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

Front Immunol. 2024 Feb 23:15:1341255. doi: 10.3389/fimmu.2024.1341255. eCollection 2024.

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.

Keywords: T-ALL; T-LBL; bioinformatics analysis; protein-protein interaction networks; serology.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Biomarkers, Tumor / genetics
  • Biomarkers, Tumor / metabolism
  • Computational Biology / methods
  • Humans
  • Lymphoma, T-Cell*
  • Precursor T-Cell Lymphoblastic Leukemia-Lymphoma* / genetics
  • Precursor T-Cell Lymphoblastic Leukemia-Lymphoma* / pathology
  • Protein Interaction Maps / genetics
  • Transcriptome

Substances

  • Biomarkers, Tumor

Grants and 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).