Bioinformatic analysis of molecular expression patterns during the development and progression of metabolic dysfunction-associated steatotic liver disease (MASLD)
Abstract The global incidence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to rise, primarily driven by the escalating obesity epidemic worldwide. MASLD, a spectrum of liver disorders, can progress to more severe conditions, metabolic dysfunction-associated steatohep...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-90744-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849767409205903360 |
|---|---|
| author | Yuanfeng Lan Ran Song Duiping Feng Junqi He |
| author_facet | Yuanfeng Lan Ran Song Duiping Feng Junqi He |
| author_sort | Yuanfeng Lan |
| collection | DOAJ |
| description | Abstract The global incidence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to rise, primarily driven by the escalating obesity epidemic worldwide. MASLD, a spectrum of liver disorders, can progress to more severe conditions, metabolic dysfunction-associated steatohepatitis (MASH), ultimately culminating in hepatocellular carcinoma (HCC). Given the complex nature of MASLD, there is an urgent need to develop robust risk prediction models and design specialized cancer screening initiatives tailored specifically for individuals with MASLD. This study aimed to identify genes exhibiting trending expression patterns that could serve as potential biomarkers or therapeutic targets. Our approach involved analyzing expression patterns across the five stages of MASLD development and progression. Notably, we introduced an innovative two-phase classification—MASLD occurrence and MASLD progression—instead of categorizing differentially expressed genes (DEGs) into multiple types. Leveraging LASSO regression models, we demonstrated their relatively strong capability to predict and distinguish both MASLD occurrence and progression. Furthermore, our analysis identified CYP7A1 and TNFRSF12A as significantly associated with the prognosis of MASLD progressing to HCC. These findings contribute to the understanding of gene expression dynamics in MASLD and may pave the way for the development of effective prognostic tools and targeted therapies in the realm of liver disease. |
| format | Article |
| id | doaj-art-15f5144f61374be994480c7d53cc7e32 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-15f5144f61374be994480c7d53cc7e322025-08-20T03:04:12ZengNature PortfolioScientific Reports2045-23222025-03-0115111910.1038/s41598-025-90744-3Bioinformatic analysis of molecular expression patterns during the development and progression of metabolic dysfunction-associated steatotic liver disease (MASLD)Yuanfeng Lan0Ran Song1Duiping Feng2Junqi He3Beijing Key Laboratory for Tumor Invasion and Metastasis, Department of Biochemistry and Molecular Biology, Capital Medical UniversityBeijing Key Laboratory for Tumor Invasion and Metastasis, Department of Biochemistry and Molecular Biology, Capital Medical UniversityDepartment of Interventional Radiology, First Hospital of Shanxi Medical UniversityBeijing Key Laboratory for Tumor Invasion and Metastasis, Department of Biochemistry and Molecular Biology, Capital Medical UniversityAbstract The global incidence of metabolic dysfunction-associated steatotic liver disease (MASLD) continues to rise, primarily driven by the escalating obesity epidemic worldwide. MASLD, a spectrum of liver disorders, can progress to more severe conditions, metabolic dysfunction-associated steatohepatitis (MASH), ultimately culminating in hepatocellular carcinoma (HCC). Given the complex nature of MASLD, there is an urgent need to develop robust risk prediction models and design specialized cancer screening initiatives tailored specifically for individuals with MASLD. This study aimed to identify genes exhibiting trending expression patterns that could serve as potential biomarkers or therapeutic targets. Our approach involved analyzing expression patterns across the five stages of MASLD development and progression. Notably, we introduced an innovative two-phase classification—MASLD occurrence and MASLD progression—instead of categorizing differentially expressed genes (DEGs) into multiple types. Leveraging LASSO regression models, we demonstrated their relatively strong capability to predict and distinguish both MASLD occurrence and progression. Furthermore, our analysis identified CYP7A1 and TNFRSF12A as significantly associated with the prognosis of MASLD progressing to HCC. These findings contribute to the understanding of gene expression dynamics in MASLD and may pave the way for the development of effective prognostic tools and targeted therapies in the realm of liver disease.https://doi.org/10.1038/s41598-025-90744-3Metabolic dysfunction-associated steatotic liver disease (MASLD)Hepatocellular carcinoma (HCC)Expression pattern cluster analysisLASSO (least absolute shrinkage and selection operator)Prognostic implications |
| spellingShingle | Yuanfeng Lan Ran Song Duiping Feng Junqi He Bioinformatic analysis of molecular expression patterns during the development and progression of metabolic dysfunction-associated steatotic liver disease (MASLD) Scientific Reports Metabolic dysfunction-associated steatotic liver disease (MASLD) Hepatocellular carcinoma (HCC) Expression pattern cluster analysis LASSO (least absolute shrinkage and selection operator) Prognostic implications |
| title | Bioinformatic analysis of molecular expression patterns during the development and progression of metabolic dysfunction-associated steatotic liver disease (MASLD) |
| title_full | Bioinformatic analysis of molecular expression patterns during the development and progression of metabolic dysfunction-associated steatotic liver disease (MASLD) |
| title_fullStr | Bioinformatic analysis of molecular expression patterns during the development and progression of metabolic dysfunction-associated steatotic liver disease (MASLD) |
| title_full_unstemmed | Bioinformatic analysis of molecular expression patterns during the development and progression of metabolic dysfunction-associated steatotic liver disease (MASLD) |
| title_short | Bioinformatic analysis of molecular expression patterns during the development and progression of metabolic dysfunction-associated steatotic liver disease (MASLD) |
| title_sort | bioinformatic analysis of molecular expression patterns during the development and progression of metabolic dysfunction associated steatotic liver disease masld |
| topic | Metabolic dysfunction-associated steatotic liver disease (MASLD) Hepatocellular carcinoma (HCC) Expression pattern cluster analysis LASSO (least absolute shrinkage and selection operator) Prognostic implications |
| url | https://doi.org/10.1038/s41598-025-90744-3 |
| work_keys_str_mv | AT yuanfenglan bioinformaticanalysisofmolecularexpressionpatternsduringthedevelopmentandprogressionofmetabolicdysfunctionassociatedsteatoticliverdiseasemasld AT ransong bioinformaticanalysisofmolecularexpressionpatternsduringthedevelopmentandprogressionofmetabolicdysfunctionassociatedsteatoticliverdiseasemasld AT duipingfeng bioinformaticanalysisofmolecularexpressionpatternsduringthedevelopmentandprogressionofmetabolicdysfunctionassociatedsteatoticliverdiseasemasld AT junqihe bioinformaticanalysisofmolecularexpressionpatternsduringthedevelopmentandprogressionofmetabolicdysfunctionassociatedsteatoticliverdiseasemasld |