Objective: To analyze the changes in the gene expression profile of T cells in CML patients after TCRζ up-regulation expression, and to explore the molecular mechanism of T cell reactivation after transgenic up-regulation of TCRζ.
Methods: The peripheral blood mononuclear cells(PBMCs) from 3 newly untreated chronic-stage CML patients were collected, and the CD3 T cells were obtained by MACS method. The TCRζ-IRES2-EGFP (experimental group) and pIRES2-EGFP (control group) plasmids were transfected into T cells by nuclear transfection technique. The gene expression profiles of CML T cells up-regulated TCRζ chain and control cells were detected by Affymetrix GeneChip Human Gene 2.0 ST Array. The differentially expressed genes were analyzed by GO functional annotation analysis and KEGG pathway enrichment analysis.
Results: A total of 2248 differentially-expressed genes were obtained, including 553 up-regulated genes and 1695 down-regulated genes in experimental group as compared with those in control group (P<0.05) . The GO and KEGG enrichment analyses showed that differentially expressed genes involved in the biological processes related to T cell immune function, such as TCR signaling pathway, T cell proliferation and activation. Some of core genes involved in promoting the TCR signaling pathway, T cell proliferation, activation and apoptosis pathways were significantly up-regulated, while some core genes involved in inhibiting T cell activation were significantly down-regulated.
Conclusion: The molecular mechanism of the significantly improved T cell activation and proliferation ability in CML patients after TCRζ up-regulation may be related to the differential transcripts mediated signaling pathways of T cell activation, proliferation and apoptosis.
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http://dx.doi.org/10.19746/j.cnki.issn.1009-2137.2021.03.003 | DOI Listing |
Brief Bioinform
November 2024
State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, 2 Sipailou, Xuanwu District, Nanjing 210096, China.
Spatial transcriptomics technologies have been extensively applied in biological research, enabling the study of transcriptome while preserving the spatial context of tissues. Paired with spatial transcriptomics data, platforms often provide histology and (or) chromatin images, which capture cellular morphology and chromatin organization. Additionally, single-cell RNA sequencing (scRNA-seq) data from matching tissues often accompany spatial data, offering a transcriptome-wide gene expression profile of individual cells.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), Buk-gu, Gwangju 61005, Republic of Korea.
Combination therapies have emerged as a promising approach for treating complex diseases, particularly cancer. However, predicting the efficacy and safety profiles of these therapies remains a significant challenge, primarily because of the complex interactions among drugs and their wide-ranging effects. To address this issue, we introduce DD-PRiSM (Decomposition of Drug-Pair Response into Synergy and Monotherapy effect), a deep-learning pipeline that predicts the effects of combination therapy.
View Article and Find Full Text PDFDermatol Ther (Heidelb)
January 2025
Department of Clinical and Molecular Sciences (DISCLIMO), Università Politecnica delle Marche, Ancona, Italy.
Introduction: Psoriasis is characterized by aberrant keratinocyte activity and immune cell infiltration, driven by immune-mediated pathways. MicroRNAs (miRNAs) play crucial roles in regulating these processes, offering insights into disease mechanisms and therapeutic targets.
Objectives: This study aimed to investigate changes in circulating miRNAs in psoriasis patients undergoing risankizumab therapy, an anti-IL-23 monoclonal antibody, to understand its impact on disease pathogenesis and treatment response.
Nat Commun
January 2025
Bioinformatics and computational systems biology of cancer, Institut Curie, Inserm U900, PSL Research University, Paris, France.
Immunotherapy is improving the survival of patients with metastatic non-small cell lung cancer (NSCLC), yet reliable biomarkers are needed to identify responders prospectively and optimize patient care. In this study, we explore the benefits of multimodal approaches to predict immunotherapy outcome using multiple machine learning algorithms and integration strategies. We analyze baseline multimodal data from a cohort of 317 metastatic NSCLC patients treated with first-line immunotherapy, including positron emission tomography images, digitized pathological slides, bulk transcriptomic profiles, and clinical information.
View Article and Find Full Text PDFBreast Cancer Res
January 2025
School of Electronic Engineering and Computer Science, Queen Mary University of London, London, UK.
Recent evidence indicates that endocrine resistance in estrogen receptor-positive (ER+) breast cancer is closely correlated with phenotypic characteristics of epithelial-to-mesenchymal transition (EMT). Nonetheless, identifying tumor tissues with a mesenchymal phenotype remains challenging in clinical practice. In this study, we validated the correlation between EMT status and resistance to endocrine therapy in ER+ breast cancer from a transcriptomic perspective.
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