■在这项研究中,从ICD的角度挖掘了mRNA疫苗的有效抗原,和PRAD的ICD亚型进一步区分以建立ICD景观,从而确定合适的疫苗接受者。
■TCGA和MSKCC数据库用于获取554和131名患者的RNA-seq数据和相应的临床数据,分别。采用GEPIA来测量预后指标。然后利用cBioPortal进行遗传改变的比较,使用TIMER分析已鉴定的ICD抗原与免疫浸润细胞的相关性。此外,ICD亚型通过共识聚类鉴定,利用基于图形学习的降维技术描绘了PRAD的ICD景观。
■总共,在PRAD中鉴定了4种PRAD抗原,包括FUS,LMNB2,RNPC3和ZNF700与APCs的不良预后和浸润有关。PRAD患者根据其分子差异分为两种ICD亚型,细胞和临床特征。此外,ICD调节剂和免疫检查点也在两种ICD亚型肿瘤之间差异表达。最后,PRAD的ICD景观在个体患者之间显示出实质性的异质性。
■总之,该研究可能为开发针对PRAD的mRNA疫苗以及确定合适的疫苗接受者提供理论基础。
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: In summary, the research may provide a theoretical foundation for developing mRNA vaccine against PRAD as well as determining appropriate vaccine recipients.