ArticleOncology letters2021
Preclinical analysis of novel prognostic transcription factors and immune-related gene signatures for bladder cancer via TCGA-based bioinformatic analysis.
Article in Oncology letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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.
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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
4 citing papers in PubMed, 12 citations in OpenAlex.
- The signature based on interleukin family and receptors identified IL19 and IL20RA in promoting nephroblastoma progression through STAT3 pathway.Scientific reports · 2025Article
- Article
- A novel immune-related gene pair prognostic signature for predicting overall survival in bladder cancer.BMC cancer · 2021Article
- A Transcription Factor-Based Risk Model for Predicting the Prognosis of Prostate Cancer and Potential Therapeutic Drugs.Evidence-based complementary and alternative medicine : eCAM · 2021Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bladder cancer (BLCA) is a common malignancy of human urinary tract, whose prognosis is influenced by complex gene interactions. Immune response activity can act as a potential prognostic factor in BLCA. The present study established a prognostic model, based on the identification of tumor transcription factors (TFs) and immune-related genes (IRGs), and further explored their therapeutic potential in BLCA. The enrichment scores of 29 IRG sets, identified in The Cancer Genome Atlas BLCA tumor samples, were quantified by single-sample Gene Set Enrichment Analysis. The abundance of infiltrated immune cells in tumor tissues was determined using the Estimating Relative algorithm. Tumor-related TFs and IRGs signatures were retrieved using Least Absolute Shrinkage and Selection Operator Cox regression analysis. A prognostic gene network was built using Pearson's correlation analysis as a means of predicting the regulatory relationship between prognostic TFs and IRGs. A nomogram was devised to also predict the overall survival (OS) rate of patients with BLCA. Based on the Genomics of Drug Sensitivity in Cancer data, potential therapeutic drugs were identified upon analyzing the relationship between the expression level of prognostic genes and respective IC
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