ReviewClinical and translational medicine2023
H1Innovative approaches to combat anti-cancer drug resistance: Targeting lncRNA and autophagy.
Review in Clinical and translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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
8 citing papers in PubMed, 17 citations in OpenAlex.
- S12micro: a machine learning-derived serum miRNA panel for high-performance pan-cancer diagnosis via liquid biopsy.International journal of surgery (London, England) · 2026Article
- Regulation of immune-mediated chemoresistance in cancer by lncRNAs: an in-depth review of signaling pathways.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- The LncRNA RMST-miR-4295-ITPR1 axis: a key mechanism in regulating autophagy in triple-negative breast cancer cells.BMC cancer · 2025Article
- Transcriptomic profiles of single-cell autophagy-related genes (ATGs) in lung diseases.Cell biology and toxicology · 2025Article
- Natural polymer derivative-based pH-responsive nanoformulations with entrapped diketo-tautomers of 5-fluorouracil for enhanced cancer therapy.ADMET & DMPK · 2025Article
- Potential Intersections between lncRNA, Vascular Cognitive Impairment, and Immunization Strategies: Insights and Future Directions.Vaccines · 2024Review
- Nrf2 in human cancers: biological significance and therapeutic potential.American journal of cancer research · 2024Review
- H1Innovative approaches to combat anti-cancer drug resistance: Targeting lncRNA and autophagy.Clinical and translational medicine · 2023Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTo date, standardizing clinical predictive biomarkers for assessing the response to immunotherapy remains challenging due to variations in personal genetic signatures, tumour microenvironment complexities and epigenetic onco-mechanisms. MAIN BODY: Early monitoring of key non-coding RNA (ncRNA) biomarkers may help in predicting the clinical efficacy of cancer immunotherapy and come up with standard predictive ncRNA biomarkers. For instance, reduced miR-125b-5p level in the plasma of non-small cell lung cancer patients treated with anti-PD-1 predicts a positive outcome. The level of miR-153 in the plasma of colorectal cancer patients treated with chimeric antigen receptor T lymphocyte (CAR-T) cell therapy may indicate the activation of T-cell killing activity. miR-148a-3p and miR-375 levels may forecast favourable responses to CAR-T-cell therapy in B-cell acute lymphoblastic leukaemia. In cancer patients treated with the GPC3 peptide vaccine, serum levels of miR-1228-5p, miR-193a-5p and miR-375-3p were reported as predictive biomarkers of good response and improved overall survival. Therefore, there is a critical need for further studies to elaborate on the key ncRNA biomarkers that have the potential to predict early clinical responses to immunotherapy.
conclusionsThis review summarises important predictive ncRNA biomarkers that were reported in cancer patients treated with different immunotherapeutic modalities including monoclonal antibodies, small molecule inhibitors, cancer vaccines and CAR-T cells. In addition, a concise discussion on forthcoming perspectives is provided, outlining technical approaches for the optimal utilisation of immune-modulatory ncRNA biomarkers as predictive tools and therapeutic targets.
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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 OpenQuestion graph.