Acute myeloid leukemia (AML) is the most common acute leukemia in adults and has the highest fatality rate. Patients aged 65 and above exhibit the poorest prognosis, with a mere 30 % survival rate within one year. One important issue in optimizing outcomes for AML patients is their limited ability to predict responses to specific therapies, response duration, and likelihood of relapse. Despite rigorous therapeutic interventions, a significant proportion of patients experience relapse. Consequently, there is a pressing need to introduce new targets for therapy. Sequencing and biotechnology have come a long way in the last ten years. This has made it easier for many omics technologies, like genomics, transcriptomics, proteomics, and metabolomics, to study molecular mechanisms of AML. An integrative approach is necessary to understand a complex biological process fully and offers an important opportunity to understand the information underlying diseases. In this review, we studied papers published between 2010 and 2024 employing omics approaches encompassing diagnosis, prognosis, and risk stratification of AML. Finally, we discuss prospects and challenges in applying -omics technologies to the discovery of novel biomarkers and therapy targets. Our review may be helpful for omics researchers who want to study AML from different molecular aspects.
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http://dx.doi.org/10.1016/j.cancergen.2024.12.006 | DOI Listing |
Bioinformatics
January 2025
Department of Pathology and Department of Immunobiology, Yale School of Medicine.
Summary: With the increased reliance on multi-omics data for bulk and single cell analyses, the availability of robust approaches to perform unsupervised learning for clustering, visualization, and feature selection is imperative. We introduce nipalsMCIA, an implementation of multiple co-inertia analysis (MCIA) for joint dimensionality reduction that solves the objective function using an extension to Non-linear Iterative Partial Least Squares (NIPALS). We applied nipalsMCIA to both bulk and single cell datasets and observed significant speed-up over other implementations for data with a large sample size and/or feature dimension.
View Article and Find Full Text PDFActa Neuropathol Commun
January 2025
Sid Faithfull Brain Cancer Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia.
Glioblastoma (GBM) is a highly aggressive adult brain cancer, characterised by poor prognosis and a dismal five-year survival rate. Despite significant knowledge gains in tumour biology, meaningful advances in patient survival remain elusive. The field of neuro-oncology faces many disease obstacles, one being the paucity of faithful models to advance preclinical research and guide personalised medicine approaches.
View Article and Find Full Text PDFInt J Biol Macromol
January 2025
Fundação de Medicina Tropical - Dr Heitor Vieira Dourado, Manaus, AM, Brazil; Universidade Nilton Lins, Manaus, AM, Brazil. Electronic address:
Snake venom galactoside-binding lectins (SVgalLs) comprise a group of toxins with the ability to bind specifically, reversibly, and non-covalently to galactose-containing carbohydrates in a Ca-dependent manner. Several SVgalLs have been identified and isolated from Bothrops snake venoms, presenting highly similar structures and biological functions. BjcuL is a galactoside binding C-type lectin isolated from the venom of South America Bothrops jararacussu and consists of the most investigated lectin.
View Article and Find Full Text PDFCancer Genet
January 2025
PhD of Hematology, Assistant Professor, Department of Medical Laboratory Sciences, School of Paramedical Sciences, Hamadan University of Medical Sciences, Hamadan, Iran. Electronic address:
Database (Oxford)
January 2025
European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, CB10 1SD, UK.
The HoloFood project used a hologenomic approach to understand the impact of host-microbiota interactions on salmon and chicken production by analysing multiomic data, phenotypic characteristics, and associated metadata in response to novel feeds. The project's raw data, derived analyses, and metadata are deposited in public, open archives (BioSamples, European Nucleotide Archive, MetaboLights, and MGnify), so making use of these diverse data types may require access to multiple resources. This is especially complex where analysis pipelines produce derived outputs such as functional profiles or genome catalogues.
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