Identification of cell surface markers for acute myeloid leukemia prognosis based on multi-model analysis
作者机构：Department of Bioinformatics School of Biomedical Engineering and Informatics Nanjing Medical University Department of Hematology of the Affiliated Huai'an No.1 People's Hospital of Nanjing Medical University Northern Jiangsu Institute of Clinical Medicine Collaborative Innovation Center for Personalized Cancer Medicine Jiangsu Key Lab of Cancer BiomarkersPrevention and Treatment Nanjing Medical University Department of Hematology the First Affiliated Hospital of Nanjing Medical University Jiangsu Province Hospital Key Laboratory of Hematology of Nanjing Medical University Department of Pharmacology School of Basic Medical Sciences Nanjing Medical University The Affiliated Cancer Hospital of Nanjing Medical University Jiangsu Cancer Hospital Jiangsu Institute of Cancer Research
出 版 物：《The Journal of Biomedical Research》 (生物医学研究杂志)
年 卷 期：2024年第04期
页 面：397-412页
核心收录：
学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学]
基 金：supported by the National Natural Science Foundation of China (Grant Nos.32200590 to K.L., 81972358 to Q.W., 91959113 to Q.W., and 82372897 to Q.W.) the Natural Science Foundation of Jiangsu Province (Grant No.B K20210530 to K.L.)
摘 要：Given the extremely high inter-patient heterogeneity of acute myeloid leukemia(AML), the identification of biomarkers for prognostic assessment and therapeutic guidance is critical. Cell surface markers(CSMs) have been shown to play an important role in AML leukemogenesis and progression. In the current study, we evaluated the prognostic potential of all human CSMs in 130 AML patients from The Cancer Genome Atlas(TCGA) based on differential gene expression analysis and univariable Cox proportional hazards regression analysis. By using multi-model analysis, including Adaptive LASSO regression, LASSO regression, and Elastic Net, we constructed a 9-CSMs prognostic model for risk stratification of the AML patients. The predictive value of the 9-CSMs risk score was further validated at the transcriptome and proteome levels. Multivariable Cox regression analysis showed that the risk score was an independent prognostic factor for the AML patients. The AML patients with high 9-CSMs risk scores had a shorter overall and event-free survival time than those with low scores. Notably,single-cell RNA-sequencing analysis indicated that patients with high 9-CSMs risk scores exhibited chemotherapy resistance. Furthermore, PI3K inhibitors were identified as potential treatments for these high-risk patients. In conclusion, we constructed a 9-CSMs prognostic model that served as an independent prognostic factor for the survival of AML patients and held the potential for guiding drug therapy.
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