ArticleTranslational oncology2018
Machine Learning Based Prediction of Brain Metastasis of Patients with IIIA-N2 Lung Adenocarcinoma by a Three-miRNA Signature.
Article in Translational oncology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 29 citations in OpenAlex.
- MOFR: Multi-omics Feature Reconstruction for Cancer Classification and Metastasis Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Circulating microRNAs as Biomarkers of Brain Metastases in Lung Cancer: A Pilot Study.Journal of clinical medicine · 2026Article
- The role of miRNAs in the development of brain metastases originating from lung adenocarcinoma.Frontiers in genetics · 2026Article
- Spatial Omics Profiling of Treatment-Naïve Lung Adenocarcinoma with Brain Metastasis as the Initial Presentation.Cancers · 2025Article
- Survival Prediction in Brain Metastasis Patients Treated with Stereotactic Radiosurgery: A Hybrid Machine Learning Approach.Brain sciences · 2025Article
- Advancing miRNA cancer research through artificial intelligence: from biomarker discovery to therapeutic targeting.Medical oncology (Northwood, London, England) · 2024Review
- MicroRNAs in Lung Cancer Brain Metastasis.International journal of molecular sciences · 2024Review
- Machine learning-driven prediction of brain metastasis in lung adenocarcinoma using miRNA profile and target gene pathway analysis of an mRNA dataset.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2024Article
- Advances in applications of artificial intelligence algorithms for cancer-related miRNA research.Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences · 2024Review
- MiRNAs in Lung Adenocarcinoma: Role, Diagnosis, Prognosis, and Therapy.International journal of molecular sciences · 2023Review
- Machine learning and deep learning methods that use omics data for metastasis prediction.Computational and structural biotechnology journal · 2021Review
- Article
- Development and validation of a 25-Gene Panel urine test for prostate cancer diagnosis and potential treatment follow-up.BMC medicine · 2020Article
- MicroRNA in Lung Cancer Metastasis.Cancers · 2019Review
- Brain imaging in early stage non-small cell lung cancer: still a controversial topic?Journal of thoracic disease · 2018Article
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
objectivesMicroRNAs (miRNAs) play a key role in governing posttranscriptional regulation through binding to the mRNAs of target genes. This study is to assess miRNAs expression profiles for identifying brain metastasis-related miRNAs to develop the predictive model by microarray in tumor tissues.
methodsFor this study, we screened the significant brain metastasis-related miRNAs from 77 lung adenocarcinoma (LUAD) patients with brain metastasis (BM+) or non-brain metastasis (BM-). A predictive model was developed from the training set (n=42) using a random Forest supervised classification algorithm and a Class Centered Method, and then validated in a test set (n=35) and further analysis in GSE62182 (n=73). The independence of this signature in BM prediction was measured by multivariate logistic regression analysis.
resultsFrom the training set, the predictive model (including hsa-miR-210, hsa-miR-214 and hsa-miR-15a) stratified the patients into two groups with significantly different BM subtypes (90.4% of accuracy). The similar predictive power (91.4% of accuracy) was obtained in the test cohort. As an independent predictive factor, it was closely associated with BM and had high sensitivity and specificity in predicting BM in clinical practice. Moreover, functional enrichment analysis demonstrated that this signature involved in the signaling pathways positively correlated with cancer metastasis.
conclusionThese results suggested that the three-miRNA signature could develop a new random Forest model to predict the BM of LUAD patients. These findings emphasized the importance of miRNAs in diagnosing BM, and provided evidence for selecting treatment decisions and designing clinical trials.
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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.