJ., JZ. and TZ. designed this study. HJ., JL., MM., JY., ZW., SZ., ZF., BG., BF. and CL. collected clinical samples and clinical information and generated metabolic data. JL. and HJ. conducted data analysis. JL., HJ., JZ. and TZ. wrote the manuscript.
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Lv, J., Jia, H., Mo, M. et al. Changes of serum metabolites levels during neoadjuvant chemoradiation and prediction of the pathological response in locally advanced rectal cancer. Metabolomics 18, 99 (2022). https://doi.org/10.1007/s11306-022-01959-8
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DOI: https://doi.org/10.1007/s11306-022-01959-8