Chen Yang, Lin Qiao-Xin, Xu Yi-Ting, Qian Fang-Jing, Lin Chen-Jing, Zhao Wen-Ya, Huang Jing-Ren, Tian Ling, Gu Dian-Na
Department of Clinical Medicine, Wenzhou Medical University, Wenzhou, China.
Department of Medical Oncology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Front Oncol. 2023 Apr 27;13:1158605. doi: 10.3389/fonc.2023.1158605. eCollection 2023.
Hepatocellular carcinoma (HCC) is a global health burden with poor prognosis. Anoikis, a novel programmed cell death, has a close interaction with metastasis and progression of cancer. In this study, we aimed to construct a novel bioinformatics model for evaluating the prognosis of HCC based on anoikis-related gene signatures as well as exploring the potential mechanisms.
We downloaded the RNA expression profiles and clinical data of liver hepatocellular carcinoma from TCGA database, ICGC database and GEO database. DEG analysis was performed using TCGA and verified in the GEO database. The anoikis-related risk score was developed univariate Cox regression, LASSO Cox regression and multivariate Cox regression, which was then used to categorize patients into high- and low-risk groups. Then GO and KEGG enrichment analyses were performed to investigate the function between the two groups. CIBERSORT was used for determining the fractions of 22 immune cell types, while the ssGSEA analyses was used to estimate the differential immune cell infiltrations and related pathways. The "pRRophetic" R package was applied to predict the sensitivity of administering chemotherapeutic and targeted drugs.
A total of 49 anoikis-related DEGs in HCC were detected and 3 genes (EZH2, KIF18A and NQO1) were selected out to build a prognostic model. Furthermore, GO and KEGG functional enrichment analyses indicated that the difference in overall survival between risk groups was closely related to cell cycle pathway. Notably, further analyses found the frequency of tumor mutations, immune infiltration level and expression of immune checkpoints were significantly different between the two risk groups, and the results of the immunotherapy cohort showed that patients in the high-risk group have a better immune response. Additionally, the high-risk group was found to have higher sensitivity to 5-fluorouracil, doxorubicin and gemcitabine.
The novel signature of 3 anoikis-related genes (EZH2, KIF18A and NQO1) can predict the prognosis of patients with HCC, and provide a revealing insight into personalized treatments in HCC.
肝细胞癌（HCC）是一种全球健康负担，预后较差。失巢凋亡是一种新型程序性细胞死亡，与癌症的转移和进展密切相关。在本研究中，我们旨在构建一种基于失巢凋亡相关基因特征评估HCC预后的新型生物信息学模型，并探索其潜在机制。
我们从TCGA数据库、ICGC数据库和GEO数据库下载了肝细胞癌的RNA表达谱和临床数据。使用TCGA进行差异基因（DEG）分析，并在GEO数据库中进行验证。通过单因素Cox回归、LASSO Cox回归和多因素Cox回归建立失巢凋亡相关风险评分，然后将患者分为高风险组和低风险组。随后进行GO和KEGG富集分析，以研究两组之间的功能差异。使用CIBERSORT确定22种免疫细胞类型的比例，而使用单样本基因集富集分析（ssGSEA）评估差异免疫细胞浸润和相关途径。应用“pRRophetic”R包预测化疗药物和靶向药物的敏感性。
在HCC中总共检测到49个与失巢凋亡相关的DEG，并选择了3个基因（EZH2、KIF18A和NQO1）构建预后模型。此外，GO和KEGG功能富集分析表明，风险组之间总生存期的差异与细胞周期途径密切相关。值得注意的是，进一步分析发现，两个风险组之间肿瘤突变频率、免疫浸润水平和免疫检查点表达存在显著差异，免疫治疗队列的结果显示，高风险组患者具有更好的免疫反应。此外，发现高风险组对5-氟尿嘧啶、阿霉素和吉西他滨具有更高的敏感性。
3个与失巢凋亡相关基因（EZH2、KIF18A和NQO1）的新型特征可以预测HCC患者的预后，并为HCC的个性化治疗提供有价值的见解。
