Exploring Serum NMR-Based Metabolomic Fingerprint of Colorectal Cancer Patients: Effects of Surgery and Possible Associations with Cancer Relapse
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Cohort
2.2. Samples Collection
2.3. NMR Analysis
2.3.1. Acquisition of NMR Data
2.3.2. Spectral Processing
2.4. Statistical Analysis
3. Results
3.1. Characteristics of Enrolled Patients
3.2. Effects of Surgery on the Metabolome of CRC Patients
3.3. Associations between Metabolome Variations after Surgery and Cancer Relapse
3.4. Associations between Metabolites and Clinical Variables
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Whole Sample (N = 41) | Stratified by Progression Status | Stratified by Chemotherapy Treatment | |||||||
---|---|---|---|---|---|---|---|---|---|
Not Relapsed (N = 33) | Relapsed (N = 8) | p-Value | Capecitabine (N = 9) | XELOX (N = 10) | No CT (N = 22) | p-Value | |||
Age at study entry | Median (min; max) | 73 (51;92) | 71 (51;92) | 78 (68;86) | 0.032 | 77 (68;86) | 65 (51;72) | 78 (54;92) | 0.001 |
Sex | F | 21 (51%) | 18 (55%) | 3 (38%) | 0.454 | 2 (22%) | 7 (70%) | 12 (55%) | 0.109 |
M | 20 (49%) | 15 (45%) | 5 (62%) | 7 (78%) | 3 (30%) | 10 (45%) | |||
ECOG PS | PS 0 | 29 (71%) | 23 (70%) | 6 (75%) | 1 | 7 (78%) | 10 (100%) | 12 (55%) | 0.102 |
PS 1 | 8 (20%) | 7 (21%) | 1 (12%) | 1 (11%) | 0 (0%) | 7 (32%) | |||
PS 2 | 4 (10%) | 3 (9%) | 1 (12%) | 1 (11%) | 0 (0%) | 3 (14%) | |||
pT | pT1 | 6 (15%) | 6 (18%) | 0 (0%) | 0.086 | 0 (0%) | 1 (10%) | 5 (23%) | 0.477 |
pT2 | 8 (20%) | 8 (24%) | 0 (0%) | 1 (11%) | 1 (10%) | 6 (27%) | |||
pT3 | 23 (56%) | 17 (52%) | 6 (75%) | 7 (78%) | 7 (70%) | 9 (41%) | |||
pT4 | 4 (10%) | 2 (6%) | 2 (25%) | 1 (11%) | 1 (10%) | 2 (9%) | |||
N | N0 | 24 (59%) | 23 (70%) | 1 (12%) | 0.005 | 3 (33%) | 3 (30%) | 18 (82%) | 0.005 |
N+ | 17 (41%) | 10 (30%) | 7 (88%) | 6 (67%) | 7 (70%) | 4 (18%) | |||
Stage risk | Stage I | 11 (27%) | 11 (33%) | 0 (0%) | 0.035 | 0 (0%) | 0 (0%) | 11 (50%) | 0.002 |
Stage II Low risk | 2 (5%) | 2 (6%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (9%) | |||
Stage II High risk | 11 (27%) | 10 (30%) | 1 (12%) | 3 (33%) | 3 (30%) | 5 (23%) | |||
Stage III | 17 (41%) | 10 (30%) | 7 (88%) | 6 (67%) | 7 (70%) | 4 (18%) | |||
Grading | G1 | 2 (5%) | 1 (3%) | 1 (12%) | 0.168 | 1 (11%) | 1 (10%) | 0 (0%) | 0.205 |
G2 | 19 (48%) | 17 (53%) | 2 (25%) | 2 (22%) | 4 (40%) | 13 (62%) | |||
G3 | 17 (42%) | 13 (41%) | 4 (50%) | 5 (56%) | 5 (50%) | 7 (33%) | |||
G4 | 2 (5%) | 1 (3%) | 1 (12%) | 1 (11%) | 0 (0%) | 1 (5%) | |||
NA | 1 | 1 | 0 | 0 | 0 | 1 | |||
Localization | Left-sided | 13 (32%) | 12 (36%) | 1 (12%) | 0.398 | 3 (33%) | 6 (60%) | 4 (18%) | 0.07 |
Right-sided | 28 (68%) | 21 (64%) | 7 (88%) | 6 (67%) | 4 (40%) | 18 (82%) | |||
Comorbidities | No com. | 13 (32%) | 8 (24%) | 5 (62%) | 0.111 | 4 (44%) | 3 (30%) | 6 (27%) | 0.519 |
No vascular com. | 8 (20%) | 8 (24%) | 0 (0%) | 0 (0%) | 3 (30%) | 5 (23%) | |||
Vascular com. | 20 (49%) | 17 (52%) | 3 (38%) | 5 (56%) | 4 (40%) | 11 (50%) | |||
MSI | Instable | 1 (11%) | 1 (14%) | 0 (0%) | 1 | 0 (0%) | 0 (0%) | 1 (33%) | 0.278 |
MSI | 1 (11%) | 1 (14%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (33%) | |||
Stable | 7 (78%) | 5 (71%) | 2 (100%) | 2 (100%) | 4 (100%) | 1 (33%) | |||
NA | 32 | 26 | 6 | 7 | 6 | 19 | |||
CDX2 | Positive | 1 (100%) | 0 (0%) | 1 (100%) | - | 1 (100%) | 0 (0%) | 0 (0%) | - |
NA | 40 | 33 | 7 | 8 | 10 | 22 | |||
KRAS | Mutated | 5 (29%) | 1 (11%) | 4 (50%) | 0.131 | 3 (50%) | 0 (0%) | 2 (67%) | 0.042 |
WT | 12 (71%) | 8 (89%) | 4 (50%) | 3 (50%) | 8 (100%) | 1 (33%) | |||
NA | 24 | 24 | 0 | 3 | 2 | 19 | |||
NRAS | WT | 13 (100%) | 8 (100%) | 5 (100%) | - | 4 (100%) | 8 (100%) | 1 (100%) | - |
NA | 28 | 25 | 3 | 5 | 2 | 21 | |||
BRAF | Mutated | 4 (24%) | 3 (33%) | 1 (12%) | 0.576 | 1 (17%) | 3 (38%) | 0 (0%) | 0.461 |
WT | 13 (76%) | 6 (67%) | 7 (88%) | 5 (83%) | 5 (62%) | 3 (100%) | |||
NA | 24 | 24 | 0 | 3 | 2 | 19 |
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Vignoli, A.; Mori, E.; Di Donato, S.; Malorni, L.; Biagioni, C.; Benelli, M.; Calamai, V.; Cantafio, S.; Parnofiello, A.; Baraghini, M.; et al. Exploring Serum NMR-Based Metabolomic Fingerprint of Colorectal Cancer Patients: Effects of Surgery and Possible Associations with Cancer Relapse. Appl. Sci. 2021, 11, 11120. https://doi.org/10.3390/app112311120
Vignoli A, Mori E, Di Donato S, Malorni L, Biagioni C, Benelli M, Calamai V, Cantafio S, Parnofiello A, Baraghini M, et al. Exploring Serum NMR-Based Metabolomic Fingerprint of Colorectal Cancer Patients: Effects of Surgery and Possible Associations with Cancer Relapse. Applied Sciences. 2021; 11(23):11120. https://doi.org/10.3390/app112311120
Chicago/Turabian StyleVignoli, Alessia, Elena Mori, Samantha Di Donato, Luca Malorni, Chiara Biagioni, Matteo Benelli, Vanessa Calamai, Stefano Cantafio, Annamaria Parnofiello, Maddalena Baraghini, and et al. 2021. "Exploring Serum NMR-Based Metabolomic Fingerprint of Colorectal Cancer Patients: Effects of Surgery and Possible Associations with Cancer Relapse" Applied Sciences 11, no. 23: 11120. https://doi.org/10.3390/app112311120