ArticleGenes2022
Extracellular Vesicle MicroRNA in Malignant Pleural Effusion.
Article in Genes, 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
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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
11 citing papers in PubMed, 13 citations in OpenAlex.
- Unraveling the multidimensional pleural ecosystem of lung cancer-associated malignant pleural effusion.Cancer cell international · 2026Review
- Effects of small extracellular vesicles isolated from pleural effusion on lung cancer cell proliferation and migration.Human cell · 2025Article
- Nucleic acids in pleural fluid for the etiological diagnosis of pleural effusion.Translational lung cancer research · 2025Article
- MicroRNA expression in pleural fluid as a diagnostic biomarker of malignant pleural effusion: promise, pragmatism, and the path forward.Translational lung cancer research · 2025Article
- Research Priorities for Malignant Pleural Organization with Loculation and Failed Drainage.Cells · 2025Review
- Exosomal miR-182-5p is a potential diagnostic marker for malignant pleural effusion.Translational lung cancer research · 2025Article
- A novel approach in the identification of microRNAs in malignant pleural effusion for lung cancer diagnosis.Oncology reviews · 2025Review
- Liquid-based biomarkers in breast cancer: looking beyond the blood.Journal of translational medicine · 2023Review
- Review
- A plasma miRNA-based classifier for small cell lung cancer diagnosis.Frontiers in oncology · 2023Article
- Malignant pleural effusion diagnosis and therapy.Open life sciences · 2023Review
Corrections and comments
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Authors and funding
16 authors at 4 institutions in 4 countries.
Funding
Abstract
Lung and breast cancer are the two most common causes of malignant pleural effusion (MPE). MPE diagnosis plays a crucial role in determining staging and therapeutic interventions in these cancers. However, our understanding of the pathogenesis and progression of MPE at the molecular level is limited. Extracellular Vesicles (EVs) and their contents, including microRNAs (miRNAs), can be isolated from all bodily fluids, including pleural fluid. This study aims to compare EV-miRNA patterns of expression in MPE caused by breast (BA-MPE) and lung (LA-MPE) adenocarcinomas compared to the control group of heart-failure-induced effusions (HF-PE). We conducted an analysis of 24 pleural fluid samples (8 LA-MPE, 8 BA-MPE, and 8 HF-PE). Using NanoString technology, we profiled miRNAs within EVs isolated from 12 cases. Bioinformatic analysis demonstrated differential expression of miR-1246 in the MPE group vs. HF-PE group and miR-150-5p and miR-1246 in the BA-MPE vs. LA-MPE group, respectively. This difference was demonstrated and validated in an independent cohort using real-time PCR (RT-PCR). miRNA-1246 demonstrated 4-fold increased expression (OR: 3.87, 95% CI: 0.43, 35) in the MPE vs. HF-PE group, resulting in an area under the curve of 0.80 (95% CI: 0.60, 0.99). The highest accuracy for differentiating MPE vs. HF-PE was seen with a combination of miRNAs compared to each miRNA alone. Consistent with prior studies, this study demonstrates dysregulation of specific EV-based miRNAs in breast and lung cancer; pleural fluid provides direct access for the analysis of these EV-miRNAs as biomarkers and potential targets and may provide insight into the underlying pathogenesis of tumor progression. These findings should be explored in large prospective studies.
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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.