Yan Yifeng, Ren Liang, Liu Yan, Liu Liang
Department of Forensic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Department of Forensic Medicine, Wannan Medical College, Wuhu, China.
Front Genet. 2022 Jan 28;12:763281. doi: 10.3389/fgene.2021.763281. eCollection 2021.
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(HCC)的病理生理学主要与基因组不稳定相关。然而,关于广泛基因组不稳定长链非编码RNA(GILnc)与HCC预后及免疫治疗相关性的研究仍然匮乏。我们将前25%的体细胞突变归入基因不稳定组,后25%的体细胞突变归入基因稳定组,进而识别两组之间GILnc的差异表达。然后,使用LASSO方法识别最具预后价值的GILnc,并根据公式计算每位患者的风险评分。基于一个计算框架,在HCC中识别出245种不同的GILnc。基于LASSO成功建立了一个包含8个GILnc的模型来预测HCC患者的总生存期,随后我们将HCC患者分为高风险组和低风险组,观察到高风险组的总生存期明显短于低风险组,这在GSE76427和同济队列中得到了验证。基因集富集分析(GSEA)显示,高风险组更有可能富集于癌症特异性通路。此外,GILnc特征比单独的TP53突变状态具有更大的预后意义,并且它能够识别TP53野生型患者中存在部分TP53功能的中间亚型组。重要的是,高风险组与PD-L1阻断治疗的疗效相关,这表明开发针对这些GILnc的潜在药物可能有助于免疫治疗的临床获益。最后,GILnc特征模型的预测性能优于最近发表的两种lncRNA特征模型。总之,我们应用生物信息学方法表明,一个包含8个GILnc的模型可作为预后生物标志物,为探索HCC的发病机制提供新方向。