Abstract

Purpose

Although mRNA vaccines have shown certain clinical benefits in multiple malignancies, their therapeutic efficacies against hepatocellular carcinoma (HCC) remains uncertain. This study focused on establishing a novel risk score system based on immune subtypes so as to identify optimal HCC mRNA vaccination population.

Methods

GEPIA, cBioPortal and TIMER databases were utilized to identify candidate genes for mRNA vaccination in HCC. Subsequently, immune subtypes were constructed based on the candidate genes. According to the differential expressed genes among various immune subtypes, a risk score system was established using machine learning algorithm. Besides, multi-color immunofluorescence of tumor tissues from 72 HCC patients were applied to validate the feasibility and efficiency of the risk score system.

Results

Twelve overexpressed and mutated genes associated with poor survival and APCs infiltration were identified as potential candidate targets for mRNA vaccination. Three immune subtypes (e.g. IS1, IS2 and IS3) with distinct clinicopathological and molecular profiles were constructed according to the 12 candidate genes. Based on the immune subtype, a risk score system was developed, and according to the risk score from low to high, HCC patients were classified into four subgroups on average (e.g. RS1, RS2, RS3 and RS4). RS4 mainly overlapped with IS3, RS1 with IS2, and RS2+RS3 with IS1. ROC analysis also suggested the significant capacity of the risk score to distinguish between the three immune subtypes. Higher risk score exhibited robustly predictive ability for worse survival, which was further independently proved by multi-color immunofluorescence of HCC samples. Notably, RS4 tumors exhibited an increased immunosuppressive phenotype, higher expression of the twelve potential candidate targets and increased genome altered fraction, and therefore might benefit more from vaccination.

Conclusions

This novel risk score system based on immune subtypes enabled the identification of RS4 tumor that, due to its highly immunosuppressive microenvironment, may benefit from HCC mRNA vaccination.

中文翻译：

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一种基于免疫亚型的新型风险评分系统，用于识别肝细胞癌的最佳 mRNA 疫苗接种群体

摘要

目的

尽管mRNA疫苗在多种恶性肿瘤中显示出一定的临床益处，但其对肝细胞癌（HCC）的治疗效果仍不确定。本研究的重点是建立一种基于免疫亚型的新型风险评分系统，以确定最佳的 HCC mRNA 疫苗接种人群。

方法

利用 GEPIA、cBioPortal 和 TIMER 数据库来识别 HCC mRNA 疫苗接种的候选基因。随后，根据候选基因构建免疫亚型。根据不同免疫亚型之间的差异表达基因，利用机器学习算法建立风险评分系统。此外，还应用72例HCC患者肿瘤组织的多色免疫荧光来验证风险评分系统的可行性和有效性。

结果

与生存率低和 APC 浸润相关的 12 个过度表达和突变基因被确定为 mRNA 疫苗接种的潜在候选靶点。根据12个候选基因构建了具有不同临床病理学和分子特征的三种免疫亚型（例如IS1、IS2和IS3）。根据免疫亚型，建立了风险评分系统，根据风险评分从低到高，将HCC患者平均分为四个亚组（例如RS1、RS2、RS3和RS4）。 RS4主要与IS3重叠，RS1与IS2主要重叠，RS2+RS3与IS1主要重叠。 ROC 分析还表明风险评分具有区分三种免疫亚型的显着能力。较高的风险评分表现出对较差生存的稳健预测能力，这一点通过 HCC 样本的多色免疫荧光进一步独立证明。值得注意的是，RS4 肿瘤表现出免疫抑制表型增加、十二种潜在候选靶点表达更高、基因组改变比例增加，因此可能从疫苗接种中获益更多。

结论

这种基于免疫亚型的新型风险评分系统能够识别 RS4 肿瘤，由于其高度免疫抑制的微环境，可能受益于 HCC mRNA 疫苗接种。