A New Cuproptosis-Related lncRNAs Model for Predicting the Prognosis of Hepatitis B Virus-Associated Hepatocellular Carcinoma and Experimental Validation of LINC01269

Chuanbing Shi; Yintao Sun; Ling Sha; Xuefeng Gu

doi:10.2147/IJGM.S489059

A New Cuproptosis-Related lncRNAs Model for Predicting the Prognosis of Hepatitis B Virus-Associated Hepatocellular Carcinoma and Experimental Validation of LINC01269

Int J Gen Med. 2024 Dec 10:17:6009-6027. doi: 10.2147/IJGM.S489059. eCollection 2024.

Authors

Chuanbing Shi^#¹, Yintao Sun^#², Ling Sha³, Xuefeng Gu^{4

5}

Affiliations

¹ Department of Pathology, Nanjing Pukou People's Hospital, Nanjing, Jiangsu, People's Republic of China.
² Department of Imaging, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou, People's Republic of China.
³ Department of Neurology, Nanjing Drum Tower Hospital Affiliated to Nanjing University Medical School, Nanjing, People's Republic of China.
⁴ Department of Central Laboratory, Jurong Hospital Affiliated to Jiangsu University, Zhenjiang, Jiangsu, People's Republic of China.
⁵ Department of Infectious Diseases, Jurong Hospital Affiliated to Jiangsu University, Zhenjiang, Jiangsu, People's Republic of China.

^# Contributed equally.

Abstract

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: HBV-HCC; LINC01269; cuproptosis; invasion; migration; proliferation.

Grants and funding

This study was supported by Project of Modern Hospital Management and Development Institute, Nanjing University, Aid project of Nanjing Drum Tower Hospital Health, Education & Research Foundation (project no. NDYG2021046), and Nursing Research Project at Nanjing Drum Tower Hospital (2021) (project no. ZSA491-3).