Background: Genomic alteration is the basis of occurrence and development of carcinoma. Specific gene mutation may be associated with the prognosis of hepatocellular carcinoma (HCC) patients without distant or lymphatic metastases. Hence, we developed a nomogram based on prognostic gene mutations that could predict the overall survival of HCC patients at early stage and provide reference for immunotherapy.

Methods: HCC cohorts were obtained from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases. The total patient was randomly assigned to training and validation sets. Univariate and multivariate cox analysis were used to select significant variables for construction of nomogram. The support vector machine (SVM) and principal component analysis (PCA) were used to assess the distinguished effect of significant genes. Besides, the nomogram model was evaluated by concordance index, time-dependent receiver operating characteristics (ROC) curve, calibration curve and decision curve analysis (DCA). Gene Set Enrichment Analysis (GSEA), CIBERSORT, Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenoscore (IPS) were utilized to explore the potential mechanism of immune-related process and immunotherapy.

Results: A total of 695 HCC patients were selected in the process including 495 training patients and 200 validation patients. Nomogram was constructed based on T stage, age, country, mutation status of DOCK2, EYS, MACF1 and TP53. The assessment showed the nomogram has good discrimination and high consistence between predicted and actual data. Furthermore, we found T cell exclusion was the potential mechanism of malignant progression in high-risk group. Meanwhile, low-risk group might be sensitive to immunotherapy and benefit from CTLA-4 blocker treatment.

Conclusion: Our research established a nomogram based on mutant genes and clinical parameters, and revealed the underlying association between these risk factors and immune-related process.

背景：基因组改变是癌症发生发展的基础。特定基因突变可能与无远处或淋巴转移的肝细胞癌（HCC）患者的预后有关。因此，我们开发了基于预后基因突变的列线图，可以预测早期 HCC 患者的总生存期，并为免疫治疗提供参考。

方法：HCC 队列来自癌症基因组图谱 (TCGA) 和国际癌症基因组联盟 (ICGC) 数据库。整个患者被随机分配到训练集和验证集。使用单变量和多变量 cox 分析选择显着变量以构建列线图。支持向量机（SVM）和主成分分析（PCA）被用来评估显着基因的区分效果。此外，列线图模型通过一致性指数、时间相关的受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）进行评估。利用基因集富集分析 (GSEA)、CIBERSORT、肿瘤免疫功能障碍和排斥 (TIDE) 和免疫表型评分 (IPS) 来探索免疫相关过程和免疫治疗的潜在机制。

结果：在该过程中总共选择了 695 名 HCC 患者，其中包括 495 名训练患者和 200 名验证患者。列线图基于 T 分期、年龄、国家、DOCK2、EYS、MACF1 和 TP53 的突变状态构建。评估表明，列线图在预测数据和实际数据之间具有良好的区分性和高度一致性。此外，我们发现 T 细胞排斥是高危组恶性进展的潜在机制。同时，低危人群可能对免疫治疗敏感并受益于 CTLA-4 阻滞剂治疗。

结论： 我们的研究建立了基于突变基因和临床参数的列线图，并揭示了这些风险因素与免疫相关过程之间的潜在关联。