Liang QY, Wang J, Yang YF, Zhao K, Luo RL, Tian Y, Li FX. Machine learning to identify potential biomarkers for sarcopenia in liver cirrhosis. World J Hepatol 2025; 17(6): 105332 [DOI: 10.4254/wjh.v17.i6.105332]
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Hepatol. Jun 27, 2025; 17(6): 105332 Published online Jun 27, 2025. doi: 10.4254/wjh.v17.i6.105332
Machine learning to identify potential biomarkers for sarcopenia in liver cirrhosis
Qian-Yu Liang, Jun Wang, Yun-Feng Yang, Kai Zhao, Rui-Li Luo, Ye Tian, Feng-Xia Li
Qian-Yu Liang, Jun Wang, Yun-Feng Yang, Kai Zhao, Rui-Li Luo, Ye Tian, Feng-Xia Li, Department of Gastroenterology, Shanxi Provincial People's Hospital, Taiyuan 030000, Shanxi Province, China
Author contributions: Liang QL and Li FX performed the experiments and drafted and revised the manuscript; Wang J, Yang YF, Zhao K, Luo RL, and Tian Y made substantial contributions to the conception and design of the work; all of the authors read and approved the final version of the manuscript to be published.
Supported by The Medical Key Science and Technology Project of Shanxi Province, No. 2020xm23.
Institutional review board statement: This study conformed to the ethical guidelines of the Declaration of Helsinki as reflected in a priori approval by Shanxi Provincial People's Hospital.
Conflict-of-interest statement: The authors declare that they have no competing interests.
Data sharing statement: The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Received: January 20, 2025 Revised: March 30, 2025 Accepted: April 27, 2025 Published online: June 27, 2025 Processing time: 158 Days and 10.2 Hours
Abstract
BACKGROUND
The prevalence of sarcopenia progressively increases with as liver function deteriorates. Muscle wasting has been shown to independently predict adverse outcomes in liver cirrhosis patients.
AIM
To screen effective biomarkers for sarcopenia in liver cirrhosis.
METHODS
Untargeted metabolomics were performed on serum from 62 liver cirrhosis patients, including 41 with sarcopenia and 21 without sarcopenia. Candidate metabolite biomarkers were screened based on three machine-learning algorithms. The diagnostic or predictive value of potential biomarkers was evaluated by drawing receiver operating characteristic curves.
RESULTS
A total of 60 differential metabolites between cirrhotic sarcopenia and the non-sarcopenia group were identified. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed differential metabolites primarily involved in glycerophospholipid metabolism, alpha-linolenic acid metabolism, retrograde endocannabinoid signaling, and choline metabolism in cancer. Finally, four potential biomarkers were screened through machine learning algorithms, namely N-Acetylcarnosine, 2-Stearylcitrate, CerP (d18:1/12:0), and 3-Methyl-alpha-ionylacetate. Among these, N-Acetylcarnosine can provide better diagnostic accuracy.
CONCLUSION
This study unveiled different plasma metabolic profiles of liver cirrhosis patients with and without sarcopenia. These valuable biomarkers have the potential to improve the prognosis of liver patients with cirrhosis by early detection or prediction of sarcopenia.
Core Tip: This study unveiled different plasma metabolic profiles of liver cirrhosis patients with and without sarcopenia, which may identify valuable biomarkers for the early detection and prognosis prediction of the disease.
