ArticleFrontiers in immunology2022
Computational Recognition of a Regulatory T-cell-specific Signature With Potential Implications in Prognosis, Immunotherapy, and Therapeutic Resistance of Prostate Cancer.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 10 citations in OpenAlex.
- Tumor‑immune spatiotemporal co‑evolution: A new paradigm for understanding and overcoming therapy resistance in metastatic castration‑resistant prostate cancer (Review).International journal of molecular medicine · 2026Review
- Targeting regulatory T cells in the prostate cancer microenvironment: From mechanisms to therapeutics (Review).Molecular medicine reports · 2026Review
- Using bioinformatics for identifying and plugging metabolic pathway holes.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Comprehensive analysis of m7G-related genes METTL1 and WDR4 for predicting prognosis and oncogenic functions in prostate cancer.Discover oncology · 2025Article
- Review
- Construction of regulatory T cells specific genes predictive models of prostate cancer patients based on machine learning: a computational analysis and in vitro experiments.Discover oncology · 2025Article
- Advances in the application of PD-1/PD-L1 immunotherapy for prostate cancer: a review.Frontiers in immunology · 2025Review
- Role of lipid metabolism gene KLF4 in osteoarthritis.Clinical rheumatology · 2024Article
- Immunological facets of prostate cancer and the potential of immune checkpoint inhibition in disease management.Theranostics · 2024Review
- Prostate cancer stem cells and their targeted therapies.Frontiers in cell and developmental biology · 2024Review
- Microphysiological systems as models for immunologically 'cold' tumors.Frontiers in cell and developmental biology · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer, recognized as a "cold" tumor, has an immunosuppressive microenvironment in which regulatory T cells (Tregs) usually play a major role. Therefore, identifying a prognostic signature of Tregs has promising benefits of improving survival of prostate cancer patients. However, the traditional methods of Treg quantification usually suffer from bias and variability. Transcriptional characteristics have recently been found to have a predictive power for the infiltration of Tregs. Thus, a novel machine learning-based computational framework has been presented using Tregs and 19 other immune cell types using 42 purified immune cell datasets from GEO to identify Treg-specific mRNAs, and a prognostic signature of Tregs (named "TILTregSig") consisting of five mRNAs (
Indexed as
Identifiers
What OpenQuestion holds
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 OpenQuestion graph.