Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach

Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach

Abstract Background Endometrial cancer represents a significant health challenge, with rising incidence and complex prognostic challenges. This study aimed to develop a robust predictive model integrating programmed cell death-related genes and advanced machine learning techniques. Methods Utilizing...

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Bibliographic Details
Main Authors: Tianshu Chen, Yuhan Yang, Zhizhong Huang, Feng Pan, Zhendi Xiao, Kunxue Gong, Wenguang Huang, Liu Xu, Xueqin Liu, Caiyun Fang
Format: Article
Language:English
Published: Springer 2025-03-01
Series:Discover Oncology
Subjects:
Endometrial cancer
Prognostic modeling
Programmed cell death
Machine learning
Precision oncology
Online Access:https://doi.org/10.1007/s12672-025-02039-8
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Summary:Abstract Background Endometrial cancer represents a significant health challenge, with rising incidence and complex prognostic challenges. This study aimed to develop a robust predictive model integrating programmed cell death-related genes and advanced machine learning techniques. Methods Utilizing transcriptomic data from TCGA-UCEC and GSE119041 datasets, we employed a comprehensive approach involving 117 machine learning algorithms. Key methodologies included differential gene expression analysis, weighted gene co-expression network analysis, functional enrichment studies, immune landscape evaluation, and multi-dimensional risk stratification. Results We identified 10 critical genes (PTGIS, TIMP3, SRPX, SNCA, HIC1, BAK1, STXBP2, TRIB3, RTKN2, E2F1) and constructed a prognostic model with superior predictive performance. The StepCox[forward] + plsRcox algorithm combination demonstrated excellent predictive accuracy (AUC > 0.8). Kaplan–Meier analysis revealed significant survival differences between high- and low-risk groups in both training (HR = 3.37, p < 0.001) and validation cohorts (HR = 2.05, p = 0.021). The model showed strong correlations with clinical characteristics, immune cell infiltration patterns, and potential therapeutic responses. Conclusions This study presents a novel, comprehensive approach to endometrial cancer prognosis, integrating machine learning and molecular insights to provide a more precise risk stratification tool with potential clinical translation.
ISSN:2730-6011

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