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©The Author(s) 2025.
World J Hepatol. Jun 27, 2025; 17(6): 105332
Published online Jun 27, 2025. doi: 10.4254/wjh.v17.i6.105332
Published online Jun 27, 2025. doi: 10.4254/wjh.v17.i6.105332
Figure 1 Analysis of serum metabolic profiling between sarcopenic and non-sarcopenic cirrhotic patients.
A: Orthogonal partial least squares discriminant analysis (OPLS-DA) of metabolites in positive mode; B: Volcano plot of metabolites by univariate analysis in positive mode; C: OPLS-DA of metabolites in negative mode; D: Volcano plot of metabolites by univariate analysis in negative mode; E: Metabolite classes with significantly different concentrations between sarcopenic and non-sarcopenic cirrhotic patients; F: Enriched metabolic pathways of differential metabolites. VIP: Variable importance in the projection.
- Citation: 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
- URL: https://www.wjgnet.com/1948-5182/full/v17/i6/105332.htm
- DOI: https://dx.doi.org/10.4254/wjh.v17.i6.105332