The role of CTGF and MFG-E8 in the prognosis assessment of SCAP: a study combining machine learning and nomogram analysis
BackgroundSevere Community-Acquired Pneumonia (SCAP) is a serious global health issue with high incidence and mortality rates. In recent years, the role of biomarkers such as Connective Tissue Growth Factor (CTGF) and Milk Fat Globule-Epidermal Growth Factor 8 (MFG-E8) in disease diagnosis and progn...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1446415/full |
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| author | Tingting Lin Tingting Lin Huimin Wan Jie Ming Yifei Liang Linxin Ran Jingjing Lu |
| author_facet | Tingting Lin Tingting Lin Huimin Wan Jie Ming Yifei Liang Linxin Ran Jingjing Lu |
| author_sort | Tingting Lin |
| collection | DOAJ |
| description | BackgroundSevere Community-Acquired Pneumonia (SCAP) is a serious global health issue with high incidence and mortality rates. In recent years, the role of biomarkers such as Connective Tissue Growth Factor (CTGF) and Milk Fat Globule-Epidermal Growth Factor 8 (MFG-E8) in disease diagnosis and prognosis has increasingly gained attention. However, their specific functions in SCAP have still remained unclear. By conducting a prospective analysis, this study has explored the relationship between these two proteins and the diagnosis and mortality of SCAP patients. Additionally, founded on comparing the applications of machine learning and nomograms as predictive models in forecasting the 28-day mortality risk of SCAP patients, this paper has discussed their performance in different medical scenarios to provide more accurate treatment options and improve prognosis.Methods198 patients diagnosed with SCAP, 80 patients with CAP and 80 healthy individuals were encompassed in the study. Demographic characteristics, clinical features and biomarkers were extracted. The ELISA method was employed to measure the levels of MFG-E8 and CTGF in the three groups. The 28-day mortality of SCAP patients was tracked. Eleven models, including XGBoost and CatBoost, were used as prediction models and compared with a nomogram. And 14 scoring methods, like F1 Score and AUC Score, were used to evaluate the prediction models.ResultsCompared to healthy controls, SCAP patients had higher serum levels of CTGF and MFG-E8, suggesting that these biomarkers are associated with poor prognosis. Compared to CAP patients, SCAP patients had lower levels of MFG-E8 and higher levels of CTGF. In the deceased group of SCAP patients, their CTGF levels were higher and MFG-E8 levels were lower. Using the CatBoost model for prediction, it performed the best, with key predictive features including Oxygenation Index, cTnT, MFG-E8, Dyspnea, CTGF and PaCO2.ConclusionThis study has highlighted the critical role of clinical and biochemical markers such as CTGF and MFG-E8 in assessing the severity and prognosis of SCAP. The CatBoost model has shown the significant potential in predicting mortality risk by virtue of its unique algorithmic advantages and efficiency. |
| format | Article |
| id | doaj-art-0fe1a9260b2e4d038d711c9cf1633be8 |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-0fe1a9260b2e4d038d711c9cf1633be82025-01-23T06:56:31ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011610.3389/fimmu.2025.14464151446415The role of CTGF and MFG-E8 in the prognosis assessment of SCAP: a study combining machine learning and nomogram analysisTingting Lin0Tingting Lin1Huimin Wan2Jie Ming3Yifei Liang4Linxin Ran5Jingjing Lu6Department of Respiratory Medicine, Xiamen Humanity Hospital, Fujian Medical University, Xiamen, ChinaShanghai East Hospital, School of Medicine, Tongji University, Shanghai, ChinaShanghai East Hospital, School of Medicine, Tongji University, Shanghai, ChinaShanghai East Hospital, School of Medicine, Tongji University, Shanghai, ChinaShanghai East Hospital, School of Medicine, Tongji University, Shanghai, ChinaShanghai East Hospital, School of Medicine, Tongji University, Shanghai, ChinaShanghai East Hospital, School of Medicine, Tongji University, Shanghai, ChinaBackgroundSevere Community-Acquired Pneumonia (SCAP) is a serious global health issue with high incidence and mortality rates. In recent years, the role of biomarkers such as Connective Tissue Growth Factor (CTGF) and Milk Fat Globule-Epidermal Growth Factor 8 (MFG-E8) in disease diagnosis and prognosis has increasingly gained attention. However, their specific functions in SCAP have still remained unclear. By conducting a prospective analysis, this study has explored the relationship between these two proteins and the diagnosis and mortality of SCAP patients. Additionally, founded on comparing the applications of machine learning and nomograms as predictive models in forecasting the 28-day mortality risk of SCAP patients, this paper has discussed their performance in different medical scenarios to provide more accurate treatment options and improve prognosis.Methods198 patients diagnosed with SCAP, 80 patients with CAP and 80 healthy individuals were encompassed in the study. Demographic characteristics, clinical features and biomarkers were extracted. The ELISA method was employed to measure the levels of MFG-E8 and CTGF in the three groups. The 28-day mortality of SCAP patients was tracked. Eleven models, including XGBoost and CatBoost, were used as prediction models and compared with a nomogram. And 14 scoring methods, like F1 Score and AUC Score, were used to evaluate the prediction models.ResultsCompared to healthy controls, SCAP patients had higher serum levels of CTGF and MFG-E8, suggesting that these biomarkers are associated with poor prognosis. Compared to CAP patients, SCAP patients had lower levels of MFG-E8 and higher levels of CTGF. In the deceased group of SCAP patients, their CTGF levels were higher and MFG-E8 levels were lower. Using the CatBoost model for prediction, it performed the best, with key predictive features including Oxygenation Index, cTnT, MFG-E8, Dyspnea, CTGF and PaCO2.ConclusionThis study has highlighted the critical role of clinical and biochemical markers such as CTGF and MFG-E8 in assessing the severity and prognosis of SCAP. The CatBoost model has shown the significant potential in predicting mortality risk by virtue of its unique algorithmic advantages and efficiency.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1446415/fullSCAPCTGFMFG-E8machine learningnomogram |
| spellingShingle | Tingting Lin Tingting Lin Huimin Wan Jie Ming Yifei Liang Linxin Ran Jingjing Lu The role of CTGF and MFG-E8 in the prognosis assessment of SCAP: a study combining machine learning and nomogram analysis Frontiers in Immunology SCAP CTGF MFG-E8 machine learning nomogram |
| title | The role of CTGF and MFG-E8 in the prognosis assessment of SCAP: a study combining machine learning and nomogram analysis |
| title_full | The role of CTGF and MFG-E8 in the prognosis assessment of SCAP: a study combining machine learning and nomogram analysis |
| title_fullStr | The role of CTGF and MFG-E8 in the prognosis assessment of SCAP: a study combining machine learning and nomogram analysis |
| title_full_unstemmed | The role of CTGF and MFG-E8 in the prognosis assessment of SCAP: a study combining machine learning and nomogram analysis |
| title_short | The role of CTGF and MFG-E8 in the prognosis assessment of SCAP: a study combining machine learning and nomogram analysis |
| title_sort | role of ctgf and mfg e8 in the prognosis assessment of scap a study combining machine learning and nomogram analysis |
| topic | SCAP CTGF MFG-E8 machine learning nomogram |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1446415/full |
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