Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models
Abstract Introduction Low back pain (LBP) is a significant global health burden, with variable treatment outcomes and an unclear underlying molecular mechanism. Effective prediction of treatment responses remains a challenge. In this study, we aimed to develop gene signature-based machine learning m...
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| Format: | Article |
| Language: | English |
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Adis, Springer Healthcare
2024-12-01
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| Series: | Pain and Therapy |
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| Online Access: | https://doi.org/10.1007/s40122-024-00700-8 |
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| author | Youzhi Lian Yinyu Shi Haibin Shang Hongsheng Zhan |
| author_facet | Youzhi Lian Yinyu Shi Haibin Shang Hongsheng Zhan |
| author_sort | Youzhi Lian |
| collection | DOAJ |
| description | Abstract Introduction Low back pain (LBP) is a significant global health burden, with variable treatment outcomes and an unclear underlying molecular mechanism. Effective prediction of treatment responses remains a challenge. In this study, we aimed to develop gene signature-based machine learning models using transcriptomic data from peripheral immune cells to predict treatment outcomes in patients with LBP. Methods The transcriptomic data of patients with LBP from peripheral immune cells were retrieved from the GEO database. Patients with LBP were recruited, and treatment outcomes were assessed after 3 months. Patients were classified into two groups: those with resolved pain and those with persistent pain. Differentially expressed genes (DEGs) between the two groups were identified through bioinformatic analysis. Key genes were selected using five machine learning models, including Lasso, Elastic Net, Random Forest, SVM, and GBM. These key genes were then used to train 45 machine learning models by combining nine different algorithms: Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting Machine, Multilayer Perceptron, Naive Bayes, and Linear Discriminant Analysis. Five-fold cross-validation was employed to ensure robust model evaluation and minimize overfitting. In each fold, the dataset was split into training and validation sets, with model performance assessed using multiple metrics including accuracy, precision, recall, and F1 score. The final model performance was reported as the mean and standard deviation across all five folds, providing a more reliable estimate of the models' ability to predict LBP treatment outcomes using gene expression data from peripheral immune cells. Results A total of 61 DEGs were identified between patients with resolved and persistent pain. From these genes, 45 machine learning models were constructed using different combinations of feature selection methods and classification algorithms. The Elastic Net with Logistic Regression achieved the highest accuracy of 88.7% ± 8.0% (mean ± standard deviation), followed closely by Elastic Net with Linear Discriminant Analysis (88.7% ± 7.5%) and Lasso with Multilayer Perceptron (87.7% ± 6.7%). Overall, 15 models demonstrated robust performance with accuracy > 80%, suggesting the reliability of our machine learning approach in predicting LBP treatment outcomes. The SHapley Additive exPlanations (SHAP) method was used to visualize the contribution of core genes to model performance, highlighting their roles in predicting treatment outcomes. Conclusion The study demonstrates the potential of using transcriptomic data from peripheral immune cells and machine learning models to predict treatment outcomes in patients with LBP. The identification of key genes and the high accuracy of certain models provide a basis for future personalized treatment strategies in LBP management. Visualizing gene importance with SHAP adds interpretability to the predictive models, enhancing their clinical relevance. |
| format | Article |
| id | doaj-art-172590420a884056a3c198985d649c8a |
| institution | Kabale University |
| issn | 2193-8237 2193-651X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Adis, Springer Healthcare |
| record_format | Article |
| series | Pain and Therapy |
| spelling | doaj-art-172590420a884056a3c198985d649c8a2025-01-26T12:13:57ZengAdis, Springer HealthcarePain and Therapy2193-82372193-651X2024-12-0114135937310.1007/s40122-024-00700-8Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning ModelsYouzhi Lian0Yinyu Shi1Haibin Shang2Hongsheng Zhan3Baoshan Hospital Affiliated to Shanghai University of Chinese MedicineShanghai University of Traditional Chinese Medicine Affiliated Shuguang HospitalBaoshan Hospital Affiliated to Shanghai University of Chinese MedicineShanghai University of Traditional Chinese Medicine Affiliated Shuguang HospitalAbstract Introduction Low back pain (LBP) is a significant global health burden, with variable treatment outcomes and an unclear underlying molecular mechanism. Effective prediction of treatment responses remains a challenge. In this study, we aimed to develop gene signature-based machine learning models using transcriptomic data from peripheral immune cells to predict treatment outcomes in patients with LBP. Methods The transcriptomic data of patients with LBP from peripheral immune cells were retrieved from the GEO database. Patients with LBP were recruited, and treatment outcomes were assessed after 3 months. Patients were classified into two groups: those with resolved pain and those with persistent pain. Differentially expressed genes (DEGs) between the two groups were identified through bioinformatic analysis. Key genes were selected using five machine learning models, including Lasso, Elastic Net, Random Forest, SVM, and GBM. These key genes were then used to train 45 machine learning models by combining nine different algorithms: Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Gradient Boosting Machine, Multilayer Perceptron, Naive Bayes, and Linear Discriminant Analysis. Five-fold cross-validation was employed to ensure robust model evaluation and minimize overfitting. In each fold, the dataset was split into training and validation sets, with model performance assessed using multiple metrics including accuracy, precision, recall, and F1 score. The final model performance was reported as the mean and standard deviation across all five folds, providing a more reliable estimate of the models' ability to predict LBP treatment outcomes using gene expression data from peripheral immune cells. Results A total of 61 DEGs were identified between patients with resolved and persistent pain. From these genes, 45 machine learning models were constructed using different combinations of feature selection methods and classification algorithms. The Elastic Net with Logistic Regression achieved the highest accuracy of 88.7% ± 8.0% (mean ± standard deviation), followed closely by Elastic Net with Linear Discriminant Analysis (88.7% ± 7.5%) and Lasso with Multilayer Perceptron (87.7% ± 6.7%). Overall, 15 models demonstrated robust performance with accuracy > 80%, suggesting the reliability of our machine learning approach in predicting LBP treatment outcomes. The SHapley Additive exPlanations (SHAP) method was used to visualize the contribution of core genes to model performance, highlighting their roles in predicting treatment outcomes. Conclusion The study demonstrates the potential of using transcriptomic data from peripheral immune cells and machine learning models to predict treatment outcomes in patients with LBP. The identification of key genes and the high accuracy of certain models provide a basis for future personalized treatment strategies in LBP management. Visualizing gene importance with SHAP adds interpretability to the predictive models, enhancing their clinical relevance.https://doi.org/10.1007/s40122-024-00700-8Low back painTreatment predictionGene signatureMachine learningTranscriptomicsPeripheral immune cells |
| spellingShingle | Youzhi Lian Yinyu Shi Haibin Shang Hongsheng Zhan Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models Pain and Therapy Low back pain Treatment prediction Gene signature Machine learning Transcriptomics Peripheral immune cells |
| title | Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models |
| title_full | Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models |
| title_fullStr | Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models |
| title_full_unstemmed | Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models |
| title_short | Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models |
| title_sort | predicting treatment outcomes in patients with low back pain using gene signature based machine learning models |
| topic | Low back pain Treatment prediction Gene signature Machine learning Transcriptomics Peripheral immune cells |
| url | https://doi.org/10.1007/s40122-024-00700-8 |
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