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...
Gespeichert in:
Veröffentlicht in: | Cancer biomarkers : section A of Disease markers 2024-01, Vol.39 (1), p.37-48 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 48 |
---|---|
container_issue | 1 |
container_start_page | 37 |
container_title | Cancer biomarkers : section A of Disease markers |
container_volume | 39 |
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. |
doi_str_mv | 10.3233/CBM-220421 |
format | Article |
fullrecord | <record><control><sourceid>proquest_pubme</sourceid><recordid>TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10977431</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2844093361</sourcerecordid><originalsourceid>FETCH-LOGICAL-c366t-8d7f21c23ec996787ee1f74f1f45802b74d4703889742b2dcb26220916c483793</originalsourceid><addsrcrecordid>eNpdkstu1TAQhiMEoqWw4QGQJTYIKeBb4niFyhE3qYgNrK2JMzlx5dgHO0HqS_GMOKRUwMpj-fv_Gc9MVT1l9JXgQrw-vP1cc04lZ_eqc9appu4aze-XuFGypqwRZ9WjnK8plYJx_bA6E6rhnOnmvPp5GcDfZJdJHIn1LjgLnmR3DG4sYbBIIAxkjh7t6iERO0ECu2ByeXH2t2zGZXIxuIBbCH30Ls-7DGyKpwmOWCf0sOBATrAUccjEBTJhuUWL3u_WkKwLcQbSQy5oDJvBtPl6hFQSHB9XD0bwGZ_cnhfVt_fvvh4-1ldfPnw6XF7VVrTtUneDGjmzXKDVulWdQmSjkiMbZdNR3is5SEVF12klec8H2_O2dFCz1spOKC0uqje772ntZxwshiWBN6fkZkg3JoIz_74EN5lj_GEY1UqVNheHF7cOKX5fMS9mdnn7KgSMaza8k5JqIdoNff4feh3XVOZSKM0aRrmQG_Vyp0pLc0443lXDqNn2wJQ9MPseFPjZ3_XfoX8GL34BO2WxdA</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2915102341</pqid></control><display><type>article</type><title>Analysis of clinical significance and molecular characteristics of methionine metabolism and macrophage-related patterns in hepatocellular carcinoma based on machine learning</title><source>MEDLINE</source><source>Sage Journals GOLD Open Access 2024</source><creator>Wen, Diguang ; Wang, Shuling ; Yu, Jiajian ; Yu, Ting ; Liu, Zuojin ; Li, Yue</creator><creatorcontrib>Wen, Diguang ; Wang, Shuling ; Yu, Jiajian ; Yu, Ting ; Liu, Zuojin ; Li, Yue</creatorcontrib><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.</description><identifier>ISSN: 1574-0153</identifier><identifier>EISSN: 1875-8592</identifier><identifier>DOI: 10.3233/CBM-220421</identifier><identifier>PMID: 37522195</identifier><language>eng</language><publisher>Netherlands: IOS Press BV</publisher><subject>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</subject><ispartof>Cancer biomarkers : section A of Disease markers, 2024-01, Vol.39 (1), p.37-48</ispartof><rights>Copyright IOS Press BV 2024</rights><rights>2024 – The authors. Published by IOS Press. 2024</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c366t-8d7f21c23ec996787ee1f74f1f45802b74d4703889742b2dcb26220916c483793</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,27922,27923</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37522195$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wen, Diguang</creatorcontrib><creatorcontrib>Wang, Shuling</creatorcontrib><creatorcontrib>Yu, Jiajian</creatorcontrib><creatorcontrib>Yu, Ting</creatorcontrib><creatorcontrib>Liu, Zuojin</creatorcontrib><creatorcontrib>Li, Yue</creatorcontrib><title>Analysis of clinical significance and molecular characteristics of methionine metabolism and macrophage-related patterns in hepatocellular carcinoma based on machine learning</title><title>Cancer biomarkers : section A of Disease markers</title><addtitle>Cancer Biomark</addtitle><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.</description><subject>Algorithms</subject><subject>Bioinformatics</subject><subject>Carcinoma, Hepatocellular - genetics</subject><subject>Clinical Relevance</subject><subject>Datasets</subject><subject>Diagnosis</subject><subject>Diagnostic systems</subject><subject>DNA microarrays</subject><subject>Drug development</subject><subject>Gene expression</subject><subject>Hepatocellular carcinoma</subject><subject>Humans</subject><subject>Infiltration</subject><subject>Learning algorithms</subject><subject>Liver cancer</subject><subject>Liver Neoplasms - genetics</subject><subject>Machine Learning</subject><subject>Macrophages</subject><subject>Metabolism</subject><subject>Metastases</subject><subject>Methionine</subject><subject>Prognosis</subject><subject>Racemethionine</subject><subject>Regression analysis</subject><subject>RNA, Messenger</subject><subject>Tumors</subject><issn>1574-0153</issn><issn>1875-8592</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkstu1TAQhiMEoqWw4QGQJTYIKeBb4niFyhE3qYgNrK2JMzlx5dgHO0HqS_GMOKRUwMpj-fv_Gc9MVT1l9JXgQrw-vP1cc04lZ_eqc9appu4aze-XuFGypqwRZ9WjnK8plYJx_bA6E6rhnOnmvPp5GcDfZJdJHIn1LjgLnmR3DG4sYbBIIAxkjh7t6iERO0ECu2ByeXH2t2zGZXIxuIBbCH30Ls-7DGyKpwmOWCf0sOBATrAUccjEBTJhuUWL3u_WkKwLcQbSQy5oDJvBtPl6hFQSHB9XD0bwGZ_cnhfVt_fvvh4-1ldfPnw6XF7VVrTtUneDGjmzXKDVulWdQmSjkiMbZdNR3is5SEVF12klec8H2_O2dFCz1spOKC0uqje772ntZxwshiWBN6fkZkg3JoIz_74EN5lj_GEY1UqVNheHF7cOKX5fMS9mdnn7KgSMaza8k5JqIdoNff4feh3XVOZSKM0aRrmQG_Vyp0pLc0443lXDqNn2wJQ9MPseFPjZ3_XfoX8GL34BO2WxdA</recordid><startdate>20240101</startdate><enddate>20240101</enddate><creator>Wen, Diguang</creator><creator>Wang, Shuling</creator><creator>Yu, Jiajian</creator><creator>Yu, Ting</creator><creator>Liu, Zuojin</creator><creator>Li, Yue</creator><general>IOS Press BV</general><general>IOS Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TM</scope><scope>7TO</scope><scope>7U7</scope><scope>C1K</scope><scope>H94</scope><scope>K9.</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20240101</creationdate><title>Analysis of clinical significance and molecular characteristics of methionine metabolism and macrophage-related patterns in hepatocellular carcinoma based on machine learning</title><author>Wen, Diguang ; Wang, Shuling ; Yu, Jiajian ; Yu, Ting ; Liu, Zuojin ; Li, Yue</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c366t-8d7f21c23ec996787ee1f74f1f45802b74d4703889742b2dcb26220916c483793</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Algorithms</topic><topic>Bioinformatics</topic><topic>Carcinoma, Hepatocellular - genetics</topic><topic>Clinical Relevance</topic><topic>Datasets</topic><topic>Diagnosis</topic><topic>Diagnostic systems</topic><topic>DNA microarrays</topic><topic>Drug development</topic><topic>Gene expression</topic><topic>Hepatocellular carcinoma</topic><topic>Humans</topic><topic>Infiltration</topic><topic>Learning algorithms</topic><topic>Liver cancer</topic><topic>Liver Neoplasms - genetics</topic><topic>Machine Learning</topic><topic>Macrophages</topic><topic>Metabolism</topic><topic>Metastases</topic><topic>Methionine</topic><topic>Prognosis</topic><topic>Racemethionine</topic><topic>Regression analysis</topic><topic>RNA, Messenger</topic><topic>Tumors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wen, Diguang</creatorcontrib><creatorcontrib>Wang, Shuling</creatorcontrib><creatorcontrib>Yu, Jiajian</creatorcontrib><creatorcontrib>Yu, Ting</creatorcontrib><creatorcontrib>Liu, Zuojin</creatorcontrib><creatorcontrib>Li, Yue</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Nucleic Acids Abstracts</collection><collection>Oncogenes and Growth Factors Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Cancer biomarkers : section A of Disease markers</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wen, Diguang</au><au>Wang, Shuling</au><au>Yu, Jiajian</au><au>Yu, Ting</au><au>Liu, Zuojin</au><au>Li, Yue</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Analysis of clinical significance and molecular characteristics of methionine metabolism and macrophage-related patterns in hepatocellular carcinoma based on machine learning</atitle><jtitle>Cancer biomarkers : section A of Disease markers</jtitle><addtitle>Cancer Biomark</addtitle><date>2024-01-01</date><risdate>2024</risdate><volume>39</volume><issue>1</issue><spage>37</spage><epage>48</epage><pages>37-48</pages><issn>1574-0153</issn><eissn>1875-8592</eissn><abstract>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.</abstract><cop>Netherlands</cop><pub>IOS Press BV</pub><pmid>37522195</pmid><doi>10.3233/CBM-220421</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1574-0153 |
ispartof | Cancer biomarkers : section A of Disease markers, 2024-01, Vol.39 (1), p.37-48 |
issn | 1574-0153 1875-8592 |
language | eng |
recordid | cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_10977431 |
source | MEDLINE; Sage Journals GOLD Open Access 2024 |
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 |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T15%3A28%3A37IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Analysis%20of%20clinical%20significance%20and%20molecular%20characteristics%20of%20methionine%20metabolism%20and%20macrophage-related%20patterns%20in%20hepatocellular%20carcinoma%20based%20on%20machine%20learning&rft.jtitle=Cancer%20biomarkers%20:%20section%20A%20of%20Disease%20markers&rft.au=Wen,%20Diguang&rft.date=2024-01-01&rft.volume=39&rft.issue=1&rft.spage=37&rft.epage=48&rft.pages=37-48&rft.issn=1574-0153&rft.eissn=1875-8592&rft_id=info:doi/10.3233/CBM-220421&rft_dat=%3Cproquest_pubme%3E2844093361%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2915102341&rft_id=info:pmid/37522195&rfr_iscdi=true |