ArticleBMC cancer2019
A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies.
Article in BMC cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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44 citing papers in PubMed, 86 citations in OpenAlex.
- The diagnostic value of CDC20 for malignant pleural effusion of lung adenocarcinoma.Scientific reports · 2026Article
- Bioinformatics-Based Discovery of Therapeutic Targets in Cadmium-Induced Lung Adenocarcinoma: The Role of Oxyresveratrol.Biological trace element research · 2026Article
- Identification of diagnostic and prognostic biomarkers in lung adenocarcinoma through integrated bioinformatics analysis and real time PCR validation.Scientific reports · 2026Article
- Application of deep learning in prognostic prediction of non-small cell lung cancer.Frontiers in cell and developmental biology · 2026Review
- Development of a machine learning-based model to predict prognosis of resected invasive pulmonary adenocarcinoma.Journal of thoracic disease · 2025Article
- Review
- Assessing the prognosis mortality in patients with cutaneous verrucous carcinoma using Lasso-cox regression model: a retrospective study.Discover oncology · 2025Article
- A machine learning approach for multimodal data fusion for survival prediction in cancer patients.NPJ precision oncology · 2025Article
- Machine learning-based model for CD4Scientific reports · 2024Article
- Diagnostic value of immune-related biomarker FAM83A in differentiating malignant from benign pleural effusion in lung adenocarcinoma.Discover oncology · 2024Article
- UPP1 promotes lung adenocarcinoma progression through the induction of an immunosuppressive microenvironment.Nature communications · 2024Article
- Improving Pancreatic Cyst Management: Artificial Intelligence-Powered Prediction of Advanced Neoplasms through Endoscopic Ultrasound-Guided Confocal Endomicroscopy.Biomimetics (Basel, Switzerland) · 2023Review
- Autoencoder-based multimodal prediction of non-small cell lung cancer survival.Scientific reports · 2023Article
- The activity of cuproptosis pathway calculated by AUCell algorithm was employed to construct cuproptosis landscape in lung adenocarcinoma.Discover oncology · 2023Article
- Clinical applications of DNA methylation profiling in lung cancer.Translational lung cancer research · 2023Article
- Identification of New Prognostic Genes and Construction of a Prognostic Model for Lung Adenocarcinoma.Diagnostics (Basel, Switzerland) · 2023Article
- An eleven-gene risk model associated with lymph node metastasis predicts overall survival in lung adenocarcinoma.Scientific reports · 2023Article
- Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma.Cancers · 2023Review
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- A nomogram and risk classification model predicts prognosis in Chinese esophageal squamous cell carcinoma patients.Translational cancer research · 2022Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
backgroundPredicting lung adenocarcinoma (LUAD) risk is crucial in determining further treatment strategies. Molecular biomarkers may improve risk stratification for LUAD.
methodsWe analyzed the gene expression profiles of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). We initially used three distinct algorithms (sigFeature, random forest, and univariate Cox regression) to evaluate each gene's prognostic relevance. Survival related genes were then fitted into the least absolute shrinkage and selection operator (LASSO) model to build a risk prediction model for LUAD. After 100,000 times of calculation and model construction, a 16-gene-based prediction model capable of classifying LUAD patients into high-risk and low-risk groups was successfully built.
resultsUsing a combined strategy, we initially identified 2472 significant survival-related genes. Functional enrichment analysis demonstrated these genes' relevance to tumor initiation and progression. Using the LASSO method, we successfully built a reliable risk prediction model. The risk model was validated in two external sets and an independent set. The expression of these 16 genes was highly correlated with patients' risk. High-risk group patients witnessed poorer recurrence-free survival (RFS) and overall survival (OS) compared to low-risk group patients. Moreover, stratification analysis and decision curve analysis (DCA) confirmed the independence and potential translational value of this predictive tool. We also built a nomogram comprising risk model and stage to predict OS for LUAD patients.
conclusionsOur risk model may serve as a practical and reliable prognosis predictive tool for LUAD and could provide novel insights into the understanding of the molecular mechanism of this disease.
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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.