Development and validation of a PBRM1‐associated immune prognostic model for clear cell renal cell carcinoma. (18th September 2021)

• Record Type: Journal Article
• Authors: Chen, Jiayi; Yao, Chunlin; Qiao, Nan; Ge, Yangyang; Li, Jianhua; Lin, Yun; Yao, Shanglong
• Is Part Of: Cancer medicine. Volume 10:Number 19(2021)
• Page Start: 6590
• Page End: 6609
• Publication Date: 2021-09-18
• Subjects: clear cell renal cell carcinoma -- immune prediction model -- mutation -- PBRM1 -- tumor immunology; 616.994005
• DOI: 10.1002/cam4.4115
• Languages: English
• ISSNs: 2045-7634

Abstract

Abstract: Alteration in the polybromo‐1 ( PBRM1 ) protein encoding gene PBRM1 is the second most frequent mutation in clear cell renal cell carcinoma (ccRCC). It causes a series of changes in the tumorigenesis, progression, prognosis, and immune response of ccRCC patients. This study explored the PBRM1 ‐associated immunological features and identified the immune‐related genes (IRGs) linked to PBRM1 mutation using bioinformatics methods. A total of 37 survival IRGs associated with PBRM1 mutation in ccRCC patients were identified. To further explore the role of these IRGs in ccRCC and their association with immune status, eight IRGs with remarkable potential as individual targets were selected. An immune model that was constructed showed good performance in stratifying patients into different subgroups, showing clinical application potential compared to traditional clinical factors. Patients in the high‐risk group were inclined to have more advanced stage and higher grade tumors with node metastasis, distant metastasis, and poorer prognosis. Furthermore, these patients had high percentages of regulatory T cells, follicular helper T cells, and M0 macrophages and exhibited high expression levels of immune checkpoints proteins, such as CTLA‐4, PD‐1, LAG‐3, TIGIT, and CD47. Finally, a nomogram integrating the model and clinical factors for clinical application showed a more robust predictive performance for prognosis. The prediction model associated with PBRM1 mutation status and immunity can serve as a promising tool to stratify patients depending upon their immune status, thus facilitating immunotherapy in the future. Abstract : In this study, we used bioinformatic methods to explore the effect of PBRM1 mutation in clear cell renal cell carcinoma samples on the immune status and identify immune‐related biomarkers related to PBRM1 mutation. As far as we know, this is the first report of a prediction model incorporating PBRM1 status and it serves as a promising tool to determine the immune status and stratify patients to judge the effectiveness of immunotherapy.