Abstract
The pathophysiology of hepatocellular carcinoma (HCC) is prevalently related to genomic instability. However, research on the association of extensive genome instability lncRNA (GILnc) with the prognosis and immunotherapy of HCC remains scarce. We placed the top 25% of somatic mutations into the genetically unstable group and placed the bottom 25% of somatic mutations into the genetically stable group, and then to identify different expression of GILnc between the two groups. Then, LASSO was used to identify the most powerful prognostic GILnc, and a risk score for each patient was calculated according to the formula. Based on a computational frame, 245 different GILncs in HCC were identified. An eight GILnc model was successfully established to predict overall survival in HCC patients based on LASSO, then we divided HCC patients into high-risk and low-risk groups, and a significantly shorter overall survival in the high-risk group was observed compared to those in the low-risk group, and this was validated in GSE76427 and Tongji cohorts. GSEA revealed that the high-risk group was more likely to be enriched in cancer-specific pathways. Besides, the GILnc signature has greater prognostic significance than TP53 mutation status alone, and it is capable of identifying intermediate subtype groups existing with partial TP53 functionality in TP53 wild-type patients. Importantly, the high-risk group was associated with the therapeutic efficacy of PD-L1 blockade, suggesting that the development of potential drugs targeting these GILnc could aid the clinical benefits of immunotherapy. Finally, the GILnc signature model is better than the prediction performance of two recently published lncRNA signatures. In summary, we applied bioinformatics approaches to suggest that an eight GILnc model could serve as prognostic biomarkers to provide a novel direction to explore the pathogenesis of HCC.