ArticleCancer biomarkers : section A of Disease markers2024
Analysis of clinical significance and molecular characteristics of methionine metabolism and macrophage-related patterns in hepatocellular carcinoma based on machine learning.
Article in Cancer biomarkers : section A of Disease markers, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 5 citations in OpenAlex.
- Dynamic remodeling of amino acid metabolism in tumor-associated macrophages: Fueling immunosuppression, reshaping tumor niches, and unlocking metabolic checkpoints.Journal of advanced research · 2026Review
- The significant role of amino acid metabolic reprogramming in cancer.Cell communication and signaling : CCS · 2024Review
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Authors and funding
6 authors at 2 institutions in 1 country.
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Abstract
backgroundIncreasing evidence has indicated that abnormal methionine metabolic activity and tumour-associated macrophage infiltration are correlated with hepatocarcinogenesis. However, the relationship between methionine metabolic activity and tumour-associated macrophage infiltration is unclear in hepatocellular carcinoma, and it contributes to the occurrence and clinical outcome of hepatocellular carcinoma (HCC). Thus, we systematically analysed the expression patterns of methionine metabolism and macrophage infiltration in hepatocellular carcinoma using bioinformatics and machine learning methods and constructed novel diagnostic and prognostic models of HCC.
methodsIn this study, we first mined the four largest HCC mRNA microarray datasets with patient clinical data in the GEO database, including 880 tissue mRNA expression datasets. Using GSVA analysis and the CIBERSORT and EPIC algorithms, we quantified the methionine metabolic activity and macrophage infiltration degree of each sample. WGCNA was used to identify the gene modules most related to methionine metabolism and tumour-associated macrophage infiltration in HCC. The KNN algorithm was used to cluster gene expression patterns in HCC. Random forest, logistic regression, Cox regression analysis and other algorithms were used to construct the diagnosis and prognosis model of HCC. The above bioinformatics analysis results were also verified by independent datasets (TCGA-LIHC, ICGC-JP and CPTAC datasets) and immunohistochemical fluorescence based on our external HCC panel. Furthermore, we carried out pancancer analysis to verify the specificity of the above model and screened a wide range of drug candidates.
resultsWe identified two methionine metabolism and macrophage infiltration expression patterns, and their prognoses were different in hepatocellular carcinoma. We constructed novel diagnostic and prognostic models of hepatocellular carcinoma with good diagnostic efficacy and differentiation ability.
conclusionsMethionine metabolism is closely related to tumour-associated macrophage infiltration in hepatocellular carcinoma and can help in the clinical diagnosis and prognosis of HCC.
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