[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128311-en":3,"doc-seo-128311-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128311,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Identifying metabolism-related genes in liver cancer through weighted gene co-expression network analysis and machine learning","Liver cancer is linked to metabolic dysregulation, motivating identification of metabolism-related prognostic biomarkers and therapeutic targets. Transcriptomic TCGA data were processed with EdgeR to detect differentially expressed genes, while WGCNA was used to uncover metabolism-associated gene modules. Random Forest, SVM, and LASSO refined marker genes, followed by GSEA/ssGSEA for pathway and immune interaction analysis; DGIdb screened candidate drugs and an external dataset GSE54236 was used for verification. RT-PCR in 10 paired samples and immune infiltration analyses supported the results.","TYPE Original Research PUBLISHED 24 September 2025 DOI 10.3389/fgene.2025.1654459  \nOPEN ACCESS  \nEDITED BY  \nMd Sadique Hussain,  \nUttaranchal University, India  \nREVIEWED BY  \nMudasir Maqbool,  \nUniversity of Kashmir, India  \nMd. Khokon Miah Akanda,  \nUniversity of Asia Pacific, Bangladesh  \n*CORRESPONDENCE  \nMingjiao Zhang,  \n [jndxzmj@163.com](jndxzmj@163.com)  \nRECEIVED 26 June 2025  \nACCEPTED 12 August 2025  \nPUBLISHED 24 September 2025  \nCITATION  \nWang T, Lai Z, Tang S, Lin L and Zhang M (2025) Identifying metabolism-related genes in liver cancer through weighted gene co-expression network analysis and machine learning.  \nFront. Genet. 16:1654459 .  \ndoi: 10.3389/fgene.2025.1654459  \nCOPYRIGHT  \n© 2025 Wang, Lai, Tang, Lin and Zhang. This isan open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nIdentifying metabolism-related genes in liver cancer through weighted gene co-expression network analysis and machine learning  \nTaorui Wang 1, Zijun Lai 2, Shengjun Tang 2, Lehang Lin 3 and Mingjiao Zhang 4*  \n1Faculty of Medicine, Macau University of Science and Technology, Taipa, China, 2Genetic Testing Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, China, 3Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA Medicine, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China, 4Department of Rheumatology and Immunology, the Third Affiliated Hospital of Southern Medical University, Guangzhou, China  \nObjective: As a leading cause of cancer-related mortality, liver cancer was associated with metabolic dysregulation. We aimed to identify metabolismrelated prognostic biomarkers and therapeutic targets.  \nMethods: Transcriptomic data from TCGA were analyzed using EdgeR to identify differentially expressed genes (DEGs) . WGCNA was applied to unveil the metabolism-related genes in liver cancer. Machine learning algorithms (RF, SVM, LASSO) refined marker genes. GSEA and ssGSEA were conducted to identify pathway associations and immune interactions of marker genes. DGIdb database predicted candidate therapeutics targeting these biomarkers. The independent queue (GSE54236) was verified as an external dataset. RT-PCR validated gene expression in clinical samples.  \nResults: A total of 234 metabolism-related genes were identified in liver cancer. Through undergoing machine learning by RF, SVM, and LASSO algorithms, seven marker genes (ACADS, ALDH8A1, COX4I2, CYP2C8, DBH, NDST3, and PLA2G6) were obtained. Except for PLA2G6, the other genes were correlated with the survival of patients with liver cancer and immune cells infiltration. Additionally, ACADS, ALDH8A1, CYP2C8, DBH, and NDST3 were downregulated, and COX4I2 was upregulated in dataset of GSE54236, which were consist with those in TCGA database. However, RT-PCR validation in 10 paired clinical samples confirmed significant downregulation of ACADS, ALDH8A1, COX4I2, CYP2C8, DBH, and NDST3 in tumor tissues (all P \u003C 0. 05) . Immune infiltration analysis revealed these genes might influence immune cell infiltration in the tumor microenvironment. And the candidate drugs were unveiled, including PAZOPANIB, SUMATRIPTAN, ETOPOSIDE, etc.  \nConclusion: The metabolism-related biomarkers ACADS, ALDH8A1, COX4I2, CYP2C8, DBH, and NDST3 demonstrated significant potential for predicting liver cancer prognosis and may serve as candidate therapeutic targets.  \nKEYWORDS  \nliver cancer, metabolism, machine learning, immune cells, therapy  \nFrontiers in Genetics 01 [fro","cbCaiontTBJo80sm","https://ap.wps.com/l/cbCaiontTBJo80sm","pdf",4466051,4,1,16,"English","en",105,"# Objective\n# Methods\n## Data processing and gene analysis\n## Machine learning and pathway/immune assessment\n## Validation and drug prediction\n# Results\n## Marker gene identification\n## Validation and expression patterns\n## Immune infiltration and therapeutic candidates\n# Conclusion","[{\"question\":\"What was the study objective in liver cancer?\",\"answer\":\"To identify metabolism-related prognostic biomarkers and potential therapeutic targets for liver cancer linked to metabolic dysregulation.\"},{\"question\":\"How were metabolism-related genes and marker genes identified?\",\"answer\":\"Differentially expressed genes from TCGA were analyzed with EdgeR, metabolism-related modules were revealed using WGCNA, and marker genes were refined using RF, SVM, and LASSO.\"},{\"question\":\"Which genes were proposed as key biomarkers and how was validation performed?\",\"answer\":\"Seven marker genes (ACADS, ALDH8A1, COX4I2, CYP2C8, DBH, NDST3, PLA2G6) were obtained; external validation used GSE54236, and RT-PCR in 10 paired clinical samples confirmed significant differential expression for ACADS, ALDH8A1, COX4I2, CYP2C8, DBH, and NDST3.\"}]","Identifying metabolism-related genes in liver cancer through weighted gene co-expression network analysis and machine learning | PDF",1785946780,40,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"identifying-metabolism-related-genes-in-liver-cancer-through-weighted-gene-co-expression-network-analysis-and-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/identifying-metabolism-related-genes-in-liver-cancer-through-weighted-gene-co-expression-network-analysis-and-machine-learning/128311/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-27","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What was the study objective in liver cancer?","Question",{"text":76,"@type":77},"To identify metabolism-related prognostic biomarkers and potential therapeutic targets for liver cancer linked to metabolic dysregulation.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were metabolism-related genes and marker genes identified?",{"text":81,"@type":77},"Differentially expressed genes from TCGA were analyzed with EdgeR, metabolism-related modules were revealed using WGCNA, and marker genes were refined using RF, SVM, and LASSO.",{"name":83,"@type":74,"acceptedAnswer":84},"Which genes were proposed as key biomarkers and how was validation performed?",{"text":85,"@type":77},"Seven marker genes (ACADS, ALDH8A1, COX4I2, CYP2C8, DBH, NDST3, PLA2G6) were obtained; external validation used GSE54236, and RT-PCR in 10 paired clinical samples confirmed significant differential expression for ACADS, ALDH8A1, COX4I2, CYP2C8, DBH, and NDST3.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":30,"slug":119},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]