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Search Results (11,190)

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15 pages, 995 KiB  
Article
Conservation Law Analysis in Numerical Schema for a Tumor Angiogenesis PDE System
by Pasquale De Luca and Livia Marcellino
Mathematics 2025, 13(1), 28; https://doi.org/10.3390/math13010028 (registering DOI) - 25 Dec 2024
Abstract
Tumor angiogenesis, the formation of new blood vessels from pre-existing vasculature, is a crucial process in cancer growth and metastasis. Mathematical modeling through partial differential equations helps to understand this complex biological phenomenon. Here, we provide a conservation properties analysis in a tumor [...] Read more.
Tumor angiogenesis, the formation of new blood vessels from pre-existing vasculature, is a crucial process in cancer growth and metastasis. Mathematical modeling through partial differential equations helps to understand this complex biological phenomenon. Here, we provide a conservation properties analysis in a tumor angiogenesis model describing the evolution of endothelial cells, proteases, inhibitors, and extracellular matrix. The adopted approach introduces a numerical framework that combines spatial and time discretization techniques. Here, we focus on maintaining solution accuracy while preserving physical quantities during the simulation process. The method achieved second-order accuracy in both space and time discretizations, with conservation errors showing consistent convergence as the mesh was refined. The numerical schema demonstrates stable wave propagation patterns, in agreement with experimental observations. Numerical experiments validate the approach and demonstrate its reliability for long-term angiogenesis simulations. Full article
(This article belongs to the Special Issue Applications of Differential Equations in Sciences)
11 pages, 1070 KiB  
Article
Mastectomy, HER2 Receptor Positivity, NPI, Late Stage and Luminal B-Type Tumor as Poor Prognostic Factors in Geriatric Patients with Breast Cancer
by Demet Nak and Mehmet Kivrak
Diagnostics 2025, 15(1), 13; https://doi.org/10.3390/diagnostics15010013 (registering DOI) - 25 Dec 2024
Abstract
Background/Objectives: This study aims to explore the risk factors associated with poor survival outcomes in geriatric female patients with breast cancer. Methods: This study utilized data from the METABRIC database to evaluate the risk factors associated with poor survival outcomes among geriatric breast [...] Read more.
Background/Objectives: This study aims to explore the risk factors associated with poor survival outcomes in geriatric female patients with breast cancer. Methods: This study utilized data from the METABRIC database to evaluate the risk factors associated with poor survival outcomes among geriatric breast cancer patients. A total of 2909 female patients, 766 of whom were geriatric, were included in the study. The effects of the type of surgery; breast cancer types; cellularity; Estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status; molecular class; axillary lymph nodes; Nottingham prognostic index (NPI); status of receiving systemic chemotherapy (SCT), hormone therapy (HT), and radiotherapy (RT); tumor size and tumor on overall survival (OS); and progression-free status (PFS) of geriatric patients were investigated. Additionally, the disease-specific survival of geriatric patients was compared with other patients. Results: HER2 receptor positivity, advanced-stage tumors (T3–T4), a high NPI, and Luminal B subtypes were significant predictors of worse outcomes. Conversely, Luminal A tumors, associated with favorable hormonal responsiveness, demonstrated the best progression-free survival (PFS). HER2-positive patients exhibited a poorer PFS compared to their HER2-negative counterparts, underscoring the need for careful management of aggressive subtypes in older adults. Additionally, patients undergoing mastectomy were less likely to receive adjuvant therapies, contributing to inferior outcomes compared to breast-conserving surgery (BCS). Conclusions: Mastectomy, HER2 positivity, high NPI, advanced stages, and Luminal B tumors are significant prognostic factors in geriatric breast cancer patients. Full article
(This article belongs to the Special Issue Advances in the Diagnosis and Management of Breast Cancer)
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<p>Kaplan–Meier survival curves depicting disease-specific overall survival (OS) among different patient groups. (<b>A</b>) Geriatric patients: 130 ± 3.22 months (95% CI 108–154); non-geriatric menopausal patients: 191 ± 5.41 months (95% CI 182–218) (<span class="html-italic">p</span> &lt; 0.001). (<b>B</b>) Geriatric patients: 130 ± 3.22 months (95% CI 108–154); non-geriatric all patients: 198 ± 4.74 months (95% CI 196–226) (<span class="html-italic">p</span> &lt; 0.001). (<b>C</b>) Geriatric patients: 130 ± 3.22 months (95% CI 108–154); premenopausal patients: 214 ± 8.36 months (95% CI 202–249) (<span class="html-italic">p</span> &lt; 0.001).</p>
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<p>HR plots of overall and progression-free survival in geriatric patients. Mastectomy was associated with poorer OS and PFS compared to breast-conserving surgery. Late-stage tumors demonstrated significantly worse OS and PFS outcomes compared to early-stage tumors. HER2-positive tumors were linked to higher risks of progression compared to HER2-negative tumors. Additionally, a higher Nottingham prognostic index (NPI) correlated with a poorer OS.</p>
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<p>HR plot and progression-free survival curves of PAM50 molecular subtypes. Luminal A demonstrated the longest PFS (200 ± 7.71 months), followed by triple-negative (187 ± 13.3 months), nonclassified (181 ± 23.25 months), HER2-enriched (173 ± 16.41 months), and Luminal B (159 ± 8.12 months). Differences in PFS across molecular subtypes were statistically significant (Log-rank <span class="html-italic">p</span> = 0.0024).</p>
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30 pages, 1346 KiB  
Review
Preclinical Models for Functional Precision Lung Cancer Research
by Jie-Zeng Yu, Zsofia Kiss, Weijie Ma, Ruqiang Liang and Tianhong Li
Cancers 2025, 17(1), 22; https://doi.org/10.3390/cancers17010022 (registering DOI) - 25 Dec 2024
Abstract
Patient-centered precision oncology strives to deliver individualized cancer care. In lung cancer, preclinical models and technological innovations have become critical in advancing this approach. Preclinical models enable deeper insights into tumor biology and enhance the selection of appropriate systemic therapies across chemotherapy, targeted [...] Read more.
Patient-centered precision oncology strives to deliver individualized cancer care. In lung cancer, preclinical models and technological innovations have become critical in advancing this approach. Preclinical models enable deeper insights into tumor biology and enhance the selection of appropriate systemic therapies across chemotherapy, targeted therapies, immunotherapies, antibody–drug conjugates, and emerging investigational treatments. While traditional human lung cancer cell lines offer a basic framework for cancer research, they often lack the tumor heterogeneity and intricate tumor–stromal interactions necessary to accurately predict patient-specific clinical outcomes. Patient-derived xenografts (PDXs), however, retain the original tumor’s histopathology and genetic features, providing a more reliable model for predicting responses to systemic therapeutics, especially molecularly targeted therapies. For studying immunotherapies and antibody–drug conjugates, humanized PDX mouse models, syngeneic mouse models, and genetically engineered mouse models (GEMMs) are increasingly utilized. Despite their value, these in vivo models are costly, labor-intensive, and time-consuming. Recently, patient-derived lung cancer organoids (LCOs) have emerged as a promising in vitro tool for functional precision oncology studies. These LCOs demonstrate high success rates in growth and maintenance, accurately represent the histology and genomics of the original tumors and exhibit strong correlations with clinical treatment responses. Further supported by advancements in imaging, spatial and single-cell transcriptomics, proteomics, and artificial intelligence, these preclinical models are reshaping the landscape of drug development and functional precision lung cancer research. This integrated approach holds the potential to deliver increasingly accurate, personalized treatment strategies, ultimately enhancing patient outcomes in lung cancer. Full article
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<p>Schematic representation of the various in vitro methods. Immortalized cell lines, primary cell cultures, and organoids established from lung cancer patients serve as essential tools in precision oncology research. Each model offers unique insights into lung cancer biology and treatment responses. Co-culture of patient-derived lung tumor cells with PBMCs isolated from the same patient’s whole blood provides a more physiologically relevant model by incorporating the patient’s immune cells, allowing for real-time study of immune–tumor interactions (by Figdraw.com, accessed on 25 August 2022). Abbreviations: CRCs, conditional reprogramming cultures; PBMC, peripheral blood mononuclear cell; ROCK, Rho kinase.</p>
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<p>A schematic representation of the in vivo mouse models currently available for cancer research. Carcinogen-induced mouse models are induced to develop certain types of cancer by exposing them to certain environmental risk factors (carcinogenic chemicals, radiation, etc.). Syngeneic mouse models are immunocompetent animals that bear tumors of mouse origin. CDX models are immunodeficient mouse models, and tumors are implanted to assess drug function on a tumor. GEMM models are mouse strains that have been manipulated genetically either by the overexpression of an oncogene or by the loss of a tumor suppressor gene function. PDX models are immunocompromised animals implanted with tumors of human origin. Humanized PDX models can represent the human immune system to a certain extent along with tumors of human origin to study tumor–immune system interactions (by Figdraw.com; accessed on 27 August 2022).</p>
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<p>Summary of key preclinical models for precision lung cancer research.</p>
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29 pages, 9628 KiB  
Review
The Role of YY1 in the Regulation of LAG-3 Expression in CD8 T Cells and Immune Evasion in Cancer: Therapeutic Implications
by Adam Merenstein, Loiy Obeidat, Apostolos Zaravinos and Benjamin Bonavida
Cancers 2025, 17(1), 19; https://doi.org/10.3390/cancers17010019 (registering DOI) - 25 Dec 2024
Abstract
The treatment of cancers with immunotherapies has yielded significant milestones in recent years. Amongst these immunotherapeutic strategies, the FDA has approved several checkpoint inhibitors (CPIs), primarily Anti-Programmed Death-1 (PD-1) and Programmed Death Ligand-1/2 (PDL-1/2) monoclonal antibodies, in the treatment of various cancers unresponsive [...] Read more.
The treatment of cancers with immunotherapies has yielded significant milestones in recent years. Amongst these immunotherapeutic strategies, the FDA has approved several checkpoint inhibitors (CPIs), primarily Anti-Programmed Death-1 (PD-1) and Programmed Death Ligand-1/2 (PDL-1/2) monoclonal antibodies, in the treatment of various cancers unresponsive to immune therapeutics. Such treatments resulted in significant clinical responses and the prolongation of survival in a subset of patients. However, not all patients responded to CPIs, due to various mechanisms of immune resistance. One such mechanism is that, in addition to PD-1 expression on CD8 T cells, other inhibitory receptors exist, such as Lymphocyte Activation Gene 3 (LAG-3), T cell Immunoglobulin Mucin 3 (TIM3), and T cell immunoreceptor with Ig and ITIM domains (TIGIT). These inhibitory receptors might be active in the presence of the above approved CPIs. Clearly, it is clinically challenging to block all such inhibitory receptors simultaneously using conventional antibodies. To circumvent this difficulty, we sought to target a potential transcription factor that may be involved in the molecular regulation of more than one inhibitory receptor. The transcription factor Yin Yang1 (YY1) was found to regulate the expression of PD-1, LAG-3, and TIM3. Therefore, we hypothesized that targeting YY1 in CD8 T cells should inhibit the expression of these receptors and, thus, prevent the inactivation of the anti-tumor CD8 T cells by these receptors, by corresponding ligands to tumor cells. This strategy should result in the prevention of immune evasion, leading to the inhibition of tumor growth. In addition, this strategy will be particularly effective in a subset of cancer patients who were unresponsive to approved CPIs. In this review, we discuss the regulation of LAG-3 by YY1 as proof of principle for the potential use of targeting YY1 as an alternative therapeutic approach to preventing the immune evasion of cancer. We present findings on the molecular regulations of both YY1 and LAG-3 expressions, the direct regulation of LAG-3 by YY1, the various approaches to targeting YY1 to evade immune evasion, and their clinical challenges. We also present bioinformatic analyses demonstrating the overexpression of LAG-3, YY1, and PD-L1 in various cancers, their associations with immune infiltrates, and the fact that when LAG-3 is hypermethylated in its promoter region it correlates with a better overall survival. Hence, targeting YY1 in CD8 T cells will result in restoring the anti-tumor immune response and tumor regression. Notably, in addition to the beneficial effects of targeting YY1 in CD8 T cells to inhibit the expression of inhibitory receptors, we also suggest targeting YY1 overexpressed in the tumor cells, which will also inhibit PD-L1 expression and other YY1-associated pro-tumorigenic activities. Full article
(This article belongs to the Special Issue Cancer Immunotherapy in Clinical and Translational Research)
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<p>YY1 gene structure. This figure provides a comprehensive depiction of the Yin Yang 1 (YY1) transcription factor, illustrating its key structural domains and functional regions. YY1 is 414 amino acids long and consists of three major domains. The transactivation domains have acidic domains of around 70 amino acids each. The histidine domains for activation of YY1 have 11 histidines in a row. The repression domains have a 32 amino acid-long Glycine Alanine Domain and a 25 amino acid-long MBTD1-binding domain. Finally, in the DNA-binding domains of YY1, there are four zinc finger domains. YY1 contributes to cancer progression and immune evasion in various ways. YY1 regulates the protein stability and expression of many different cancer-associated genes. YY1 also contributes to the upregulation or downregulation of various T cell stability and regulation processes, contributing to a much stronger immune evasion response. Prepared by BioRender, Inc. (Toronto, ON, Canada).</p>
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<p>The KIELLE domain in LAG-3. This figure depicts the structural organization of the LAG-3 protein, a key inhibitory receptor involved in the regulation of immune responses. The KIELLE domain, the CP domain, and the IgG domains. Created by BioRender, Inc.</p>
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<p>LAG-3 higher affinity for MHC-II. CD4 cells use four extracellular immunoglobulin superfamily-like domains (d1–d4). LAG-3 utilizes the extra loop with 30 amino acids in D1 to bind to MHC class II with greater affinity. Ligation of MHC class II, by antigen presenting cells or aberrantly by melanoma cells, with LAG-3 mediates an intrinsic negative inhibitory signal, in which the KIEELE motif in the cytoplasmic domain is indispensable. LAG-3 is highly glycosylated with LSECtin, expressed on melanoma cells, and Galectin-3 is expressed on stromal cells and CD8<sup>+</sup> T cells in the tumor microenvironment. This figure shows the interaction between LAG-3 and these three ligands and how it interacts with CD4 and CD8 T cells. Created by BioRender, Inc.</p>
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<p>Regulation of PD-1 and LAG-3 by YY1 in tumor-infiltrating CD8 T lymphocytes. This figure depicts p38MAPK/JNK/YY1/LAG-3-PD-1 pathway in tumor-infiltrating lymphocytes. MAP3K activation increases JNK and p38, leading to an increase in YY1 expression. This pathway, which drives YY1 expression, leads to YY1-mediated transcriptional PD-1 and LAG-3 upregulations. The anti-tumor CD8 T cells, expressing both PD-1 and LAG-3 inhibitory receptors, will bind the tumor target cells, leading to the inactivation of the CD8 T cells through their interactions with the PDL-1/2 and MHC-II, respectively. Thus, tumors escape via immune evasion and tumor growth. Created by BioRender, Inc.</p>
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<p>The expression of YY1, CD274 (PD-L1), and LAG-3 in pan-cancer using normalized and batch-corrected RSEM mRNA expression data for 14 TCGA cancer types paired with their normal tissue. (<b>a</b>) The bubble plot presents the fold change and FDR for gene expression across different cancer types, represented by bubble color and size. Rows indicate gene symbols, while columns correspond to selected cancer types. Bubble color transitions from purple to red, reflecting fold change (tumor vs. normal), and bubble size is proportional to FDR significance. (<b>b</b>,<b>c</b>) Boxplots display the expression levels of YY1, LAG-3, and CD274 between tumor and normal tissues across multiple cancers. A detailed example focusing on lung cancer is provided in panel (<b>c</b>), highlighting the differential expression patterns that may suggest varying roles in tumorigenesis.</p>
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<p>Correlation between YY1, CD274 and LAG-3 expression and immune cell infiltration in squamous cell lung carcinoma (LUSC) (<b>a</b>) and breast cancer (BRCA) (<b>b</b>). The Spearman’s test was used for correlations. The infiltrates of 24 immune cells were quantified using ImmuCellAI. Bubble size correlates with FDR significance. The black outline border indicates FDR ≤ 0.05. (<b>c</b>) Each gene’s mRNA expression was correlated with a specific immune cell’s infiltrates using scatter plots with a fitting line.</p>
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<p>Expression of YY1 and LAG-3 in five independent scRNA-seq datasets of breast cancer (GSE110686, GSE114727_10X, GSE114727_inDrop, GSE176078 and EMTAB8107). The global-scaling normalization method (‘NormalizeData’ function) in Seurat was used to scale the raw counts (UMI) in each cell to 10,000, and to log-transform the results. YY1 and LAG-3 expression levels were calculated in log2(TPM/10+1) values and displayed using UMAP.</p>
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<p>Expression of YY1 and LAG3 across different immune cells in breast cancer, using multiple GEO datasets. CD8<sup>+</sup> T cells express higher levels of YY1 compared to LAG-3.</p>
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<p>The GDC TCGA Breast Cancer (BRCA) dataset on the UCSC Xena browser was explored for LAG-3 and FGL1 methylation. (<b>a</b>) Electrophoresis result (2% agarose gel) of two methylation-specific PCR amplicons for LAG-3 and FGL1 (methylated and unmethylated DNA). (<b>b</b>) In silico analysis of LAG-3 promoter methylation using the beta values of specific markers (Illumina Human Methylation 450) shows that LAG-3 promoter is hypermethylated in breast cancer. (<b>c</b>) The Kaplan–Meier curves depict that breast cancer patients with LAG-3 hypermethylation (red curve) have better overall survival compared to those with LAG-3 hypomethylation (white curve) (<span class="html-italic">p</span> &lt; 0.05, Log-rank test). (<b>d</b>) In silico analysis shows no significant methylation levels in the promoter region of FGL1. (<b>e</b>) The Kaplan–Meier curves depict no difference in the overall survival between FLG1 hyper- and hypo-methylated breast cancer patients (<span class="html-italic">p</span> &gt; 0.05, Log-rank test). (<b>f</b>) Proposed model for LAG-3-expressing breast tumors (LAG-3 hyper-methylated), which could be targeted with Relatimab (anti-LAG-3) alone or in combination with anti-PD-1/PD-L1.</p>
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13 pages, 2838 KiB  
Article
Anti-Tumor Effects of Vespa bicolor Venom on Liver Cancer: In Vitro and In Vivo Studies
by Yong-Hua Wu, Feng Xiong, Zheng-Wen Ou, Jing-An Wang, Jing Cui, Lin Jiang and Wen-Jian Lan
Toxins 2025, 17(1), 4; https://doi.org/10.3390/toxins17010004 (registering DOI) - 25 Dec 2024
Abstract
Despite the popular belief in the anti-tumor properties of Vespa bicolor venom (VBV), there is limited scientific evidence to support this claim. This study is the first to examine the anti-tumor effects of VBV on liver cancer, both alone and in combination with [...] Read more.
Despite the popular belief in the anti-tumor properties of Vespa bicolor venom (VBV), there is limited scientific evidence to support this claim. This study is the first to examine the anti-tumor effects of VBV on liver cancer, both alone and in combination with cisplatin (DDP), through in vitro and in vivo experiments. In vitro experiments evaluated VBV and its combination with DDP on HepG2 cell proliferation, invasion, migration, and apoptosis. Animal studies examined the tumor-suppressive effects, safety (hepatotoxicity and nephrotoxicity), and immune impact of these treatments in tumor-bearing mice. VBV monotherapy significantly inhibited the growth of HepG2 cells by suppressing their proliferation and invasion and induced apoptosis in vitro. Notably, low VBV concentrations significantly promoted the proliferation of normal liver cells (L-02), suggesting a hepatoprotective effect. In vivo, VBV monotherapy enhanced immune function and exhibited tumor suppression comparable to DDP monotherapy but did not induce significant liver or kidney damage. In addition, VBV combined with DDP synergistically enhanced the anti-tumor effects of DDP, compensating for its limited apoptosis-inducing activity and insufficient enhancement of immune function. Initial studies have shown the strong potential of VBV as an anti-liver-tumor drug, highlighting its unique clinical value. Full article
(This article belongs to the Special Issue Clinical Evidence for Therapeutic Effects and Safety of Animal Venoms)
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<p>Quality evaluation of collected <span class="html-italic">Vespa bicolor</span> venom (VBV) samples, including (<b>a</b>) analysis of protein components by SDS-PAGE electrophoresis and (<b>b</b>) HPLC fingerprint analysis.</p>
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<p>In vitro efficacy of VBV as a treatment for liver cancers either as a monotherapy or in conjunction with the chemotherapeutic drug cisplatin (DDP). (<b>a</b>) Cytotoxic effects of varying concentrations of VBV on HepG2 cells and L-02 cells. (<b>b</b>) Comparative analysis of the cytotoxicity of DDP alone versus its combination with 15 µg/mL VBV on HepG2 cells. (<b>c</b>) Comparative analysis of the cytotoxicity of DDP alone and in combination with 15 µg/mL VBV on L-02 cells. Data are expressed as means ± SD. Each experiment represents the mean values of six independent experiments. * <span class="html-italic">p</span> &lt; 0.05 compared with negative controls.</p>
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<p>In vitro anti-tumor efficacy of VBV as a monotherapy against HepG2 cells. The effects of varying concentrations of VBV on HepG2 cells: (<b>a</b>) proliferation assessed using EdU, (<b>b</b>) migration assessed using scratch assays, (<b>c</b>) invasion measured using Transwell assays, and (<b>d</b>) apoptosis analyzed via flow cytometry. Data are expressed as means ± SD. Each experiment represents the mean values of three independent experiments. *** <span class="html-italic">p</span> &lt; 0.001, significant difference; ns, no significant difference.</p>
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<p>In vitro anti-tumor efficacy of combined with DDP. The effects of 15 µg/mL VBV combined with 4 µg/mL DDP on HepG2 cells: (<b>a</b>) proliferation assessed using EdU assays; (<b>b</b>) migration assessed using scratch assay; (<b>c</b>) invasion measured using Transwell assays; and (<b>d</b>) apoptosis analyzed via flow cytometry. Data are expressed as means ± SD. Each experiment represents the mean values of three independent experiments. ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, significant difference; ns, no significant difference.</p>
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<p>Tumor-suppressive effects of VBV alone and in combination with DDP in the murine model. (<b>a</b>) Weight changes of mice across different treatment groups over 20 days after tumor implantation. (<b>b</b>) Changes in tumor volume among the treatment groups. (<b>c</b>) Tumor weights in each group following 20 days of drug treatment. (<b>d</b>) Images of tumor tissue in each experimental group. (<b>e</b>) HE stained images of tumors in each experimental group. Data represent mean ± SD; n = 6 mice/group. * <span class="html-italic">p</span> &lt; 0.05, significant difference.</p>
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<p>Hepatorenal toxicity and immunomodulatory effects of VBV alone and in combination with DDP evaluated in a murine model. Following 20 days of various drug treatments, we analyzed the differences in the following: (<b>a</b>) liver index, (<b>b</b>) kidney index, (<b>d</b>) spleen index, and (<b>e</b>) thymus index (<b>f</b>), as well as (<b>c</b>) ALT and (<b>d</b>) AST levels in serum among the different groups of mice. Data represent mean ± SD; n = 6 mice/group. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, significant difference; ns, no significant difference.</p>
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16 pages, 2704 KiB  
Article
The RAGE Inhibitor TTP488 (Azeliragon) Demonstrates Anti-Tumor Activity and Enhances the Efficacy of Radiation Therapy in Pancreatic Cancer Cell Lines
by Kumari Alka, Jacob F. Oyeniyi, Ghulam Mohammad, Yi Zhao, Stephen Marcus and Prakash Chinnaiyan
Cancers 2025, 17(1), 17; https://doi.org/10.3390/cancers17010017 (registering DOI) - 24 Dec 2024
Abstract
Pancreatic cancer is the third leading cause of cancer-related mortality in the United States, with rising incidence and mortality. The receptor for advanced glycation end products (RAGE) and its ligands significantly contribute to pancreatic cancer progression by enhancing cell proliferation, fostering treatment resistance, [...] Read more.
Pancreatic cancer is the third leading cause of cancer-related mortality in the United States, with rising incidence and mortality. The receptor for advanced glycation end products (RAGE) and its ligands significantly contribute to pancreatic cancer progression by enhancing cell proliferation, fostering treatment resistance, and promoting a pro-tumor microenvironment via activation of the nuclear factor-kappa B (NF-κB) signaling pathways. This study validated pathway activation in human pancreatic cancer and evaluated the therapeutic efficacy of TTP488 (Azeliragon), a small-molecule RAGE inhibitor, alone and in combination with radiation therapy (RT) in preclinical models of pancreatic cancer. Human (Panc1) and murine (Pan02) pancreatic cancer cell lines exhibited elevated levels of RAGE and its ligands compared to normal pancreatic tissue. In vitro, Azeliragon inhibited RAGE-mediated NF-κB activation and ligand-mediated cell proliferation in pancreatic cancer cell lines. Target engagement of Azeliragon was confirmed in vivo, as determined by decreased NF-κB activation. Azeliragon demonstrated significant growth delay in mouse models of pancreatic cancer and additive effects when combined with RT. Additionally, Azeliragon modulated the immune suppressive tumor microenvironment in pancreatic cancer by reducing immunosuppressive cells, including M2 macrophages, regulatory T cells, and myeloid-derived suppressor cells, while enhancing CD8+ T cell infiltration. These findings suggest that Azeliragon, by inhibiting RAGE-mediated signaling and modulating immune response, may serve as an effective anti-cancer agent in pancreatic cancer. Full article
(This article belongs to the Special Issue Management of Pancreatic Cancer)
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<p>Chemical Structure of Azeliragon (retrieved from PubChem) [<a href="#B34-cancers-17-00017" class="html-bibr">34</a>].</p>
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<p><b>Expression and clustering of RAGE (AGER) and its ligands in pancreatic cancer:</b> (<b>A</b>) Expression levels of RAGE (Receptor for Advanced Glycation End Products (encoded by the AGER gene)) and its ligands in pancreatic cancer patient samples obtained from The Cancer Genome Atlas (TCGA) database; (<b>B</b>) Clustering of RAGE ligands and activators within distinct pancreatic cancer subtypes (progenitor and squamous subtypes) as described by Bailey et al. [<a href="#B35-cancers-17-00017" class="html-bibr">35</a>]; (<b>C</b>) Variable Importance Plot (VIP) analysis highlights key ligands (e.g., S100A2, HMGA2) that differentiate the pancreatic progenitor and squamous subtypes. Red indicates upregulated genes, while blue indicates downregulated genes, underscoring subtype-specific mechanisms of RAGE pathway activation.</p>
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<p><b>Expression of RAGE and its ligands in pancreatic cancer cell lines and the inhibitory effect of Azeliragon on RAGE signaling:</b> (<b>A</b>) Expression levels of RAGE (Receptor for Advanced Glycation End Products) and its ligands—S100P, S100A2, and HMGB1—in human (Panc1) and murine (Pan02) pancreatic cancer cell lines; (<b>B</b>–<b>E</b>) Azeliragon (AZ), a RAGE inhibitor, inhibits the downstream signaling pathway of RAGE in pancreatic cancer cell lines: (<b>B</b>,<b>C</b>) Expression levels of phosphorylated NF-κB (pNF-κB) in Panc1 (<b>B</b>) and Pan02 (<b>C</b>) cells treated with indicated concentrations of AZ for 3 h; (<b>D</b>,<b>E</b>) Expression levels of pNF-κB in Panc1 (<b>D</b>) and Pan02 (<b>E</b>) cells treated with 1 µM AZ at indicated time points. Statistical significance: ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 compared to the control group. The corresponding uncropped blots are provided in <a href="#app1-cancers-17-00017" class="html-app">Supplementary Figures S2 and S3</a>.</p>
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<p><b>Expression of RAGE and its ligands in pancreatic cancer cell lines and the inhibitory effect of Azeliragon on RAGE signaling:</b> (<b>A</b>) Expression levels of RAGE (Receptor for Advanced Glycation End Products) and its ligands—S100P, S100A2, and HMGB1—in human (Panc1) and murine (Pan02) pancreatic cancer cell lines; (<b>B</b>–<b>E</b>) Azeliragon (AZ), a RAGE inhibitor, inhibits the downstream signaling pathway of RAGE in pancreatic cancer cell lines: (<b>B</b>,<b>C</b>) Expression levels of phosphorylated NF-κB (pNF-κB) in Panc1 (<b>B</b>) and Pan02 (<b>C</b>) cells treated with indicated concentrations of AZ for 3 h; (<b>D</b>,<b>E</b>) Expression levels of pNF-κB in Panc1 (<b>D</b>) and Pan02 (<b>E</b>) cells treated with 1 µM AZ at indicated time points. Statistical significance: ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 compared to the control group. The corresponding uncropped blots are provided in <a href="#app1-cancers-17-00017" class="html-app">Supplementary Figures S2 and S3</a>.</p>
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<p><b>Azeliragon inhibits proliferation and NF-κB activation in pancreatic cancer cell lines:</b> (<b>A</b>,<b>B</b>) Azeliragon (AZ) suppresses proliferation in pancreatic cancer cell lines: (<b>A</b>) Proliferation was determined by treating human Panc1 (left) and murine Pan02 (right) cells with indicated concentrations of AZ for 24 h. Live cells were counted using the trypan blue exclusion; (<b>B</b>) Clonogenic assay of Panc1 (left) and Pan02 (right) cells treated with 1 µM AZ for 24 h, followed by exposure to radiation doses of 2 Gy, 4 Gy, and 6 Gy. Results are representative of at least three independent experiments. Data represent mean ± standard deviation (SD). Significant results are indicated as: ** <span class="html-italic">p</span> = 0.01; *** <span class="html-italic">p</span> = 0.001; **** <span class="html-italic">p</span> &lt; 0.001; (<b>C</b>) Azeliragon inhibits RAGE ligand-induced expression of phosphorylated NF-κB (pNF-κB). Expression level of pNF-κB in Panc1 (left) and Pan02 (right) cells stimulated with RAGE ligands (S100P, S100A2, HMGB1; 0.1 µM) for 24 h then treated with Azeliragon (1 µM AZ for 24 h). (<b>D</b>,<b>E</b>) Azeliragon inhibits RAGE ligand-induced cell proliferation. Effect of RAGE ligand stimulation (0.1 µM ligands for 24 h) on the proliferation of Panc1 (<b>D</b>) and Pan02 (<b>E</b>) cells with and without Azeliragon treatment (1 µM AZ for 24 h). Data represent mean ± SD of three or more independent experiments. Statistical significance: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 for ligands compared to the control group; <sup>#</sup> <span class="html-italic">p</span> = 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>###</sup> <span class="html-italic">p</span> = 0.001 for AZ-treated groups compared to their respective ligand-treated groups. The corresponding uncropped blots are provided in <a href="#app1-cancers-17-00017" class="html-app">Supplementary Figure S4</a>.</p>
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<p><b>Azeliragon inhibits proliferation and NF-κB activation in pancreatic cancer cell lines:</b> (<b>A</b>,<b>B</b>) Azeliragon (AZ) suppresses proliferation in pancreatic cancer cell lines: (<b>A</b>) Proliferation was determined by treating human Panc1 (left) and murine Pan02 (right) cells with indicated concentrations of AZ for 24 h. Live cells were counted using the trypan blue exclusion; (<b>B</b>) Clonogenic assay of Panc1 (left) and Pan02 (right) cells treated with 1 µM AZ for 24 h, followed by exposure to radiation doses of 2 Gy, 4 Gy, and 6 Gy. Results are representative of at least three independent experiments. Data represent mean ± standard deviation (SD). Significant results are indicated as: ** <span class="html-italic">p</span> = 0.01; *** <span class="html-italic">p</span> = 0.001; **** <span class="html-italic">p</span> &lt; 0.001; (<b>C</b>) Azeliragon inhibits RAGE ligand-induced expression of phosphorylated NF-κB (pNF-κB). Expression level of pNF-κB in Panc1 (left) and Pan02 (right) cells stimulated with RAGE ligands (S100P, S100A2, HMGB1; 0.1 µM) for 24 h then treated with Azeliragon (1 µM AZ for 24 h). (<b>D</b>,<b>E</b>) Azeliragon inhibits RAGE ligand-induced cell proliferation. Effect of RAGE ligand stimulation (0.1 µM ligands for 24 h) on the proliferation of Panc1 (<b>D</b>) and Pan02 (<b>E</b>) cells with and without Azeliragon treatment (1 µM AZ for 24 h). Data represent mean ± SD of three or more independent experiments. Statistical significance: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 for ligands compared to the control group; <sup>#</sup> <span class="html-italic">p</span> = 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01, <sup>###</sup> <span class="html-italic">p</span> = 0.001 for AZ-treated groups compared to their respective ligand-treated groups. The corresponding uncropped blots are provided in <a href="#app1-cancers-17-00017" class="html-app">Supplementary Figure S4</a>.</p>
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<p><b>The in vivo antitumor activity of Azeliragon in pancreatic cancer mouse models:</b> (<b>A</b>,<b>B</b>) Panc1 cells were injected into the flank of NU/NU mice. On day 14 post-tumor implantation, AZ treatment (1 mg/kg, i.p.) was initiated, and one day later, RT treatment (2 Gy X 5 days) was initiated. AZ treatment continued daily: (<b>A</b>) Tumor growth curves of mice. Data represent mean ± SD. Statistical significance calculated on day 34: * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 compared to the control group; <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 RT + AZ significantly different from the AZ group; <sup><span>$</span><span>$</span></sup> <span class="html-italic">p</span> &lt; 0.01 RT + AZ significantly different from the RT group. For correlative studies, mice were euthanized 10 days after the first day of treatment, and tumors were harvested for further analysis; (<b>B</b>) Expression levels of phosphorylated NF-κB (pNF-κB) in tumors in indicated treatment groups; (<b>C</b>–<b>E</b>): Pan02 cells were injected subcutaneously into the flank of C57BL/6 mice. On day 14 post-tumor implantation, AZ treatment (1 mg/kg) was initiated, and one day later, RT treatment (2 Gy X 5 days) was initiated. AZ treatment continued daily until the mice met the criteria requiring euthanization; (<b>C</b>) Tumor growth of C57BL/6 mice. Data represents mean ± SD. Statistical significance as calculated on day 24: * <span class="html-italic">p</span> &lt; 0.01 significantly different from the control group. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05 RT + AZ significantly different from RT group. For correlative studies, tumors were harvested 24 days post-tumor implantation; (<b>D</b>) Expression of pNF-kB in tumors in indicated treatment groups; (<b>E</b>) Immune profiling of tumors was performed using flow cytometry. Data represents mean ± SD. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 significantly different from control group. <sup>#</sup> <span class="html-italic">p</span> &lt; 0.05, <sup>##</sup> <span class="html-italic">p</span> &lt; 0.01 significantly different from RT group. The corresponding uncropped blots are provided in <a href="#app1-cancers-17-00017" class="html-app">Supplementary Figure S4</a>.</p>
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14 pages, 877 KiB  
Review
Hypoxia-Inducible Factor in Renal Cell Carcinoma: From Molecular Insights to Targeted Therapies
by Giandomenico Roviello, Irene De Gennaro, Ismaela Vascotto, Giulia Venturi, Alberto D’Angelo, Costanza Winchler, Adriana Guarino, Salvatore Cacioppo, Mikol Modesti, Marinella Micol Mela, Edoardo Francini, Laura Doni, Virginia Rossi, Elisabetta Gambale, Roberta Giorgione, Lorenzo Antonuzzo, Gabriella Nesi and Martina Catalano
Genes 2025, 16(1), 6; https://doi.org/10.3390/genes16010006 (registering DOI) - 24 Dec 2024
Abstract
Mutations of the von Hippel–Lindau (VHL) tumor suppressor gene occur frequently in clear cell renal cell carcinoma (RCC), the predominant histology of kidney cancer, and have been associated with its pathogenesis and progression. Alterations of VHL lead to impaired degradation of [...] Read more.
Mutations of the von Hippel–Lindau (VHL) tumor suppressor gene occur frequently in clear cell renal cell carcinoma (RCC), the predominant histology of kidney cancer, and have been associated with its pathogenesis and progression. Alterations of VHL lead to impaired degradation of hypoxia-inducible factor 1α (HIF1α) and HIF2α promoting neoangiogenesis, which is pivotal for cancer growth. As such, targeting the VHL-HIF axis holds relevant potential for therapeutic purposes. Belzutifan, an HIF-2α inhibitor, has been recently indicated for metastatic RCC and other antiangiogenic drugs directed against HIF-2α are currently under investigation. Further, clinical and preclinical studies of combination approaches for metastatic RCC including belzutifan with cyclin-dependent kinase 4–6 inhibitors, tyrosine kinase inhibitors, or immune checkpoint inhibitors achieved promising results or are ongoing. This review aims to summarize the existing evidence regarding the VHL/HIF pathway, and the approved and emerging treatment strategies that target this pivotal molecular axis and their mechanisms of resistance. Full article
(This article belongs to the Section Molecular Genetics and Genomics)
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<p>The role of HIFs in renal tumor development. The figure illustrates the involvement of HIFs in the pathogenesis of RCC. HIFs are key transcription factors activated under hypoxic conditions and are commonly dysregulated in RCC due to mutations in the VHL tumor suppressor gene. In normal oxygen levels, VHL targets HIF for proteasomal degradation. In RCC, the loss of VHL function leads to constitutive stabilization of the HIF-α subunit that accumulates and translocates to the nucleus and binds to HIF-β, forming an active transcriptional complex. This HIF complex binds to hypoxia-responsive elements (HREs) within the promoter regions of hypoxia-related genes, activating a broad transcriptional program that includes genes regulating angiogenesis, metabolism, erythropoiesis, and cell survival. Hypoxia-inducible factors (HIFs); renal cell carcinoma (RCC); von Hippel–Lindau (VHL).</p>
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<p>Belzutifan inhibits hypoxia-inducible factor-2 alpha (HIF-2α), a key transcription factor involved in cellular adaptation to hypoxia. By targeting HIF-2α, belzutifan disrupts tumor growth and survival pathways in hypoxia-driven cancers.</p>
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18 pages, 14945 KiB  
Article
Long-Term Therapeutic Effects of 225Ac-DOTA-E[c(RGDfK)]2 Induced by Radiosensitization via G2/M Arrest in Pancreatic Ductal Adenocarcinoma
by Mitsuyoshi Yoshimoto, Kohshin Washiyama, Kazunobu Ohnuki, Ayano Doi, Miki Inokuchi, Motohiro Kojima, Brian W. Miller, Yukie Yoshii, Anri Inaki and Hirofumi Fujii
Pharmaceutics 2025, 17(1), 9; https://doi.org/10.3390/pharmaceutics17010009 - 24 Dec 2024
Abstract
Background: Alpha radionuclide therapy has emerged as a promising novel strategy for cancer treatment; however, the therapeutic potential of 225Ac-labeled peptides in pancreatic cancer remains uninvestigated. Methods: In the cytotoxicity study, tumor cells were incubated with 225Ac-DOTA-RGD2. [...] Read more.
Background: Alpha radionuclide therapy has emerged as a promising novel strategy for cancer treatment; however, the therapeutic potential of 225Ac-labeled peptides in pancreatic cancer remains uninvestigated. Methods: In the cytotoxicity study, tumor cells were incubated with 225Ac-DOTA-RGD2. DNA damage responses (γH2AX and 53BP1) were detected using flowcytometry or immunohistochemistry analysis. Biodistribution and therapeutic studies were carried out in BxPC-3-bearing mice. Results: 225Ac-DOTA-RGD2 demonstrated potent cytotoxicity against cells expressing αvβ3 or αvβ6 integrins and induced G2/M arrest and γH2AX expression as a marker of double-stranded DNA breaks. 225Ac-DOTA-RGD2 (20, 40, 65, or 90 kBq) showed favorable pharmacokinetics and remarkable tumor growth inhibition without severe side effects in the BxPC-3 mouse model. In vitro studies revealed that 5 and 10 kBq/mL of 225Ac-DOTA-RGD2 swiftly induced G2/M arrest and elevated γH2AX expression. Furthermore, to clarify the mechanism of successful tumor growth inhibition for a long duration in vivo, we investigated whether short-term high radiation exposure enhances radiation sensitivity. Initially, a 4 h induction treatment with 5 and 10 kBq/mL of 225Ac-DOTA-RGD2 enhanced both cytotoxicity and γH2AX expression with 0.5 kBq/mL of 225Ac-DOTA-RGD2 compared to a treatment with only 0.5 kBq/mL of 225Ac-DOTA-RGD2. Meanwhile, the γH2AX expression induced by 5 or 10 kBq/mL of 225Ac-DOTA-RGD2 alone decreased over time. Conclusions: These findings highlight the potential of using 225Ac-DOTA-RGD2 in the treatment of intractable pancreatic cancers, as its ability to induce G2/M cell cycle arrest enhances radiosensitization, resulting in notable growth inhibition. Full article
(This article belongs to the Section Clinical Pharmaceutics)
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Figure 1
<p>In vitro cytotoxicity. (<b>a</b>) Cytotoxicity of <sup>225</sup>Ac-DOTA-RGD<sub>2</sub> in human pancreatic tumor cell lines. (<b>b</b>) Comparison of cytotoxicity between <sup>225</sup>Ac-DOTA-RGD<sub>2</sub> and <sup>225</sup>AcDOTA in BxPC-3. All assays were performed in triplicate. Data are presented as mean ± standard deviation.</p>
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<p>Induction of γH2AX and 53BP1 foci formation in response to increasing doses of <sup>225</sup>Ac-DOTA-RGD<sub>2</sub> at 24 h. (<b>a</b>) Representative images of γH2AX and 53BP1 foci obtained by immunofluorescence microscopy in BxPC-3 cells. Scale bar, 20 μm. (<b>b</b>) The number of γH2AX and 53BP1 foci per cell. Induction of γH2AX and 53BP1 foci in response to increasing doses of <sup>225</sup>Ac-DOTA-RGD<sub>2</sub> was monitored at 24 h. The number of γH2AX and 53BP1 foci per cell was counted, and 50–100 cells were analyzed. All assays were performed in triplicate. Data are presented as mean ± standard deviation and analyzed using a one-way analysis of variance with Dunn’s multiple-comparisons test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01).</p>
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<p>Flow cytometric analysis of BxPC-3 after incubation with <sup>225</sup>Ac-DOTA-RGD<sub>2</sub>. (<b>a</b>) Representative fluorescence-activated cell sorting plots for γH2AX. The <span class="html-italic">y</span>-axis indicates γH2AX staining, and the <span class="html-italic">x</span>-axis is the DNA content. (<b>b</b>) Percentage of cells with γH2AX staining. All assays were performed in triplicate. (<b>c</b>) Percentage of cell cycle distribution (G1, S, and G2/M). All assays were performed in triplicate. Data are presented as the mean ± standard deviation (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, and **** <span class="html-italic">p</span> &lt; 0.0001).</p>
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<p>Biodistribution of <sup>225</sup>Ac-DOTA-RGD<sub>2</sub> in BxPC-3-bearing mice. (<b>a</b>) Pharmacokinetics of <sup>225</sup>Ac-DOTA-RGD<sub>2</sub>. Data are expressed as % ID/g for organs and blood and as % ID for carcass, urine, and feces. Data are shown as the mean ± standard deviation (<span class="html-italic">n</span> = 3–4). (<b>b</b>) Alpha camera imaging of intratumoral distribution and corresponding hematoxylin and eosin images. The scale bars indicate 100 μm. ID, injected dose.</p>
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<p>Therapeutic efficacy of <sup>225</sup>Ac-DOTA-RGD<sub>2</sub> in BxPC-3-bearing mice. (<b>a</b>) Individual tumor responses. Each solid color line represents a tumor from a single mouse. (<b>b</b>) Relative tumor growth of the mice groups treated with a single dose of <sup>225</sup>Ac-DOTA-RGD<sub>2</sub> compared to the control group (untreated). Data are shown as the mean ± standard deviation. (<b>c</b>) Kaplan–Meier survival curves of the mice treated with <sup>225</sup>Ac-DOTA-RGD<sub>2</sub>. Log-rank (Mantel–Cox) test; <span class="html-italic">p</span> = 0.0192, hazard ratio [HR] 2.415, 95% CI 0.7639–7.636 (control vs. 20 kBq); <span class="html-italic">p</span> = 0.0014, HR 3.342, 95% CI 0.9631–11.60 (control vs. 40 kBq); <span class="html-italic">p</span> = 0.0002, HR 3.774, 95% CI 1.042–13.67 (control vs. 65 kBq); <span class="html-italic">p</span> = 0.0009, HR 3.786, 95% CI 1.062–13.49 (control vs. 90 kBq). (<b>d</b>) Change in body weight after administration of <sup>225</sup>Ac-DOTA-RGD<sub>2</sub>. Data are shown as the mean ± standard deviation.</p>
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<p>Cytotoxicity, cell cycle, and γH2AX expression by low-dose <sup>225</sup>Ac-DOTA-RGD<sub>2</sub> after 4 h of treatment with high-dose (5 or 10 kBq/mL) of <sup>225</sup>Ac-DOTA-RGD<sub>2</sub> in BxPC-3 and PANC-1 cells. (<b>a</b>) Cell viability. The white, grey, and blue columns indicate the pretreatment with 0, 5, and 10 kBq/mL of <sup>225</sup>Ac-DOTA-RGD<sub>2</sub>, respectively. (<b>b</b>) Percentage of cell cycle distribution. (<b>c</b>) Time course of γH2AX expression. The significance of γH2AX expression at each time point was compared to 0, 5, or 10 kBq/mL as the control in each graph. Data represent the mean ± standard deviation (<span class="html-italic">n</span> = 2–4). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, and **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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27 pages, 7809 KiB  
Article
Study on SHP2 Conformational Transition and Structural Characterization of Its High-Potency Allosteric Inhibitors by Molecular Dynamics Simulations Combined with Machine Learning
by Baerlike Wujieti, Mingtian Hao, Erxia Liu, Luqi Zhou, Huanchao Wang, Yu Zhang, Wei Cui and Bozhen Chen
Molecules 2025, 30(1), 14; https://doi.org/10.3390/molecules30010014 - 24 Dec 2024
Abstract
The src-homology 2 domain-containing phosphatase 2 (SHP2) is a human cytoplasmic protein tyrosine phosphatase that plays a crucial role in cellular signal transduction. Aberrant activation and mutations of SHP2 are associated with tumor growth and immune suppression, thus making it a potential target [...] Read more.
The src-homology 2 domain-containing phosphatase 2 (SHP2) is a human cytoplasmic protein tyrosine phosphatase that plays a crucial role in cellular signal transduction. Aberrant activation and mutations of SHP2 are associated with tumor growth and immune suppression, thus making it a potential target for cancer therapy. Initially, researchers sought to develop inhibitors targeting SHP2’s catalytic site (protein tyrosine phosphatase domain, PTP). Due to limitations such as conservativeness and poor membrane permeability, SHP2 was once considered a challenging drug target. Nevertheless, with the in-depth investigations into the conformational switch mechanism from SHP2’s inactive to active state and the emergence of various SHP2 allosteric inhibitors, new hope has been brought to this target. In this study, we investigated the interaction models of various allosteric inhibitors with SHP2 using molecular dynamics simulations. Meanwhile, we explored the free energy landscape of SHP2 activation using enhanced sampling technique (meta-dynamics simulations), which provides insights into its conformational changes and activation mechanism. Furthermore, to biophysically interpret high-dimensional simulation trajectories, we employed interpretable machine learning methods, specifically extreme gradient boosting (XGBoost) with Shapley additive explanations (SHAP), to comprehensively analyze the simulation data. This approach allowed us to identify and highlight key structural features driving SHP2 conformational dynamics and regulating the activity of the allosteric inhibitor. These studies not only enhance our understanding of SHP2’s conformational switch mechanism but also offer crucial insights for designing potent allosteric SHP2 inhibitors and addressing drug resistance issues. Full article
(This article belongs to the Special Issue Chemical Biology in Asia)
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<p>The overall structure of SHP2 and its allosteric regulatory mechanism.</p>
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<p>Scatter plot of binding free energy (ΔG<sub>bind</sub>) vs. experimental inhibitory activity (ΔG<sub>exp</sub>) for allosteric inhibitors to SHP2. The scatters were color-coded according to the mother nucleus of the allosteric inhibitors. The structural details were given in <a href="#app1-molecules-30-00014" class="html-app">Figure S1</a>.</p>
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<p>Interaction energies between allosteric inhibitors and SHP2 residues (<b>A</b>) and the interaction analysis of five representative allosteric inhibitors with SHP2 (<b>B</b>).</p>
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<p>Scatter plot of binding free energy (ΔG<sub>bind</sub>) between N-SH2 and PTP domains vs. experimental inhibitory activity (ΔG<sub>exp</sub>) of allosteric inhibitors. The scatters are color-coded according to the mother nucleus of the allosteric inhibitors. The structural details are given in <a href="#app1-molecules-30-00014" class="html-app">Figure S1</a>.</p>
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<p>Interaction energies between N-SH2 and PTP domain residues (<b>A</b>) and the interaction analysis between N-SH2 and PTP domains (<b>B</b>).</p>
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<p>Top 20 most important simulated trajectory analysis data (<b>A</b>) and their SHAP value distribution (<b>B</b>).</p>
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<p>Top 20 most important ligand–receptor interaction fingerprints (<b>A</b>) and their SHAP value distribution (<b>B</b>).</p>
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<p>Top 20 most important contact residue pairs (<b>A</b>) and their SHAP value distribution (<b>B</b>).</p>
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<p>Kinetic model of enzyme-catalyzed reaction of allosteric inhibitor-bound SHP2.</p>
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<p>Free energy landscapes of apo-SHP2 and SHP099-bound SHP2 activation processes (<b>A</b>,<b>C</b>) and their minimum free energy paths (<b>B</b>,<b>D</b>).</p>
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<p>(<b>A</b>) Top 20 most important contact residue pairs identified from the machine learning model trained on the meta-dynamics simulations of apo-SHP2; (<b>B</b>) distribution of SHAP values of top 20 most important contact residue pairs; (<b>C</b>) spatial distribution of top 20 most important contact residue pairs in the three-dimensional structure of SHP2.</p>
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<p>(<b>A</b>) Top 20 most important contact residue pairs identified from the machine learning model trained on meta-dynamics simulations of SHP099-bound SHP2; (<b>B</b>) distribution of SHAP values of top 20 most important contact residue pairs; (<b>C</b>) spatial distribution of the top 20 most important contact residue pairs in the three-dimensional structure of SHP2.</p>
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<p>Structural generalizations of allosteric inhibitors and five representative inhibitors.</p>
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15 pages, 3162 KiB  
Article
4-Pyridone-3-carboxamide-1-β-D-ribonucleoside Reduces Cyclophosphamide Effects and Induces Endothelial Inflammation in Murine Breast Cancer Model
by Paulina Mierzejewska, Agnieszka Denslow, Diana Papiernik, Alicja Zabrocka, Barbara Kutryb-Zając, Karol Charkiewicz, Alicja Braczko, Ryszard T. Smoleński, Joanna Wietrzyk and Ewa M. Słomińska
Int. J. Mol. Sci. 2025, 26(1), 35; https://doi.org/10.3390/ijms26010035 - 24 Dec 2024
Abstract
4-pyridone-3-carboxamide-1-β-D-ribonucleoside (4PYR) is a nicotinamide derivative, considered a new oncometabolite. 4PYR formation induced a cytotoxic effect on the endothelium. Elevated blood 4PYR concentration was observed in patients with cancer. Still, little is known about the metabolic and functional effects of 4PYR in this [...] Read more.
4-pyridone-3-carboxamide-1-β-D-ribonucleoside (4PYR) is a nicotinamide derivative, considered a new oncometabolite. 4PYR formation induced a cytotoxic effect on the endothelium. Elevated blood 4PYR concentration was observed in patients with cancer. Still, little is known about the metabolic and functional effects of 4PYR in this pathology. The study aimed to investigate whether this toxic accumulation of 4PYR may affect the activity of anticancer therapy with cyclophosphamide in the orthotropic model of breast cancer. Female Balb/c mice were injected with 4T1 breast cancer cells and assigned into three groups: treated with PBS (Control), cyclophosphamide-treated (+CP), 4PYR-treated (+4PYR), and mice treated with both 4PYR and CP(+4PYR+CP) for 28 days. Afterward, blood and serum samples, liver, muscle, spleen, heart, lungs, aortas, and tumor tissue were collected for analysis of concentrations of nucleotides, nicotinamide metabolites, and 4PYR with its metabolites, as well as the liver level of cytochrome P450 enzymes. 4PYR treatment caused elevation of blood 4PYR, its monophosphate and a nicotinamide adenine dinucleotide (NAD+) analog—4PYRAD. Blood 4PYRAD concentration in the +4PYR+CP was reduced in comparison to +4PYR. Tumor growth and final tumor mass were significantly decreased in +CP and did not differ in +4PYR in comparison to Control. However, we observed a substantial increase in these parameters in +4PYR+CP as compared to +CP. The extracellular adenosine deamination rate was measured to assess vascular inflammation, and it was higher in +4PYR than the Control. Treatment with 4PYR and CP caused the highest vascular ATP hydrolysis and adenosine deamination rate. 4PYR administration caused significant elevation of CYP2C9 and reduction in CYP3A4 liver concentrations in both +4PYR and +4PYR+CP as compared to Control and +CP. In additional experiments, we compared healthy mice without cancer, treated with 4PYR (4PYR w/o cancer) and PBS (Control w/o cancer), where 4PYR treatment caused an increase in the serum proinflammatory cytokine expression as compared to Control w/o cancer. 4PYR accumulation in the blood interferes with cyclophosphamide anticancer activity and induces a pro-inflammatory shift of endothelial extracellular enzymes, probably by affecting its metabolism by cytochrome P450 enzymes. This observation may have crucial implications for the activity of various anticancer drugs metabolized by cytochrome P450. Full article
(This article belongs to the Section Biochemistry)
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Graphical abstract
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<p>Cyclophposphamide metabolic pathway. Hepatic cytochrome P450 enzymes activate CP (prodrug) to 4-hydroxycyclophosphamide, which is in an equilibrium state with aldophosphamide. Both metabolites diffuse into cells, where aldophosphamide is converted to phosphoramide mustard and acrolein.</p>
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<p>(<b>a</b>) Blood 4PYR and 4PYR metabolite concentration of mammary 4T1 carcinoma mice (Control) treated with cyclophosphamide (+CP), 4PYR (+4PYR) and 4PYR with cyclophosphamide (+4PYR+CP); (<b>b</b>) 4PYMP and (<b>c</b>) 4PYRAD concentration in the tissues of 4PYR (+4PYR) and 4PYR with cyclophosphamide (+4PYR+CP) receiving mice. Mean ± SEM, <span class="html-italic">n</span> = 10; two-way ANOVA with post hoc Tukey test and Student <span class="html-italic">t</span>-test: * <span class="html-italic">p</span> &lt; 0.05; ** <span class="html-italic">p</span> &lt; 0.01; *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>(<b>a</b>) The kinetics of murine mammary 4T1 carcinoma tumor growth and (<b>b</b>) tumor weight of mammary 4T1 carcinoma mice (Control) treated with cyclophosphamide (+CP), 4PYR (+4PYR) and 4PYR with cyclophosphamide (+4PYR+CP). Mean ± SEM, <span class="html-italic">n</span> = 10; two-way ANOVA with post hoc Tukey test and Student <span class="html-italic">t</span>-test: *** <span class="html-italic">p</span> &lt; 0.001; ** <span class="html-italic">p</span> &lt; 0.01; * <span class="html-italic">p</span> &lt; 0.05 vs. control; ### <span class="html-italic">p</span> &lt; 0.001; ## <span class="html-italic">p</span> &lt; 0.01; vs. CP and 4PYR+CP and <span>$</span><span>$</span><span>$</span> <span class="html-italic">p</span> &lt; 0.001; vs. 4PYR and CP.</p>
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<p>(<b>a</b>) ATP, (<b>b</b>) AMP hydrolysis and (<b>c</b>) adenosine deamination on the aorta of mammary 4T1 carcinoma mice (Control) treated with cyclophosphamide (+CP), 4PYR (+4PYR) and 4PYR with cyclophosphamide (+4PYR+CP). Mean ± SEM, <span class="html-italic">n</span> = 10; two-way ANOVA with post hoc Tukey test and Student t test: *** <span class="html-italic">p</span> &lt; 0.001; ** <span class="html-italic">p</span> &lt; 0.01; * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Changes in nicotinamide metabolites in the serum of mammary 4T1 carcinoma mice (Control) treated with cyclophosphamide (+CP), 4PYR (+4PYR) and 4PYR with cyclophosphamide (+4PYR+CP): the concentration of (<b>a</b>) nicotinamide (NA); (<b>b</b>) N-methylnicotinamide (MetNA); (<b>c</b>) nicotinamide riboside (NR); (<b>d</b>) N-methyl-2-pyridone-5-carboxamide (Met2PY); (<b>e</b>) N-methyl-4-pyridone-3-carboxamide (Met4PY) and (<b>f</b>) 4PYR. Mean ± SEM, <span class="html-italic">n</span> = 10; two-way ANOVA with post hoc Tukey test and Student <span class="html-italic">t</span>-test: *** <span class="html-italic">p</span> &lt; 0.001; ** <span class="html-italic">p</span> &lt; 0.01; * <span class="html-italic">p</span> &lt; 0.05. Mean ± SEM, <span class="html-italic">n</span> = 10; *** <span class="html-italic">p</span> &lt; 0.001; ** <span class="html-italic">p</span> &lt; 0.01; * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Liver concentration of cytochrome P450 enzymes involved in cyclophosphamide metabolism: (<b>a</b>) CYP2A6, (<b>b</b>) CYP2C9 and (<b>c</b>) CYP3A4 of mammary 4T1 carcinoma mice (Control) treated with cyclophosphamide (+CP), 4PYR (+4PYR) and 4PYR with cyclophosphamide (+4PYR+CP). Mean ± SEM, <span class="html-italic">n</span> = 5; two-way ANOVA with post hoc Tukey test and Student <span class="html-italic">t</span>-test: * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Additional measurements of the serum expression of cytokines associated with inflammation or cancer progression: CCL19, CCL3, SDF-1α, CX3CL1, CCL5, FAS-ligand, IL-1β and IL-1α in healthy mice without cancer treated with 4PYR (4PYR w/o cancer) or PBS (Control w/o cancer). Mean ± SEM, <span class="html-italic">n</span> = 5; Student’s <span class="html-italic">t</span>-test: * <span class="html-italic">p</span> &lt; 0.05.</p>
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19 pages, 1146 KiB  
Review
Vascular Endothelial Growth Factor (VEGF) Family and the Immune System: Activators or Inhibitors?
by Cristina Maria Failla, Maria Luigia Carbone, Carmela Ramondino, Emanuele Bruni and Angela Orecchia
Biomedicines 2025, 13(1), 6; https://doi.org/10.3390/biomedicines13010006 - 24 Dec 2024
Abstract
The vascular endothelial growth factor (VEGF) family includes key mediators of vasculogenesis and angiogenesis. VEGFs are secreted by various cells of epithelial and mesenchymal origin and by some immune cells in response to physiological and pathological stimuli. In addition, immune cells express VEGF [...] Read more.
The vascular endothelial growth factor (VEGF) family includes key mediators of vasculogenesis and angiogenesis. VEGFs are secreted by various cells of epithelial and mesenchymal origin and by some immune cells in response to physiological and pathological stimuli. In addition, immune cells express VEGF receptors and/or co-receptors and can respond to VEGFs in an autocrine or paracrine manner. This immunological role of VEGFs has opened the possibility of using the VEGF inhibitors already developed to inhibit tumor angiogenesis also in combination approaches with different immunotherapies to enhance the action of effector T lymphocytes against tumor cells. This review pursues to examine the current understanding of the interplay between VEGFs and the immune system, while identifying key areas that require further evaluation. Full article
(This article belongs to the Special Issue Angiogenesis)
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<p>Schematic representation of the different interactions among VEGFs and immune cells. VEGF-A has been reported to interact with monocytes/macrophages, dendritic cells, mesenchymal-derived suppressor cells (MDSCs), and regulatory T cells (T-reg) by acting as a chemoattractant factor [<a href="#B17-biomedicines-13-00006" class="html-bibr">17</a>,<a href="#B19-biomedicines-13-00006" class="html-bibr">19</a>,<a href="#B22-biomedicines-13-00006" class="html-bibr">22</a>]. VEGF-A increases polarization of T cells towards the T helper (h) 1 phenotype [<a href="#B21-biomedicines-13-00006" class="html-bibr">21</a>] and inhibits differentiation of dendritic cells and B cells [<a href="#B20-biomedicines-13-00006" class="html-bibr">20</a>]. PlGF increases chemokine release by monocytes/macrophages and induces differentiation of Natural killer cells and Th-17 lymphocytes [<a href="#B23-biomedicines-13-00006" class="html-bibr">23</a>,<a href="#B24-biomedicines-13-00006" class="html-bibr">24</a>,<a href="#B25-biomedicines-13-00006" class="html-bibr">25</a>]. VEGF-C and, probably, also VEGF-D, act as chemoattractants for monocytes/macrophages and induce activation of CD8+ T cells [<a href="#B26-biomedicines-13-00006" class="html-bibr">26</a>]. Conversely, VEGF-C can modulate lymphatic-mediated presentation of tumor antigens and inactivate CD8+ T cells [<a href="#B27-biomedicines-13-00006" class="html-bibr">27</a>]. Parts of the figure are drawn using pictures from Servier Medical Art (<a href="https://smart.servier.com" target="_blank">https://smart.servier.com</a>).</p>
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<p>Immune cell-mediated mechanisms of regulation of VEGF-A expression. Neutrophils and monocytes/macrophages release inflammatory cytokines (TNFα and IL-1β), which stimulate the secretion of VEGF-A by both endothelial and non-endothelial cells [<a href="#B40-biomedicines-13-00006" class="html-bibr">40</a>]. IL-9 induces VEGF-A release by human mast cells [<a href="#B41-biomedicines-13-00006" class="html-bibr">41</a>], whereas IL-4 inhibits the release of VEGF-A by human macrophages [<a href="#B42-biomedicines-13-00006" class="html-bibr">42</a>]. CD40-CD40L-mediated cell–cell contact can induce VEGF-A secretion by different cell types [<a href="#B43-biomedicines-13-00006" class="html-bibr">43</a>,<a href="#B44-biomedicines-13-00006" class="html-bibr">44</a>]. VEGF-A secretion by dendritic cells is induced by molecules such as TNFα, calcitriol, lipopolysaccharide (LPS), or prostaglandin (PG)E<sub>2</sub>. Neutrophils augment VEGF-A availability by secretion of matrix metalloproteinases (MMPs) and heparinases [<a href="#B38-biomedicines-13-00006" class="html-bibr">38</a>], and hypoxia can induce T cells to secrete VEGF-A through hypoxia-inducible factor (HIF)-1 [<a href="#B21-biomedicines-13-00006" class="html-bibr">21</a>,<a href="#B45-biomedicines-13-00006" class="html-bibr">45</a>]. Parts of the figure are drawn using pictures from Servier Medical Art (<a href="https://smart.servier.com" target="_blank">https://smart.servier.com</a>).</p>
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<p>NRP-1 roles in immune cells. NRP-1 is expressed in different cells of the immune system. When expressed in T follicular helper (Tfh) cells, NRP-1 mediates B cell differentiation [<a href="#B81-biomedicines-13-00006" class="html-bibr">81</a>], whereas in plasmacytoid DCs, it induces the secretion of IFNα [<a href="#B82-biomedicines-13-00006" class="html-bibr">82</a>]. Human T cell lymphotropic virus type 1 (HTLV-1) enters myeloid DCs through NRP-1 and VEGF-A can block this infection [<a href="#B84-biomedicines-13-00006" class="html-bibr">84</a>]. Myeloid DCs can transfer NRP-1 and VEGF-A to T cells by trogocytosis [<a href="#B83-biomedicines-13-00006" class="html-bibr">83</a>]. DCs directly activate T cells through NRP-1 but also block T cell activation by secretion of SEMA3 [<a href="#B78-biomedicines-13-00006" class="html-bibr">78</a>]. Regulatory T cells (Tregs) are also directly activated by DCs but also indirectly by secretion of TGF-β that also blocks T cell activation [<a href="#B80-biomedicines-13-00006" class="html-bibr">80</a>]. SEMA-3A/NRP-1 interaction leads to increased migration of monocytes [<a href="#B73-biomedicines-13-00006" class="html-bibr">73</a>]. Parts of the figure are drawn using pictures from Servier Medical Art (<a href="https://smart.servier.com" target="_blank">https://smart.servier.com</a>).</p>
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22 pages, 1918 KiB  
Review
Resveratrol, Piceatannol, Curcumin, and Quercetin as Therapeutic Targets in Gastric Cancer—Mechanisms and Clinical Implications for Natural Products
by Paulina Warias, Paulina Plewa and Agata Poniewierska-Baran
Molecules 2025, 30(1), 3; https://doi.org/10.3390/molecules30010003 - 24 Dec 2024
Abstract
Gastric cancer remains a significant global health challenge, driving the need for innovative therapeutic approaches. Natural polyphenolic compounds such as resveratrol, piceatannol, curcumin, and quercetin currently show promising results in the prevention and treatment of various cancers, due to their diverse biological activities. [...] Read more.
Gastric cancer remains a significant global health challenge, driving the need for innovative therapeutic approaches. Natural polyphenolic compounds such as resveratrol, piceatannol, curcumin, and quercetin currently show promising results in the prevention and treatment of various cancers, due to their diverse biological activities. This review presents the effects of natural compounds on important processes related to cancer, such as apoptosis, proliferation, migration, invasion, angiogenesis, and autophagy. Resveratrol, naturally found in red grapes, has been shown to induce apoptosis and inhibit the proliferation, migration, and invasion of gastric cancer cells. Piceatannol, a metabolite of resveratrol, shares similar anticancer properties, particularly in modulating autophagy. Curcumin, derived from turmeric, is known for its anti-inflammatory and antioxidant properties, and its ability to inhibit tumor growth and metastasis. Quercetin, a flavonoid found in various fruits and vegetables, induces cell cycle arrest and apoptosis while enhancing the efficacy of conventional therapies. Despite their potential, challenges such as low bioavailability limit their clinical application, necessitating further research into novel delivery systems. Collectively, these compounds represent a promising avenue for enhancing gastric cancer treatment and improving patient outcomes through their multifaceted biological effects. Full article
(This article belongs to the Section Natural Products Chemistry)
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<p>Forms of resveratrol metabolites.</p>
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<p>Form of piceatannol.</p>
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<p>Forms of curcumin metabolites.</p>
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<p>Forms of quercetin metabolites.</p>
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<p>Summary of how natural products can influence the process of carcinogenesis in gastric cancer, with the objectives of molecular research highlighted. Created in BioRender. Poniewierska-Baran, A. (2024) <a href="https://BioRender.com/y52c684" target="_blank">https://BioRender.com/y52c684</a> (accessed on 30 October 2024).</p>
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19 pages, 671 KiB  
Review
Neutrophil and Colorectal Cancer
by Hideyuki Masui, Kenji Kawada and Kazutaka Obama
Int. J. Mol. Sci. 2025, 26(1), 6; https://doi.org/10.3390/ijms26010006 - 24 Dec 2024
Abstract
Colorectal cancer (CRC) is often associated with metastasis and recurrence and is the leading cause of cancer-related mortality. In the progression of CRC, recent studies have highlighted the critical role of neutrophils, particularly tumor-associated neutrophils (TANs). TANs have both tumor-promoting and tumor-suppressing activities, [...] Read more.
Colorectal cancer (CRC) is often associated with metastasis and recurrence and is the leading cause of cancer-related mortality. In the progression of CRC, recent studies have highlighted the critical role of neutrophils, particularly tumor-associated neutrophils (TANs). TANs have both tumor-promoting and tumor-suppressing activities, contributing to metastasis, immunosuppression, angiogenesis, and epithelial-to-mesenchymal transition. Tumor-promoting TANs promote tumor growth by releasing proteases, reactive oxygen species, and cytokines, whereas tumor-suppressing TANs enhance immune responses by activating T cells and natural killer cells. Understanding the mechanisms underlying TAN mobilization, plasticity, and their role in the tumor microenvironment has revealed potential therapeutic targets. This review provides a comprehensive overview of TAN biology in CRC and discusses both the tumor-promoting and tumor-suppressing functions of neutrophils. Novel therapeutic approaches targeting TANs, such as chemokine receptor antagonists, aim to modulate neutrophil reprogramming and offer promising avenues for improving treatment outcomes of CRC. Full article
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<p>Multiple functions of tumor-associated neutrophils (TANs). (<b>A</b>) Recruitment of TANs by tumor-derived chemokines. (<b>B</b>) Induction of genetic instability through ROS production and microRNAs (e.g., miR-23a, miR-155), leading to DNA damage. (<b>C</b>) Promotion of extracellular matrix (ECM) remodeling via degranulation and degradation involving MMPs, Bv8, and S100A8/A9, facilitating angiogenesis driven by VEGF. (<b>D</b>) Immunosuppression mediated by N2 TANs through interactions with T cells, NK cells, and T-reg cells, and polarization of macrophages into the M2 phenotype. Created with BioRender.com.</p>
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19 pages, 19454 KiB  
Article
mTOR Inhibitor Everolimus Modulates Tumor Growth in Small-Cell Carcinoma of the Ovary, Hypercalcemic Type and Augments the Drug Sensitivity of Cancer Cells to Cisplatin
by Kewei Zheng, Yi Gao, Jing Xu, Mingyi Kang, Ranran Chai, Guanqin Jin and Yu Kang
Biomedicines 2025, 13(1), 1; https://doi.org/10.3390/biomedicines13010001 - 24 Dec 2024
Abstract
Background: Small-cell carcinoma of the ovary, hypercalcemic type (SCCOHT), is a rare and aggressive cancer with a poor prognosis and limited treatment options. Current chemotherapy regimens are predominantly platinum-based; however, the development of platinum resistance during treatment significantly worsens patient outcomes. Everolimus, [...] Read more.
Background: Small-cell carcinoma of the ovary, hypercalcemic type (SCCOHT), is a rare and aggressive cancer with a poor prognosis and limited treatment options. Current chemotherapy regimens are predominantly platinum-based; however, the development of platinum resistance during treatment significantly worsens patient outcomes. Everolimus, an mTOR inhibitor, has been widely used in combination cancer therapies and has successfully enhanced the efficacy of platinum-based treatments. Method: In this study, we investigated the combined effects of everolimus and cisplatin on SCCOHT through both in vitro and in vivo experiments, complemented by RNA sequencing (RNA-seq) analyses to further elucidate the therapeutic impact. Result: Our findings revealed that everolimus significantly inhibits the proliferation of SCCOHT cells, induces cell cycle arrest, and accelerates apoptosis. When combined with cisplatin, everolimus notably enhances the therapeutic efficacy without increasing the toxicity typically associated with platinum-based drugs. RNA-seq analysis uncovered alterations in the expression of apoptosis-related genes, suggesting that the underlying mechanism involves autophagy regulation. Conclusions: Despite the current challenges in treating SCCOHT and the suboptimal efficacy of platinum-based therapies, the addition of everolimus significantly suppresses tumor growth. This indicates that everolimus enhances cisplatin efficacy by disrupting survival-promoting signaling cascades and inducing cell cycle arrest. Furthermore, it points to potential biomarkers for predicting therapeutic response. Full article
(This article belongs to the Section Cancer Biology and Oncology)
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<p>Effect of everolimus combined with cisplatin on proliferation of SCCOHT cells. (<b>A</b>) GSEA enrichment analysis. (<b>B</b>) IC50 of everolimus and cisplatin against SCCOHT cells. Data are expressed as mean ± SD. (<b>C</b>) Heatmaps of drug combination responses. Everolimus and cisplatin act synergistically in SCCOHT-CH-1 cells. Everolimus and cisplatin at the indicated concentrations were used to treat cells for 48 h, and cell viability was assessed by CCK-8 assay. The ZIP synergy scores were calculated using SynergyFinder 3.0, with scores greater than 0 indicating synergism and scores exceeding 10 reflecting a strong synergistic interaction. The white rectangle on the heatmap delineates the concentrations that correspond to the highest degree of synergy. (<b>D</b>) The proliferation of everolimus and cisplatin on SCCOHT cells was assessed by CCK-8 assay. (<b>E</b>) The colony forming ability of SCCOHT cells treated with everolimus or cisplatin was assessed. (<b>F</b>) EdU staining was used to analyze the effects of everolimus and cisplatin on the proliferation of SCCOHT cells (Scale bar = 200 μm).</p>
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<p>Everolimus in combination with cisplatin induced cell cycle arrest and increased apoptosis in SCCOHT cells. (<b>A</b>,<b>B</b>) apoptosis, (<b>C</b>,<b>D</b>) cell cycle analysis, and (<b>E</b>,<b>F</b>) wound healing assay. Error bars represent the standard deviation (Scale bar = 100 μm). Error bars represent the standard deviation, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Everolimus combined with cisplatin enhanced the inhibition of tumor growth in vivo. (<b>A</b>) Administration regimen for tumor-bearing mice. (<b>B</b>) Mouse tumor images. Tumor volume and spaghetti curves of tumor volume (<b>C</b>), tumor weight (<b>D</b>), and mouse weight (<b>E</b>) in different groups of mice. (<b>F</b>–<b>H</b>) HE staining of tumor tissues, immunohistochemical analysis of Ki67 expression, and TUNEL staining in different groups of tumor tissues (Scale bar<sup>HE, Ki67</sup> = 1000 µm, Scale bar<sup>TUNEL</sup> = 500 µm). Error bars represent the standard deviation, ns: not significant, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>In vivo toxicity analysis. (<b>A</b>) The heart, liver, spleen, lung, kidney, and other organ indices of the animal at the end of the in vivo experiment. (<b>B</b>) HE staining of mouse organs (Scale bar = 100 µm). Error bars represent the standard deviation, ns: not significant.</p>
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<p>(<b>A</b>,<b>B</b>) Western blot analysis of phosphorylation of AKT and mTOR in SCCOHT cells of the control group and everolimus group. (<b>C</b>,<b>D</b>) Western blot analysis of changes in autophagy-associated protein levels in the control group and the everolimus group. (<b>E</b>) Representative images for the detection of the autophagy flux by staining with DAL<sup>®</sup>Green (green) (Scale bar = 100 µm). (<b>F</b>,<b>G</b>) The expression levels of autophagy related proteins and autophagy substrates in SCCOHT cells treated with everolimus, 3-MA, or everolimus + 3-MA were detected by Western blot analysis. Error bars represent the standard deviation, ns: not significant, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001.</p>
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<p>Regulation of apoptosis-related genes by everolimus and cisplatin. (<b>A</b>) Volcano maps showed differential expressions of apoptosis-related genes after different treatments. Genes with log2(FC) &gt; 2 or log2(FC) &lt; −2 are considered biologically significant. The red picture shows significantly upregulated gene (adjP &lt; 0.05); The blue chart shows well downregulated genes (adjP &lt; 0.05). (<b>B</b>) Venn diagram showed the differential expression of apoptosis-related genes after different treatments. (<b>C</b>) Heat maps showing the fold changes (logarithmic transformation) in apoptosis-related genes in different treatment groups. Calculate the fold change relative to the average of the control group. The shaded range from blue to red indicates downregulated genes to upregulated genes. (<b>D</b>) GO analysis of the 40 unique apoptosis-related genes. (<b>E</b>) Venn maps of upregulated and downregulated genes in cisplatin group and two-drug combination group. (<b>F</b>) GO analysis of the 11 reversed genes. (<b>G</b>) The PPI network for the 40 unique apoptosis-related genes and two specific reversed resistance genes.</p>
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41 pages, 2458 KiB  
Review
Cancer-Targeting Applications of Cell-Penetrating Peptides
by Liliana Marisol Moreno-Vargas and Diego Prada-Gracia
Int. J. Mol. Sci. 2025, 26(1), 2; https://doi.org/10.3390/ijms26010002 - 24 Dec 2024
Abstract
Cell-penetrating peptides (CPPs) offer a unique and efficient mechanism for delivering therapeutic agents directly into cancer cells. These peptides can traverse cellular membranes, overcoming one of the critical barriers in drug delivery systems. In this review, we explore recent advancements in the application [...] Read more.
Cell-penetrating peptides (CPPs) offer a unique and efficient mechanism for delivering therapeutic agents directly into cancer cells. These peptides can traverse cellular membranes, overcoming one of the critical barriers in drug delivery systems. In this review, we explore recent advancements in the application of CPPs for cancer treatment, focusing on mechanisms, delivery strategies, and clinical potential. The review highlights the use of CPP-drug conjugates, CPP-based vaccines, and their role in targeting and inhibiting tumor growth. Full article
(This article belongs to the Section Molecular Oncology)
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<p>CPPs with Intrinsic Anticancer Activity. This figure presents CPPs that exert therapeutic effects through intrinsic biological functions, independent of cargo. Highlighted CPPs include ATX-101, which disrupts PCNA interactions; PEP-010, which induces caspase-9-mediated apoptosis; SAHBD, which inhibits MCL-1 to promote apoptosis; p28, stabilizing p53 for cell-cycle arrest; P1pal-7, which targets PAR1 to reduce tumor angiogenesis; EN1-iPEP, a transcription factor inhibitor triggering selective apoptosis; and Bac1-24, enhancing nuclear localization of therapeutic peptides. Only selected examples of the CPPs discussed in this communication are shown. [Created in BioRender. Moreno-Vargas, L. (2024) BioRender.com/c08a709 | CC-BY 4.0].</p>
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<p>CPPs as Cargo Carriers for Targeted Delivery. This figure highlights the role of CPPs as efficient drug delivery vehicles, enabling targeted transportation of therapeutic agents into cancer cells. Included CPPs are AVB-620, pVEC, PEGA, Z12, Pep-1, MAP, and SAP(E), which exhibit diverse cargo-loading mechanisms: AVB-620 for real-time imaging, pVEC for direct cell translocation, PEGA for selective tumor targeting, Z12 and Pep-1 for immune modulation and selective cell penetration, MAP for membrane disruption, and SAP(E) as a complex-forming agent to enhance cellular uptake and cytotoxicity of its payloads in target cells, minimizing off-target toxicity. Only selected examples of the CPPs discussed in this communication are shown. [Created in BioRender. Moreno-Vargas, L. (2024) BioRender.com/u64r118 | CC-BY 4.0].</p>
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<p>Internalization Mechanisms of CPPs Across Cell Membranes. CPPs employ diverse mechanisms to cross cell membranes, including direct translocation, endocytosis, and interactions with receptors overexpressed in membranes. Despite shared characteristics, CPPs exhibit distinct internalization routes that vary across peptide families and depend on experimental conditions. Factors such as charge, length, structure, and peptide concentration play critical roles in determining the internalization route. The CPPs listed in this communication are highlighted, noting that individual CPPs can often engage multiple internalization pathways. [Created in BioRender. Moreno-Vargas, L. (2024) BioRender.com/j68n394 | CC-BY 4.0].</p>
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