Gao Yukui, Wang Guixin, Chen Yanzhuo, Zhang Mingpeng, Gao Wenlong, Shang Zhiqun, Niu Yuanjie
Tianjin Institute of Urology, the Second Hospital of Tianjin Medical University, Tianjin, China.
Front Genet. 2022 Apr 25;13:886983. doi: 10.3389/fgene.2022.886983. eCollection 2022.
Messenger ribonucleic acid (mRNA) vaccine has been considered as a potential therapeutic strategy and the next research hotspot, but their efficacy against prostate adenocarcinoma (PRAD) remains undefined. This study aimed to find potential antigens of PRAD for mRNA vaccine development and identify suitable patients for vaccination through immunophenotyping. Gene expression profiles and clinical information were obtained from TCGA and ICGC. GEPIA2 was used to calculate the prognostic index of the selected antigens. The genetic alterations were compared on cBioPortal and the correlation between potential antigen and immune infiltrating cells was explored by TIMER. ConsensusClusterPlus was used to construct a consistency matrix, and identify the immune subtypes. Graph learning-based dimensional reduction was performed to depict immune landscape. Boruta algorithm and LASSO logistic analysis were used to screen PRAD patients who may benefit from mRNA vaccine. Seven potential tumor antigens selected were significantly positively associated with poor prognosis and the antigen-presenting immune cells (APCs) in PRAD, including ADA, FYN, HDC, NFKBIZ, RASSF4, SLC6A3, and UPP1. Five immune subtypes of PRAD were identified by differential molecular, cellular, and clinical characteristics in both cohorts. C3 and C5 had immune "hot" and immunosuppressive phenotype, On the contrary, C1&C2 had immune "cold" phenotype. Finally, the immune landscape characterization showed the immune heterogeneity among patients with PRAD. ADA, FYN, HDC, NFKBIZ, RASSF4, SLC6A3, and UPP1 are potential antigens for mRNA vaccine development against PRAD, and patients in type C1 and C2 are suitable for vaccination.
信使核糖核酸(mRNA)疫苗已被视为一种潜在的治疗策略和下一个研究热点,但其对前列腺腺癌(PRAD)的疗效仍不明确。本研究旨在寻找用于mRNA疫苗开发的PRAD潜在抗原,并通过免疫表型分析确定适合接种疫苗的患者。从TCGA和ICGC获取基因表达谱和临床信息。使用GEPIA2计算所选抗原的预后指数。在cBioPortal上比较基因改变,并通过TIMER探索潜在抗原与免疫浸润细胞之间的相关性。使用ConsensusClusterPlus构建一致性矩阵,并识别免疫亚型。进行基于图学习的降维以描绘免疫格局。使用Boruta算法和LASSO逻辑分析筛选可能从mRNA疫苗中获益的PRAD患者。所选的七种潜在肿瘤抗原与PRAD的不良预后和抗原呈递免疫细胞(APC)显著正相关,包括ADA、FYN、HDC、NFKBIZ、RASSF4、SLC6A3和UPP1。通过两个队列中不同的分子、细胞和临床特征识别出PRAD的五种免疫亚型。C3和C5具有免疫“热”和免疫抑制表型,相反,C1和C2具有免疫“冷”表型。最后,免疫格局特征显示了PRAD患者之间的免疫异质性。ADA、FYN、HDC、NFKBIZ、RASSF4、SLC6A3和UPP1是针对PRAD的mRNA疫苗开发的潜在抗原,C1和C2型患者适合接种疫苗。