Department of Urology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Department of Intensive Care Unit, Suzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University, Suzhou, Jiangsu, China.
Expert Rev Vaccines. 2024 Jan-Dec;23(1):830-844. doi: 10.1080/14760584.2024.2396086. Epub 2024 Sep 2.
In this study, effective antigens of mRNA vaccine were excavated from the perspective of ICD, and ICD subtypes of PRAD were further distinguished to establish an ICD landscape, thereby determining suitable vaccine recipients.
TCGA and MSKCC databases were applied to acquire RNA-seq data and corresponding clinical data of 554 and 131 patients, respectively. GEPIA was employed to measure prognostic indices. Then, a comparison of genetic alterations was performed utilizing cBioPortal, and correlation of identified ICD antigens with immune infiltrating cells was analyzed employing TIMER. Moreover, ICD subtypes were identified by means of consensus cluster, and ICD landscape of PRAD was depicted utilizing graph learning-based dimensional reduction.
In total, 4 PRAD antigens were identified in PRAD, including FUS, LMNB2, RNPC3, and ZNF700, which had association with adverse prognosis and infiltration of APCs. PRAD patients were classified as two ICD subtypes based on their differences in molecular, cellular, and clinical features. Furthermore, ICD modulators and immune checkpoints were also differentially expressed between two ICD subtype tumors. Finally, the ICD landscape of PRAD showed substantial heterogeneity among individual patients.
In summary, the research may provide a theoretical foundation for developing mRNA vaccine against PRAD as well as determining appropriate vaccine recipients.
本研究从 ICD 的角度挖掘 mRNA 疫苗的有效抗原,并进一步区分 PRAD 的 ICD 亚型,建立 ICD 景观,从而确定合适的疫苗接种者。
分别应用 TCGA 和 MSKCC 数据库获取 554 名和 131 名患者的 RNA-seq 数据和相应的临床数据。使用 GEPIA 测量预后指标。然后,利用 cBioPortal 比较遗传改变,利用 TIMER 分析鉴定的 ICD 抗原与免疫浸润细胞的相关性。此外,通过共识聚类识别 ICD 亚型,利用基于图学习的降维方法描绘 PRAD 的 ICD 景观。
总共在 PRAD 中鉴定出 4 种 PRAD 抗原,包括 FUS、LMNB2、RNPC3 和 ZNF700,它们与不良预后和 APC 浸润有关。基于分子、细胞和临床特征的差异,将 PRAD 患者分为两种 ICD 亚型。此外,两种 ICD 亚型肿瘤之间的 ICD 调节剂和免疫检查点也存在差异表达。最后,PRAD 的 ICD 景观显示个体患者之间存在显著异质性。
总之,该研究可为开发针对 PRAD 的 mRNA 疫苗以及确定合适的疫苗接种者提供理论基础。