Machine learning potential predictor of idiopathic pulmonary fibrosis
IntroductionIdiopathic pulmonary fibrosis (IPF) is a severe chronic respiratory disease characterized by treatment challenges and poor prognosis. Identifying relevant biomarkers for effective early-stage risk prediction is therefore of critical importance.MethodsIn this study, we obtained gene expre...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Genetics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fgene.2024.1464471/full |
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| author | Chenchun Ding Quan Liao Renjie Zuo Shichao Zhang Zhenzhen Guo Junjie He Ziwei Ye Weibin Chen Sunkui Ke |
| author_facet | Chenchun Ding Quan Liao Renjie Zuo Shichao Zhang Zhenzhen Guo Junjie He Ziwei Ye Weibin Chen Sunkui Ke |
| author_sort | Chenchun Ding |
| collection | DOAJ |
| description | IntroductionIdiopathic pulmonary fibrosis (IPF) is a severe chronic respiratory disease characterized by treatment challenges and poor prognosis. Identifying relevant biomarkers for effective early-stage risk prediction is therefore of critical importance.MethodsIn this study, we obtained gene expression profiles and corresponding clinical data of IPF patients from the GEO database. GO enrichment and KEGG pathway analyses were performed using R software. To construct an IPF risk prediction model, we employed LASSO-Cox regression analysis and the SVM-RFE algorithm. PODNL1 and PIGA were identified as potential biomarkers associated with IPF onset, and their predictive accuracy was confirmed using ROC curve analysis in the test set. Furthermore, GSEA revealed enrichment in multiple pathways, while immune function analysis demonstrated a significant correlation between IPF onset and immune cell infiltration. Finally, the roles of PODNL1 and PIGA as biomarkers were validated through in vivo and in vitro experiments using qRT-PCR, Western blotting, and immunohistochemistry.ResultsThese findings suggest that PODNL1 and PIGA may serve as critical biomarkers for IPF onset and contribute to its pathogenesis.DiscussionThis study highlights their potential for early biomarker discovery and risk prediction in IPF, offering insights into disease mechanisms and diagnostic strategies. |
| format | Article |
| id | doaj-art-73ca4bb207a54e7da31972b671594316 |
| institution | Kabale University |
| issn | 1664-8021 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Genetics |
| spelling | doaj-art-73ca4bb207a54e7da31972b6715943162025-01-28T09:37:53ZengFrontiers Media S.A.Frontiers in Genetics1664-80212025-01-011510.3389/fgene.2024.14644711464471Machine learning potential predictor of idiopathic pulmonary fibrosisChenchun Ding0Quan Liao1Renjie Zuo2Shichao Zhang3Zhenzhen Guo4Junjie He5Ziwei Ye6Weibin Chen7Sunkui Ke8Department of Thoracic Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, ChinaDepartment of Thoracic Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, ChinaDepartment of Thoracic Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, ChinaDepartment of Urology, Tianjin Institute of Urology, The Second Hospital of Tianjin Medical University, Tianjin, ChinaSchool of Pharmaceutical Sciences, Xiamen University, Xiamen, Fujian, ChinaDepartment of Thoracic Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, ChinaSchool of Pharmaceutical Sciences, Xiamen University, Xiamen, Fujian, ChinaDepartment of Thoracic Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, ChinaDepartment of Thoracic Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, ChinaIntroductionIdiopathic pulmonary fibrosis (IPF) is a severe chronic respiratory disease characterized by treatment challenges and poor prognosis. Identifying relevant biomarkers for effective early-stage risk prediction is therefore of critical importance.MethodsIn this study, we obtained gene expression profiles and corresponding clinical data of IPF patients from the GEO database. GO enrichment and KEGG pathway analyses were performed using R software. To construct an IPF risk prediction model, we employed LASSO-Cox regression analysis and the SVM-RFE algorithm. PODNL1 and PIGA were identified as potential biomarkers associated with IPF onset, and their predictive accuracy was confirmed using ROC curve analysis in the test set. Furthermore, GSEA revealed enrichment in multiple pathways, while immune function analysis demonstrated a significant correlation between IPF onset and immune cell infiltration. Finally, the roles of PODNL1 and PIGA as biomarkers were validated through in vivo and in vitro experiments using qRT-PCR, Western blotting, and immunohistochemistry.ResultsThese findings suggest that PODNL1 and PIGA may serve as critical biomarkers for IPF onset and contribute to its pathogenesis.DiscussionThis study highlights their potential for early biomarker discovery and risk prediction in IPF, offering insights into disease mechanisms and diagnostic strategies.https://www.frontiersin.org/articles/10.3389/fgene.2024.1464471/fullbioinformaticsbiomarkersimmune cell infiltrationmachine-learningidiopathic pulmonary fibrosis |
| spellingShingle | Chenchun Ding Quan Liao Renjie Zuo Shichao Zhang Zhenzhen Guo Junjie He Ziwei Ye Weibin Chen Sunkui Ke Machine learning potential predictor of idiopathic pulmonary fibrosis Frontiers in Genetics bioinformatics biomarkers immune cell infiltration machine-learning idiopathic pulmonary fibrosis |
| title | Machine learning potential predictor of idiopathic pulmonary fibrosis |
| title_full | Machine learning potential predictor of idiopathic pulmonary fibrosis |
| title_fullStr | Machine learning potential predictor of idiopathic pulmonary fibrosis |
| title_full_unstemmed | Machine learning potential predictor of idiopathic pulmonary fibrosis |
| title_short | Machine learning potential predictor of idiopathic pulmonary fibrosis |
| title_sort | machine learning potential predictor of idiopathic pulmonary fibrosis |
| topic | bioinformatics biomarkers immune cell infiltration machine-learning idiopathic pulmonary fibrosis |
| url | https://www.frontiersin.org/articles/10.3389/fgene.2024.1464471/full |
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