Chen Lin, Wu Guo-Zhi, Wu Tao, Shang Hao-Hu, Wang Wei-Juan, Fisher David, Hiens Nguyen Thi Thu, Musabaev Erkin, Zhao Lei
Department of Infectious Diseases, Tsinghua University Affiliated Chuiyangliu Hospital, Beijing, 100021, China.
The First Clinical Medical College, Lanzhou University, Lanzhou, 730000, China.
Curr Med Sci. 2024 Dec;44(6):1217-1231. doi: 10.1007/s11596-024-2924-9. Epub 2024 Dec 17.
Hepatocellular carcinoma (HCC) presents substantial genetic and phenotypic diversity, making it challenging to predict patient outcomes. There is a clear need for novel biomarkers to better identify high-risk individuals. Long non-coding RNAs (lncRNAs) are known to play key roles in cell cycle regulation and genomic stability, and their dysregulation has been closely linked to HCC progression. Developing a prognostic model based on cell cycle-related lncRNAs could open up new possibilities for immunotherapy in HCC patients.
Transcriptomic data and clinical samples were obtained from the TCGA-HCC dataset. Cell cycle-related gene sets were sourced from existing studies, and coexpression analysis identified relevant lncRNAs (correlation coefficient >0.4, P<0.001). Univariate analysis identified prognostic lncRNAs, which were then used in a LASSO regression model to create a risk score. This model was validated via cross-validation. HCC samples were classified on the basis of their risk scores. Correlations between the risk score and tumor mutational burden (TMB), tumor immune infiltration, immune checkpoint gene expression, and immunotherapy response were evaluated via R packages and various methods (TIMER, CIBERSORT, CIBERSORT-ABS, QUANTISEQ, MCP-COUNTER, XCELL, and EPIC).
Four cell cycle-related lncRNAs (AC009549.1, AC090018.2, PKD1P6-NPIPP1, and TMCC1-AS1) were significantly upregulated in HCC. These lncRNAs were used to create a risk score (risk score=0.492×AC009549.1+1.390×AC090018.2+1.622×PKD1P6-NPIPP1+0.858×TMCC1-AS1). This risk score had superior predictive value compared to traditional clinical factors (AUC=0.738). A nomogram was developed to illustrate the 1-year, 3-year, and 5-year overall survival (OS) rates for individual HCC patients. Significant differences in TMB, immune response, immune cell infiltration, immune checkpoint gene expression, and drug responsiveness were observed between the high-risk and low-risk groups.
The risk score model we developed enhances the prognostication of HCC patients by identifying those at high risk for poor outcomes. This model could lead to new immunotherapy strategies for HCC patients.
肝细胞癌（HCC）具有显著的遗传和表型多样性，这使得预测患者预后具有挑战性。显然需要新的生物标志物来更好地识别高危个体。已知长链非编码RNA（lncRNA）在细胞周期调控和基因组稳定性中起关键作用，其失调与HCC进展密切相关。基于细胞周期相关lncRNA开发预后模型可能为HCC患者的免疫治疗开辟新的可能性。
从TCGA-HCC数据集中获取转录组数据和临床样本。细胞周期相关基因集来自现有研究，共表达分析确定相关lncRNA（相关系数>0.4，P<0.001）。单变量分析确定预后lncRNA，然后将其用于LASSO回归模型以创建风险评分。该模型通过交叉验证进行验证。HCC样本根据其风险评分进行分类。通过R包和各种方法（TIMER、CIBERSORT、CIBERSORT-ABS、QUANTISEQ、MCP-COUNTER、XCELL和EPIC）评估风险评分与肿瘤突变负担（TMB）、肿瘤免疫浸润、免疫检查点基因表达和免疫治疗反应之间的相关性。
四种细胞周期相关lncRNA（AC009549.1、AC090018.2、PKD1P6-NPIPP1和TMCC1-AS1）在HCC中显著上调。这些lncRNA用于创建风险评分（风险评分=0.492×AC009549.1+1.390×AC090018.2+1.622×PKD1P6-NPIPP1+0.858×TMCC1-AS1）。与传统临床因素相比，该风险评分具有更高的预测价值（AUC=0.738）。开发了列线图以说明个体HCC患者的1年、3年和5年总生存率（OS）。在高风险和低风险组之间观察到TMB、免疫反应、免疫细胞浸润、免疫检查点基因表达和药物反应性的显著差异。
我们开发的风险评分模型通过识别预后不良的高危患者来增强HCC患者的预后评估。该模型可能为HCC患者带来新的免疫治疗策略。
