Activated NK Cells Research Articles

An integrative analysis combining bioinformatics, network pharmacology and experimental methods identified key genes of EGCG targets in Nasopharyngeal Carcinoma

BackgroundEpigallocatechin gallate (EGCG), a frequently studied catechin in green tea, has been shown to be involved in the antiproliferation and apoptosis of human Nasopharyngeal carcinoma (NPC) cells. However, the pharmacological targets and mechanism by which EGCG can combat NPC patients remain to be studied in detail.MethodsNetwork pharmacology and bioinformatics were employed to investigate the molecular mechanisms underlying EGCG’s therapeutic effects on NPC, with an emphasis on developing a prognostic risk model and identifying potential therapeutic targets.ResultsA novel prognostic risk model was developed using univariate Cox regression, LASSO regression and multivariable Cox regression analyses, incorporating six genes to stratify patients into low- and highrisk groups. Kaplan–Meier analysis demonstrated significantly shorter progression-free survival in the high-risk group. The model’s accuracy was further validated using time-dependent Receiver Operating Characteristic (ROC) curves. ESTIMATE analysis revealed significantly higher immune, stromal and overall ESTIMATE scores in the low-risk group compared to the high-risk group. Immune profiling indicated significant differences in five immune cell subtypes (memory B cells, regulatory T cells (Tregs), gamma delta T cells, activated NK cells and activated dendritic cells) between the two risk groups. Additionally, the low-risk group showed greater sensitivity to conventional chemotherapeutic agents. Immunohistochemistry and molecular docking analyses identified CYCS and MYL12B as promising targets for EGCG treatment.ConclusionThis study utilised network pharmacology and bioinformatics to identify shared genes between EGCG and NPC, aiming to elucidate the molecular mechanisms through which EGCG inhibits NPC and to develop a prognostic model for assessing patient outcomes. The findings provide potential insights for the development of anti-NPC therapies and their clinical applications.

Tumor-associated MerTK promotes a pro-inflammatory microenvironment and enhances immune checkpoint inhibitor response in triple-negative breast cancer

IntroductionTriple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with no targeted treatment modalities. Currently, combination chemotherapy and immune checkpoint inhibitor (ICI) therapy are options for many TNBC patients; however, their efficacy is limited. Understanding what makes TNBCs responsive to immune therapy is crucial for improving patient outcomes.MethodsWe investigated the role of MerTK expression in TNBC using syngeneic and immunodeficient mouse models, human and murine cells lines, and human clinical samples. Flow cytometry, immunohistochemistry, RNA, multiplex ELISA, immunohistochemistry and multiplex immunofluorescence analysis were used to probe the effects of MerTK expression on the tumor immune microenvironment.ResultsOverexpression of MerTK in TNBC syngeneic mouse models leads to a marked delay in tumor growth, coupled with significant increases in anti-tumor M1 macrophage, CD4+ T cell, active CD8+ T cell, active NK cell, and NKT cell populations. This increase in pro-inflammatory cells contrasted with decreased anti-inflammatory polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) and regulatory T cells (Tregs) in the TIME. In addition, tumors overexpressing MerTK exhibited very high sensitivity to both aPDL1 and aCTLA4 therapies, leading to durable tumor control and, in some cases, complete tumor regression without recurrence. Further, using Vectra multispectral analysis, elevated MerTK expression in human clinical samples was associated with increased levels of pro-inflammatory immune cells. In vivo and human clinical data suggest that tumor-bound MerTK expression is independent of PD-L1 expression in TNBC.ConclusionThese preclinical findings indicate that MerTK could serve as an independent predictive biomarker for ICI response in TNBC, potentially expanding the cohort of late-stage TNBC patients eligible for ICI therapy while reducing toxicity in early-stage patients by treating only those predicted to respond.

Bioinformatics Analysis Reveals Hub Genes Linked to Programmed Cell Death in Intervertebral Disc Degeneration.

Intervertebral disc degeneration (IVDD) represents a severe chronic condition characterized by diverse programmed cell death (PCD) mechanisms serving as critical pathological features. The identification of key genes associated with cellular demise in IVDD is crucial for enhancing diagnostic and prognostic strategies. We extracted microarray-based transcriptomic multi-datasets from the GEO database, comprising 34 normal specimens (grade I/II) and 38 IVDD cases (grade III/IV). Nineteen PCD-associated genes encompassing multiple death modalities (including apoptosis, pyroptosis, ferroptosis, autophagy, necroptosis, cuproptosis, parthanatos, entotic cell death, netotic cell death, lysosome-dependent cell death, alkaliptosis, oxeiptosis, NETosis, immunogenic cell death, anoikis, paraptosis, methuosis, entosis, and disulfidptosis) were systematically curated from established studies. Pathway enrichment was evaluated through gene set variation analysis (GSVA), while weighted gene co-expression network analysis (WGCNA) facilitated the identification of core cell death-related genes, ultimately constructing a cell death signature (CDS) risk model via LASSO regression. Then, we found the significant upregulation of specific PCD pathways in IVDD specimens, particularly apoptosis, ferroptosis, autophagy, necroptosis, immunogenic cell death, anoikis, and disulfidptosis. Immune profiling revealed substantial infiltration of M0 macrophages in IVDD tissues, contrasting with predominant activated NK cells and M2 macrophages in control groups. Through integrative analysis by limma and WGCNA, we discerned 19 key PCD-related genes, subsequently identifying three gene targets (YWHAB, BID, and GSDME) for IVDD pathogenesis. This investigation culminated in developing a machine learning-driven prognostic model based on these biomarkers. Our study establishes a novel and comprehensive framework integrating IVDD with PCD mechanisms, proposing YWHAB, BID, and GSDME as promising diagnostic biomarkers and therapeutic targets for IVDD management.

Abstract 5358: A comparison of tumor microenvironment immune profiles using CIBERSORTx in breast cancer patients from Kenya and Hong Kong

Abstract Background: Tumor-infiltrating immune cells in the tumor microenvironment (TME) are critical for breast cancer (BC) progression and treatment outcomes, with notable variations observed across populations. Unique BC risk factors, such as germline genetics, mammographic density, infectious exposures, and lifestyle factors, may shape distinctive immune profiles and lead to distinct tumor characteristics and clinical outcomes in different populations. Comparing immune cell compositions across populations can improve our understanding of BC racial disparity and guide personalized treatment strategies. Aims: This study aims to characterize and compare immune composition in the BC TME between East Africa (Kenya) and East Asia (Hong Kong) when accounting for age, intrinsic subtype, and analytical methods. Methods: We collected formalin-fixed paraffin-embedded (FFPE) tumor blocks from BC patients in Kenya and Hong Kong, processed them for nucleic acid extraction and analyzed them using NanoString nCounter from a custom-designed code set including 50 PAM50 genes and 547 CIBERSORT panel genes. Samples were selected to match age and estrogen receptor (ER) status distributions. After removing duplicates and QC-failed samples, 467 samples from Kenya (mean age=50.57, ER+: 71.18%) and 118 samples from Hong Kong (mean age=54.94, ER+: 72.03%) were included in the subsequent analyses. CIBERSORTx was used to estimate relative fractions of 22 immune cell types. PAM50 subtype was defined as Luminal A, Luminal B, HER2-enriched, Basal-like, or Normal-like using the nearest centroid method. Multiple linear regression models were performed to identify the differences in immune cell proportions by ethnicity, adjusting for age, PAM50 subtype, and tumor grade. Results: After filtering cells with mean fractions <5%, this analysis was focused on T follicular helper, CD8+, resting CD4+, activated NK, resting mast, and macrophage subsets (M0, M1, M2). Compared to Hong Kong, Kenyan samples exhibited higher relative fractions of CD8+ T cells (p < 0.01), activated NK cells (p < 0.001), and macrophages M2 (p < 0.05) but lower fractions of resting CD4+ T cells (p < 0.05) and macrophages M0 (p < 0.01). Similar results were observed with absolute abundance analyses. Conclusion: This study reveals distinct immune profiles between Kenyan and Hong Kong BC TMEs. Kenyan samples exhibit a relatively “hot” immune TME with both pro-inflammatory and immunosuppressive components, while Hong Kong samples show a more quiescent or relatively “cold” immune environment. These population-specific differences highlight the potential need for tailored immunotherapy strategies considering unique immune characteristics shaped by genetic and environmental factors. Future analyses will explore associations with clinical outcomes and risk factors to elucidate mechanisms driving these racial disparities. Citation Format: Li Feng, Shahin Sayed, Hela Koka, Viviane Oluoch, Veronica Ngundo, Alfred M. Githuka, Zaitun Ajuoga, Kristine Jones, Belynda Hicks, Amy Hutchinson, Maria Brown, Petra Lenz, Aaron M. Rozeboom, Difei Wang, Francis Makokha, Stefan Ambs, Jonine Figueroa, Xiaohong R. Yang. A comparison of tumor microenvironment immune profiles using CIBERSORTx in breast cancer patients from Kenya and Hong Kong [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5358.

Abstract 2122: FS-8002, a bifunctional antibody fusion protein of anti-VEGF and anti-TGF-β, exhibits significant anti-tumor activity in preclinical studies

Abstract Background: FS-8002 is a bifunctional antibody fusion protein targeting vascular endothelial growth factor (VEGF) and transforming growth factor β (TGF-β), which is generated by fusing a humanized IgG1 antibody against VEGF and the extracellular domain of TGF-β receptor II (TGF-βRII). FS-8002 can simultaneously bind to and neutralize VEGF and TGF-β, thus achieving anti-tumor effects through the following proposed mechanisms: 1) inhibiting tumor neovascularization and normalizing tumor blood vessels; 2) relieving the suppression of anti-tumor immune cells in the tumor microenvironment (TME); 3) inhibiting the formation of the immune barrier or extracellular matrix (ECM) and promoting cytotoxic T cell infiltration into tumors, and 4) blocking the invasion and metastasis of tumor cells. Methods: The mechanism of action (MOA) and anti-tumor effects of FS-8002 were evaluated in a series of studies including in vitro target binding, biological activity assays, and in vivo efficacy study in xenograft tumor models. Results: in vitro, FS-8002 binds to VEGF and TGF-β simultaneously. FS-8002 bound to VEGF165 and VEGF121 and thus inhibited VEGF-induced human umbilical vein endothelial cell (HUVEC) proliferation. FS-8002 also specifically bound to TGF-β1 and TGF-β3, but only had weak affinity to TGF-β2. By neutralizing TGF-β, FS-8002 could block TGF-β/Smad signaling pathway, inhibit TGF-β1-induced tumor cell metastasis, and relieve immunosuppression in the TME by activating NK cells and inhibiting Treg proliferation. FS-8002 exhibited significant anti-tumor effect in two "cold tumor" xenograft models (human PBMC reconstituted MDA-MB-231 model and HT-29 cells model) as single agent or in combination with anti-PD-1 antibody (Keytruda®). IHC analysis of the tumors from the two mouse xenograft models revealed that FS-8002 treatment could inhibit the tumor neovascularization, inhibit the formation of the immune barrier by reducing fibrosis formation in tumor tissues, and promote T cells infiltration into tumor tissue to kill tumor cells, therefore exhibit anti-tumor effect. The results of GLP repeat-dose toxicity study in cynomolgus monkeys combined with safety pharmacology assessment indicated that FS-8002 was well tolerated under the doses ranged from 15 to 135 mg/kg/dose (HNSTD) after four weeks of weekly intravenous infusion administration, and the adverse effects were within expectations and can be managed in clinical trials. FS-8002 is currently under clinical evaluation for safety and activity in both US (IND approved) and China. Conclusion: All the preclinical data supported FS-8002 to be studied in clinical trials. Citation Format: Libo Zhou, Qiran Guo, Ruxia Zhou, Hua Jiang, Chaoyi Xu, Xinzhong Jon Wang. FS-8002, a bifunctional antibody fusion protein of anti-VEGF and anti-TGF-β, exhibits significant anti-tumor activity in preclinical studies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2122.

Abstract 925: Golgi-derived extracellular vesicles lack conventional tetraspanins and mediate immune evasion in cancer

Immunotherapy has significantly improved the survival of patients with various malignancies. However, immunotherapies have largely remained ineffective in tumors that have reduced infiltration of T cells, also called immune-cold tumors. Despite an extensive understanding of factors that mediate poor T cell infiltration, current therapies that target these factors have shown modest clinical benefit, indicating that other factors contribute to this process. One such factor that has been shown to contribute to immunosuppression are extracellular vesicles (EVs). However, due to significant EV heterogeneity, discovery of specific EV subpopulations that are responsible for these immunosuppressive effects has remained a challenge. Overcoming this challenge, we first established relevant lung cancer models to study EV-mediated T cell suppression and then identified a previously uncharacterized population of lung cancer-derived EVs that strongly inhibit the proliferation and activation of T cells. We further identified that this novel T cell-suppressive EV subpopulation originates from the trans-Golgi network and expresses the classic trans-Golgi marker protein, TGN46. In addition, the TGN46+ EV subpopulation lacks conventional EV markers CD81 and CD63. In the immunosuppressive lung cancer model, while total EVs suppress T cells, specifically removing the TGN46+ EV subpopulation ameliorated this effect. Conversely, overexpressing TGN46 in a lung cancer model that had no EV-mediated effect on T cells was sufficient to increase TGN46+ EV release and the ability of the EVs to suppress T cells, underscoring the physiological significance of the TGN46+ EV subpopulation. Analysis of patient data from The Cancer Genome Atlas (TCGA) indicates that high TGN46 expression (both transcript and protein) correlates with worse progression-free survival in lung cancer and overall survival in other cancer types - glioblastoma, lower grade glioma, testicular cancer, and large B-cell lymphoma. Most importantly, tumor-immune association analysis of TCGA data suggests that high tumor TGN46 expression significantly correlates with an immunologically-cold tumor signature, i.e., low infiltration of CD8+ T cells and activated NK cells, and high infiltration of M2 macrophages, across several cancer types, stressing the clinical relevance of our findings. The data elucidates that TGN46+ EVs are a novel factor that potentially contributes to immune-cold tumor microenvironment. How TGN46+ EVs suppress T cells mechanistically and their role in overall tumor microenvironment modulation are currently under investigation, which may inform strategies to enhance immunotherapy effectiveness in immune-cold tumors. Citation Format: Ikjot Singh Sohal, Sydney N. Shaw, Lauren N. Meeks, Andrea L. Kasinski. Golgi-derived extracellular vesicles lack conventional tetraspanins and mediate immune evasion in cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 925.

Abstract 1235: Complex in vitro tumor models screen NK therapy and reveal mode of action

Abstract NK cell therapy is an emerging and increasingly promising modality for various cancers. Whereas T cell-based adoptive cell therapy (e.g. tumor infiltrating lymphocytes or TILs, and CAR-T) is mainly limited to patient’s autologous cells due to concerns around allogeneic responses with MHC (major histocompatibility complex) mismatch, NK therapies do not suffer from this due to their low risk of introducing GvHD (Graft-versus-host disease), enabling the development of NK therapies using either patient-sourced NK cells or allogeneic, healthy donor-sourced cells. NK therapies have demonstrated early success in some hematologic cancer, however, the inadequate infiltration and persistence in the tumor microenvironment (TME) limits their application in solid tumors. Therefore, the ability to model solid tumors in vitro to screen efficacy and study mode of action would be highly beneficial to gaining early and actionable insights for clinical translation. Here, we developed several patient-derived xenograft (PDX) 3D in vitro non-small cell lung or colorectal cancer models using a proprietary hydrogel platform. The platform resembles the TME to enable consistent growth of tumor cells, fibroblast elongation, and immune cell interactions. An activated NK cell population (“NK therapy”), featuring CD56bright CD16+ (∼50%) and CD56bright CD16- (∼50%) phenotypes by flow cytometry, was highly potent in the 3D models. These NK therapy cells demonstrated a significant increase in tumor infiltration and tumor cytotoxicity compared to the non-activated NK (“NK control”) counterparts from the same donor. Interestingly, key cytokines like IFNg were downregulated in the NK therapy as compared to the NK control. These data demonstrate a robust and comprehensive in vitro analysis for NK therapy, offering a new preclinical tool for validating their effectiveness and mode of action. Citation Format: Bin Xue, Christopher Harrod, Long Le, Julia Schueler, Kolin C. Hribar. Complex in vitro tumor models screen NK therapy and reveal mode of action [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 1235.

Machine learning-based prognostic modelling of NK cells in PAAD for immunotherapy guidance

Pancreatic cancer’s high incidence and mortality rates are underscored by ineffective treatments, particularly immunotherapy’s poor performance. This could stem from an unclear immune microenvironment, where NK cells may play a unique role. Analyzing the NK cell-differentially expressed genes (NKDEGs) from the PAAD_GSE162708 single-cell dataset and utilizing the TCGA-PAAD and ICGC-PACA-AU datasets, we identified 11 NKDEGs linked to pancreatic adenocarcinoma (PAAD) prognosis and developed a prognostic model. This model’s risk scores significantly outperformed traditional grading and TNM staging systems, validated through clinical and pathological analyses. Functional enrichment analysis pointed to the Neuroactive ligand-receptor interaction and MAPK signaling pathways, suggesting NK cells’ distinctive role in PAAD. High-risk groups showed decreased overall NK cells but increased activated NK cells, which may mediate adverse inflammatory responses. NK cells exhibit synergistic interactions with plasma cells and macrophages and negative regulation by monocytes and naive B cells. Our model accurately predicts immunotherapy responses, indicating potential for targeted drugs to enhance treatment. Additionally, we introduced an NKDEGs-based immunotyping approach for personalized medicine and clinical decision-making in PAAD. This study emphasizes NK cells’ potential in PAAD treatment, offering precise patient stratification and therapeutic targets for immunotherapy.

Reengineering the Tumor Oxygen Microenvironment in Radiotherapy to Enhance T‐Cell Function for Radio‐Immunotherapy

AbstractAnalyzing the relationship between tumor O2 microenvironment (TOME) and immune microenvironment during radiotherapy and regulating it will provide new insights into the combination of radiotherapy and immunotherapy. In the experiments, it is observed that the exacerbation of tumor hypoxia after radiotherapy activated the HIF pathway, which, in turn, led to T‐cell exhaustion by upregulating PD‐L1 expression in the tumor and promoting the infiltration of MDSCs. To address this, a manganese ion‐doped Au–Pt nanozyme‐coated O2 modulator (Mn‐Nz‐M) is designed and prepared to reengineer the TOME. First, the high catalase activity of Mn‐Nz‐M efficiently generated O2 at the tumor‐site, modulating the TOME and improving radiotherapy efficacy. More importantly, studies in vitro and in vivo showed that Mn‐Nz‐M‐mediated improvement of TOME can reverse PD‐1+ T cell exhaustion post radiotherapy and activate T cell function. Additionally, the Mn2+ released from Mn‐Nz‐M can simultaneously amplify the cGAS‐STING pathway to activate NK cells for anti‐tumor activity. This reengineering strategy of the TOME for radio‐immunotherapy not only eliminates primary solid tumors but also inhibits tumor metastasis, providing new insights and potential therapeutic targets for enhancing the interaction between radiotherapy and immunotherapy.

Analysis of the Relationship Between NLRP3 and Alzheimer's Disease in Oligodendrocytes based on Bioinformatics and In Vitro Experiments.

This study aims to explore the potential association between nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) in oligodendrocytes and Alzheimer's disease (AD), utilizing a combination of bioinformatics analysis and molecular biology experiments to validate this relationship. Public datasets related to AD were systematically retrieved and downloaded from the Gene Expression Omnibus (GEO) database at the National Center for Biotechnology Information (NCBI). Subsequently, the SVA package was employed to merge the data and eliminate batch effects, allowing for the precise identification of differentially expressed genes (DEGs) between AD patients and healthy controls. Advanced machine learning techniques, including LASSO regression analysis, random forest algorithms, and support vector machines (SVM), were utilized to analyze further the DEGs associated with the NLRP3 inflammasome to determine the gene set most closely related to AD. The effectiveness and clinical value of the gene-based diagnostic model were comprehensively assessed through receiver operating characteristic (ROC) curve analysis, nomogram construction, and decision curve analysis (DCA). Immune infiltration analysis evaluated the extent of various immune cell infiltrations in the brain tissue of AD patients. Single-cell transcriptomics and in vitro experiments were conducted to verify the molecular expression of NLRP3 in oligodendrocytes within the AD model. A total of 11 significant DEGs were identified, with 4 genes showing downregulation and 7 genes exhibiting upregulation. All three algorithms-LASSO regression, random forest, and SVM-consistently identified PANX1, APP, P2RX7, MEFV, and NLRP3 as key genes closely associated with AD. ROC curve analysis, nomogram modeling, and DCA results demonstrated that the diagnostic model constructed based on these five genes exhibited high diagnostic accuracy and clinical applicability. Immune infiltration analysis revealed a significant correlation between key genes associated with AD and various immune cells, particularly CD8+ T cells, monocytes, activated NK cells, and neutrophils, suggesting that these cells may play important roles in the immunopathological process of AD. Single-cell transcriptomics indicated that the expression level of NLRP3 in oligodendrocytes was higher in the AD group compared to the control group (p < 0.05). Additionally, in vitro cell experiments using RT-PCR, immunofluorescence, and Western blot analysis confirmed that the expression level of NLRP3 in oligodendrocytes was elevated in the AD model relative to the control group (p < 0.05). This study corroborates the high expression of NLRP3 in AD and its close relationship with the disease through integrated bioinformatics analysis and molecular biology experiments. Furthermore, the diagnostic model constructed based on the five key genes-PANX1, APP, P2RX7, MEFV, and NLRP3-not only provides a robust tool for early diagnosis of AD but also offers new insights for the development of treatment targets for AD.

Identification of Anoikis-Related Genes in Gastric Cancer: Bioinformatics and Experimental Validation.

Distant metastasis is the main reason for the poor prognosis of gastric cancer, and anoikis refers to the cell death caused when cells detach from the extracellular matrix or adhere in incorrect locations, playing an important role in the distant metastasis of gastric cancer. Download the TCGA-STAD dataset and the anoikis gene set, and filter out the differentially expressed anoikis genes. Perform consensus clustering of gastric cancer samples, and conduct Weighted Gene Correlation Network Analysis (WGCNA), enrichment analysis, and immune infiltration analysis for the expression characteristics of each subtype, while also filtering the genes with differential expression between subtypes. Additionally, through COX survival analysis, identify anoikis genes related to gastric cancer prognosis and establish a nomogram. Finally, validate the differentially expressed gene CYP1B1 invivo and invitro through clinical samples, cell culture, and the establishment of an anoikis model. Three subtypes of gastric cancer with anoikis genes were identified, each exhibiting different expression characteristics, biological pathways, and immune cell infiltration. The abundance of activated NK cells, memory B cells, and M2 macrophages showed significant differences among the three subtypes. We screened four differentially expressed gene sets and five genes (CYP1B1, EQTN, NRXN2, TBC1D3E, TCEAL5) among the three subtypes. Through survival analysis, we identified 33 independent prognostic genes and constructed a nomogram, with calibration curves indicating good consistency. Finally, we selected CYP1B1 for experimental validation, and invivo and invitro experiments demonstrated that CYP1B1 is highly expressed in gastric cancer, participates in the resistance to cell death in gastric cancer cells, and promotes the invasion, migration, and tumor progression of gastric cancer cells. The expression patterns of subtypes based on differentially expressed genes related to anoikis in gastric cancer vary, providing theoretical support for the future of personalized treatment for gastric cancer.

Supercharged NK cells, unlike primary activated NK cells, effectively target ovarian cancer cells irrespective of MHC-class I expression

ObjectiveTo demonstrate the significance of supercharged natural killer (sNK) cells to target aggressive gynecological tumours.Methods and analysisWe used cell cultures of peripheral blood-derived mononuclear cells (PBMCs) and purified NK cells...

Machine learning-based transcriptmics analysis reveals BMX, GRB10, and GADD45A as crucial biomarkers and therapeutic targets in sepsis.

Sepsis is a life-threatening condition characterized by a dysregulated host response to infection, resulting in high mortality rates and complex clinical management. This study leverages transcriptomics and machine learning (ML) to identify critical biomarkers and therapeutic targets in sepsis. Analyzing microarray data from the Gene Expression Omnibus (GEO) datasets GSE28750, GSE26440, GSE13205, and GSE9960, we discovered three pivotal biomarkers that BMX (bone marrow tyrosine kinase gene on chromosome X), GRB10 (growth factor receptor bound protein 10), and GADD45A (growth arrest and DNA damage inducible alpha), exhibiting exceptional diagnostic accuracy (AUC >0.9). Functional enrichment analyses revealed that these genes play key roles in reactive oxygen species metabolism and immune response regulation. Specifically, GADD45A was positively correlated with eosinophils and inversely associated with activated NK cells, CD8 T cells, and activated memory CD4 T cells. BMX showed positive correlations with eosinophils, mast cells, and neutrophils, while GRB10 was linked to eosinophils and M2 macrophages. Additionally, we constructed a comprehensive mRNA-miRNA-lncRNA regulatory network, identifying key interactions that may drive sepsis pathogenesis. Molecular docking and dynamics simulations validated Bendroflumethiazide, Cianidanol, and Hexamidine as promising therapeutic agents targeting these biomarkers. In conclusion, this integrated approach provides profound insights into the molecular mechanisms underlying sepsis, pinpointing BMX, GRB10, and GADD45A as pivotal biomarkers and therapeutic targets. These findings significantly enhance our understanding of sepsis pathophysiology and lay the groundwork for developing personalized diagnostic and therapeutic strategies aimed at improving patient outcomes.

Toll Like Receptor 7/8 agonists antitumor effects on NK cells in murine melanoma model

Background: Toll-like receptors (TLRs) are a subclass of molecules that regulate both innate and adaptive immune cells, and this ability makes them attractive targets for cancer immunotherapy. Synthetic TLR agonists, such as imidazoquinolines, activate TLR7/8 receptors on various immune cells and offer exciting perspectives for cancer therapy. Aim: In this study we aimed to investigate the effects of TLR7/8 agonists, imiquimod and gardiquimod, on NK cell phenotype and tumor growth in a mouse model of melanoma. Methods: C57BL/6J mice were injected subcutaneously with B16-F10 melanoma cells on day one. The TLR7/8 agonists used were imiquimod (IMQ), gardiquimod (GDQ) and imiquimod cream (ALDARA), and treatment was administrated after tumor development. IMQ and GDQ were administrated intratumorally on days 8, 10, 15, and 18, while ALDARA was applied topically starting on day 14, for 5 consecutive days. The untreated control group received phosphate-buffered saline solution. Tumor size was measured weekly to determine the impact of treatment. To examine the effects of TLR7/8 agonists on NK cells, mice were euthanized on day 21 and spleens were harvested and assessed immediately using flow cytometry analysis for NK cell surface markers. A control group of healthy mice was considered. Results: ALDARA treatment induced significant inhibition of tumor growth, while no significant differences were observed in the IMQ and GDQ groups, except for one mouse that received GDQ. In this case, a tumor regression was observed and after two months the tumor started to grow. At necropsy, macroscopically visible melanoma metastases were observed on the lungs. A decrease in the percentage of NK1.1+ and CD49+ cells in the spleen was observed in all groups compared with healthy mice. Evaluation of NK cell phenotype showed an increase of CD69+ and B220+CD11c+NK1.1+ cells in all treated groups. Analysis of NK cell subsets indicated a decrease of the mature cell subset and an increase of the immature cell subset in tumor-bearing mice, with or without treatment, except for the ALDARA treatment group. Conclusions: Our study showed that treatment with TLR7/8 agonists induced a slowing of tumor growth and activated NK cells. These data can provide new insights into the knowledge of the NK cell phenotype and function in solid tumors treated with TLR7/8 agonists and also new approaches in cancer immunotherapy. Acknowledgement: This work was funded by Ministry of Education and Research, projects no. PN 18.21.02.01, PN 23.16.02.02 and PN-IV-P2-2.1-TE-2023-1169.

Modification of soluble dietary fiber from quinoa bran and investigation of its anticancer activity in vitro.

To investigate the immunological and anticancer actions of quinoa bran soluble dietary fiber (SDF), this study used a simple approach to develop an SDF-zinc complex (SDF-Zn) to enhance the biological activity of SDF. SDF-Zn was co-cultured with NK cells to investigate its ability to activate NK cells. Then, its ability to induce apoptosis in HepG2 human hepatoma cells was investigated using cell proliferation, scratch wound healing assay, flow cytometry, real-time polymerase chain reaction and western blot. The results revealed that chelating SDF and zinc ions considerably increased the cytotoxicity of NK cells against HeLa cells (48.76% at 150 μg mL-1) and significantly (P < 0.05) increased the levels of IFN-γ, TNF-α, granzyme-B and NKp44. SDF-Zn was co-cultured with common human cancer cells including AGS, HeLa, HCT116 and HepG2, and the results revealed that SDF-Zn significantly (P < 0.05) inhibited HepG2 cell proliferation. The overproduction of reactive oxygen species increased the expression of apoptotic genes including Bax and caspase-3, and the number of dead cells reached 45.91%. The inhibitory effect of SDF-Zn on HepG2 cells occurred through the MAPK and NF-κB signaling pathways. Overall, the SDF-Zn complex has the potential to be used as a therapeutic agent for human liver cancer. © 2025 Society of Chemical Industry.

Identification of novel inflammatory response-related biomarkers in patients with ischemic stroke based on WGCNA and machine learning

BackgroundIschemic stroke (IS) is one of the most common causes of disability in adults worldwide. This study aimed to identify key genes related to the inflammatory response to provide insights into the mechanisms and management of IS.MethodsTranscriptomic data for IS were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) and differential expression analysis were used to identify inflammation-related genes (IRGs) associated with IS. Hub IRGs were screened using Lasso, SVM-RFE, and random forest algorithms, and a nomogram diagnostic model was constructed. The diagnostic performance of the model was assessed using receiver operating characteristic (ROC) curves and calibration plots. Additionally, immune cell infiltration and potential small molecule drugs targeting IRGs were analyzed. The expression of IRG was verified by qRT-PCR in healthy controls and IS patients.ResultsNine differentially expressed IRGs were identified in IS, including NMUR1, AHR, CD68, OSM, CDKN1A, RGS1, BTG2, ATP2C1, and TLR3. Machine learning algorithms selected three hub IRGs (AHR, OSM, and NMUR1). A diagnostic model based on these three genes showed excellent diagnostic performance for IS, with an area under the curve (AUC) greater than 0.9 in both the training and validation sets. Immune infiltration analysis revealed higher levels of neutrophils and activated CD4 + T cells, and lower levels of CD8 + T cells, activated NK cells, and naive B cells in IS patients. The hub IRGs exhibited significant correlations with immune cell infiltration. Furthermore, small molecule drugs targeting hub IRGs were identified, including chrysin, piperine, genistein, and resveratrol, which have potential therapeutic effects for IS. qRT-PCR evaluation demonstrated that the levels of blood biomarkers (AHR, OSM, and NMUR1) in IS patients could serve as distinguishing indicators between IS patients and healthy controls (P < 0.05).ConclusionThis study confirmed the significant impact of IRGs on the progression of IS and provided new diagnostic and therapeutic targets for personalized treatment of IS.

Analysis and identification of potential biomarkers for dysfunctional uterine bleeding.

Analysis and identification of potential biomarkers for dysfunctional uterine bleeding.

Estrogen-related gene INPP5D is a potential biomarker for Alzheimer’s disease

Objective: This study aims to leverage bioinformatics methodologies to screen genes related to Alzheimer’s disease (AD) and estrogen, unravel the interplay between estrogen and Alzheimer’s pathophysiology, and furnish a foundation for the clinical treatment of AD. Methods: Through accessing AD transcriptomics data from the GEO database, we examined differentially expressed genes (DEGs) and elucidated their functional roles via GO and KEGG analyses. Also, we constructed a protein interaction network. Furthermore, we took the intersection between genes identified by the weighted gene co-expression network and estrogen-related DEGs, then obtained key genes through machine learning and verified them through internal data sets. Finally, immune infiltration analysis was performed on the data of patients with AD. Results: 326 DEGs were obtained from GSE118553, predominantly enriched in nucleotide diphosphorylation (biological process), nucleotide excision repair complexes (cellular composition), neurotrophic factor binding (molecular functions), and sugar metabolism (signaling pathway). Through weighted gene co-expression network analysis and the intersection of DEGs, three estrogen-related genes were screened out: INPP5D, ENO1, and NOP16. Next, INPP5D was selected as the pivotal gene by the random forest tree algorithm. The prediction model was built on INPP5D, and the AUC of the ROC curve equals 0.876. After single-sample gene enrichment analysis, the samples clustered into high- and low-immune groups. Intriguingly, there was a positive correlation between these two groups on activated NK cells and M1 macrophages, and a negative correlation between M1 macrophages and T cells CD4 memory resting. Conclusion: The estrogen-related gene INPP5D is a potential biomarker for AD, which provides a new perspective and scientific basis for the development of AD treatment strategies based on hormone replacement therapy.

Investigating HLA-E Peptide Binding on Natural Killer Cells for Targeting Breast Cancer Stem Cells

-Breast cancer stem cells (BCSCs) are a major challenge for many conventional therapies such as chemotherapy and radiotherapy. Natural killer cells are often mentioned in immunotherapy, but the need for activation makes NK cell immunotherapy less than satisfactory. Recent studies have found that the MHC HLA-E protein has an abundant peptide expression that can activate NK cells. This study will focus on using HLA-E-expressed peptides in MHC proteins to activate NK cells and develop new possibilities for immunotherapy.

Super-Charged Natural Killer Cells: A Promising Immunotherapeutic Strategy for Oral Cancer

NK cells have traditionally been classified as effectors of innate immunity, even though they also exhibit some features of adaptive immunity such as memory. NK cells contribute to the lysis and growth inhibition of cancer, mediating direct cytotoxicity and antibody-dependent cellular cytotoxicity (ADCC) and regulating the functions of other immune cells, respectively. NK cells regulate the function of other immune cells via the release of inflammatory cytokines and chemokines. Currently, NK cell therapeutics in oral cancer have been less efficient due to several limitations, as follows: (a) lower percentages of NK cells in peripheral blood immune cells; (b) limited survival and decreased function of NK cells, especially in the tumor microenvironment; and (c) a lack of tools or methodologies to expand and activate NK cells to the levels that are required for the effective targeting of oral cancer. To overcome these limitations, we established and demonstrated a novel technology for activating and expanding highly functional NK cells coined as supercharged NK (sNK) cells. This review summarizes the characteristics of sNK cells and highlights their superior anti-cancer activity when compared to primary activated NK cells.