A Prognostic Survival Model Based on Endocrine-Related Gene Expression in Acute Myelogenous Leukemia
Accurate prediction of survival in patients with acute myelogenous leukemia (AML) is challenging. Therefore, we developed a predictive survival model using endocrine-related gene expression to identify an endocrine signature for accurate stratification of AML prognosis. RNA matrices and clinical dat...
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Published in | Acta haematologica p. 1 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
14.01.2025
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Abstract | Accurate prediction of survival in patients with acute myelogenous leukemia (AML) is challenging. Therefore, we developed a predictive survival model using endocrine-related gene expression to identify an endocrine signature for accurate stratification of AML prognosis.
RNA matrices and clinical data for AML were downloaded from a training dataset (Gene Expression Omnibus) and two validation datasets (the Cancer Genome Atlas and Therapeutically Applicable Research to Generate Effective Treatments).
In relation to the survival outcome, a risk model was constructed by incorporating seven endocrine-related genes. The model exhibited favorable predictive efficacy in estimating 5-year survival rates, as demonstrated by both the training and validation cohorts. Multivariable analysis revealed that the endocrine signature demonstrated autonomous prognostic significance in the aforementioned cohorts. Prediction accuracy for 5-year overall survival increased using a nomogram combining endocrine risk score and classical prognostic factors compared with using classical prognostic factors alone. The model predictions were confirmed using AML cell lines.
The endocrine-related prognostic model established in this study improves AML survival prediction accuracy. |
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AbstractList | Accurate prediction of survival in patients with acute myelogenous leukemia (AML) is challenging. Therefore, we developed a predictive survival model using endocrine-related gene expression to identify an endocrine signature for accurate stratification of AML prognosis.
RNA matrices and clinical data for AML were downloaded from a training dataset (Gene Expression Omnibus) and two validation datasets (the Cancer Genome Atlas and Therapeutically Applicable Research to Generate Effective Treatments).
In relation to the survival outcome, a risk model was constructed by incorporating seven endocrine-related genes. The model exhibited favorable predictive efficacy in estimating 5-year survival rates, as demonstrated by both the training and validation cohorts. Multivariable analysis revealed that the endocrine signature demonstrated autonomous prognostic significance in the aforementioned cohorts. Prediction accuracy for 5-year overall survival increased using a nomogram combining endocrine risk score and classical prognostic factors compared with using classical prognostic factors alone. The model predictions were confirmed using AML cell lines.
The endocrine-related prognostic model established in this study improves AML survival prediction accuracy. |
Author | Wu, Bingyi Hu, Fang Song, Yuanbin Huang, Hanying Chen, Lezong Liang, Yang Lv, Weiran Cui, Yingying Wang, Yun |
Author_xml | – sequence: 1 givenname: Weiran surname: Lv fullname: Lv, Weiran email: lvwr@sysucc.org.cn organization: Department of Hematologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China, lvwr@sysucc.org.cn – sequence: 2 givenname: Yun surname: Wang fullname: Wang, Yun organization: Department of Hematologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China – sequence: 3 givenname: Fang surname: Hu fullname: Hu, Fang organization: Department of Hematologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China – sequence: 4 givenname: Hanying surname: Huang fullname: Huang, Hanying organization: Department of Hematologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China – sequence: 5 givenname: Yingying surname: Cui fullname: Cui, Yingying organization: Department of Hematologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China – sequence: 6 givenname: Yuanbin surname: Song fullname: Song, Yuanbin organization: Department of Hematologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China – sequence: 7 givenname: Lezong surname: Chen fullname: Chen, Lezong organization: Department of Hematologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China – sequence: 8 givenname: Bingyi surname: Wu fullname: Wu, Bingyi organization: Department of Hematologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China – sequence: 9 givenname: Yang surname: Liang fullname: Liang, Yang organization: Department of Hematologic Oncology, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China |
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Keywords | Clinical prognostic model Endocrine Acute myelogenous leukemia Experimental verification Nomogram |
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Title | A Prognostic Survival Model Based on Endocrine-Related Gene Expression in Acute Myelogenous Leukemia |
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