Machine Learning Based Analysis of Human Serum N-glycome Alterations to Follow up Lung Tumor Surgery
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Chemicals and Reagents
2.2. Sample Preparation
2.3. Capillary Electrophoresis
2.4. Data Analysis
3. Results and Discussion
4. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Patient # | Age | Sex | Ethnicity | Histology | Stadium |
---|---|---|---|---|---|
1 | 78 | male | Caucasian | squamous cell carcinoma | I/b |
2 | 70 | male | Caucasian | small-cell neuroendocrine carcinoma | I/a |
3 | 61 | female | Caucasian | adenocarcinoma | II/b |
4 | 79 | female | Caucasian | adenocarcinoma | I/b |
5 | 52 | male | Caucasian | adenocarcinoma | III/b |
6 | 63 | male | Caucasian | squamous cell carcinoma | II/b |
7 | 68 | female | Caucasian | adenocarcinoma | I/a |
8 | 53 | male | Caucasian | adenocarcinoma | I/a |
9 | 58 | female | Caucasian | adenocarcinoma | I/a |
10 | 75 | male | Caucasian | adenocarcinoma | III/a |
11 | 66 | female | Caucasian | adenocarcinoma | I/a |
12 | 61 | male | Caucasian | anaplasticus-cell carcinoma | II/b |
13 | 63 | female | Caucasian | adenocarcinoma | I/a |
14 | 70 | male | Caucasian | adenocarcinoma | I/a |
15 | 68 | male | Caucasian | squamous cell carcinoma | II/a |
16 | 75 | male | Caucasian | adenocarcinoma | I/a |
17 | 77 | male | Caucasian | adenocarcinoma | I/a |
Age average: 66.8, Age median: 68, Age range: 52–79 |
Peak Notation | Structures | Glycan Structures |
---|---|---|
1 | FA4BG4S(3)4 | |
2 | A2G2S(6)2 | |
3 | FA3G3S(6)3 | |
4 | A2G2S(3)2 | |
5 | A2BG2S2 | |
6 | FA2G2S2 | |
7 | FA2BG2S2, FA3G3S(3)3 | |
8 | FA2[6]G1S1 | |
9 | A3G3S(3)2 | |
10 | A2G2S(6)1 | |
11 | A2BG2S1 | |
12 | FA2G2S1 | |
13 | FA2BG2S1,M5 | |
14 | A4G4S(6)2 | |
15 | FA2, M6 | |
16 | FA2B | |
17 | FA2[6]G1, M7 | |
18 | FA2[3]G1 | |
19 | FA2B [6]G1, M8 | |
20 | FA2G2 | |
21 | M9 |
Clinical Parameters | Results | Accuracy |
---|---|---|
Positive outcome of the surgery | ΔA2G2S(6)2 ≥ −38.1 and ΔFA2G2S2 < 13.58 | 0.69 |
Smoker | ΔFA2, M6 < 11.35 | 0.75 |
Have atherosclerosis | ΔA2BG2S2 ≥ −19.10 and ΔA2G2S(6)2 < 6.06 | 0.63 |
Have other disease | ΔM9 ≥ 35 | 0.75 |
Positive outcome of the surgery and smoker | ΔA2G2S(6)2 ≥ 9.34 or ΔA2G2S(6)2 < 9.34 and ΔFA3G3S(6)3 ≥ 3.96 | 0.63 |
Positive outcome of the surgery and non-smoker | ΔFA2, M6 ≥ 16.14 | 0.88 |
Negative outcome of the surgery and smoker | ΔFA2BG2S1 ≥ 19.26 | 0.81 |
Negative outcome of the surgery and non-smoker | ΔA2G2S(3)2 < −28.26 or ΔA2G2S(3)2 ≥ −28.26 and ΔFA2[3]G1 < −18.79 | 0.69 |
Negative outcome of the surgery and have COPD | ΔFA3G3S(6)3 < −34.01 or ΔFA3G3S(6)3 ≥ −34.01 and ΔFA2BG2S1 ≥ 19.26 | 0.69 |
Positive outcome of the surgery and have atherosclerosis | ΔA2BG2S1 ≥ 18.37 | 0.75 |
Positive outcome of the surgery and not have atherosclerosis | ΔA2BG2S2 < −19.10 or ΔA2BG2S2 ≥ −19.10 and ΔA2G2S(6)2 ≥ 9.34 | 0.63 |
Negative outcome of the surgery and not have atherosclerosis | ΔFA2BG2S2, FA3G3S(3)3 ≥ 35.24 | 0.88 |
Have COPD and atherosclerosis | ΔFA4BG4[3,3,3,3]S4 ≥ 18.51 or ΔFA4BG4[3,3,3,3]S4 < 18.51 and ΔFA2G2 < −28.15 | 0.63 |
Have COPD and not have atherosclerosis | ΔFA2G2S1 ≥ 10.97 | 0.81 |
Positive outcome of the surgery and have COPD | ΔFA2G2S2 ≥ 31.67 | 0.69 |
Non-smoker and not have atherosclerosis | ΔM9 ≥ 17.80 | 0.81 |
Clinical Parameters | Results | Accuracy |
---|---|---|
Positive outcome of the surgery | Δ total afucosylated ≥ −21% | 0.69 |
Have diabetes | Δ total terminal galactosylated ≥ 81.53% | 0.81 |
Have other disease | Δ total terminal galactosylated ≥ 81.53% or Δ total terminal galactosylated < 81.53% and Δ total sialylated ≥ 9.065% | 0.69 |
Positive outcome of the surgery and smoker | Δ neutral < −9.26% | 0.63 |
Positive outcome of the surgery and non-smoker | Δ total sialylated < −1.05% | 0.81 |
Negative outcome of the surgery and non-smoker | Δ total afucosylated < −21% | 0.7 |
Negative outcome of the surgery and no COPD | Δ total afucosylated < −21% | 0.69 |
Negative outcome of the surgery and atherosclerosis | Δ total afucosylated < −21% | 0.75 |
Have COPD and atherosclerosis | Δ total sialylated ≥ 7.4% or Δ total sialylated < 7.4% and −5.56% ≤ Δ total afucosylated < −2.21% | 0.63 |
Not have COPD but diabetes | Δ total afucosylated < −21% | 0.75 |
Not have COPD and diabetes | Δ total afucosylated ≥ 4.57% or Δ total afucosylated < 1.4% | 0.63 |
Negative outcome of the surgery and have diabetes | Δ total afucosylated < −21% | 0.75 |
Not have diabetes but atherosclerosis | Δ total sialylated ≥ 7.41% | 0.75 |
Not have atherosclerosis but diabetes | Δ total afucosylated < −21% | 0.81 |
Smoker and have diabetes | Δ total afucosylated < −21% | 0.81 |
Non-smoker and not have diabetes | Δ total afucosylated ≥ −21 | 0.63 |
Smoker and not have COPD | Δ total afucosylated < −21 | 0.69 |
Non-smoker and have atherosclerosis | Δ total sialylated ≥ 7.4% or Δ total sialylated < 7.4% and −5.56% ≤ Δ total afucosylated <−2.21% | 0.63 |
Smoker and not have atherosclerosis | Δ total afucosylated < −21% | 0.69 |
Formalization of the Relationship | R2 | # |
---|---|---|
0.99 | Equation (1) | |
0.95 | Equation (2) | |
0.81 | Equation (3) | |
0.77 | Equation (4) | |
0.99 | Equation (5) | |
0.96 | Equation (6) |
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Mészáros, B.; Járvás, G.; Kun, R.; Szabó, M.; Csánky, E.; Abonyi, J.; Guttman, A. Machine Learning Based Analysis of Human Serum N-glycome Alterations to Follow up Lung Tumor Surgery. Cancers 2020, 12, 3700. https://doi.org/10.3390/cancers12123700
Mészáros B, Járvás G, Kun R, Szabó M, Csánky E, Abonyi J, Guttman A. Machine Learning Based Analysis of Human Serum N-glycome Alterations to Follow up Lung Tumor Surgery. Cancers. 2020; 12(12):3700. https://doi.org/10.3390/cancers12123700
Chicago/Turabian StyleMészáros, Brigitta, Gábor Járvás, Renáta Kun, Miklós Szabó, Eszter Csánky, János Abonyi, and András Guttman. 2020. "Machine Learning Based Analysis of Human Serum N-glycome Alterations to Follow up Lung Tumor Surgery" Cancers 12, no. 12: 3700. https://doi.org/10.3390/cancers12123700