State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
National Medical Center for Infectious Diseases, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Front Immunol. 2022 Aug 23;13:973649. doi: 10.3389/fimmu.2022.973649. eCollection 2022.
Hepatocellular carcinoma remains the third most common cause of cancer-related deaths worldwide. Although great achievements have been made in resection, chemical therapies and immunotherapies, the pathogenesis and mechanism of HCC initiation and progression still need further exploration. Necroptosis genes have been reported to play an important role in HCC malignant activities, thus it is of great importance to comprehensively explore necroptosis-associated genes in HCC.
We chose the LIHC cohort from the TCGA, ICGC and GEO databases for this study. ConsensusClusterPlus was adopted to identify the necroptosis genes-based clusters, and LASSO cox regression was applied to construct the prognostic model based on necroptosis signatures. The GSEA and CIBERSORT algorithms were applied to evaluate the immune cell infiltration level. QPCR was also applied in this study to evaluate the expression level of genes in HCC.
We identified three clusters, C1, C2 and C3. Compared with C2 and C3, the C1 cluster had the shortest overall survival time and highest immune score. The C1 was samples were significantly enriched in cell cycle pathways, some tumor epithelial-mesenchymal transition related signaling pathways, among others. The DEGs between the 3 clusters showed that C1 was enriched in cell cycle, DNA replication, cellular senescence, and p53 signaling pathways. The LASSO cox regression identified KPNA2, SLC1A5 and RAMP3 as prognostic model hub genes. The high risk-score subgroup had an elevated expression level of immune checkpoint genes and a higher TIDE score, which suggested that the high risk-score subgroup had a lower efficiency of immunotherapies. We also validated that the necroptosis signatures-based risk-score model had powerful prognosis prediction ability.
Based on necroptosis-related genes, we classified patients into 3 clusters, among which C1 had significantly shorter overall survival times. The proposed necroptosis signatures-based prognosis prediction model provides a novel approach in HCC survival prediction and clinical evaluation.
肝细胞癌仍然是全球癌症相关死亡的第三大常见原因。尽管在肝切除术、化学疗法和免疫疗法方面取得了巨大成就，但 HCC 发生和发展的发病机制仍需进一步探索。坏死性凋亡基因已被报道在 HCC 的恶性活动中发挥重要作用，因此全面探索 HCC 中与坏死性凋亡相关的基因具有重要意义。
我们选择了 TCGA、ICGC 和 GEO 数据库中的 LIHC 队列进行本研究。采用 ConsensusClusterPlus 识别基于坏死性凋亡基因的聚类，LASSO cox 回归构建基于坏死性凋亡特征的预后模型。采用 GSEA 和 CIBERSORT 算法评估免疫细胞浸润水平。本研究还应用 QPCR 评估 HCC 中基因的表达水平。
我们确定了三个聚类，C1、C2 和 C3。与 C2 和 C3 相比，C1 聚类的总生存时间最短，免疫评分最高。C1 样本显著富集于细胞周期途径、一些肿瘤上皮-间充质转化相关信号通路等。三个聚类之间的 DEGs 显示，C1 富集于细胞周期、DNA 复制、细胞衰老和 p53 信号通路。LASSO cox 回归鉴定 KPNA2、SLC1A5 和 RAMP3 为预后模型的关键基因。高风险评分亚组的免疫检查点基因表达水平升高，TIDE 评分较高，提示高风险评分亚组的免疫治疗效率较低。我们还验证了基于坏死性凋亡相关基因的风险评分模型具有强大的预后预测能力。
基于坏死性凋亡相关基因，我们将患者分为 3 个聚类，其中 C1 的总生存时间明显缩短。所提出的基于坏死性凋亡特征的预后预测模型为 HCC 生存预测和临床评估提供了一种新方法。
