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1.
Front Immunol ; 15: 1405249, 2024.
Article in English | MEDLINE | ID: mdl-38742110

ABSTRACT

Introduction: Exploring monocytes' roles within the tumor microenvironment is crucial for crafting targeted cancer treatments. Methods: This study unveils a novel methodology utilizing four 20-color flow cytometry panels for comprehensive peripheral immune system phenotyping, specifically targeting classical, intermediate, and non-classical monocyte subsets. Results: By applying advanced dimensionality reduction techniques like t-distributed stochastic neighbor embedding (tSNE) and FlowSom analysis, we performed an extensive profiling of monocytes, assessing 50 unique cell surface markers related to a wide range of immunological functions, including activation, differentiation, and immune checkpoint regulation. Discussion: This in-depth approach significantly refines the identification of monocyte subsets, directly supporting the development of personalized immunotherapies and enhancing diagnostic precision. Our pioneering panel for monocyte phenotyping marks a substantial leap in understanding monocyte biology, with profound implications for the accuracy of disease diagnostics and the success of checkpoint-inhibitor therapies. Key findings include revealing distinct marker expression patterns linked to tumor progression and providing new avenues for targeted therapeutic interventions.


Subject(s)
Biomarkers , Flow Cytometry , Immunophenotyping , Monocytes , Humans , Monocytes/immunology , Monocytes/metabolism , Flow Cytometry/methods , Cluster Analysis , Immunophenotyping/methods , Tumor Microenvironment/immunology , Neoplasms/immunology , Neoplasms/diagnosis
2.
Hematology ; 28(1): 2288475, 2023 Dec.
Article in English | MEDLINE | ID: mdl-38038045

ABSTRACT

This study delves into the emerging role of ferroptosis in Myelodysplastic Neoplasms (MDS) and aims to identify a prognostic ferroptosis-related gene signature for MDS. Utilizing RNA-seq data and clinical information from the Gene Expression Omnibus database, the researchers extracted ferroptosis-related genes from the FerrDb website and conducted differential expression analysis using the 'limma' package in R. Hub ferroptosis-related genes in MDS were screened using the "RandomForest" and "carat" R packages. Kaplan -Meier and Cox regression analyses were employed to assess the prognostic role of three identified hub genes (BNIP3, MDM2, and RRM2). Receiver operator characteristic curve analysis confirmed the diagnostic efficacy of these genes. The study delved further into immune infiltration correlations, ncRNA-transcription factor coregulatory network analysis, and the identification of potential therapeutic drugs targeting hub ferroptosis-related genes in MDS. The researchers constructed a 3-gene signature-based risk score using datasets GSE58831 and GSE19429, demonstrating high accuracy (AUC > 0.75) in both datasets for survival prediction in MDS. A nomogram analysis reinforced the prognostic value of the risk-scoring model. Immunological analysis revealed an association between the risk score and immune infiltration. Quantitative reverse transcription polymerase chain reaction (qPCR) data indicated significant expression differences in MDM2, RRM2, and BNIP3 between MDS and healthy bone marrow samples. Notably, MDM2 and RRM2 showed decreased expression, while BNIP3 exhibited increased expression in MDS samples. This comprehensive study concludes that BNIP3, MDM2, and RRM2 hold diagnostic and prognostic significance in MDS and provide valuable insights into immune cell landscapes and potential therapeutic avenues for this condition.


Subject(s)
Ferroptosis , Myelodysplastic Syndromes , Neoplasms , Humans , Prognosis , Ferroptosis/genetics , Nomograms , Databases, Factual , Myelodysplastic Syndromes/diagnosis , Myelodysplastic Syndromes/genetics
3.
Mol Med ; 29(1): 59, 2023 04 25.
Article in English | MEDLINE | ID: mdl-37098464

ABSTRACT

BACKGROUND: Myofibroblasts (MFB), one of the major effectors of pathologic fibrosis, mainly derived from the activation of fibroblast to myofibroblast transition (FMT). Although MFBs were historically considered terminally differentiated cells, their potential for de-differentiation was recently recognized and implied with therapeutic value in treating fibrotic diseases, for instance, idiopathic pulmonary fibrosis (IPF) and post allogeneic hematopoietic stem cell transplantation bronchiolitis obliterans (BO). During the past decade, several methods were reported to block or reverse MFB differentiation, among which mesenchymal stem cells (MSC) have demonstrated potential but undetermined therapeutic values. However, the MSC-mediated regulation of FMT and underlying mechanisms remained largely undefined. METHOD: By identifying TGF-ß1 hypertension as the pivotal landmark during the pro-fibrotic FMT, TGF-ß1-induced MFB and MSC co-culture models were established and utilized to investigate regulations by MSC on FMT in vitro. Methods including RNA sequencing (RNA-seq), Western blot, qPCR and flow cytometry were used. RESULT: Our data revealed that TGF-ß1 readily induced invasive signatures identified in fibrotic tissues and initiated MFB differentiation in normal FB. MSC reversibly de-differentiated MFB into a group of FB-like cells by selectively inhibiting the TGF-ß-SMAD2/3 signaling. Importantly, these proliferation-boosted FB-like cells remained sensitive to TGF-ß1 and could be re-induced into MFB. CONCLUSION: Our findings highlighted the reversibility of MSC-mediated de-differentiation of MFB through TGF-ß-SMAD2/3 signaling, which may explain MSC's inconsistent clinical efficacies in treating BO and other fibrotic diseases. These de-differentiated FB-like cells are still sensitive to TGF-ß1 and may further deteriorate MFB phenotypes unless the pro-fibrotic microenvironment is corrected.


Subject(s)
Mesenchymal Stem Cells , Myofibroblasts , Humans , Cell Differentiation , Fibroblasts/metabolism , Fibrosis , Mesenchymal Stem Cells/metabolism , Myofibroblasts/metabolism , Smad2 Protein/metabolism , Transforming Growth Factor beta1/pharmacology , Transforming Growth Factor beta1/metabolism , Smad3 Protein/metabolism
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