Loading
Life sciences · Journal article
npj Precision Oncology · October 12, 2026
No summary has been generated for this record yet. What follows is drawn from its source metadata only.
Journal article.
No findings were extractable from the material analysed.
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
This record has not been graded across any dimension yet. Treat the label above as provisional and read the source.
What is missing. This record has no bottom line, key findings, reported figures, evidence dimensions. That is a gap in the analysis, not a judgement about the study.
Bladder cancer (BLCA) is a highly heterogeneous malignancy with significant variability in clinical outcomes. The interplay between inflammation and cellular senescence, referred to as inflammaging, plays a critical role in tumor initiation and progression. However, the molecular mechanisms underlying inflammaging and its clinical relevance in BLCA remain poorly understood. Inflammaging signature hallmark genes (ISH genes) were identified through correlation analysis using the MSigDB database. A machine learning-based ISH-Riskscore model was developed and validated to predict BLCA prognosis. To explore the biological and clinical relevance of ISH genes and the ISH-Riskscore model, comprehensive analyses were conducted, including functional enrichment, immune infiltration, and drug sensitivity assessments based on multi-omics and single-cell RNA sequencing (scRNA-seq) data. Additionally, the role of SCEL, a key ISH gene, was experimentally validated through functional assays. The ISH-Riskscore model demonstrated robust prognostic performance across multiple independent cohorts. Patients in the low-risk group exhibited higher infiltration of antitumor immune cells, correlating with more favorable clinical outcomes and potentially enhanced responses to immune checkpoint therapy. In contrast, the high-risk group displayed enrichment of pro-inflammatory, invasive, and immunosuppressive pathways, which were associated with poorer survival. scRNA-seq analysis revealed significant upregulation of SCEL in malignant epithelial cells, suggesting its role in tumor progression by promoting proliferation and migration, a finding further validated by in vitro experiments. An inflammaging signature was identified in BLCA, revealing its biological characteristics and clinical relevance. Based on this signature, a novel machine learning-based prognostic model was developed and validated, effectively predicting patient survival and therapeutic responses. Additionally, SCEL was identified as a promising therapeutic target for BLCA management.