ArticleACS omega2025
Integrated Multiomics Analysis and Machine Learning Approaches in Bladder Cancer: Unveiling the Impact of Immunogenic Cell Death and Its Key Gene SLC2A3 on Prognosis and Personalized Treatment Strategies.
Article in ACS omega, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- E-ABIN: an explainable module for anomaly detection in biological networks.NAR genomics and bioinformatics · 2025Article
- Pan-cancer analysis and experimental verification of its roles and clinical significance of SLC2A3 in kidney renal clear cell carcinoma.Frontiers in immunology · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, immunogenic cell death (ICD) has been identified as a regulatory cell death mechanism that induces an adaptive immune response, thereby improving enhancing the efficacy of immunotherapy and contributing to improved prognosis in bladder cancer (BLCA). This study established a risk signature based on ICD and identified ICD-related genes as diagnostic markers and therapeutic targets for BLCA. Thirty-two key ICD-risk genes (IRGs) were screened from correlation and univariate Cox regression analyses. Data obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases regarding BLCA and normal samples were categorized based on IRGs. ICD-based prognostic signature was built based on IRGs, stratifying BLCA patients into high- and low-risk groups. SLC2A3 was identified as a hub gene by 101 combinatorial machine learning algorithms and 10 individual machine learning algorithms. Based on single-cell sequencing data, we identified the cancer-associated fibroblasts (CAFs), the key cell population exhibiting high SLC2A3 expression. Functional analyses were performed to explore the potential value of SLC2A3 as a target for precision therapy. A prognostic signature was constructed using IRGs, indicating significant differences in the tumor microenvironment (TME) and treatment response between different risk groups. We identified SLC2A3 as the most critical IRG exhibiting high expression in the fibroblast population of patients with BLCA, especially in CAFs, which play an important role in BLCA progression. We found that inhibiting SLC2A3 expression may enhance the effectiveness of immunotherapy and the identified potential drugs targeting SLC2A3. We demonstrated that the identified IRGs serve as risk factors for clinical prognosis in BLCA and successfully constructed an ICD-based prognostic signature. Additionally, SLC2A3 holds potential as a therapeutic target to advance precision and personalized treatment strategies for BLCA, in combination with immunotherapy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.