Background:Hepatocellular carcinoma (HCC) is an aggressive carcinoma with genome instability. Long non-coding RNAs (LncRNAs) have been functionally associated with genomic instability in cancers. However, the identi cation and prognostic value of lncRNAs related to genome instability have not been explored in hepatocellular carcinoma. In this study, we aim to identify a genomic instability-related lncRNA signature for predicting prognosis and the e cacy of immunotherapy in HCC patients. Methods:According to the somatic mutation and transcript data of 364 patients with HCC, we determined differentially expressed genome instability-related lncRNAs (GInLncRNAs). Gene Ontology (GO) enrichment analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed the potential functions of genes co-expressed with those lncRNAs involved in cancer development and immune function. We further determined a genome instability-related lncRNA signature (GInLncSig) through Cox regression analysis and LASSO regression analysis. Thereafter, we performed correlation analyses with mutations, clinical strati cation analyses, survival analysis to evaluate GInLncSig predictive function. Subsequently, we construct a nomogram model for prognostic assessments of patients with HCC. Finally, we performed Immunocytes in ltration analysis, gene set enrichment analysis (ssGSEA) of immunity circle associated pathways, and T cell-in amed score to explore GInLncSig's potential value of guiding immunotherapy. Results:We identi ed 11 independent prognosis-associated GInLncRNAs (AC002511.

Background:Hepatocellular carcinoma (HCC) is an aggressive carcinoma with genome instability. Long non-coding RNAs (LncRNAs) have been functionally associated with genomic instability in cancers. However, the identification and prognostic value of lncRNAs related to genome instability have not been explored in hepatocellular carcinoma. In this study, we aim to identify a genomic instability-related lncRNA signature for predicting prognosis and the efficacy of immunotherapy in HCC patients.Methods:According to the somatic mutation and transcript data of 364 patients with HCC, we determined differentially expressed genome instability-related lncRNAs (GInLncRNAs). Gene Ontology (GO) enrichment analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed the potential functions of genes co-expressed with those lncRNAs involved in cancer development and immune function. We further determined a genome instability-related lncRNA signature (GInLncSig) through Cox regression analysis and LASSO regression analysis. Thereafter, we performed correlation analyses with mutations, clinical stratification analyses, survival analysis to evaluate GInLncSig predictive function. Subsequently, we construct a nomogram model for prognostic assessments of patients with HCC. Finally, we performed Immunocytes infiltration analysis, gene set enrichment analysis (ssGSEA) of immunity circle associated pathways, and T cell-inflamed score to explore GInLncSig’s potential value of guiding immunotherapy.Results:We identified 11 independent prognosis-associated GInLncRNAs (AC002511.2, LINC00501, LINC02055, LINC02714, LINC01508, LOC105371967, RP11_96A15.1, RP11_305F18.1, RP11_342M1.3, RP11_432J24.3, U95743.1) to construct a GInLncSig. According to the risk score calculated by GInLncSig, the high-risk group was characterized by a higher somatic mutation count, significantly poorer clinical prognosis, higher T cell-inflamed score, and specific tumor immune infiltration status compared with the low-risk group. Furthermore, we constructed a nomogram model to improve the reliability and clinical utility for predicting the prognosis of patients with HCC. Conclusion：our study established a reliable prognostic prediction signature that could be a tool for prognosis prediction and a promising predictive biomarker of immunotherapy in hepatocellular carcinoma.