Volume 23 Issue 1
Jan.  2025
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Article Navigation >  Chinese Journal of General Practice  > 2025  >  23(1): 21-25
CHEN Shaoming, HU Yan, HONG Xudong, ZHENG Wei, HU Xugang. Construction and validation for a prognostic model of pyroptosis from lung adenocarcinoma based on machine learning[J]. Chinese Journal of General Practice, 2025, 23(1): 21-25. doi: 10.16766/j.cnki.issn.1674-4152.003827
Citation: CHEN Shaoming, HU Yan, HONG Xudong, ZHENG Wei, HU Xugang. Construction and validation for a prognostic model of pyroptosis from lung adenocarcinoma based on machine learning[J]. Chinese Journal of General Practice, 2025, 23(1): 21-25. doi: 10.16766/j.cnki.issn.1674-4152.003827
shu

Construction and validation for a prognostic model of pyroptosis from lung adenocarcinoma based on machine learning

doi: 10.16766/j.cnki.issn.1674-4152.003827

  • Department of General Practice, No. 903 Hospital of PLA Joint Logistic Support Force (Affiliated Xihu Hospital Hangzhou Medical College), Hangzhou, Zhejiang 310000, China

Funds:

 2021KY946

 Y202456549

 B20252302

  • Received Date: 2024-01-30
    Available Online: 2025-02-13
    Abstract
  •   Objective  To construct a prognostic model for lung adenocarcinoma (LUAD) based on pyroptosis related genes (PRGs) and evaluate its association with immune regulation.  Methods  LUAD gene expression and clinicopathological data were obtained from the TCGA database, and differentially expressed PRGs were obtained by the DESeq2 method; the patients were divided into training and validation sets in a 7∶3 ratio by stratified random sampling, and prognosisVrelated genes were screened, and prognostic models were constructed by Cox and Lasso regression; the predictive ability of the prognostic model was evaluated by Kaplan-Meier analysis, ROC curve, and Cox regression analysis in the training set, validation set, and the three test sets (GSE30219, GSE31210, and GSE50081); a nomogram model was constructed to observe the role of the prognostic model combined with the clinical features in the prognostic assessment of LUAD; the correlation between the prognostic model and the immune infiltration was analyzed.  Results  A prognostic model for LUAD consisting of four PRGs, including CPA3, FAT1, MST1, and TFAP2A, was constructed. Patients were divided into the high-risk group and the low-risk group based on median risk scores. Patients in the high-risk group had a poorer prognosis (P < 0.05); The prognostic model risk score has good survival prediction value for patients and was an independent poor prognostic indicator; the nomogram model could effectively predict the survival status. Immune infiltration analysis showed that patients in the high-risk group were in a state of immune dysregulation, and the expression of CD274 and CD276 was significantly elevated.  Conclusion  The prognostic model based on CPA3, FAT1, MST1, and TFAP2A can effectively predict the prognosis of patients with LUAD and correlate with the immune status of the patients, which can be used as an indicator for the evaluation of patient treatment and prognosis.

     

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