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      Application of omics in the diagnosis, prognosis, and treatment of acute myeloid leukemia

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          Abstract

          Acute myeloid leukemia (AML) is the most frequent leukemia in adults with a high mortality rate. Current diagnostic criteria and selections of therapeutic strategies are generally based on gene mutations and cytogenetic abnormalities. Chemotherapy, targeted therapies, and hematopoietic stem cell transplantation (HSCT) are the major therapeutic strategies for AML. Two dilemmas in the clinical management of AML are related to its poor prognosis. One is the inaccurate risk stratification at diagnosis, leading to incorrect treatment selections. The other is the frequent resistance to chemotherapy and/or targeted therapies. Genomic features have been the focus of AML studies. However, the DNA-level aberrations do not always predict the expression levels of genes and proteins and the latter is more closely linked to disease phenotypes. With the development of high-throughput sequencing and mass spectrometry technologies, studying downstream effectors including RNA, proteins, and metabolites becomes possible. Transcriptomics can reveal gene expression and regulatory networks, proteomics can discover protein expression and signaling pathways intimately associated with the disease, and metabolomics can reflect precise changes in metabolites during disease progression. Moreover, omics profiling at the single-cell level enables studying cellular components and hierarchies of the AML microenvironment. The abundance of data from different omics layers enables the better risk stratification of AML by identifying prognosis-related biomarkers, and has the prospective application in identifying drug targets, therefore potentially discovering solutions to the two dilemmas. In this review, we summarize the existing AML studies using omics methods, both separately and combined, covering research fields of disease diagnosis, risk stratification, prognosis prediction, chemotherapy, as well as targeted therapy. Finally, we discuss the directions and challenges in the application of multi-omics in precision medicine of AML. Our review may inspire both omics researchers and clinical physicians to study AML from a different angle.

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          Most cited references286

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          WGCNA: an R package for weighted correlation network analysis

          Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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            UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses1

            Genomics data from The Cancer Genome Atlas (TCGA) project has led to the comprehensive molecular characterization of multiple cancer types. The large sample numbers in TCGA offer an excellent opportunity to address questions associated with tumo heterogeneity. Exploration of the data by cancer researchers and clinicians is imperative to unearth novel therapeutic/diagnostic biomarkers. Various computational tools have been developed to aid researchers in carrying out specific TCGA data analyses; however there is need for resources to facilitate the study of gene expression variations and survival associations across tumors. Here, we report UALCAN, an easy to use, interactive web-portal to perform to in-depth analyses of TCGA gene expression data. UALCAN uses TCGA level 3 RNA-seq and clinical data from 31 cancer types. The portal's user-friendly features allow to perform: 1) analyze relative expression of a query gene(s) across tumor and normal samples, as well as in various tumor sub-groups based on individual cancer stages, tumor grade, race, body weight or other clinicopathologic features, 2) estimate the effect of gene expression level and clinicopathologic features on patient survival; and 3) identify the top over- and under-expressed (up and down-regulated) genes in individual cancer types. This resource serves as a platform for in silico validation of target genes and for identifying tumor sub-group specific candidate biomarkers. Thus, UALCAN web-portal could be extremely helpful in accelerating cancer research. UALCAN is publicly available at http://ualcan.path.uab.edu.
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              Genomic Classification and Prognosis in Acute Myeloid Leukemia

              New England Journal of Medicine, 374(23), 2209-2221
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                Author and article information

                Contributors
                baosuyixueye@163.com
                gux2002@suda.edu.cn
                chensuning@suda.edu.cn
                Journal
                Biomark Res
                Biomark Res
                Biomarker Research
                BioMed Central (London )
                2050-7771
                10 June 2024
                10 June 2024
                2024
                : 12
                : 60
                Affiliations
                [1 ]National Clinical Research Center for Hematologic Diseases, Jiangsu Institute of Hematology, the First Affiliated Hospital of Soochow University, ( https://ror.org/051jg5p78) Suzhou, China
                [2 ]Jiangsu Key Laboratory of Neuropsychiatric Diseases and College of Pharmaceutical Sciences, Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Suzhou Key Laboratory of Drug Research for Prevention and Treatment of Hyperlipidemic Diseases, Soochow University, ( https://ror.org/05kvm7n82) Suzhou, 215123 Jiangsu China
                [3 ]GRID grid.263761.7, ISNI 0000 0001 0198 0694, Suzhou International Joint Laboratory for Diagnosis and Treatment of Brain Diseases, College of Pharmaceutical Sciences, , Soochow University, ; Suzhou, 215123 Jiangsu China
                [4 ]GRID grid.263761.7, ISNI 0000 0001 0198 0694, MOE Key Laboratory of Geriatric Diseases and Immunology, , Suzhou Medical College of Soochow University, ; Suzhou, 215123 Jiangsu Province China
                Article
                600
                10.1186/s40364-024-00600-1
                11165883
                38858750
                780aebf1-70f6-4eed-999f-a19ef25c71ce
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 20 March 2024
                : 17 May 2024
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 82170158
                Award ID: 82170158
                Award ID: 82170158
                Award ID: 31971353
                Award ID: 82170158
                Categories
                Review
                Custom metadata
                © Yumed Inc. and BioMed Central Ltd., part of Springer Nature 2024

                aml,omics,biomarker,risk stratification,targeted therapy,venetoclax,flt3,menin inhibitor

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