Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosis
Abstract Non-alcoholic fatty liver disease (NAFLD) affects about 25% of adults worldwide. Its advanced form, non-alcoholic steatohepatitis (NASH), is a major cause of liver fibrosis, but there are no non-invasive tests for diagnosing or preventing it. In our study, we analyzed data from multiple sou...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-04-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-97670-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850145605704220672 |
|---|---|
| author | Jiahui Feng Zheng Gong Jialing Yang Yuting Mo Fengqian Song |
| author_facet | Jiahui Feng Zheng Gong Jialing Yang Yuting Mo Fengqian Song |
| author_sort | Jiahui Feng |
| collection | DOAJ |
| description | Abstract Non-alcoholic fatty liver disease (NAFLD) affects about 25% of adults worldwide. Its advanced form, non-alcoholic steatohepatitis (NASH), is a major cause of liver fibrosis, but there are no non-invasive tests for diagnosing or preventing it. In our study, we analyzed data from multiple sources to find crucial genes linked to NASH fibrosis. We built diagnostic models using 103 machine learning algorithms and validated them with two external datasets. All models performed well, with the best one (RF + Enet[alpha = 0.6]) achieving an average AUC of 0.822. This model used five key genes: LUM, COL1A2, THBS2, COL5A2, and NTS. Our findings show that these genes are important in collagen and extracellular matrix pathways, shedding light on how NASH progresses to liver fibrosis. We also found that certain immune cells, like M1 macrophages, are involved in this process. This study provides a reliable diagnostic tool for assessing fibrosis risk in NASH patients and suggests potential for immunotherapy, laying a foundation for future treatments. |
| format | Article |
| id | doaj-art-be9870e39606483e8bec5f40899cd79b |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-be9870e39606483e8bec5f40899cd79b2025-08-20T02:28:03ZengNature PortfolioScientific Reports2045-23222025-04-0115111210.1038/s41598-025-97670-4Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosisJiahui Feng0Zheng Gong1Jialing Yang2Yuting Mo3Fengqian Song4Department of Gastroenterology, Loudi Central HospitalDepartment of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and TechnologySchool of Basic Medical Sciences, Nanjing Medical UniversityDepartment of Geriatrics, Zhongnan Hospital of Wuhan UniversityDepartment of Gastroenterology, Loudi Central HospitalAbstract Non-alcoholic fatty liver disease (NAFLD) affects about 25% of adults worldwide. Its advanced form, non-alcoholic steatohepatitis (NASH), is a major cause of liver fibrosis, but there are no non-invasive tests for diagnosing or preventing it. In our study, we analyzed data from multiple sources to find crucial genes linked to NASH fibrosis. We built diagnostic models using 103 machine learning algorithms and validated them with two external datasets. All models performed well, with the best one (RF + Enet[alpha = 0.6]) achieving an average AUC of 0.822. This model used five key genes: LUM, COL1A2, THBS2, COL5A2, and NTS. Our findings show that these genes are important in collagen and extracellular matrix pathways, shedding light on how NASH progresses to liver fibrosis. We also found that certain immune cells, like M1 macrophages, are involved in this process. This study provides a reliable diagnostic tool for assessing fibrosis risk in NASH patients and suggests potential for immunotherapy, laying a foundation for future treatments.https://doi.org/10.1038/s41598-025-97670-4Machine learningNASHFibrosisBiomarkersImmune microenvironment |
| spellingShingle | Jiahui Feng Zheng Gong Jialing Yang Yuting Mo Fengqian Song Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosis Scientific Reports Machine learning NASH Fibrosis Biomarkers Immune microenvironment |
| title | Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosis |
| title_full | Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosis |
| title_fullStr | Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosis |
| title_full_unstemmed | Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosis |
| title_short | Machine learning-based integration reveals reliable biomarkers and potential mechanisms of NASH progression to fibrosis |
| title_sort | machine learning based integration reveals reliable biomarkers and potential mechanisms of nash progression to fibrosis |
| topic | Machine learning NASH Fibrosis Biomarkers Immune microenvironment |
| url | https://doi.org/10.1038/s41598-025-97670-4 |
| work_keys_str_mv | AT jiahuifeng machinelearningbasedintegrationrevealsreliablebiomarkersandpotentialmechanismsofnashprogressiontofibrosis AT zhenggong machinelearningbasedintegrationrevealsreliablebiomarkersandpotentialmechanismsofnashprogressiontofibrosis AT jialingyang machinelearningbasedintegrationrevealsreliablebiomarkersandpotentialmechanismsofnashprogressiontofibrosis AT yutingmo machinelearningbasedintegrationrevealsreliablebiomarkersandpotentialmechanismsofnashprogressiontofibrosis AT fengqiansong machinelearningbasedintegrationrevealsreliablebiomarkersandpotentialmechanismsofnashprogressiontofibrosis |