目的: 筛选肝细胞癌（hepatellular carcinoma,HCC）的特征基因,构建有效的HCC预后模型,为HCC患者的预后提供更准确的评估。方法: 基于癌症基因组图谱（the cancer genome atlas,TCGA）和基因表达综合数据库（gene expression omnibus,GEO）,筛选癌组织与正常组织之间的差异表达基因（differentially expressed genes,DEG）。采用单变量Cox回归分析和Lasso-cox 回归分析筛选出HCC特征基因;通过多元Cox回归分析构建风险预后模型和列线图,将患者按风险评分划为高、低风险组,根据高、低风险组差异表达的基因进行富集分析和基因组富集分析;采用CIBERSORT算法评估肿瘤的免疫细胞浸润水平。结果: 从613 个DEGs中鉴定出与预后相关的特征基因（CCNB2、CCNB1、NDC80、RRM2和MCM2）,以及上述基因的预后风险模型。高风险组患者的总生存期明显短于低风险组患者。单因素与多因素 Cox回归分析表明该评分模型和肿瘤分期是HCC患者独立的危险因素。此外,基于风险评分和临床信息构建列线图。进一步免疫浸润分析发现激活的记忆性CD4+T细胞与上述特征基因的表达均呈正相关,而静息的记忆性CD4+T细胞与之均呈负相关。结论: 本研究基于5个特征基因构建了预后风险模型,为HCC的预后和治疗提供了新的见解和策略。
Abstract
Objective To screen signature genes and construct an effective prognostic model for hepatocellular carcinoma (HCC), ultimately providing a more accurate evaluation on prognosis for patients with HCC. Method DEGs between carcinoma tissue and normal tissue were identified based on the cancer genome atlas (TCGA) and gene expression omnibus (GEO) database. The signature genes for HCC were screened via univariate cox regression and the least absolute shrinkage and selection operator-cox regression. The multivariate cox analysis was used to construct a monogram and a prognostic risk score model. Based on this model, the patients were grouped into low-risk group and high-risk group. Enrichment analysis and gene set enrichment analysis was performed on DEGs between these two subgroups. CIBERSORT algorithm was employed to evaluate the level of tumor-infiltrating immune cells. Results A total of 5 signature genes, including CCNB2, CCNB1, NDC80, RRM2 and MCM2, were identified from 613 overlapping DEGs in HCC. The prognostic risk score model was constructed based on these candidate genes. Patients in high-risk group had a significantly shorter OS compared with those in low-risk group. Univariate and multivariate cox regression analysis showed that both the risk scores from prognostic model and tumor stages were independent prognostic factors for HCC patients. Furthermore, based on risk scores and clinical information, the nomogram plot was constructed. It was shown that high-risk group was related to immune reactions according to gene set enrichment analysis. Further immune-infiltrating analysis revealed that the expression of the signature genes was positively correlated with the level of activated CD4+T cell memory but negatively related with the level of resting CD4+T cell memory. Conclusion This study screened 5 signature genes and constructed a prognostic risk model, providing novel insights and strategies for the prognosis and therapy of HCC.
关键词
预后 /
风险特征 /
肝细胞癌 /
免疫浸润
Key words
prognostic /
risk signature /
hepatocellular carcinoma /
immune infiltration
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参考文献
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基金
湖南省自然科学基金科药联合项目“基于智能化主动监测开展司美格鲁肽真实世界安全性研究及ADR风险预测模型的构建”（2024JJ8202）;湖南省自然科学基金青年基金“tRNA修饰酶通过调控ERK/c-Myc信号通路介导肝癌发生发展的机制研究”（2024JJ6080）
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