ArticleCancer medicine2021
Development and validation of a PBRM1-associated immune prognostic model for clear cell renal cell carcinoma.
Article in Cancer medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Emerging roles for the epigenetic modifiers PBRM1 SETD2 and BAP1 in clear cell renal cell carcinoma pathogenesis and prognosis beyond VHL.Discover oncology · 2026Review
- PBRM1 deficiency enhances PD1 immunotherapeutic sensitivity via chromosomal accessibility in colorectal cancer.Theranostics · 2025Article
- A CDKN2B-Associated Immune Prognostic Model for Predicting Immune Cell Infiltration and Prognosis in Esophageal Carcinoma.Clinical and experimental gastroenterology · 2025Article
- PD1/PD-L1 blockade in clear cell renal cell carcinoma: mechanistic insights, clinical efficacy, and future perspectives.Molecular cancer · 2024Review
- Article
- Biological Biomarkers of Response and Resistance to Immune Checkpoint Inhibitors in Renal Cell Carcinoma.Cancers · 2023Review
- Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma.Frontiers in pharmacology · 2022Article
- Review
- Development and validation of a PBRM1-associated immune prognostic model for clear cell renal cell carcinoma.Cancer medicine · 2021Article
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Authors and funding
7 authors.
Funding
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.
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