A novel risk score system based on immune subtypes for identifying optimal mRNA vaccination population in hepatocellular carcinoma.

IF 4.9 2区医学 Q2 CELL BIOLOGY

Cellular Oncology Pub Date : 2024-08-01 Epub Date: 2024-02-05 DOI:10.1007/s13402-024-00921-1

Hongkai Zhuang, Chenwei Tang, Han Lin, Zedan Zhang, Xinming Chen, Wentao Wang, Qingbin Wang, Wenliang Tan, Lei Yang, Zhiqin Xie, Bingkun Wang, Bo Chen, Changzhen Shang, Yajin Chen

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引用次数: 0

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.

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基于免疫亚型的新型风险评分系统，用于确定肝细胞癌的最佳 mRNA 疫苗接种人群。

目的：尽管 mRNA 疫苗在多种恶性肿瘤中显示出一定的临床疗效，但其对肝细胞癌（HCC）的疗效仍不确定。本研究的重点是建立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 数据库确定 HCC mRNA 疫苗接种的候选基因。随后，根据候选基因构建了免疫亚型。根据不同免疫亚型的差异表达基因，利用机器学习算法建立了风险评分系统。此外，还对 72 例 HCC 患者的肿瘤组织进行了多色免疫荧光检测，以验证风险评分系统的可行性和有效性：结果：12个与生存率低和APCs浸润相关的过表达和突变基因被确定为mRNA疫苗接种的潜在候选靶点。根据这 12 个候选基因，构建了三种具有不同临床病理和分子特征的免疫亚型（如 IS1、IS2 和 IS3）。根据免疫亚型建立了风险评分系统，按照风险评分从低到高将 HCC 患者平均分为四个亚组（如 RS1、RS2、RS3 和 RS4）。RS4 主要与 IS3 重叠，RS1 与 IS2 重叠，RS2+RS3 与 IS1 重叠。ROC 分析还表明，风险评分在区分三种免疫亚型方面具有显著的能力。风险评分越高，预测生存率越低的能力越强，HCC样本的多色免疫荧光进一步证明了这一点。值得注意的是，RS4肿瘤表现出更高的免疫抑制表型、更高的12个潜在候选靶点表达量和更高的基因组改变部分，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，由于其具有高度免疫抑制的微环境，RS4 肿瘤可能会从 HCC mRNA 疫苗接种中获益。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Cellular Oncology ONCOLOGY-CELL BIOLOGY

CiteScore

10.30

自引率

1.50%

发文量

审稿时长

12 months

期刊介绍： The Official Journal of the International Society for Cellular Oncology Focuses on translational research Addresses the conversion of cell biology to clinical applications Cellular Oncology publishes scientific contributions from various biomedical and clinical disciplines involved in basic and translational cancer research on the cell and tissue level, technical and bioinformatics developments in this area, and clinical applications. This includes a variety of fields like genome technology, micro-arrays and other high-throughput techniques, genomic instability, SNP, DNA methylation, signaling pathways, DNA organization, (sub)microscopic imaging, proteomics, bioinformatics, functional effects of genomics, drug design and development, molecular diagnostics and targeted cancer therapies, genotype-phenotype interactions. A major goal is to translate the latest developments in these fields from the research laboratory into routine patient management. To this end Cellular Oncology forms a platform of scientific information exchange between molecular biologists and geneticists, technical developers, pathologists, (medical) oncologists and other clinicians involved in the management of cancer patients. In vitro studies are preferentially supported by validations in tumor tissue with clinicopathological associations.