ReviewTranslational lung cancer research2024
Research into overcoming drug resistance in lung cancer treatment using CRISPR-Cas9 technology: a narrative review.
Review in Translational lung cancer research, 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
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
6 citing papers in PubMed.
- CRISPR-Cas9 in Cancer Therapy: Overcoming Delivery Barriers and Off-Target Effects for Clinical Translation.Health science reports · 2026Article
- TrxR Inhibition and Nrf2-FOXO3 Modulation by Repurposed Drugs: A Redox Strategy to Reverse Cancer Multidrug Resistance.Drug development research · 2026Review
- Role of the SOX family in non‑small cell lung cancer: Molecular mechanisms and therapeutic implications (Review).Oncology reports · 2026Review
- CRISPR-driven diagnostics: Molecular mechanisms, clinical efficacy and translational challenges.Clinical and translational medicine · 2025Review
- Review
- Biomarkers for immunotherapy resistance in non-small cell lung cancer.Frontiers in oncology · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: Lung cancer remains a leading cause of cancer-related mortality globally, with drug resistance posing a significant challenge to effective treatment. The advent of clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated protein 9 (CRISPR-Cas9) technology offers a novel and precise gene-editing technology for targeting and negating drug resistance mechanisms in lung cancer. This review summarizes the research progress in the use of CRISPR-Cas9 technology for investigating and managing drug resistance in lung cancer treatment. Methods: A literature search was conducted using the Web of Science and PubMed databases, with the following keywords: [CRISPR-Cas9], [lung cancer], [drug resistance], [gene editing], and [gene therapy]. The search was limited to articles published in English from 2002 to September 2023. From the search results, studies that utilized CRISPR-Cas9 technology in the context of lung cancer drug resistance were selected for further analysis and summarize. Key Content and Findings: CRISPR-Cas9 technology enables precise DNA-sequence editing, allowing for the targeted addition, deletion, or modification of genes. It has been applied to investigate drug resistance in lung cancer by focusing on key genes such as epidermal growth factor receptor ( Conclusions: The CRISPR-Cas9 technology holds great promise for advancing lung cancer treatment, particularly in addressing drug resistance. The ability to precisely target and edit genes involved in resistance pathways offers a powerful tool for developing more effective and personalized therapies. While challenges remain in terms of delivery, safety, and ethical considerations, ongoing research and technological refinements are expected to further enhance the role of CRISPR-Cas9 in improving patient outcomes in lung cancer treatment.
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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.