Construction of a prognostic model for lung cancer based on acrolein-related genes
10.19405/j.cnki.issn1000-1492.2025.11.001
- VernacularTitle:丙烯醛相关基因的肺癌预后预测模型
- Author:
Yiting Feng
1
;
Liangliang Ren
2
;
Lijuan Lou
2
;
Yuxian Shen
3
;
Ying Jiang
1
Author Information
1. Dept of Biochemistry and Molecular Biology , School of Basic Medical Sciences , Anhui Medical University , Hefei 230032; State Key Laboratory of Medical Proteomics , Beijing Proteome Research Center , National Center for Protein Sciences (Beijing) , Beijing Institute of Lifeomics , Beijing 102206
2. State Key Laboratory of Medical Proteomics , Beijing Proteome Research Center , National Center for Protein Sciences (Beijing) , Beijing Institute of Lifeomics , Beijing 102206
3. Dept of Biochemistry and Molecular Biology , School of Basic Medical Sciences , Anhui Medical University , Hefei 230032
- Publication Type:Journal Article
- Keywords:
acrolein;
lung cancer;
environmental pollutants;
bioinformatics;
machine learning;
prognostic model
- From:
Acta Universitatis Medicinalis Anhui
2025;60(11):1985-1995
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct and validate a prognostic model for lung cancer based on acrolein-related genes using bioinformatics methods .
Methods:Lung cancer datasets GSE30219 and GSE68465 were obtained from the GEO database , and acrolein-related gene sets were retrieved from the CTD database . Differentially expressed genes (DEGs) between cancer and adjacent tissues were identified in the GSE30219 dataset. The intersection of these DEGs and acrolein-related genes was then used to identify candidate genes . Gene set variation analysis ( GSVA) was performed to assess functional alterations based on the intersection genes . A protein-protein interaction (PPI) network was constructed based on the STRING database to identify core hub genes . Subsequently , support vector machine recursive feature elimination (SVM-RFE) and LASSO-Cox regression analyses were employed to develop a prognostic model based on acrolein-related genes , which was independently validated using the GSE68465 dataset. The CIBERSORT algorithm was applied to evaluate the immune cell infiltration characteristics between high- and low-risk groups , and functional enrichment analysis of DEGs between the two groups was conducted to further ex- plore the potential molecular mechanisms underlying the prognostic model .
Results :A total of 361 acrolein-related DEGs were identified in lung cancer , and 7 key genes were selected for model construction . Kaplan-Meier survival analysis revealed that patients in the high-risk group had significantly lower survival rates compared to those in the low-risk group (P < 0. 000 1) . Receiver operating characteristic (ROC) curve analysis demonstrated that the mod- el possessed good predictive performance . Moreover , immune infiltration analysis indicated that the risk score was closely associated with multiple immune cell subsets , suggesting a potential role of acrolein-related genes in modula- ting the lung cancer immune microenvironment.
Conclusion:The prognostic model for lung cancer based on acro- lein-related genes demonstrates significant application value in predicting the prognosis of lung cancer , providing new insights into the potential mechanisms of acrolein in the onset and progression of lung cancer.
- Full text:202603041121129874丙烯醛相关基因的肺癌预后预测模型_冯祎婷.pdf
