ArticleScientific reports2024
Identifying microRNAs associated with tumor immunotherapy response using an interpretable machine learning model.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Deciphering the mechanistic roles of ADARs in cancer pathogenesis, tumor immune evasion, and drug resistance.Frontiers in immunology · 2025Review
- Phenotypic Plasticity, Non-genetic Mechanisms, and Immune Drug Resistance in Cancer.Cancer treatment and research · 2025Review
- Revealing intra-group immunotherapy response heterogeneity in metastatic urothelial carcinoma through interpretable feature extraction and spectral clustering.Frontiers in immunology · 2025Article
- MicroRNAs as key regulators of cancer drug resistance: insights and future directions in chemotherapy, targeted-therapy, radiotherapy, and immunotherapy.Cancer drug resistance (Alhambra, Calif.) · 2025Review
- Advancing miRNA cancer research through artificial intelligence: from biomarker discovery to therapeutic targeting.Medical oncology (Northwood, London, England) · 2024Review
- Molecular functions of microRNAs in colorectal cancer: recent roles in proliferation, angiogenesis, apoptosis, and chemoresistance.Naunyn-Schmiedeberg's archives of pharmacology · 2024Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Predicting clinical responses to tumor immunotherapy is essential to reduce side effects and the potential for sustained clinical responses. Nevertheless, preselecting patients who are likely to respond to such treatments remains highly challenging. Here, we explored the potential of microRNAs (miRNAs) as predictors of immune checkpoint blockade responses using a machine learning approach. First, we constructed random forest models to predict the response to tumor ICB therapy using miRNA expression profiles across 19 cancer types. The contribution of individual miRNAs to each prediction process was determined by employing SHapley Additive exPlanations (SHAP) for model interpretation. Remarkably, the predictive performance achieved by using a small number of miRNAs with high feature importance was similar to that achieved by using the entire miRNA set. Additionally, the genes targeted by these miRNAs were closely associated with tumor- and immune-related pathways. In conclusion, this study demonstrates the potential of miRNA expression data for assessing tumor immunotherapy responses. Furthermore, we confirmed the potential of informative miRNAs as biomarkers for the prediction of immunotherapy response, which will advance our understanding of tumor immunotherapy mechanisms.
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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.