Prediction of Multiple Degenerative Diseases Based on DNA Methylation in a Co-Physiology Mechanisms Perspective
Abstract
:1. Introduction
2. Results
2.1. Validation and Performance Enhancement with Selected CpG Sites
2.2. Model Performance Analysis
3. Discussions
3.1. Comparative Analysis of Machine Learning Models
3.2. Performance Variability among Different Diseases
3.3. Insights from DNA Methylation Data Analysis
3.4. Limitations and Future Work
4. Materials and Methods
4.1. Datasets
4.2. Model Construction
4.3. Epigenetic Clock Based on Deep Learning
4.4. Identification of Age-Related CpG Sites
4.5. Enrichment Analysis
4.6. Hyperparameter Tuning and Evaluation
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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Degenerative Disease Type | AUC | ACC | Recall | Precision |
---|---|---|---|---|
Alzheimer’s Disease | 0.96 | 0.92 | 0.97 | 0.92 |
Parkinson’s Disease | 0.92 | 0.90 | 0.96 | 0.87 |
Breast Cancer | 0.93 | 0.91 | 0.97 | 0.91 |
Colon Cancer | 0.85 | 0.83 | 0.97 | 0.79 |
Lung Cancer | 0.85 | 0.77 | 0.89 | 0.77 |
Liver Cancer | 0.83 | 0.73 | 0.62 | 0.83 |
Kidney Cancer | 0.81 | 0.83 | 0.94 | 0.83 |
Osteoporosis | 0.71 | 0.63 | 0.82 | 0.50 |
Brain Cancer | 0.80 | 0.71 | 1.00 | 0.63 |
(a) | |||||
Disease Type | Model | AUC | ACC | Precision | Recall |
Alzheimer’s Disease | MLP | 0.96 | 0.92 | 0.97 | 0.92 |
LR | 0.93 | 0.93 | 0.99 | 0.91 | |
SVM | 0.92 | 0.93 | 0.99 | 0.91 | |
RF | 0.91 | 0.93 | 0.99 | 0.91 | |
XGBoost | 0.90 | 0.93 | 0.99 | 0.91 | |
KNN | 0.91 | 0.93 | 0.97 | 0.93 | |
NB | 0.90 | 0.93 | 0.98 | 0.92 | |
Parkinson’s Disease | MLP | 0.92 | 0.90 | 0.96 | 0.87 |
LR | 0.89 | 0.90 | 0.98 | 0.86 | |
SVM | 0.87 | 0.91 | 1.00 | 0.86 | |
RF | 0.87 | 0.90 | 0.98 | 0.86 | |
XGBoost | 0.88 | 0.88 | 1.00 | 0.82 | |
KNN | 0.85 | 0.85 | 0.89 | 0.84 | |
NB | 0.90 | 0.91 | 1.00 | 0.86 | |
Breast Cancer | MLP | 0.93 | 0.91 | 0.97 | 0.91 |
LR | 0.84 | 0.79 | 1.00 | 0.77 | |
SVM | 0.85 | 0.86 | 1.00 | 0.84 | |
RF | 0.82 | 0.74 | 0.79 | 0.85 | |
(b) | |||||
Disease Type | Model | AUC | ACC | Precision | Recall |
Breast Cancer | XGBoost | 0.65 | 0.70 | 0.76 | 0.82 |
KNN | 0.84 | 0.89 | 0.96 | 0.90 | |
NB | 0.81 | 0.72 | 0.69 | 0.91 | |
Colon Cancer | MLP | 0.85 | 0.83 | 0.97 | 0.79 |
LR | 0.78 | 0.59 | 1.00 | 0.59 | |
SVM | 0.79 | 0.59 | 1.00 | 0.59 | |
RF | 0.76 | 0.79 | 1.00 | 0.74 | |
XGBoost | 0.75 | 0.79 | 1.00 | 0.74 | |
KNN | 0.82 | 0.83 | 0.88 | 0.83 | |
NB | 0.77 | 0.78 | 0.91 | 0.76 | |
Lung Cancer | MLP | 0.85 | 0.77 | 0.89 | 0.77 |
LR | 0.78 | 0.63 | 1.00 | 0.63 | |
SVM | 0.80 | 0.77 | 0.89 | 0.77 | |
RF | 0.75 | 0.83 | 1.00 | 0.79 | |
XGBoost | 0.77 | 0.83 | 1.00 | 0.79 | |
KNN | 0.69 | 0.70 | 0.74 | 0.78 | |
NB | 0.75 | 0.83 | 1.00 | 0.79 | |
Liver Cancer | MLP | 0.83 | 0.73 | 0.62 | 0.83 |
LR | 0.64 | 0.55 | 1.00 | 0.55 | |
SVM | 0.69 | 0.64 | 0.79 | 0.63 | |
RF | 0.66 | 0.61 | 0.71 | 0.63 | |
XGBoost | 0.70 | 0.73 | 0.96 | 0.68 | |
KNN | 0.82 | 0.82 | 0.83 | 0.83 | |
NB | 0.69 | 0.70 | 0.83 | 0.69 | |
Kidney Cancer | MLP | 0.81 | 0.83 | 0.94 | 0.83 |
LR | 0.77 | 0.67 | 1.00 | 0.67 | |
SVM | 0.48 | 0.67 | 1.00 | 0.67 | |
RF | 0.72 | 0.79 | 0.94 | 0.79 | |
XGBoost | 0.69 | 0.75 | 0.88 | 0.78 | |
KNN | 0.59 | 0.58 | 0.56 | 0.75 | |
NB | 0.77 | 0.79 | 0.94 | 0.79 | |
Osteoporosis | MLP | 0.71 | 0.63 | 0.82 | 0.50 |
LR | 0.68 | 0.37 | 1.00 | 0.37 | |
SVM | 0.68 | 0.37 | 1.00 | 0.37 | |
RF | 0.52 | 0.57 | 0.36 | 0.40 | |
XGBoost | 0.69 | 0.63 | 0.91 | 0.50 | |
KNN | 0.61 | 0.60 | 0.64 | 0.47 | |
NB | 0.70 | 0.60 | 1.00 | 0.48 | |
(c) | |||||
Disease Type | Model | AUC | ACC | Precision | Recall |
Brain Cancer | MLP | 0.80 | 0.71 | 1.00 | 0.63 |
LR | 0.66 | 0.50 | 1.00 | 0.50 | |
SVM | 0.76 | 0.50 | 1.00 | 0.50 | |
RF | 0.67 | 0.67 | 1.00 | 0.60 | |
XGBoost | 0.58 | 0.58 | 0.75 | 0.56 | |
KNN | 0.75 | 0.75 | 1.00 | 0.67 | |
NB | 0.76 | 0.75 | 0.83 | 0.71 |
Disease | Stage | Sample Count |
---|---|---|
Alzheimer’s Disease | Early | 401 |
Late | 321 | |
Parkinson’s Disease | Early | 229 |
Late | 238 | |
Breast Cancer | Early | 148 |
Late | 134 | |
Colon Cancer | Early | 146 |
Late | 121 | |
Lung Cancer | Early | 84 |
Late | 104 | |
Liver Cancer | Early | 68 |
Late | 46 | |
Kidney Cancer | Early | 37 |
Late | 39 | |
Osteoporosis | Early | 38 |
Late | 34 | |
Brain Cancer | Early | 33 |
Late | 27 |
CpG ID | SHAP Values | Gene Name | Chromosome Position |
---|---|---|---|
cg00008493 | −3.31 × 10−5 | COX8C | 14 |
cg00024812 | −5.67 × 10−5 | CPSF3 | 2 |
cg00033773 | −4.54 × 10−5 | MORG1 | 19 |
cg00094319 | −2.96 × 10−5 | PABPC3 | 13 |
cg00103783 | −8.55 × 10−5 | MPDU1 | 17 |
cg00136477 | −7.97 × 10−5 | C1QC | 1 |
cg00187686 | −2.08 × 10−5 | TCN1 | 11 |
cg00234961 | −2.22 × 10−5 | ZBED4 | 22 |
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Zhang, L.; Cai, R.; Wang, C.; Liu, J.; Kuang, Z.; Wang, H. Prediction of Multiple Degenerative Diseases Based on DNA Methylation in a Co-Physiology Mechanisms Perspective. Int. J. Mol. Sci. 2024, 25, 9514. https://doi.org/10.3390/ijms25179514
Zhang L, Cai R, Wang C, Liu J, Kuang Z, Wang H. Prediction of Multiple Degenerative Diseases Based on DNA Methylation in a Co-Physiology Mechanisms Perspective. International Journal of Molecular Sciences. 2024; 25(17):9514. https://doi.org/10.3390/ijms25179514
Chicago/Turabian StyleZhang, Li, Ruirui Cai, Chencai Wang, Jialong Liu, Zhejun Kuang, and Han Wang. 2024. "Prediction of Multiple Degenerative Diseases Based on DNA Methylation in a Co-Physiology Mechanisms Perspective" International Journal of Molecular Sciences 25, no. 17: 9514. https://doi.org/10.3390/ijms25179514