Integrative Analysis of Multi-Omics Identified the Prognostic Biomarkers in Acute Myelogenous Leukemia

被引:19
|
作者
Zheng, Jiafeng [1 ]
Zhang, Tongqiang [1 ]
Guo, Wei [1 ]
Zhou, Caili [2 ]
Cui, Xiaojian [3 ]
Gao, Long [4 ]
Cai, Chunquan [5 ]
Xu, Yongsheng [1 ]
机构
[1] Tianjin Univ, Dept Pediat Resp Med, Childrens Hosp, Tianjin Childrens Hosp, Tianjin, Peoples R China
[2] Tianjin Univ, Dept Sci & Educ, Childrens Hosp, Tianjin Childrens Hosp, Tianjin, Peoples R China
[3] Tianjin Univ, Dept Clin Lab, Childrens Hosp, Tianjin Childrens Hosp, Tianjin, Peoples R China
[4] Tianjin Univ, Dept Pediat Endocrinol, Childrens Hosp, Tianjin Childrens Hosp, Tianjin, Peoples R China
[5] Tianjin Univ, Tianjin Inst Pediat, Tianjin Key Lab Birth Defects Prevent & Treatment, Tianjin Childrens Hosp,Childrens Hosp, Tianjin, Peoples R China
来源
FRONTIERS IN ONCOLOGY | 2020年 / 10卷
基金
中国国家自然科学基金;
关键词
multi-omics; prognostic; acute myelogenous leukemia; children; methylation; ABERRANT DNA METHYLATION; EXPRESSION; CANCER; TUMOR;
D O I
10.3389/fonc.2020.591937
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background Acute myelogenous leukemia (AML) is a common pediatric malignancy in children younger than 15 years old. Although the overall survival (OS) has been improved in recent years, the mechanisms of AML remain largely unknown. Hence, the purpose of this study is to explore the differentially methylated genes and to investigate the underlying mechanism in AML initiation and progression based on the bioinformatic analysis. Methods Methylation array data and gene expression data were obtained from TARGET Data Matrix. The consensus clustering analysis was performed using ConsensusClusterPlus R package. The global DNA methylation was analyzed using methylationArrayAnalysis R package and differentially methylated genes (DMGs), and differentially expressed genes (DEGs) were identified using Limma R package. Besides, the biological function was analyzed using clusterProfiler R package. The correlation between DMGs and DEGs was determined using psych R package. Moreover, the correlation between DMGs and AML was assessed using varElect online tool. And the overall survival and progression-free survival were analyzed using survival R package. Results All AML samples in this study were divided into three clusters at k = 3. Based on consensus clustering, we identified 1,146 CpGs, including 40 hypermethylated and 1,106 hypomethylated CpGs in AML. Besides, a total 529 DEGs were identified, including 270 upregulated and 259 downregulated DEGs in AML. The function analysis showed that DEGs significantly enriched in AML related biological process. Moreover, the correlation between DMGs and DEGs indicated that seven DMGs directly interacted with AML. CD34, HOXA7, and CD96 showed the strongest correlation with AML. Further, we explored three CpG sites cg03583857, cg26511321, cg04039397 of CD34, HOXA7, and CD96 which acted as the clinical prognostic biomarkers. Conclusion Our study identified three novel methylated genes in AML and also explored the mechanism of methylated genes in AML. Our finding may provide novel potential prognostic markers for AML.
引用
收藏
页数:12
相关论文
共 50 条
  • [1] Integrative Placental Multi-Omics Analysis Reveals Perturbed Pathways and Potential Prognostic Biomarkers in Gestational Hypertension
    Varghese, Bincy
    Babu, Sreeranjini
    Jala, Aishwarya
    Das, Panchanan
    Raju, Rajesh
    Borkar, Roshan M.
    Adela, Ramu
    ARCHIVES OF MEDICAL RESEARCH, 2024, 55 (01)
  • [2] Multi-omics analysis identified extracellular vesicles as biomarkers for cardiovascular diseases
    Meng, Ke
    Meng, Fanqi
    Wu, Yuan
    Lin, Ling
    TALANTA, 2024, 280
  • [3] Machine learning and integrative multi-omics network analysis for survival prediction in acute myeloid leukemia
    Kosvyra Α.
    Karadimitris Α.
    Papaioannou Μ.
    Chouvarda I.
    Computers in Biology and Medicine, 2024, 178
  • [4] Prognostic Biomarkers in Breast Cancer via Multi-Omics Clustering Analysis
    Malighetti, Federica
    Villa, Matteo
    Villa, Alberto Maria
    Pelucchi, Sara
    Aroldi, Andrea
    Cortinovis, Diego Luigi
    Canova, Stefania
    Capici, Serena
    Cazzaniga, Marina Elena
    Mologni, Luca
    Ramazzotti, Daniele
    Cordani, Nicoletta
    INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES, 2025, 26 (05)
  • [5] Multi-Omics Data Analysis Identifies Prognostic Biomarkers across Cancers
    Demir Karaman, Ezgi
    Isik, Zerrin
    MEDICAL SCIENCES, 2023, 11 (03)
  • [6] Identification of prognostic biomarkers in neuroblastoma using WGCNA and multi-omics analysis
    Ke, Yuhan
    Ge, Wenliang
    DISCOVER ONCOLOGY, 2024, 15 (01)
  • [7] INTEGRATIVE MULTI-OMICS ANALYSIS FOR UNDERSTANDING ACUTE PROMYELOCYTIC LEUKEMIA RESISTANCE: EZH2 ON THE ROAD
    Poplineau, Mathilde
    Herault, Leonard
    Mazuel, Adrien
    Platet, Nadine
    Koide, Shuhei
    Kuribayashi, Wakako
    Carbuccia, Nadine
    N'Guyen, Lia
    Vernerey, Julien
    Oshima, Motohiko
    Birnbaum, Daniel
    Lachaud, Christophe
    Iwama, Atsushi
    Duprez, Estelle
    EXPERIMENTAL HEMATOLOGY, 2021, 100 : S98 - S98
  • [8] A Customizable Analysis Flow in Integrative Multi-Omics
    Lancaster, Samuel M.
    Sanghi, Akshay
    Wu, Si
    Snyder, Michael P.
    BIOMOLECULES, 2020, 10 (12) : 1 - 15
  • [9] Integrative multi-omics analysis of muscle-invasive bladder cancer identifies prognostic biomarkers for frontline chemotherapy and immunotherapy
    Qianxing Mo
    Roger Li
    Dennis O. Adeegbe
    Guang Peng
    Keith Syson Chan
    Communications Biology, 3
  • [10] Integrative multi-omics analysis of muscle-invasive bladder cancer identifies prognostic biomarkers for frontline chemotherapy and immunotherapy
    Mo, Qianxing
    Li, Roger
    Adeegbe, Dennis O.
    Peng, Guang
    Chan, Keith Syson
    COMMUNICATIONS BIOLOGY, 2020, 3 (01)