Hepatocellular carcinoma (HCC) represents a formidable malignancy with a high lethality. Nonetheless, the development of vaccine and the establishment of prognostic models for precise and personalized treatment of HCC still encounter big challenges. Thus, the aim of this study was to develop HCC vaccines and explore anoikis-based prognostic models based on RNA sequencing data in GEO datasets (GSE10143, GSE76427) and the TCGA-LIHC cohort. Potential HCC antigens were identified using GEPIA2, cBioPortal, and TIMER2. Anoikis-related subtypes and gene clusters were defined by consensus clustering of 566 liver cancer samples based on 28 anoikis regulators, and we further analyzed their relationship with the immune microenvironment of HCC. A predictive model based on anoikis-related long noncoding RNAs (lncRNAs) was developed to accurately predict HCC prognosis. Seven overexpressed genes associated with HCC prognosis and tumor-infiltrating antigen-presenting cells were identified as potential tumor antigens for the development of HCC mRNA vaccines. Two subtypes based on anoikis-related genes (ARGs) and two gene clusters with different characteristics were identified and validated in defined cohorts. The tumor immune microenvironment between the two subtypes showed different cell infiltration and molecular characteristics. Furthermore, a prognostic score based on seven lncRNAs identified by LASSO regression was constructed, with the low-risk group having favorable prognosis, a “hot” immune microenvironment, and better response to immunotherapy. CCNB1, CDK1, DNASE1L3, KPNA2, PRC1, PTTG, and UBE2S were first identified as promising tumor antigens for mRNA vaccine development in HCC. Besides, we innovatively propose anoikis-based molecular subtypes, which not only enable personalized prognostic stratification of HCC patients but also provide a blueprint for identifying optimal candidates for tumor vaccines, enhancing immunotherapeutic strategies.

肝细胞癌（HCC）是一种具有高致死率的可怕恶性肿瘤。尽管如此，HCC精准个性化治疗的疫苗研发和预后模型的建立仍面临巨大挑战。因此，本研究的目的是开发 HCC 疫苗，并基于 GEO 数据集（GSE10143、GSE76427）和 TCGA-LIHC 队列中的 RNA 测序数据探索基于失巢凋亡的预后模型。使用 GEPIA2、cBioPortal 和 TIMER2 鉴定潜在的 HCC 抗原。基于28个失巢凋亡调节因子，对566个肝癌样本进行共识聚类，定义了失巢凋亡相关亚型和基因簇，并进一步分析了它们与肝癌免疫微环境的关系。开发了基于失巢凋亡相关长非编码 RNA (lncRNA) 的预测模型，以准确预测 HCC 预后。七个与 HCC 预后和肿瘤浸润抗原呈递细胞相关的过表达基因被确定为开发 HCC mRNA 疫苗的潜在肿瘤抗原。在定义的队列中鉴定并验证了基于失巢凋亡相关基因（ARG）的两种亚型和具有不同特征的两个基因簇。两种亚型之间的肿瘤免疫微环境表现出不同的细胞浸润和分子特征。此外，基于LASSO回归确定的7个lncRNA构建了预后评分，低风险组具有良好的预后、“热”的免疫微环境以及对免疫治疗的更好的反应。CCNB1、CDK1、DNASE1L3、KPNA2、PRC1、PTTG 和 UBE2S 首次被确定为 HCC mRNA 疫苗开发的有希望的肿瘤抗原。此外，我们创新性地提出了基于失巢凋亡的分子亚型，不仅能够对 HCC 患者进行个性化预后分层，而且还为识别最佳候选肿瘤疫苗、增强免疫治疗策略提供了蓝图。