---
title: "Cancer stem cell programs, S‑nitrosylation and deubiquitination of FoxP3: full text not available"
id: "frontiers-in-immunology-14-cancer-stem-cell-programs-s-nitrosylation-and-deubiquitination-of-foxp3-protein"
canonical_url: "https://medichelpline.com/clinical-feed/frontiers-in-immunology-14-cancer-stem-cell-programs-s-nitrosylation-and-deubiquitination-of-foxp3-protein"
content_type: "clinical_feed_article"
specialty: "Infectious Disease"
source_name: "Frontiers in Immunology"
source_url: "https://www.frontiersin.org/articles/10.3389/fimmu.2026.1961819"
published_at: "2026-09-15T00:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Cancer stem cell programs, S‑nitrosylation and deubiquitination of FoxP3: full text not available
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- **Specialty:** [Infectious Disease](https://medichelpline.com/clinical-feed/infectious-disease.md)
- **Primary Source:** Frontiers in Immunology
- **Source URL:** [Original Journal Publication](https://www.frontiersin.org/articles/10.3389/fimmu.2026.1961819)
- **Published At:** 2026-09-15T00:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
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## Clinical Analysis & Structured Key Points
Frontiers | Cancer stem cell programs S-nitrosylation and deubiquitination of FoxP3 protein to promote regulatory T cell differentiation ORIGINAL RESEARCH article Front. Immunol. , 15 September 2026 Sec. Cancer Immunity and Immunotherapy Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1961819 Published in Frontiers in Immunology Cancer Immunity and Immunotherapy 7 impact factor 11.3 citescore Part of a Research Topic Post-Translational Modifications in Tumor Immunotherapy 12k views 5 articles Editor & Reviewers Edited by Q X Qian Xiao Reviewed by H Z Hui Zhang D L DAN LU Outline Figures and Tables Figure 1 View in article Figure 2 View in article Figure 3 View in article Figure 4 View in article Figure 5 View in article Figure 6 View in article Table 1 Clinical features of NSCLC patients. View in article Table 2 Sequence of primers targeting different genes for qPCR. View in article ORIGINAL RESEARCH article Front. Immunol. , 15 September 2026 Sec. Cancer Immunity and Immunotherapy Volume 17 - 2026 | https://doi.org/10.3389/fimmu.2026.1961819 Cancer stem cell programs S-nitrosylation and deubiquitination of FoxP3 protein to promote regulatory T cell differentiation X L Xiyu Liu 1 J L Jiaxin Lei 2 X J Xinhang Jiang 1 L L Lingyi Li 2 L Y Longhao Yu 3 G W Gengxue Wang 4 K G Kaixun Guo 4 Z W Zhenke Wen 2 Y W Yan Wang 1 * 1. Department of Thoracic Surgery, China-Japan Union Hospital of Jilin University, Jilin University, Changchun, China 2. Jiangsu Key Laboratory of Infection and Immunity, Institutes of Biology and Medical Sciences, Suzhou Medical College of Soochow University, Suzhou, China 3. Department of Cardiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, China 4. Department of Thoracic Surgery, Central Hospital of Gongzhuling City, Siping, China See more Article metrics View details Abstract Background: Cancer stem cells (CSCs) are critical drivers of tumor progression and therapeutic resistance in non-small cell lung cancer (NSCLC). However, how CSCs remodel the immunosuppressive tumor microenvironment (TME) of NSCLC remains largely unclear. Methods: Flow cytometry was performed to evaluate the immunomodulatory effects of NSCLC CSCs on T cell differentiation. RNA-sequencing-based metabolic profiling was conducted to identify pivotal metabolic pathways activated in CSCs. Mitochondrial reactive oxygen species (ROS) encapsulated in CSC-derived exosomes were quantified, and the molecular mechanism by which exosomal ROS modulates intracellular nitric oxide (NO) production and FoxP3 post-translational modifications in T cells was further explored. Patient-derived organoids (PDOs) were utilized as a preclinical model to verify the therapeutic potential of glutamine metabolism targeting. Results: NSCLC CSCs potently induced tumor immunosuppression by promoting regulatory T (Treg) cell differentiation. Mechanistically, hyperactive glutamine metabolism in CSCs substantially increased mitochondrial ROS generation. Exosomal ROS secreted by CSCs was transferred to T cells, thereby elevating intracellular NO synthesis. Increased NO further triggered S-nitrosylation and deubiquitination of FoxP3, which ultimately stabilized FoxP3 expression and facilitated Treg cell differentiation. In NSCLC PDO models, pharmacological inhibition of glutamine metabolism reversed the immunosuppressive T cell phenotype and efficiently suppressed PDO growth. Conclusion: NSCLC CSCs mediate TME immunosuppression via a glutamine metabolism-dependent regulatory axis. Exosomal ROS-initiated FoxP3 post-translational modification is a novel mechanism underlying CSC-driven Treg differentiation. This study reveals an unreported immune evasion pathway in NSCLC and identifies glutamine metabolism as a viable therapeutic target for overcoming tumor immunosuppression. Introduction Non-small-cell lung cancer (NSCLC) poses a significant challenge in clinical settings, standing as the foremost cause of cancer-related deaths globally ( 1 ). Current approaches to treating NSCLC typically encompass a blend of interventions such as surgery, chemotherapy, radiation therapy, targeted therapy, and immunotherapy, tailored to the clinical stage and specific characteristics of the tumor ( 2 ). While surgery is commonly employed for early-stage NSCLC, chemotherapy and radiation therapy may serve to reduce tumor size pre-surgery, eradicate residual cancer cells post-surgery, or function as primary treatment for advanced-stage NSCLC ( 1 , 2 ). Targeted therapy medications are frequently utilized for advanced NSCLC featuring specific mutations, while immunotherapy, including immune checkpoint inhibitors, is frequently employed either independently or in conjunction with other modalities ( 1 , 2 ). Ongoing clinical trials continue to explore and assess novel treatments for NSCLC, including innovative targeted therapies, immunotherapies, and combination approaches, highlighting the need to investigate key factors influencing anti-tumor immunity within the tumor microenvironment (TME). CD4 + T cells recognize specific antigens presented by antigen-presenting cells and play a pivotal role in coordinating the immune responses ( 3 , 4 ). Upon activation, they release cytokines and signaling molecules, which stimulate the proliferation and activation of other immune cells, including CD8 + cytotoxic T cells and macrophages ( 3 , 5 ). Notably, CD4 + T cells aid CD8 + cytotoxic T cells by secreting cytokines such as interleukin-2 (IL-2), IL-12, and interferon-gamma (IFN-γ), thus promoting the expansion and activation of CD8 + T cells, augmenting their capacity to directly target and eliminate tumor cells ( 3 , 6 ). Additionally, within the TME, CD4 + T cells can produce IFN-γ and tumor necrosis factor-alpha (TNF-α), exerting anti-tumor effects ( 3 , 7 ). While regulatory CD4 + T (Tregs) cells function to dampen excessive immune responses, their presence in the context of cancer hinders the effectiveness of anti-tumor immunity ( 3 , 8 , 9 ). Consequently, the multifaceted roles of CD4 + T cells in tumor immunity underscore their significance as central regulators of the anti-tumor immune response ( 3 ). Understanding and leveraging the functions of CD4 + T cells are imperative for the development of efficacious immunotherapeutic strategies for cancer treatment. Cancer cells exhibit heterogeneity, comprising both cancer stem cells (CSCs) and non-CSCs ( 10 – 12 ). CSCs, distinguished by their self-renewal capacity, multilineage differentiation potential, and ability to initiate tumor formation, constitute a distinct subpopulation contributing to tumor initiation, progression, metastasis, and treatment resistance ( 10 , 11 ). Phenotypically, CSCs express specific stem cell markers include CD44, CD133, ALDH (aldehyde dehydrogenase), and certain embryonic stem cell markers such as OCT4, SOX2, and NANOG. CSCs significantly contribute to tumor heterogeneity by generating a variety of cell types within the tumor and propelling tumor initiation and progression, thereby fueling tumor growth and enabling metastasis ( 10 , 11 ). Importantly, CSCs frequently evade conventional cancer treatments and play a pivotal role in metastasis by infiltrating blood or lymphatic vessels, surviving in circulation, extravasating into distant organs, and initiating secondary tumor growth ( 13 , 14 ). Therefore, understanding CSC biology in NSCLC is crucial for developing innovative therapeutic approaches targeting these cells ( 10 , 11 ). Targeting CSC-specific pathways or vulnerabilities shows potential for improving treatment effectiveness, reducing tumor recurrence, and enhancing patient outcomes ( 13 , 14 ). However, the potential impact of CSCs on CD4 + T cell differentiation remains largely unknown. In this study, we investigated the role of NSCLC CSCs in regulating CD4 + T cell responses, and identified a novel function of CSCs: promoting Treg cell differentiation while suppressing pro-inflammatory T cells. These CSCs showed enriched glutamine metabolism, which increased reactive oxygen species (ROS) levels in T cells. Elevated ROS further enhanced nitric oxide (NO) levels in T cells, thereby promoting S-nitrosylation and deubiquitination of FoxP3 to facilitate Treg cell differentiation. Importantly, inhibition of glutamine metabolism effectively suppressed tumor growth in NSCLC patient-derived organoids (PDOs). Collectively, these findings provide new insights into CSC biology and offer potential avenues for the development of therapeutic strategies for NSCLC management. Materials and methods Study period Human sample experiments were performed from December 2020 to April 2026, while animal studies were conducted between January 2023 and December 2025. All data analysis was finalized by May 2026. Patients A total of 33 patients diagnosed with NSCLC and 89 age-matched healthy volunteers were recruited into this study from 2020 to 2026. Patient characteristics were provided in Table 1 . Following written informed consent, peripheral blood and tumor tissue samples were collected to investigate the effect of CSCs on T cell differentiations. All experimental protocols involving human samples were carried out in adherence with the Declaration of Helsinki and approval by the Ethics Committee of China-Japan Union Hospital of Jilin University (202202005). Table 1 Demographic parameters NSCLC patients Healthy donors No. of subjects 33 89 Sex (F/M) 15/18 43/46 Age (mean ± SEM [years]) 51.73 ± 8.11 49.65 ± 5.94 TNM staging (I/II/III) 12/19/2 N/A Disease duration (mean ± SEM [months]) 2.31 ± 1.55 N/A Histological types Adenocarcinoma 29 N/A Squamous carcinoma 4 N/A Clinical features of NSCLC patients. T cell preparation and cell culture PBMCs were obtained from either healthy donors or NSCLC patients utilizing the Lymphocyte Isolation Solution (Dakewe Biotech). CD4 + T cells were subsequently isolated from PBMCs employing Human CD4 + T Cell Isolation Kit (STEMCELL Technologies). Naïve CD4 + T cells were isolated from PBMCs utilizing Human Naïve CD4 + T Cell Isolation Kit (STEMCELL Technologies) ( 15 , 16 ). All cells were cultured in RPMI 1640 medium (Corning) supplemented with 10% FBS (Sigma) and 50 units/mL penicillin/streptomycin (Beyotime). Tumor-spheres and adherent cells Tumor-spheres were enriched using DMEM/F12 medium (Corning, Cat. 10-092-cv) supplemented with 1x B-27 (GIBCO), EGF (20 ng/mL; Novoprotein, Cat. C029), bFGF (20 ng/mL; Novoprotein, C046), insulin (Beyotime, P3376-100IU), and penicillin/streptomycin (50 units/mL, Beyotime, Cat. C0222) as previously described ( 10 , 11 ). For serial passaging, spheres were harvested after 6 days using a 40 mm cell strainer, dissociated into single cells by continuous pipetting, and then cultured under the same conditions. Sphere formation assays were conducted by quantifying the number of spheres with diameters greater than 40 mm using a CASY Cell Counter (Biocentury). CSC-T cell co-culture and T cell differentiations The CSC-T cell co-culture was established according to previously described method ( 3 ). T cells were initially co-cultured with CSCs at a ratio of 4:1 for 12 hours, followed by activation using beads coated with anti-CD3/CD28 beads (at a 1:1 ratio, Gibco). T-cell differentiation was assessed by quantifying intracellular lineage-determining transcription factors and cytokines using flow cytometry ( 3 ). For the in vivo differentiation of T cells, CD4 + T cells from healthy donor PBMCs were preincubated with or without CSCs at a ratio of 4:1 (T cells: CSCs) for 12 hours. After co-culture, the T cells were reintroduced into the autologous PBMC population (1×10 7 cells/mouse) and adoptively transferred into 6-week-old immunodeficient NOD-Prkdc scid Il2rg em1 /Smoc (M-NSG) mice (Shanghai Model Organisms) via an intraperitoneal injection without anesthesia. Both male and female mice were used in equal numbers. Sample size was determined by power analysis. A total of 12 mice (6 per group) were used. No inclusion/exclusion criteria were applied; and no animals were excluded from the analysis. Mice were randomly assigned to control and treated groups using a computer-generated random number sequence ( www.random.org ), and the analysts were not aware of the group allocations. One week later, mice were euthanized with carbon dioxide in their home cage, and the splenic frequencies of human T cells subsets were determined using flow cytometry. All mice were housed in a standard SPF facility of our institution. The exact number of mice in each group and statistics were clarified in the corresponding figure legends. All procedures were performed in accordance with the ARRIVE guidelines. Experiments were carried out in accordance with our Institutional Animal Care and Use Committee guidelines and approved by the Institutional Ethics Committee of China-Japan Union Hospital of Jilin University (202202072). Transfections and reagents Lentiviral particles for gene overexpression or knockdown in cancer stem cells (CSCs) were generated in HEK293T packaging cells by co-transfecting the transfer plasmid with the packaging plasmids pVSVg and psPAX2. Viral supernatants were collected 48 h post-transfection, filtered through a 0.45-μm PES membrane, and used to transduce target cells at a 1:1 ratio with fresh medium containing 8 μg/mL polybrene. All plasmids were obtained from MIAOLING BIOLOGY. The following reagents were purchased from the indicated suppliers: DETA-NONOate, MG132 (proteasome inhibitor), Cycloheximide (protein synthesis inhibitor), MAO-IN-M30 dihydrochloride (MAO inhibitor), L-Methionine-DL-sulfoximine (GLUL inhibitor), BPTES (GLS inhibitor), L-NAME (NOS inhibitor), C75 (FASN inhibitor), and NAC (ROS inhibitor) from MedChemExpress; ARL67156 (CD39 inhibitor), and GW4869 (exosome inhibitor) from Sigma-Aldrich; and JSH23 (NF-κB inhibitor), SCH772984 (ERK inhibitor), and rapamycin (mTOR inhibitor) from TargetMol. The following assay kits were used: Reactive Oxygen Species Detection Kit (Beyotime, S0033S), Mitochondrial Superoxide Detection Kit (Beyotime, S0061S), Annexin V-FITC/PI Apoptosis Kit (Elabscience, E-CK-A211), and Annexin V-APC/7-AAD Apoptosis Kit (Elabscience, E-CK-A218), Nitric Oxide Assay Kit (Beyotime, S0021S). All reagents and kits were used in accordance with the manufacturers’ instructions. Flow cytometry For intracellular staining, cells were fixed with Fix Buffer I (BD Biosciences) and permeabilized with Perm Buffer III (BD Biosciences). Multiparametric flow cytometry panels were constructed using the following anti-human antibodies: Alexa Fluor ® 647 anti-T-bet (BioLegend, 644804), PE anti-GATA3 (BioLegend, 653804), APC anti-RORγt (Invitrogen, 17-6988-82), PE-Cyanine7 anti-FoxP3 (Invitrogen, 25-4776-42), APC/Cyanine7 anti-CD4 (BioLegend, 317450), PE-Cyanine7 anti-CD8 (BioLegend, 344712), APC anti-CD69 (BioLegend, 310910), and CoraLite ® Plus 488-Annexin V (Proteintech, PF00005). For quantification of intracellular cytokines, cells were stimulated for 6 hours with PMA (50 ng/mL, Tocris), ionomycin (500 ng/mL, Tocris), and brefeldin A (5 µg/mL, BioLegend), followed by fixation, permeabilization, and staining with APC anti-IL-10 (Biolegend, 501410), PE anti-IFN-γ (BioLegend, 986702), FITC anti-IL-17 (BioLegend, 512306), PE/Cy7 anti-IL-4 (BioLegend, 500810), and FITC anti-Granzyme B (BioLegend, 372206). Staining was performed for 45 minutes at 4 °C in the dark, followed by extensive washing and analysis using flow cytometry with a Canto II instrument (BD Biosciences). Data analysis was conducted using FlowJo software, with fluorescence minus one (FMO) utilized as the gating control. Immunoblotting and immunoprecipitation Cellular proteins were extracted using RIPA buffer (NCM Biotech), and their expression levels were assessed according to standard Western blotting or immunoprecipitation protocols ( 3 , 10 , 11 ). Primary anti-human antibodies were used as follows: Anti-Ki67 antibody (Abcam, ab16667), Anti-Alix (Cell Signaling Technology, 92880), Anti-CD63 (Cell Signaling Technology, 52090), Anti-CD9 (Cell Signaling Technology, 13174), Anti-FoxP3 (Proteintech, 22228-1-AP), Anti-Ubiquitin (Proteintech, 10201-2-AP), and Anti-USP7 (Proteintech, 66514-1-Ig). β-Actin, detected with the corresponding antibody (Santa Cruz Biotechnology, sc-47778), served as an internal control for normalization. A protein molecular weight marker (Epizyme Biomedical Technology, WJ103) was used in these experiments. Biotin‐switching assay The level of S-nitrosylated FoxP3 (SNO-FoxP3) was measured using a biotin switch assay performed with the S-nitrosylation Protein Detection Assay Kit (Cayman, 10006518), following previously described protocols ( 17 , 18 ). Briefly, cell lysates were first incubated with blocking buffer for 30 minutes to block free thiols, after which proteins were precipitated with cold acetone. The samples were then sequentially treated with reducing buffer and labeling buffer, which converted S-nitrosothiols into free thiols and subsequently labeled them with biotin. Following continuous rotation, biotinylated proteins were captured using streptavidin agarose (Beyotime, P2159) and then subjected to SDS-PAGE followed by immunoblotting with an anti-FoxP3 antibody. Real-time PCR Total RNA was isolated using Trizol (Takara Bio) and then reverse transcribed into cDNA utilizing a reverse transcription Kit (Vazyme Biotech). Quantitative PCR analyses were performed using SYBR Green qPCR Master Mix (Bimake). The primers used are listed in Table 2 , as previously described, and gene expression was normalized to 18S rRNA ( 3 , 19 ). Table 2 Oligo name Forward primer (5’ to 3’) Reverse primer (5’ to 3’) 18SrRNA AGTCCCTGCCCTTTGTACACA GATCCGAGGGCCTCACTAAAC FOXP3 AGATGGTACAGTCTCTGGAGCAG AAGTAGTCCATGTTGTGGAGGAA NOS2 GCAGCTCAGCCTGTACT CACCATCCTCTTTGCGACA NOS3 ACGATGGTGACTTTGGCTA TGGAGGATGTGGCTGTCT NOX1 CACAAGAAAAATCCTTGGGTCAA GACAGCAGATTGCGACACACA NOX2 TCACTTCCTCCACCAAAACC CACCTTCTGTTGAGATCGCC NOX3 CCAGGGCAGTACATCTTGGT CCGTGTTTCCAGGGAGAGTA NOX4 TGGCTGCCCATCTGGTGAATG CAGCAGCCCTCCTGAAACATGC NOX5 CAGGCACCAGAAAAGAAAGCAT ATGTTGTCTTGGACACCTTCGA GLS1 GTCACGATCTTGTTTCTCTGTG GTCCAAAGAGCAGTGCTTCATCCATG GLS2 TGCCTATAGTGGCGATGTCTCA GTTCCATATCCATGGCTGACAA GLUL CCTGCTTGTATGCTGGAGTC GATCTCCCATGCTGATTCCT USP7 CGTTCGGAATCCCGTTTTTGCT TCAAGGTAAGTGTAGCGACTCC USP11 GAAGAGAACGGACGGCGAT CGTGCTGTGGCTCTCTATCC USP21 AGGTGTCTCTGCGGGATTGTT CGATTCAGATGGAGCACGAGG USP44 ACAACTTATGATATGCCACC GATTTCCTCAAAGCCAAC USP19 CGGCACAAGATGAGGAATGA GGCACCGGCAGATAAAGAAA USP10 TTTTAAATGCCACCGAACCTATC CCAGCCATTCAGACCGATCT USP3 GTTTCAACGGTGTTTCCC AATGCCTCCGAATATAGCC USP33 AAAATCCCTTGGTACTTGTCAGG TCGAAGAGTGGTAAGGTTCACA USP9X CAATGGATAGATCGCTTTATA CTTCTTGCCATGGCCTTAAAT USP17L2 ACACTTTTGACCCTTACCTGG GCTTCACCAACTGTTCCAAAG Sequence of primers targeting different genes for qPCR. Exosome and macrovesicle isolation Cell culture supernatants were collected and subjected to sequential centrifugation to remove cells and debris: first at 200 × g for 10 min, then at 2,700 × g for 10 min, and finally at 14,000 × g for 2 min. The resulting supernatant was then divided for parallel isolation of macrovesicles and exosomes. For macrovesicle isolation, the supernatant was centrifuged at 14,000 × g for 1 h, and the pellet was collected as the macrovesicle fraction. For exosome isolation, the supernatant was filtered through 0.22-μm filters and ultracentrifuged at 100,000 × g (or 31,000 rpm) for 70 min at 4 °C. The resulting exosome pellets from multiple tubes were pooled, resuspended in PBS, and subjected to a second ultracentrifugation under the same conditions. The final exosome pellet was resuspended in PBS and quantified using the ExoELISA-ULTRA Complete Kit (System Biosciences). Exosome identity was further confirmed by immunoblotting for the exosomal markers Alix, CD63, and CD9 ( 20 ). Patient-derived organoids (PDOs) PDOs were derived from NSCLC tissue specimens obtained from resected patients, fo
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