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用于预测乙型肝炎病毒相关肝细胞癌预后的铜中毒相关lncrna新模型及LINC01269的实验验证
Authors Shi C, Sun Y, Sha L , Gu X
Received 31 July 2024
Accepted for publication 7 December 2024
Published 10 December 2024 Volume 2024:17 Pages 6009—6027
DOI https://doi.org/10.2147/IJGM.S489059
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 3
Editor who approved publication: Professor Hyam Leffert
Chuanbing Shi,1,* Yintao Sun,2,* Ling Sha,3 Xuefeng Gu4,5
1Department of Pathology, Nanjing Pukou People’s Hospital, Nanjing, Jiangsu, People’s Republic of China; 2Department of Imaging, The Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University, Changzhou, People’s Republic of China; 3Department of Neurology, Nanjing Drum Tower Hospital Affiliated to Nanjing University Medical School, Nanjing, People’s Republic of China; 4Department of Central Laboratory, Jurong Hospital Affiliated to Jiangsu University, Zhenjiang, Jiangsu, People’s Republic of China; 5Department of Infectious Diseases, Jurong Hospital Affiliated to Jiangsu University, Zhenjiang, Jiangsu, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xuefeng Gu, Department of Central Laboratory, Jurong Hospital Affiliated to Jiangsu University, 66 Ersheng Road, Jurong, Zhenjiang, Jiangsu, 212400, People’s Republic of China, Tel/Fax +86- 0511-87205671, Email guxuefeng@seu.edu.cn
Background: Hepatocellular carcinoma (HCC) triggered by Hepatitis B virus (HBV) remains a significant clinical challenge, necessitating novel therapeutic interventions. Copper ionophores, recognized for introducing an innovative type of programmed cell death termed cuproptosis, present promising potentials for cancer therapy. Nevertheless, The role of cuproptosis-related lncRNAs (CRLRs) in HBV-HCC has not been clearly elucidated.
Methods: This study utilised univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariable Cox regression analyses to establish a signature for CRLRs in HBV-HCC. This prognostic model was validated with an independent internal validation cohort, combined with clinical parameters, and used to construct a nomogram for patient survival predictions. Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) were employed to explore associated biological pathways. Additionally, a protein-protein interaction (PPI) network was developed, and implications for tumour mutational burden (TMB) and drug response were examined. A comprehensive bioinformatics analysis of these hub CRLRs was performed, followed by experimental validation through quantitative real-time PCR (qRT-PCR) and functional cellular assays.
Results: The nomogram showed high predictive accuracy for HBV-HCC patient survival. GO and GSEA analyses indicated that these lncRNAs are involved in pathways related to cancer and oestrogen metabolism. A PPI network consisting of 201 nodes and 568 edges was developed, and the TMB and drug response differed significantly between high- and low-risk groups. Analyses identified three hub CRLRs, SOS1-IT1, AC104695.3, and LINC01269, which were significantly differentially expressed in HCC tissues. In vitro, LINC01269 was found to enhance HCC cell proliferation, invasion, and migration.
Conclusion: The first systematic exploration of the roles of CRLRs in HBV-HCC demonstrates their critical involvement in the disease’s pathogenesis and possible therapeutic implication. The distinct expression patterns and significant biological pathways suggest that these lncRNAs may facilitate novel therapeutic targets.
Keywords: cuproptosis, HBV-HCC, proliferation, invasion, migration, LINC01269