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

作者全名："Wen, Diguang; Wang, Shuling; Yu, Jiajian; Yu, Ting; Liu, Zuojin; Li, Yue"

作者地址："[Wen, Diguang; Liu, Zuojin; Li, Yue] Chongqing Med Univ, Hepatobiliary Surg Dept, Affiliated Hosp 2, Chongqing 400010, Peoples R China; [Wen, Diguang; Wang, Shuling] Chongqing Med Univ, Dept Gastroenterol, Affiliated Hosp 2, Chongqing, Peoples R China; [Yu, Jiajian; Yu, Ting] Chongqing Univ, Dept Hepatol, Filing Hosp, Chongqing, Peoples R China"

通信作者："Li, Y (通讯作者)，Chongqing Med Univ, Hepatobiliary Surg Dept, Affiliated Hosp 2, Chongqing 400010, Peoples R China."

来源：CANCER BIOMARKERS

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001168492800004

JCR分区：Q3

影响因子：2.2

年份：2024

卷号：39

期号：1

开始页：37

结束页：48

文献类型：Article

关键词：HCC; macrophages; immune microenvironment; bioinformatics; methionine metabolism; machine learning

摘要："BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: Methionine metabolism is closely related to tumour-associated macrophage infiltration in hepatocellular carcinoma and can help in the clinical diagnosis and prognosis of HCC."

基金机构："National Science Foundation of China [8207034238, 81470899, 81702357]; Natural Science Foundation of Chongqing, China [cstc2020jcyj-msxmX0902, cstc2019jcyj-msxm X0659, cstc2020jcyj-msxmX0655, cstc2021jcyj-bsh X0193]"

基金资助正文："This project was supported by the National Science Foundation of China (Nos. 8207034238, 81470899, and 81702357); Natural Science Foundation of Chongqing, China (cstc2020jcyj-msxmX0902; cstc2019jcyj-msxm X0659; cstc2020jcyj-msxmX0655; cstc2021jcyj-bsh X0193.)."