Cancer stem cells and niches: challenges in immunotherapy resistance

Molecular Cancer, Год журнала: 2025, Номер 24(1)

Опубликована: Фев. 25, 2025

Cancer stem cells (CSCs) are central to tumor progression, metastasis, immune evasion, and therapeutic resistance. Characterized by remarkable self-renewal adaptability, CSCs can transition dynamically between stem-like differentiated states in response external stimuli, a process termed "CSC plasticity." This adaptability underpins their resilience therapies, including checkpoint inhibitors adoptive cell therapies (ACT). Beyond intrinsic properties, reside specialized microenvironment—the CSC niche—which provides immune-privileged protection, sustains stemness, fosters suppression. review highlights the critical role of niche driving immunotherapy resistance, emphasizing need for integrative approaches overcome these challenges.

Язык: Английский

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Reprogramming tumor-associated macrophages in gastric cancer: a pathway to enhanced immunotherapy

Frontiers in Immunology, Год журнала: 2025, Номер 16

Опубликована: Март 3, 2025

Gastric cancer (GC) remains a significant global health concern due to its poor prognosis and limited therapeutic options, particularly in advanced stages. Tumor microenvironment (TME), tumor-associated macrophages (TAMs), plays key role tumor progression, immune evasion, therapy resistance. TAMs exhibit plasticity, shifting between pro-inflammatory M1 immunosuppressive M2 phenotypes, with the latter predominating GC contributing outcomes. Recent advancements focus on targeting TAMs, including inhibiting polarization, reprogramming combining TAM-targeted approaches checkpoint inhibitors. Innovations nanotechnology, metabolic reprogramming, pathways such as interleukin-6 C-C motif ligand 2/C-C chemokine receptor 2 further enhance these strategies. However, challenges remain, spatial functional heterogeneity of within TME need for selective avoid disrupting homeostasis. Ongoing research TAM origins, functions, interactions is crucial developing precise effective therapies. These advances hold promise not only improving outcomes but also addressing other cancers similarly complex microenvironments.

Язык: Английский

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Overcoming immune evasion with innovative multi-target approaches for glioblastoma

Frontiers in Immunology, Год журнала: 2025, Номер 16

Опубликована: Янв. 22, 2025

Glioblastoma (GBM) cells leverage complex endogenous and environmental regulatory mechanisms to drive proliferation, invasion, metastasis. Tumor immune evasion, facilitated by a multifactorial network, poses significant challenge effective therapy, as evidenced the limited clinical benefits of monotherapies, highlighting adaptive nature evasion. This review explores glioblastoma’s evasion mechanisms, role ICIs in tumor microenvironment, recent advancements, offering theoretical insights directions for monotherapy combination therapy glioblastoma management.

Язык: Английский

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Spatial‒temporal heterogeneities of liver cancer and the discovery of the invasive zone

Clinical and Translational Medicine, Год журнала: 2025, Номер 15(2)

Опубликована: Фев. 1, 2025

Solid tumours are intricate and highly heterogeneous ecosystems, which grow in invade normal organs. Their progression is mediated by cancer cells' interaction with different cell types, such as immune cells, stromal cells endothelial the extracellular matrix. Owing to its high incidence, aggressive growth resistance local systemic treatments, liver has particularly mortality rates worldwide. In recent decades, spatial heterogeneity garnered significant attention an unfavourable biological characteristic of tumour microenvironment, prompting extensive research into role development. Advances omics have facilitated detailed analysis states cell‒cell interactions, allowing a thorough understanding temporal heterogeneities microenvironment informing development novel therapeutic approaches. This review illustrates latest discovery invasive zone, systematically introduced specific macroscopic heterogeneities, pathological cancer.

Язык: Английский

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DET induces apoptosis and suppresses tumor invasion in glioma cells via PI3K/AKT pathway

Frontiers in Oncology, Год журнала: 2025, Номер 14

Опубликована: Янв. 9, 2025

Gliomas, particularly glioblastomas (GBM), are highly aggressive with a poor prognosis and low survival rate. Currently, deoxyelephantopin (DET) has shown promising anti-inflammatory anti-tumor effects. Using clinical prognostic analysis, molecular docking, network pharmacology, this study aims to explore the primary targets signaling pathways identify novel GBM treatment approaches. PharmMapper, chemical structure of DET was examined for possible after being acquired from PubChem. GBM-related were obtained through multi-omics A protein-protein interaction (PPI) constructed using Cytoscape STRING, target binding evaluated docking. Enrichment analysis conducted Metascape. The effects on cell invasion, apoptosis, proliferation assessed in vitro assays, including Transwell, EDU, CCK8, flow cytometry. Western blot performed examine components PI3K/AKT pathway. Among sixty-four shared identified, JUN CCND1 most frequently observed. demonstrated that influenced MAPK pathways. In Transwell significantly inhibited invasive behavior glioma cells. further confirmed downregulation EGFR, JUN, PI3K/AKT. inhibits proliferation, apoptosis via modulating pathway, highlighting its potential as therapeutic strategy treatment.

Язык: Английский

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Develop a prognostic and drug therapy efficacy prediction model for hepatocellular carcinoma based on telomere maintenance-associated genes

Frontiers in Oncology, Год журнала: 2025, Номер 15

Опубликована: Фев. 14, 2025

Background Hepatocellular carcinoma (HCC) poses a substantial global health challenge because of its grim prognosis and limited therapeutic options. Telomere maintenance mechanisms (TMM) significantly influence cancer progression, yet their prognostic value in HCC remains largely unexamined. This research aims to establish telomere maintenance-associated genes(TMGs)-based model using transcriptomic clinical data evaluate effectiveness predicting patient outcomes HCC. Methods The identified differentially expressed genes (DEGs) were derived from the analysis information sourced database Cancer Genome Atlas (TCGA) cross-referenced with TMGs. Candidate risk factors initially assessed univariate Cox regression, subsequently followed by LASSO, then refined through multivariate regression prediction model. model’s predictive accuracy was validated Kaplan-Meier(K-M) survival analysis, external validation Gene Expression Omnibus (GEO) dataset. Additionally, nomogram incorporating age tumor stage developed. Tumor mutation burden (TMB), immune profile, drug sensitivity also analyzed. Furthermore, we employed RT-PCR confirm expression levels related TMGs HepG2 cell lines. Results A comprising 3 core constructed, high-risk individuals showing lower overall (OS). association between elevated TMB diminished patients uncovered analysis. Immune profiling indicated notable disparities infiltration among these groups, displaying Dysfunction Exclusion (TIDE) scores, suggesting potential evasion. Conclusion In short, our based on effectively categorized enabling dependable forecasts identification targets for personalized treatment management. Future studies should explore integrating this into practice improve outcomes.

Язык: Английский

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Machine learning-derived diagnostic model of epithelial ovarian cancer based on gut microbiome signatures

Journal of Translational Medicine, Год журнала: 2025, Номер 23(1)

Опубликована: Март 13, 2025

Prior studies have elucidated that alterations in gut microbiota are associated with a spectrum of tumors and metabolic disorders. However, the diagnostic value epithelial ovarian cancer remains insufficiently investigated. A total 34 patients diagnosis (EOC), 15 benign (TB), 30 healthy volunteers (NOR) were enrolled this study. Fecal samples collected, followed by sequencing V3–V4 region 16S rRNA gene. The clinical data pathological characteristics comprehensively recorded for further analysis, PICRUSt2 was utilized to conduct an analysis microbial functional predictions, WGCNA networks constructed integrating microbiome data. LEfSe employed identify markers, LASSO SVM analyses used screen markers conjunction Cally index, establish Microbial-Cally model. Bootstrap resampling internal validation model, whereas Hosmer–Lemeshow test decision curve (DCA) evaluate performance Plasma subjected untargeted metabolomics profiling, differential key metabolites significantly altered cancer. At same time, Spearman correlation study association between metabolites. supernatants from Escherichia coli Bifidobacterium cultures co-cultured SKOV3 cells. Cell proliferation, migration, invasion evaluated using Counting Kit-8 (CCK-8) assay, Transwell migration assays. Apoptosis assessed flow cytometry fluorescence signals Annexin V propidium iodide (PI) staining. Compared Nor TB populations, individuals diagnosed EOC demonstrated diminished diversity when contrasted both normal controls those presenting conditions. Specifically, relative abundance Bilophila, Bifidobacterium, other probiotics reduced while Shigella marked enrichment within cohort. Differential microorganisms identified through application machine learning techniques delineate characteristic profiles patients. significant index microorganisms. In conclusion, we biomarkers alongside model cancer, receiver operating (ROC) Area Under Curve (AUC) 0.976 (95%CI 0.943–1.00), AUC obtained 0.974. revealed robust concordance observed probabilities predicted generated provided net benefit. 233 group NT (NOR TB) groups. Among these, eight specific (HMDB0243492, C09265, HMDB0242046, HMDB0240606, C04171, HMDB0060557, HMDB0252797, C21412) exclusively derived microbiome. Notably, metabolite HMDB0240606 exhibited positive Shigella, it showed negative Ruminococcus. vitro possessed anti-tumor activity, pro-tumor activity. This provides inaugural comprehensive composition its among tumors, Hunan province, China.

Язык: Английский

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Tumor–lymph cross-plane projection reveals spatial relationship features: a ResNet-CBAM model for prognostic prediction in esophageal cancer

Frontiers in Oncology, Год журнала: 2025, Номер 15

Опубликована: Март 21, 2025

Background Prognostic models for esophageal cancer based on contrast-enhanced chest CT can aid thoracic surgeons in developing personalized treatment plans to optimize patient outcomes. However, the extensive lymphatic drainage and early lymph node metastasis of esophagus present significant challenges extracting analyzing meaningful characteristics. Previous studies have primarily focused tumor features separately, overlooking spatial correlations such as position, direction, volumetric ratio. Methods A total 285 patients who underwent radical resection surgery at Fujian Provincial Hospital from 2018 2022 were retrospectively analyzed. This study introduced a tumor–lymph projection plane, created by projecting ROIs onto ROI plane. ResNet-CBAM model, integrating residual convolutional neural network with CBAM attention module, was employed feature extraction survival prediction. The PJ group utilized planes training data, while TM ZC groups concatenated images ROIs, respectively, controls. Additional comparisons made traditional machine learning (support vector machines, logistic regression, K-nearest neighbors). Survival outcomes (median, 1-year, 3-year, 5-year) used target labels evaluate model performance distinguishing high-risk predicting both short- long-term survival. Results In group, achieved accuracy rates 0.766, 0.981, 0.883, 0.778 median, 5-year survival, respectively. Its corresponding AUC values 1-, 3-, 0.992, 0.913, 0.835. Kaplan–Meier analysis revealed differences between high- low-risk identified model. outperformed those Compared models, it demonstrated superior Conclusion trained effectively distinguished By capturing relationships tumors nodes, enhanced predictive efficiency.

Язык: Английский

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Depletion of tumor-derived CXCL5 improves T cell infiltration and anti-PD-1 therapy response in an obese model of pancreatic cancer

Journal for ImmunoTherapy of Cancer, Год журнала: 2025, Номер 13(3), С. e010057 - e010057

Опубликована: Март 1, 2025

Background CXCR1/2 inhibitors are being implemented with immunotherapies in PDAC clinical trials. CXC-ligands a family of cytokines responsible for stimulating these receptors; while typically secreted by activated immune cells, fibroblasts, and even adipocytes, they also immune-evasive cancer cells. CXC-ligand release is known to occur response inflammatory stimuli. Adipose tissue an endocrine organ source signaling peptides. Importantly, adipose-derived chemokines implicated as potential drivers tumor cell evasion; cumulatively, findings suggest that targeting may be beneficial the context obesity. Methods RNA-sequencing human lines was used assess influences adipose conditioned media on transcriptome. The adipose-induced secretome cells validated ELISA induction CXCL5 secretion. Human data from CPTAC correlate IL-1β TNF expression both mRNA protein levels. CRISPR-Cas9 knockout murine KPC line orthotopic studies syngeneic, diet-induced obese mice. Flow cytometry immunohistochemistry were compare profiles between tumors or without CXCL5. Mice-bearing competent deficient monitored differential size anti-PD-1 checkpoint blockade therapy. Results stimulates secretion via either TNF; neutralization required significantly block Ablation promoted enriched phenotype unanticipatedly increased number exhausted CD8 T Application treatment control failed alter growth, yet CXCL5-deficient showed diminished mass. Conclusions In summary, our show can stimulate vitro, which correlates patient data. depletion vivo alone sufficient promote infiltration into tumors, increasing efficacy requiring inhibition alleviate burden.

Язык: Английский

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METTL1-driven nucleotide metabolism reprograms the immune microenvironment in hepatocellular carcinoma: a multi-omics approach for prognostic biomarker discovery

Frontiers in Immunology, Год журнала: 2025, Номер 16

Опубликована: Апрель 22, 2025

Hepatocellular carcinoma (HCC) remains one of the leading causes cancer-related mortality worldwide, partly due to an incomplete understanding metabolic and immune dysregulation driving its progression. Here, we uncover a novel role METTL1 in nucleotide metabolism reprogramming, which significantly modulates tumor microenvironment. Utilizing integrated multi-omics approach, analyzed metabolism-related genes derived from TCGA, GEO, ICGC datasets. Non-negative matrix factorization (NMF) clustering stratified HCC patients into distinct subgroups with varied clinical features. Weighted Gene Co-expression Network Analysis (WGCNA) identified hub that were subsequently used construct robust prognostic models via multiple machine learning algorithms. These computational findings validated through vitro experiments, infiltration assessments, single-cell RNA sequencing analysis. Our analyses demonstrate is markedly upregulated HCC, reprogramming expression key checkpoints, including PD-L1 CTLA-4. This regulation associated immunosuppressive microenvironment, reduced activated T cells, poorer outcomes. Moreover, model integrating checkpoint profiles shows strong predictive performance across independent cohorts, highlighting potential utility. study highlights innovative METTL1-driven reshaping microenvironment HCC. The provide insights pathogenesis pave way for developing personalized therapeutic strategies based on targeting pathways.

Язык: Английский

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