ReviewTranslational lung cancer research2026
Application of organoid models in basic and translational research of lung cancer: a narrative review.
Review in Translational lung cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Objective: Lung cancer remains the leading cause of cancer-related mortality worldwide. Despite advances in targeted and immunotherapy, challenges such as drug resistance persist. Traditional experimental models often fail to recapitulate human tumor complexity, limiting translational success. Patient-derived organoids (PDOs), which retain key characteristics of original tumors, have emerged as powerful tools. This review aims to systematically examine the methodologies, characterizations, and applications of lung cancer organoids (LCOs) in both basic and translational research. Methods: A narrative review was conducted based on literature retrieved from PubMed, Scopus, and Web of Science up to March 2026. Search terms included "lung cancer organoid/LCO", "patient-derived organoid/PDO", "three-dimensional (3D) culture", "2.5-dimensional (2.5D) culture", "drug susceptibility testing", and "precision medicine". Studies focusing on culture techniques, molecular characterization, and preclinical/clinical applications were included. Key Content and Findings: This study summarizes current LCO culture systems, comparing conventional 3D platforms and emerging 2.5D systems that offer significant advantages in cost, operational simplicity, and imaging convenience for rapid clinical applications. We detail multi-dimensional characterization approaches (morphological, molecular, functional) and discuss critical quality control standards. LCOs have been instrumental in studying tumorigenesis mechanisms, signaling pathways, and metabolic reprogramming. In translational research, LCOs show high predictive value for drug susceptibility to targeted agents, chemotherapy, and immunotherapy, and serve as platforms for developing novel therapeutics such as antibody-drug conjugates (ADCs). Conclusions: LCO models faithfully recapitulate tumor heterogeneity and are increasingly integral to precision medicine and drug development. While challenges remain in vascularization, immune microenvironment reconstitution, and standardization, ongoing integration with bioengineering and artificial intelligence (AI) promises to enhance their translational utility. This review underscores the potential of LCOs to bridge basic research and clinical practice, accelerating personalized therapeutic strategies in lung cancer.
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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.