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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| Format: | Article |
| Language: | English |
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Springer
2025-03-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-02039-8 |
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| author | Tianshu Chen Yuhan Yang Zhizhong Huang Feng Pan Zhendi Xiao Kunxue Gong Wenguang Huang Liu Xu Xueqin Liu Caiyun Fang |
| author_facet | Tianshu Chen Yuhan Yang Zhizhong Huang Feng Pan Zhendi Xiao Kunxue Gong Wenguang Huang Liu Xu Xueqin Liu Caiyun Fang |
| author_sort | Tianshu Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c930af13a6284b54b462431507a8cdac |
| institution | DOAJ |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-c930af13a6284b54b462431507a8cdac2025-08-20T02:59:57ZengSpringerDiscover Oncology2730-60112025-03-0116111810.1007/s12672-025-02039-8Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approachTianshu Chen0Yuhan Yang1Zhizhong Huang2Feng Pan3Zhendi Xiao4Kunxue Gong5Wenguang Huang6Liu Xu7Xueqin Liu8Caiyun Fang9Department of Gynecology, Taihe Hospital, Hubei University of MedicineDepartment of Gynecology, Taihe Hospital, Hubei University of MedicineDepartment of Gynecology, Taihe Hospital, Hubei University of MedicineDepartment of Gynecology, Taihe Hospital, Hubei University of MedicineDepartment of Gynecology, Taihe Hospital, Hubei University of MedicineDepartment of Gynecology, Taihe Hospital, Hubei University of MedicineDepartment of Gynecology, Taihe Hospital, Hubei University of MedicineDepartment of Gynecology, Taihe Hospital, Hubei University of MedicineDepartment of Gynecology, Taihe Hospital, Hubei University of MedicineDepartment of Gynecology, Taihe Hospital, Hubei University of MedicineAbstract 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.https://doi.org/10.1007/s12672-025-02039-8Endometrial cancerPrognostic modelingProgrammed cell deathMachine learningPrecision oncology |
| spellingShingle | Tianshu Chen Yuhan Yang Zhizhong Huang Feng Pan Zhendi Xiao Kunxue Gong Wenguang Huang Liu Xu Xueqin Liu Caiyun Fang Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach Discover Oncology Endometrial cancer Prognostic modeling Programmed cell death Machine learning Precision oncology |
| title | Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach |
| title_full | Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach |
| title_fullStr | Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach |
| title_full_unstemmed | Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach |
| title_short | Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach |
| title_sort | prognostic risk modeling of endometrial cancer using programmed cell death related genes a comprehensive machine learning approach |
| topic | Endometrial cancer Prognostic modeling Programmed cell death Machine learning Precision oncology |
| url | https://doi.org/10.1007/s12672-025-02039-8 |
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