Nie Ye, Li Jianhui, Wu Wenlong, Guo Dongnan, Lei Xinjun, Zhang Tianchen, Wang Yanfang, Mao Zhenzhen, Zhang Xuan, Song Wenjie
Xi'an Medical University, Xi'an, China.
Department of Hepatobiliary Surgery, Xijing Hospital, The Fourth Military Medical University, Xi'an, China.
Front Oncol. 2021 Sep 15;11:706915. doi: 10.3389/fonc.2021.706915. eCollection 2021.
Hepatocellular carcinoma is one of the most common malignant tumors with a very high mortality rate. The emergence of immunotherapy has brought hope for the cure of hepatocellular carcinoma. Only a small number of patients respond to immune checkpoint inhibitors, and ferroptosis and tertiary lymphoid structure contribute to the increased response rate of immune checkpoint inhibitors; thus, we first need to identify those who are sensitive to immunotherapy and then develop different methods to improve sensitivity for different groups.
The sequencing data of hepatocellular carcinoma from The Cancer Genome Atlas and Gene Expression Omnibus was downloaded to identify the immune-related long non-coding RNAs (lncRNAs). LncRNAs related to survival data were screened out, and a risk signature was established using Cox proportional hazard regression model. R software was used to calculate the riskScore of each patient, and the patients were divided into high- and low-risk groups. The prognostic value of riskScore and its application in clinical chemotherapeutic drugs were confirmed. The relationship between riskScore and immune checkpoint genes, ferroptosis genes, and genes related to tertiary lymphoid structure formation was analyzed by Spearman method. TIMER, CIBERSORT, ssGSEA, and ImmuCellAI were used to evaluate the relative number of lymphocytes in tumor. The Wilcoxon signed-rank test confirmed differences in immunophenoscore between the high- and low-risk groups.
Data analysis revealed that our signature could well predict the 1-, 2-, 3-, and 5-year survival rates of hepatocellular carcinoma and to predict susceptible populations with Sorafenib. The risk signature were significantly correlated with immune checkpoint genes, ferroptosis genes, and tertiary lymphoid structure-forming genes, and predicted tumor-infiltrating lymphocyte status. There was a significant difference in IPS scores between the low-risk group and the high-risk group, while the low-risk group had higher scores.
The riskScore obtained from an immune-related lncRNA signature could successfully predict the survival time and reflect the efficacy of immune checkpoint inhibitors. More importantly, it is possible to select different treatments for different hepatocellular carcinoma patients that increase the response rate of immune checkpoint inhibitors and will help improve the individualized treatment of hepatocellular carcinoma.
肝细胞癌是最常见的恶性肿瘤之一，死亡率极高。免疫疗法的出现为肝细胞癌的治愈带来了希望。只有少数患者对免疫检查点抑制剂有反应，铁死亡和三级淋巴结构有助于提高免疫检查点抑制剂的反应率；因此，我们首先需要识别那些对免疫疗法敏感的患者，然后针对不同群体开发不同的方法来提高敏感性。
从癌症基因组图谱（The Cancer Genome Atlas）和基因表达综合数据库（Gene Expression Omnibus）下载肝细胞癌的测序数据，以鉴定免疫相关的长链非编码RNA（lncRNA）。筛选出与生存数据相关的lncRNA，使用Cox比例风险回归模型建立风险特征。使用R软件计算每位患者的风险评分（riskScore），并将患者分为高风险组和低风险组。确认了riskScore的预后价值及其在临床化疗药物中的应用。采用Spearman法分析riskScore与免疫检查点基因、铁死亡基因以及与三级淋巴结构形成相关基因之间的关系。使用TIMER、CIBERSORT、单样本基因集富集分析（ssGSEA）和免疫细胞人工智能（ImmuCellAI）评估肿瘤中淋巴细胞的相对数量。Wilcoxon符号秩检验证实了高风险组和低风险组之间免疫表型评分的差异。
数据分析表明，我们的特征能够很好地预测肝细胞癌的1年、2年、3年和5年生存率，并预测对索拉非尼敏感的人群。风险特征与免疫检查点基因、铁死亡基因和三级淋巴结构形成基因显著相关，并能预测肿瘤浸润淋巴细胞状态。低风险组和高风险组的免疫表型评分（IPS）存在显著差异，低风险组评分更高。
从免疫相关lncRNA特征获得的riskScore能够成功预测生存时间，并反映免疫检查点抑制剂的疗效。更重要的是，有可能为不同的肝细胞癌患者选择不同的治疗方法，提高免疫检查点抑制剂的反应率，这将有助于改善肝细胞癌的个体化治疗。
