ArticleJournal of thoracic disease2022
Machine learning-based screening of the diagnostic genes and their relationship with immune-cell infiltration in patients with lung adenocarcinoma.
Article in Journal of thoracic disease, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A multi-algorithm machine learning framework identifies FGD5, LRRC36, C8B, and MYOC as novel diagnostic biomarkers in lung adenocarcinoma.Discover oncology · 2026Article
- Systems-level analyses and clinical validation highlight CD53 as a diagnostic and prognostic marker in lung adenocarcinoma.Frontiers in cell and developmental biology · 2026Article
- Tumor immune microenvironment in non-small cell lung cancer progression.Frontiers in immunology · 2026Review
- Mutually reinforcing relationship between Sjögren's syndrome and lung adenocarcinoma: insights from Mendelian randomisation, single-cell, and transcriptomic analyses.Frontiers of medicine · 2025Article
- Development and validation of M2 macrophage-related genes in a prognostic model of lung adenocarcinoma based on bulk RNA and ScRNA datasets.Discover oncology · 2025Article
- RhoJ: an emerging biomarker and target in cancer research and treatment.Cancer gene therapy · 2024Review
- A literature review: machine learning-based stem cell investigation.Annals of translational medicine · 2024Review
- Article
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Article
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Authors and funding
7 authors.
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Abstract
Background: Lung adenocarcinoma (LUAD) is the most common type of lung cancer, and has a dismal mortality rate of 80%, mainly due to diagnosis at an advanced stage. Biomarkers with high specificity and sensitivity for the early diagnosis of LUAD are sparse. This study aimed to identify markers for the early diagnosis of LUAD. Methods: The GSE32863 and GSE75037 data sets were standardized and merged to screen for differentially expressed genes (DEGs). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. The intersected DEGs from the least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM) regression analyses were considered the hub genes. Then the diagnostic ability and expression of hub genes was tested in GSE63459 data set, Finally, CIBERSORT was used to analyze the correlation between the immune-infiltrating cells and hub genes. Results: The following 7 DEGs were intersected by the LASSO and SVM regression analyses: Locus 401286 ( Conclusions: We identified 7 DEGs in LUAD tissue that can be considered diagnostic genes based on 2 machine-learning regression methods, which could be very helpful for the early diagnosis of LUAD in clinical practice.
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