Zhilan Zhang, Jie Zhou, Ruiru Huang, Xingxing Zhuang, Shoudong Ni
{"title":"Identification of CCNB1 as a biomarker for cellular senescence in hepatocellular carcinoma: a bioinformatics and experimental validation study.","authors":"Zhilan Zhang, Jie Zhou, Ruiru Huang, Xingxing Zhuang, Shoudong Ni","doi":"10.1007/s12672-025-02182-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Hepatocellular carcinoma (HCC), originating in the liver and often asymptomatic in early stages, frequently metastasises and recures post-surgery. Currently, reliable diagnostic biomarkers and therapeutic targets for HCC are lacking. This study investigates the influence of cellular senescence on HCC, employing bioinformatics analysis and in vitro experiments to identify potential biomarkers.</p><p><strong>Methods: </strong>We integrated data from GEO microarrays (GSE14520, GSE45267 and GSE64041) to analyse differentially expressed genes (DEGs) using the R package limma. WGCNA identified gene modules highly correlated to HCC. Then, ageing-highly related differentially expressed genes (AgHDEGs) were identified. Correlation analysis, GO and KEGG functional enrichment analysis, and gene co-expression network analysis further elucidated the functions of AgHDEGs. The STRING database identified hub AgHDEGs with CCNB1 subsequently evaluated for diagnostic value using ROC curve analysis. Additionally, we explored the correlation between CCNB1 and immune cells and assessed its biological functions via GSEA. Ultimately, the conclusions from bioinformatics analysis were confirmed via in vitro experiments, complemented by molecular docking simulations of gene-drug interactions.</p><p><strong>Results: </strong>Eight AgHDEGs (KPNA2, CCT3, CCNB1, RACGAP1, CDKN3, FEN1, MT1X and FOXM1) were identified. PPI network analysis highlighted CCNB1 as hub AgHDEGs with ROC analysis confirming its strong diagnostic potential. Analysis of immune infiltration revealed a significant correlation between CCNB1 and M0 macrophages. Subsequent studies showed CCNB1's critical role in regulating the cell cycle. Validation experiments illustrated an upregulation of CCNB1 expression in HCC, while inhibiting CCNB1 may reduce HepG2 cell proliferation by promoting cellular senescence. Moreover, molecular docking indicated CCNB1 as a potential therapeutic target.</p><p><strong>Conclusion: </strong>Our study underscores CCNB1's potential impact on HCC senescence and progression, suggesting its candidacy as a biomarker for HCC.</p>","PeriodicalId":11148,"journal":{"name":"Discover. Oncology","volume":"16 1","pages":"384"},"PeriodicalIF":2.8000,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11933616/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Discover. Oncology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12672-025-02182-2","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENDOCRINOLOGY & METABOLISM","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Hepatocellular carcinoma (HCC), originating in the liver and often asymptomatic in early stages, frequently metastasises and recures post-surgery. Currently, reliable diagnostic biomarkers and therapeutic targets for HCC are lacking. This study investigates the influence of cellular senescence on HCC, employing bioinformatics analysis and in vitro experiments to identify potential biomarkers.
Methods: We integrated data from GEO microarrays (GSE14520, GSE45267 and GSE64041) to analyse differentially expressed genes (DEGs) using the R package limma. WGCNA identified gene modules highly correlated to HCC. Then, ageing-highly related differentially expressed genes (AgHDEGs) were identified. Correlation analysis, GO and KEGG functional enrichment analysis, and gene co-expression network analysis further elucidated the functions of AgHDEGs. The STRING database identified hub AgHDEGs with CCNB1 subsequently evaluated for diagnostic value using ROC curve analysis. Additionally, we explored the correlation between CCNB1 and immune cells and assessed its biological functions via GSEA. Ultimately, the conclusions from bioinformatics analysis were confirmed via in vitro experiments, complemented by molecular docking simulations of gene-drug interactions.
Results: Eight AgHDEGs (KPNA2, CCT3, CCNB1, RACGAP1, CDKN3, FEN1, MT1X and FOXM1) were identified. PPI network analysis highlighted CCNB1 as hub AgHDEGs with ROC analysis confirming its strong diagnostic potential. Analysis of immune infiltration revealed a significant correlation between CCNB1 and M0 macrophages. Subsequent studies showed CCNB1's critical role in regulating the cell cycle. Validation experiments illustrated an upregulation of CCNB1 expression in HCC, while inhibiting CCNB1 may reduce HepG2 cell proliferation by promoting cellular senescence. Moreover, molecular docking indicated CCNB1 as a potential therapeutic target.
Conclusion: Our study underscores CCNB1's potential impact on HCC senescence and progression, suggesting its candidacy as a biomarker for HCC.