ArticleInfectious Disease Modelling2022
A Naive Bayes model on lung adenocarcinoma projection based on tumor microenvironment and weighted gene co-expression network analysis.
Article in Infectious Disease Modelling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Machine learning prediction of STEMI incidence with SHAP interpretation of environmental determinants.Scientific reports · 2025Article
- Early Lung Cancer Detection via AI-Enhanced CT Image Processing Software.Diagnostics (Basel, Switzerland) · 2025Article
- Construction and validation of prognostic models for acute kidney disease and mortality in patients at risk of malnutrition: an interpretable machine learning approach.Clinical kidney journal · 2025Article
- A Machine Learning Model for the Prediction of COVID-19 Severity Using RNA-Seq, Clinical, and Co-Morbidity Data.Diagnostics (Basel, Switzerland) · 2024Article
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Abstract
Based on the lung adenocarcinoma (LUAD) gene expression data from the cancer genome atlas (TCGA) database, the Stromal score, Immune score and Estimate score in tumor microenvironment (TME) were computed by the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm. And gene modules significantly related to the three scores were identified by weighted gene co-expression network analysis (WGCNA). Based on the correlation coefficients and P values, 899 key genes affecting tumor microenvironment were obtained by selecting the two most correlated modules. It was suggested through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis that these key genes were significantly involved in immune-related or cancer-related terms. Through univariate cox regression and elastic network analysis, genes associated with prognosis of the LUAD patients were screened out and their prognostic values were further verified by the survival analysis and the University of ALabama at Birmingham CANcer (UALCAN) database. The results indicated that eight genes were significantly related to the overall survival of LUAD. Among them, six genes were found differentially expressed between tumor and control samples. And immune infiltration analysis further verified that all the six genes were significantly related to tumor purity and immune cells. Therefore, these genes were used eventually for constructing a Naive Bayes projection model of LUAD. The model was verified by the receiver operating characteristic (ROC) curve where the area under curve (AUC) reached 92.03%, which suggested that the model could discriminate the tumor samples from the normal accurately. Our study provided an effective model for LUAD projection which improved the clinical diagnosis and cure of LUAD. The result also confirmed that the six genes in the model construction could be the potential prognostic biomarkers of LUAD.
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