P1.063. Development of a Machine Learning-Based Programmed Cell Death Genes Prognostic Model for Esophagea Carcinoma

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ID: 325649
2026
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Ranked #30 of 453 articles by views in diseases of the esophagus : official journal of the international society for diseases of the esophagus

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Abstract
Abstract Topic Esophageal Cancer: Molecular Biology/Pathology Background To use bioinformatics methods to evaluate the prognostic value of Programmed Cell Death Related Genes (PCDRGs) in esophageal carcinoma (EC), and to explore the development and immune regulatory mechanisms of EC from multiple perspectives. Methods Using TCGA, GSE53622 data sets, and downloaded key regulatory genes of 18 PCD patterns, combined with 10 different machine learning methods to develop a prediction model, named this model ‘Characteristics of Cell Deaths’ (CDS). Seven prognosis-related genes were screened out by the model. The correlationbetween these seven genes and EC was analyzed. Results The PCDRGs prognostic model developed using the StepCox[both] + RSF method performed the best. CDS showed significant and powerful performance in predicting EC clinical outcomes and was able to serve as an independent risk factor in TCGA and GEO datasets. Conclusion This study successfully developed a novel EC PCDRGs model, which could predict the prognosis and drug treatment sensitivity of EC patients in the future based on further validation.
Reference Key
openalex_W7203894094 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Simiao Lu, Yi Zhu, Yongtao Han, Qiuling Shi, Xuefeng Leng
Journal diseases of the esophagus : official journal of the international society for diseases of the esophagus
Year 2026
DOI
10.1093/dote/doag077.211
URL
https://doi.org/10.1093/dote/doag077.211
Keywords Keywords not found

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