Table1_Development and Validation of Genome Instability-Associated lncRNAs to Predict Prognosis and Immunotherapy of Patients With Hepatocellular Carcinoma.xlsx

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.

肝细胞癌（hepatocellular carcinoma, HCC）的病理生理学过程普遍与基因组不稳定密切相关，但目前针对广泛基因组不稳定长链非编码RNA（extensive genome instability lncRNA, GILnc）与HCC预后及免疫治疗关联的研究仍较为匮乏。本研究将体细胞突变数目位居前25%的样本划分为基因组不稳定组，将体细胞突变数目位居后25%的样本划分为基因组稳定组，以此鉴定两组间差异表达的GILnc。随后采用LASSO筛选出预后效能最优的GILnc，并基于对应公式计算每位患者的风险评分。基于该计算框架，本研究共鉴定出HCC组织中245个差异表达的GILnc。基于LASSO方法成功构建了由8个GILnc组成的HCC患者总生存期预测模型，随后将HCC患者划分为高风险组与低风险组，结果显示高风险组患者的总生存期显著短于低风险组，该结论在GSE76427队列及同济队列中得到验证。基因集富集分析（Gene Set Enrichment Analysis, GSEA）结果显示，高风险组显著富集于肿瘤特异性通路。此外，相较于单纯的TP53（tumor protein p53）突变状态，GILnc特征模型具备更优的预后价值，且可在TP53野生型患者中鉴定出携带部分TP53功能的中间亚型群体。值得注意的是，高风险组与PD-L1（programmed death-ligand 1, PD-L1）阻断治疗的疗效密切相关，提示靶向此类GILnc的潜在药物开发可提升免疫治疗的临床获益。最后，本GILnc特征模型的预测性能优于两项近期发表的长链非编码RNA（long non-coding RNA, lncRNA）特征模型。综上，本研究通过生物信息学方法证实，由8个GILnc组成的模型可作为预后生物标志物，为探索HCC的发病机制提供了全新方向。