Analysis of clinical significance and molecular characteristics of methionine metabolism and macrophage-related patterns in hepatocellular carcinoma based on machine learning

Increasing 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 hepatocell...

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Veröffentlicht in:Cancer biomarkers : section A of Disease markers 2024-01, Vol.39 (1), p.37-48
Hauptverfasser: Wen, Diguang, Wang, Shuling, Yu, Jiajian, Yu, Ting, Liu, Zuojin, Li, Yue
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container_title Cancer biomarkers : section A of Disease markers
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creator Wen, Diguang
Wang, Shuling
Yu, Jiajian
Yu, Ting
Liu, Zuojin
Li, Yue
description Increasing 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. In 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. We 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. Methionine 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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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. In 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. We 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. 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subjects Algorithms
Bioinformatics
Carcinoma, Hepatocellular - genetics
Clinical Relevance
Datasets
Diagnosis
Diagnostic systems
DNA microarrays
Drug development
Gene expression
Hepatocellular carcinoma
Humans
Infiltration
Learning algorithms
Liver cancer
Liver Neoplasms - genetics
Machine Learning
Macrophages
Metabolism
Metastases
Methionine
Prognosis
Racemethionine
Regression analysis
RNA, Messenger
Tumors
title Analysis of clinical significance and molecular characteristics of methionine metabolism and macrophage-related patterns in hepatocellular carcinoma based on machine learning
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