Integrated analyses and a novel nomogram for the prediction of significant fibrosis in patients
Introduction and Objectives: This study aimed to explore the key genes involved in the pathophysiological process of liver fibrosis and develop a novel predictive model for noninvasive assessment of significant liver fibrosis patients. Patients and Methods: Differentially expressed genes (DEGs) were...
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Elsevier
2025-01-01
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| Series: | Annals of Hepatology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1665268124005271 |
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| author | Mengxin Lu Shuai Tao Xinyan Li Qunling Yang Cong Du Weijia Lin Shuangshuang Sun Conglin Zhao Neng Wang Qiankun Hu Yuxian Huang Qiang Li Yi Zhang Liang Chen |
| author_facet | Mengxin Lu Shuai Tao Xinyan Li Qunling Yang Cong Du Weijia Lin Shuangshuang Sun Conglin Zhao Neng Wang Qiankun Hu Yuxian Huang Qiang Li Yi Zhang Liang Chen |
| author_sort | Mengxin Lu |
| collection | DOAJ |
| description | Introduction and Objectives: This study aimed to explore the key genes involved in the pathophysiological process of liver fibrosis and develop a novel predictive model for noninvasive assessment of significant liver fibrosis patients. Patients and Methods: Differentially expressed genes (DEGs) were identified using the Limma package. The hub genes were explored using the CytoHubba plugin app and validated in GEO datasets and cell models. Furthermore, serum LTBP2 was measured in liver fibrosis (LF) patients with biopsy-proven by ELISA. All patients' clinical characteristics and laboratory results were analyzed. Finally, multivariate logistic regression analysis was used to construct the model for visualization by nomogram. Area under the receiver operating characteristic curve (AUROC) analysis, calibration curves, and decision curve analysis (DCA) certify the accuracy of the nomogram. Results: RNA sequencing was performed on the liver tissues of 66 biopsy-proven HBV-LF patients. After multiple analyses and in vitro simulation of HSC activation, LTBP2 was found to be the most associated with HSC activation regardless of the causes. Serum LTBP2 expression was measured in 151 patients with biopsy, and LTBP2 was found to increase in parallel with the fibrosis stage. Multivariate logistic regression analysis showed that LTBP2, PLT and AST levels were demonstrated as the independent prediction factors. A nomogram that included the three factors was tabled to evaluate the probability of significant fibrosis occurrence. The AUROC of the nomogram model was 0.8690 in significant fibrosis diagnosis. Conclusions: LTBP2 may be a new biomarker for liver fibrosis patients. The nomogram showed better diagnostic performance in patients. |
| format | Article |
| id | doaj-art-3f940289fdee4f3d8baa9d0d08690f41 |
| institution | DOAJ |
| issn | 1665-2681 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Annals of Hepatology |
| spelling | doaj-art-3f940289fdee4f3d8baa9d0d08690f412025-08-20T03:18:27ZengElsevierAnnals of Hepatology1665-26812025-01-0130110174410.1016/j.aohep.2024.101744Integrated analyses and a novel nomogram for the prediction of significant fibrosis in patientsMengxin Lu0Shuai Tao1Xinyan Li2Qunling Yang3Cong Du4Weijia Lin5Shuangshuang Sun6Conglin Zhao7Neng Wang8Qiankun Hu9Yuxian Huang10Qiang Li11Yi Zhang12Liang Chen13Department of Liver Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaScientific Research Center, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Liver Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Liver Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Liver Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Hepatobiliary Medicine, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of liver disease center, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Liver Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Liver Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Liver Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Liver Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai, ChinaDepartment of Liver Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China; Corresponding authors.Department of Liver Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China; Corresponding authors.Department of Liver Disease, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China; Corresponding authors.Introduction and Objectives: This study aimed to explore the key genes involved in the pathophysiological process of liver fibrosis and develop a novel predictive model for noninvasive assessment of significant liver fibrosis patients. Patients and Methods: Differentially expressed genes (DEGs) were identified using the Limma package. The hub genes were explored using the CytoHubba plugin app and validated in GEO datasets and cell models. Furthermore, serum LTBP2 was measured in liver fibrosis (LF) patients with biopsy-proven by ELISA. All patients' clinical characteristics and laboratory results were analyzed. Finally, multivariate logistic regression analysis was used to construct the model for visualization by nomogram. Area under the receiver operating characteristic curve (AUROC) analysis, calibration curves, and decision curve analysis (DCA) certify the accuracy of the nomogram. Results: RNA sequencing was performed on the liver tissues of 66 biopsy-proven HBV-LF patients. After multiple analyses and in vitro simulation of HSC activation, LTBP2 was found to be the most associated with HSC activation regardless of the causes. Serum LTBP2 expression was measured in 151 patients with biopsy, and LTBP2 was found to increase in parallel with the fibrosis stage. Multivariate logistic regression analysis showed that LTBP2, PLT and AST levels were demonstrated as the independent prediction factors. A nomogram that included the three factors was tabled to evaluate the probability of significant fibrosis occurrence. The AUROC of the nomogram model was 0.8690 in significant fibrosis diagnosis. Conclusions: LTBP2 may be a new biomarker for liver fibrosis patients. The nomogram showed better diagnostic performance in patients.http://www.sciencedirect.com/science/article/pii/S1665268124005271Liver fibrosisLTBP2NomogramBiomarker |
| spellingShingle | Mengxin Lu Shuai Tao Xinyan Li Qunling Yang Cong Du Weijia Lin Shuangshuang Sun Conglin Zhao Neng Wang Qiankun Hu Yuxian Huang Qiang Li Yi Zhang Liang Chen Integrated analyses and a novel nomogram for the prediction of significant fibrosis in patients Annals of Hepatology Liver fibrosis LTBP2 Nomogram Biomarker |
| title | Integrated analyses and a novel nomogram for the prediction of significant fibrosis in patients |
| title_full | Integrated analyses and a novel nomogram for the prediction of significant fibrosis in patients |
| title_fullStr | Integrated analyses and a novel nomogram for the prediction of significant fibrosis in patients |
| title_full_unstemmed | Integrated analyses and a novel nomogram for the prediction of significant fibrosis in patients |
| title_short | Integrated analyses and a novel nomogram for the prediction of significant fibrosis in patients |
| title_sort | integrated analyses and a novel nomogram for the prediction of significant fibrosis in patients |
| topic | Liver fibrosis LTBP2 Nomogram Biomarker |
| url | http://www.sciencedirect.com/science/article/pii/S1665268124005271 |
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