ArticleOncology letters2019
Decision tree-based classifiers for lung cancer diagnosis and subtyping using TCGA miRNA expression data.
Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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34 citing papers in PubMed, 64 citations in OpenAlex.
- Integrative transcriptomic and experimental analysis identifies GREM1 as a pro-metastatic mediator in lung adenocarcinoma.Journal of translational medicine · 2026Article
- Current trends and future directions of artificial intelligence in lung cancer diagnosis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Review
- Artificial intelligence-based miRNA analysis for precision oncology: diagnostic and prognostic insights.Frontiers in molecular biosciences · 2026Review
- The clinical significance of circulating microRNAs as biomarkers in lung cancer diagnosis and prognosis.Discover oncology · 2025Review
- Multi-omic data integration and exploiting metabolic models using systems biology approach increase precision in subtyping and early diagnosis of cancer.Quantitative biology (Beijing, China) · 2025Article
- Opportunities and challenges in lung cancer care in the era of large language models and vision language models.Translational lung cancer research · 2025Review
- Profiling the cell-specific small non-coding RNA transcriptome of the human placenta.Scientific reports · 2025Article
- Exploring Feature Selection with Deep Learning for Kidney Tissue Microarray Classification Using Infrared Spectral Imaging.Bioengineering (Basel, Switzerland) · 2025Article
- The Epithelial Cell-Associated Gene PMAIP1 Serves as a Prognostic Biomarker for Lung Adenocarcinoma and Can Regulate the Stemness of Lung Cancer.Stem cells international · 2025Article
- Investigating the role of prognostic mitophagy-related genes in non-small cell cancer pathogenesis via multiomics and network-based approach.3 Biotech · 2024Article
- Decoding core genes and intercellular communication in osteosarcoma: bioinformatic investigation and immune cell profiling for diagnostic and therapeutic insights.Discover oncology · 2024Article
- Evaluating Generative AI's Ability to Identify Cancer Subtypes in Publicly Available Structured Genetic Datasets.Journal of personalized medicine · 2024Article
- "Crosstalk between non-coding RNAs and transcription factor LRF in non-small cell lung cancer".Non-coding RNA research · 2024Article
- A model-free and distribution-free multi-omics integration approach for detecting novel lung adenocarcinoma genes.Scientific reports · 2024Article
- Robust and consistent biomarker candidates identification by a machine learning approach applied to pancreatic ductal adenocarcinoma metastasis.BMC medical informatics and decision making · 2024Article
- Survival Rate and Chronic Diseases of TCGA Cancer and KoGES Normal Samples by Clustering for DNA Methylation.Life (Basel, Switzerland) · 2024Article
- Molecular Subtypes and Prognostic Models for Predicting Prognosis of Lung Adenocarcinoma based on MiRNA-related Genes.Current medicinal chemistry · 2024Article
- MicroRNA-875-5p inhibits the growth and metastasis of cervical cancer cells by promoting autophagy and apoptosis and inhibiting the epithelial-mesenchymal transition.Frontiers in oncology · 2024Article
- Early Recognition of Secondary Asthma Caused by Lower Respiratory Tract Infection in Children Based on Multi-Omics Signature: A Retrospective Cohort Study.International journal of general medicine · 2024Article
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2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Lung cancer has the world's highest cancer- associated mortality rate, making biomarker discovery for this cancer a pressing issue. Machine learning approaches to identify molecular biomarkers are not as prevalent as screening of potential biomarkers by differential expression analysis. However, several differentially expressed miRNAs involved in cancer have been identified using this approach. The availability of The Cancer Genome Atlas (TCGA) allows the use of machine-learning methods for the molecular profiling of tumors. The present study employed empirical negative control microRNAs (miRs) in lung cancer to normalize lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) datasets from TCGA to model decision trees in order to classify lung cancer status and subtype. The two primary classification models consisted of four miRNAs for lung cancer diagnosis and subtyping. hsa-miR-183 and hsa-miR-135b were used to distinguish lung tumors from normal samples taken from tissues adjacent to the tumor site, and hsa-miR-944 and hsa-miR-205 to further classify the tumors into LUAD and LUSC major subtypes. Specific cancer status classification models were also presented for each subtype.
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