Construction of a prognostic prediction model for invasive lung adenocarcinoma based on machine learning
- VernacularTitle:基于机器学习算法构建浸润性肺腺癌预后预测模型
- Author:
Yanqi CUI
1
;
Jingrong YANG
2
;
Lin NI
3
;
Duohuang LIAN
2
;
Shixin YE
2
;
Yi LIAO
1
;
Jincan ZHANG
2
;
Zhiyong ZENG
4
Author Information
1. Fuzong Clinical College of Fujian Medical University, Fuzhou, 350025, P. R. China
2. Department of Cardiothoracic Surgery, The 900th Hospital of the Joint Support Force, Fuzhou, 350025, P. R. China
3. Department of General Surgery, The 900th Hospital of the Joint Support Force, Fuzhou, 350025, P. R. China
4. 1. Fuzong Clinical College of Fujian Medical University, Fuzhou, 350025, P. R. China 2. Department of Cardiothoracic Surgery, The 900th Hospital of the Joint Support Force, Fuzhou, 350025, P. R. China
- Publication Type:Journal Article
- Keywords:
Invasive lung adenocarcinoma;
machine learning;
biomarker;
risk assessment model;
nomogram
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(01):80-86
- CountryChina
- Language:Chinese
-
Abstract:
Objective To determine the prognostic biomarkers and new therapeutic targets of the lung adenocarcinoma (LUAD), based on which to establish a prediction model for the survival of LUAD patients. Methods An integrative analysis was conducted on gene expression and clinicopathologic data of LUAD, which were obtained from the UCSC database. Subsequently, various methods, including screening of differentially expressed genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Set Enrichment Analysis (GSEA), were employed to analyze the data. Cox regression and least absolute shrinkage and selection operator (LASSO) regression were used to establish an assessment model. Based on this model, we constructed a nomogram to predict the probable survival of LUAD patients at different time points (1-year, 2-year, 3-year, 5-year, and 10-year). Finally, we evaluated the predictive ability of our model using Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves. The validation group further verified the prognostic value of the model. Results The different-grade pathological subtypes' DEGs were mainly enriched in biological processes such as metabolism of xenobiotics by cytochrome P450, natural killer cell-mediated cytotoxicity, antigen processing and presentation, and regulation of enzyme activity, which were closely related to tumor development. Through Cox regression and LASSO regression, we constructed a reliable prediction model consisting of a five-gene panel (MELTF, MAGEA1, FGF19, DKK4, C14ORF105). The model demonstrated excellent specificity and sensitivity in ROC curves, with an area under the curve (AUC) of 0.675. The time-dependent ROC analysis revealed AUC values of 0.893, 0.713, and 0.632 for 1-year, 3-year, and 5-year survival, respectively. The advantage of the model was also verified in the validation group. Additionally, we developed a nomogram that accurately predicted survival, as demonstrated by calibration curves and C-index. Conclusion We have developed a prognostic prediction model for LUAD consisting of five genes. This novel approach offers clinical practitioners a personalized tool for making informed decisions regarding the prognosis of their patients.
