ArticleFrontiers in pharmacology2024
Prediction of immunotherapy response of bladder cancer with a pyroptosis-related signature indicating tumor immune microenvironment.
Article in Frontiers in pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Identification of pyroptosis-related molecular subtypes and development of a subtype-derived six-gene prognostic signature for bladder cancer.Translational andrology and urology · 2026Article
- MircoRNAs as regulators of regulated cell death in cancer progression: a focus on ferroptosis, anoikis and pyroptosis.Frontiers in pharmacology · 2026Review
- Microsatellite instability and mismatch repair deficiency in bladder urothelial carcinoma: a Tunisian single-center study.Journal of the Egyptian National Cancer Institute · 2025Article
- Targeting pyroptosis for cancer immunotherapy: mechanistic insights and clinical perspectives.Molecular cancer · 2025Review
- Construction and immunohistochemical validation of a necroptosis-related prognostic signature in bladder cancer and its association with tumor immune infiltration.Frontiers in genetics · 2025Article
- The Role of Pyroptosis-Related Gene Signature and Immune Infiltration in Juvenile Dermatomyositis.Clinical, cosmetic and investigational dermatology · 2025Article
- Comprehensive analysis of cuproptosis-related genes involved in prognosis and tumor microenvironment infiltration of colorectal cancer.Translational cancer research · 2024Article
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9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Although prognostic models based on pyroptosis-related genes (PRGs) have been constructed in bladder cancer (BLCA), the comprehensive impact of these genes on tumor microenvironment (TME) and immunotherapeutic response has yet to be investigated. Methods: Based on expression profiles of 52 PRGs, we utilized the unsupervised clustering algorithm to identify PRGs subtypes and ssGSEA to quantify immune cells and hallmark pathways. Moreover, we screened feature genes of distinct PRGs subtypes and validated the associations with immune infiltrations in tissue using the multiplex immunofluorescence. Univariate, LASSO, and multivariate Cox regression analyses were employed to construct the scoring scheme. Results: Four PRGs clusters were identified, samples in cluster C1 were infiltrated with more immune cells than those in others, implying a favorable response to immunotherapy. While the cluster C2, which shows an extremely low level of most immune cells, do not respond to immunotherapy. CXCL9/CXCL10 and SPINK1/DHSR2 were identified as feature genes of cluster C1 and C2, and the specimen with high CXCL9/CXCL10 was characterized by more CD8 + T cells, macrophages and less Tregs. Based on differentially expressed genes (DEGs) among PRGs subtypes, a predictive model (termed as PRGs score) including five genes (CACNA1D, PTK2B, APOL6, CDK6, ANXA2) was built. Survival probability of patients with low-PRGs score was significantly higher than those with high-PRGs score. Moreover, patients with low-PRGs score were more likely to benefit from anti-PD1/PD-L1 regimens. Conclusion: PRGs are closely associated with TME and oncogenic pathways. PRGs score is a promising indicator for predicting clinical outcome and immunotherapy response.
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