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Integrated machine learning identifies a cellular senescence-related prognostic model to improve outcomes in uterine corpus endometrial carcinoma.
Wei, Changqiang; Lin, Shanshan; Huang, Yanrong; Wei, Yiyun; Mao, Jingxin; Fan, Jiangtao.
Afiliación
  • Wei C; Department of Obstetrics and Gynecology, The First Affiliated Hospital of Guangxi Medical University, Guangxi, China.
  • Lin S; Department of Obstetrics and Gynecology, The First Affiliated Hospital of Guangxi Medical University, Guangxi, China.
  • Huang Y; Department of Obstetrics and Gynecology, The First Affiliated Hospital of Guangxi Medical University, Guangxi, China.
  • Wei Y; Department of Obstetrics and Gynecology, The First Affiliated Hospital of Guangxi Medical University, Guangxi, China.
  • Mao J; Department of Science and Technology Industry, Chongqing Medical and Pharmaceutical College, Chongqing, China.
  • Fan J; Department of Obstetrics and Gynecology, The First Affiliated Hospital of Guangxi Medical University, Guangxi, China.
Front Immunol ; 15: 1418508, 2024.
Article en En | MEDLINE | ID: mdl-38994352
ABSTRACT

Background:

Uterine Corpus Endometrial Carcinoma (UCEC) stands as one of the prevalent malignancies impacting women globally. Given its heterogeneous nature, personalized therapeutic approaches are increasingly significant for optimizing patient outcomes. This study investigated the prognostic potential of cellular senescence genes(CSGs) in UCEC, utilizing machine learning techniques integrated with large-scale genomic data.

Methods:

A comprehensive analysis was conducted using transcriptomic and clinical data from 579 endometrial cancer patients sourced from the Cancer Genome Atlas (TCGA). A subset of 503 CSGs was assessed through weighted gene co-expression network analysis (WGCNA) alongside machine learning algorithms, including Gaussian Mixture Model (GMM), support vector machine - recursive feature elimination (SVM-RFE), Random Forest, and eXtreme Gradient Boosting (XGBoost), to identify key differentially expressed cellular senescence genes. These genes underwent further analysis to construct a prognostic model.

Results:

Our analysis revealed two distinct molecular clusters of UCEC with significant differences in tumor microenvironment and survival outcomes. Utilizing cellular senescence genes, a prognostic model effectively stratified patients into high-risk and low-risk categories. Patients in the high-risk group exhibited compromised overall survival and presented distinct molecular and immune profiles indicative of tumor progression. Crucially, the prognostic model demonstrated robust predictive performance and underwent validation in an independent patient cohort.

Conclusion:

The study emphasized the significance of cellular senescence genes in UCEC progression and underscored the efficacy of machine learning in developing reliable prognostic models. Our findings suggested that targeting cellular senescence holds promise as a strategy in personalized UCEC treatment, thus warranting further clinical investigation.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Endometriales / Senescencia Celular / Aprendizaje Automático Límite: Female / Humans / Middle aged Idioma: En Revista: Front Immunol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Endometriales / Senescencia Celular / Aprendizaje Automático Límite: Female / Humans / Middle aged Idioma: En Revista: Front Immunol Año: 2024 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza