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      Key candidate genes and pathways in T lymphoblastic leukemia/lymphoma identified by bioinformatics and serological analyses

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          Abstract

          T-cell acute lymphoblastic leukemia (T -ALL)/T-cell lymphoblastic lymphoma (T-LBL) is an uncommon but highly aggressive hematological malignancy. It has high recurrence and mortality rates and is challenging to treat. This study conducted bioinformatics analyses, compared genetic expression profiles of healthy controls with patients having T-ALL/T-LBL, and verified the results through serological indicators. Data were acquired from the GSE48558 dataset from Gene Expression Omnibus (GEO). T-ALL patients and normal T cells-related differentially expressed genes (DEGs) were investigated using the online analysis tool GEO2R in GEO, identifying 78 upregulated and 130 downregulated genes. Gene Ontology (GO) and protein-protein interaction (PPI) network analyses of the top 10 DEGs showed enrichment in pathways linked to abnormal mitotic cell cycles, chromosomal instability, dysfunction of inflammatory mediators, and functional defects in T-cells, natural killer (NK) cells, and immune checkpoints. The DEGs were then validated by examining blood indices in samples obtained from patients, comparing the T-ALL/T-LBL group with the control group. Significant differences were observed in the levels of various blood components between T-ALL and T-LBL patients. These components include neutrophils, lymphocyte percentage, hemoglobin (HGB), total protein, globulin, erythropoietin (EPO) levels, thrombin time (TT), D-dimer (DD), and C-reactive protein (CRP). Additionally, there were significant differences in peripheral blood leukocyte count, absolute lymphocyte count, creatinine, cholesterol, low-density lipoprotein, folate, and thrombin times. The genes and pathways associated with T-LBL/T-ALL were identified, and peripheral blood HGB, EPO, TT, DD, and CRP were key molecular markers. This will assist the diagnosis of T-ALL/T-LBL, with applications for differential diagnosis, treatment, and prognosis.

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          NCBI GEO: archive for functional genomics data sets—update

          The Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high-throughput microarray and next-generation sequence functional genomic data sets submitted by the research community. The resource supports archiving of raw data, processed data and metadata which are indexed, cross-linked and searchable. All data are freely available for download in a variety of formats. GEO also provides several web-based tools and strategies to assist users to query, analyse and visualize data. This article reports current status and recent database developments, including the release of GEO2R, an R-based web application that helps users analyse GEO data.
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            Hypoxic control of metastasis.

            Metastatic disease is the leading cause of cancer-related deaths and involves critical interactions between tumor cells and the microenvironment. Hypoxia is a potent microenvironmental factor promoting metastatic progression. Clinically, hypoxia and the expression of the hypoxia-inducible transcription factors HIF-1 and HIF-2 are associated with increased distant metastasis and poor survival in a variety of tumor types. Moreover, HIF signaling in malignant cells influences multiple steps within the metastatic cascade. Here we review research focused on elucidating the mechanisms by which the hypoxic tumor microenvironment promotes metastatic progression. These studies have identified potential biomarkers and therapeutic targets regulated by hypoxia that could be incorporated into strategies aimed at preventing and treating metastatic disease.
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              The genetic basis of early T-cell precursor acute lymphoblastic leukaemia.

              Early T-cell precursor acute lymphoblastic leukaemia (ETP ALL) is an aggressive malignancy of unknown genetic basis. We performed whole-genome sequencing of 12 ETP ALL cases and assessed the frequency of the identified somatic mutations in 94 T-cell acute lymphoblastic leukaemia cases. ETP ALL was characterized by activating mutations in genes regulating cytokine receptor and RAS signalling (67% of cases; NRAS, KRAS, FLT3, IL7R, JAK3, JAK1, SH2B3 and BRAF), inactivating lesions disrupting haematopoietic development (58%; GATA3, ETV6, RUNX1, IKZF1 and EP300) and histone-modifying genes (48%; EZH2, EED, SUZ12, SETD2 and EP300). We also identified new targets of recurrent mutation including DNM2, ECT2L and RELN. The mutational spectrum is similar to myeloid tumours, and moreover, the global transcriptional profile of ETP ALL was similar to that of normal and myeloid leukaemia haematopoietic stem cells. These findings suggest that addition of myeloid-directed therapies might improve the poor outcome of ETP ALL.
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                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                23 February 2024
                2024
                : 15
                : 1341255
                Affiliations
                [1] 1State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College , Tianjin, China
                [2] 2Tianjin Institutes of Health Science , Tianjin, China
                [3] 3Clinical Laboratory of Zhengning County People's Hospital , Qingyang, Gansu, China
                [4] 4Department of Geriatrics, Tianjin Medical University General Hospital, Tianjin Geriatrics Institute , Tianjin, China
                Author notes

                Edited by: Omer Jamy, University of Alabama at Birmingham, United States

                Reviewed by: Manuel Espinoza-Gutarra, University of Alabama at Birmingham, United States

                Antonio Di Stasi, University of Alabama at Birmingham, United States

                †These authors have contributed equally to this work

                Article
                10.3389/fimmu.2024.1341255
                10920334
                38464517
                c105b77d-d7ac-4db2-9b7f-618a797e0541
                Copyright © 2024 Ren, Liang, Huang, Miao, Li, Qiang, Wu, Qi, Li, Xia, Huang, Wang, Kong, Zhou, Zhang and Zhu

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 20 November 2023
                : 08 February 2024
                Page count
                Figures: 8, Tables: 0, Equations: 0, References: 83, Pages: 18, Words: 9343
                Funding
                The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by grants from the Major Research Plan of National Natural Science Foundation of China (Grant Number: 92163213), General Program of National Natural Science Foundation of China (Grant Number: 81970085), Tianjin science and technology plan project (Grant Number: 21JCZDJC00940) and Tianjin health science and technology projects (Grant Number: TJWJ2022XK001). This work was supported funded by Tianjin Key Medical Discipline (Specialty) Construction Project (Grant Number: TJYXZDXK-006A). This work was supported by grants from National Key Research and Development Program of China (2020YFE0203000), National Natural Science Foundation of China (81890990, 82270148) and CAMS Innovation Fund for Medical Sciences (2022-I2M-2-003).
                Categories
                Immunology
                Original Research
                Custom metadata
                Cancer Immunity and Immunotherapy

                Immunology
                t-all,t-lbl,bioinformatics analysis,serology,protein-protein interaction networks
                Immunology
                t-all, t-lbl, bioinformatics analysis, serology, protein-protein interaction networks

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