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Search Results (3,994)

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Keywords = pancreatic cancer

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19 pages, 954 KiB  
Review
Vascular Endothelial Growth Factor Receptors in the Vascularization of Pancreatic Tumors: Implications for Prognosis and Therapy
by Craig Grobbelaar, Vanessa Steenkamp and Peace Mabeta
Curr. Issues Mol. Biol. 2025, 47(3), 179; https://doi.org/10.3390/cimb47030179 - 10 Mar 2025
Viewed by 61
Abstract
In pancreatic cancer (PC), vascular endothelial growth factor (VEGF) and its primary receptor, vascular endothelial growth factor receptor (VEGFR)-2, are central drivers of angiogenesis and metastasis, with their overexpression strongly associated with poor prognosis. In some PC patients, VEGF levels correlate with disease [...] Read more.
In pancreatic cancer (PC), vascular endothelial growth factor (VEGF) and its primary receptor, vascular endothelial growth factor receptor (VEGFR)-2, are central drivers of angiogenesis and metastasis, with their overexpression strongly associated with poor prognosis. In some PC patients, VEGF levels correlate with disease stage, tumor burden, and survival outcomes. However, therapies targeting VEGF and VEGFR-2, including tyrosine kinase inhibitors (TKIs) and monoclonal antibodies, have demonstrated limited efficacy, partly due to the emergence of resistance mechanisms. Resistance appears to stem from the activation of alternative vascularization pathways. This review explores the multifaceted roles of VEGFRs in pancreatic cancer, including VEGFR-1 and VEGFR-3. Potential strategies to improve VEGFR-targeting therapies, such as combination treatments, the development of more selective inhibitors, and the use of biomarkers, are discussed as promising approaches to enhance treatment efficacy and outcomes. Full article
(This article belongs to the Special Issue Angiogenesis in Diseases: Molecular Mechanism and Regulation)
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<p>Diagram indicating vascular endothelial growth factor receptor (VEGFR) signaling in pancreatic cancer. VEGFR-1 binds VEGF-A, Placental Growth Factor (PlGF), and VEGF-B to regulate angiogenesis. VEGFR-2 binds VEGF-A to primarily drive angiogenesis. VEGFR-3 binds VEGF-C and VEGF-D to elicit lymphangiogenesis. Key signaling pathways involved include extracellular signal-regulated kinase (ERK), phosphoinositide 3-kinase (PI3K), and protein kinase C (PKC). The figure was constructed using drawing tools and Sketch.</p>
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<p>Diagram indicating how vascular endothelial growth factors receptor-1 and -2 regulate angiogenesis. VEGF-VEGFR-2 activates Notch signaling, stimulating tip cell formation in one cell while inhibiting neighboring cells from differentiating into tip cells. Notch also upregulates sVEGFR-1. The latter receptor traps VEGF and prevents excessive angiogenesis. The figure was generated by Biorender.</p>
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<p>Proposed approaches to improve VEGFR targeting in pancreatic cancer treatment. The diagram illustrates the central role of VEGFR targeting and outlines several strategies to enhance its efficacy. Examples of ongoing clinical trials and specific drugs are highlighted to demonstrate the practical application of these strategies. The figure was constructed using drawing tools and Sketch.</p>
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15 pages, 802 KiB  
Article
Seasonal Change in Microbial Diversity: Bile Microbiota and Antibiotics Resistance in Patients with Bilio-Pancreatic Tumors: A Retrospective Monocentric Study (2010–2020)
by Paola Di Carlo, Nicola Serra, Consolato Maria Sergi, Francesca Toia, Emanuele Battaglia, Teresa Maria Assunta Fasciana, Vito Rodolico, Anna Giammanco, Giuseppe Salamone, Adriana Cordova, Angela Capuano, Giovanni Francesco Spatola, Ginevra Malta and Antonio Cascio
Antibiotics 2025, 14(3), 283; https://doi.org/10.3390/antibiotics14030283 - 9 Mar 2025
Viewed by 238
Abstract
Background: Bilio-pancreatic tumors are a severe form of cancer with a high rate of associated mortality. These patients showed the presence of bacteria such as Escherichia coli and Pseudomonas spp. in the bile-pancreatic tract. Therefore, efficient antibiotic therapy is essential to reduce bacterial [...] Read more.
Background: Bilio-pancreatic tumors are a severe form of cancer with a high rate of associated mortality. These patients showed the presence of bacteria such as Escherichia coli and Pseudomonas spp. in the bile-pancreatic tract. Therefore, efficient antibiotic therapy is essential to reduce bacterial resistance and adverse events in cancer patients. Recent studies on the seasonality of infectious diseases may aid in developing effective preventive measures. This study examines the seasonal impact on the bile microbiota composition and the antibiotic resistance of its microorganisms in patients with hepato-pancreatic-biliary cancer. Methods: We retrospectively evaluated the effect of the seasonally from 149 strains isolated by 90 Italian patients with a positive culture of bile samples collected through endoscopic retrograde cholangiopancreatography between 2010 and 2020. Results: Across all seasons, the most frequently found bacteria were E. coli, Pseudomonas spp., and Enterococcus spp. Regarding antibiotic resistance, bacteria showed the highest resistance to 3GC, fluoroquinolones, aminoglycosides, fosfomycin, and piperacillin-tazobactam in the summer and the lowest resistance in the spring, except for carbapenems and colistin. Conclusions: Antibiotic resistance has negative effects in cancer patients who rely on antibiotics to prevent and treat infections. Knowing whether bacterial and fungal resistance changes with the seasons is key information to define adequate and more effective antibiotic therapy. Full article
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<p>Gram-negative isolated from 90 patients.</p>
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<p>Gram-positive isolated from 90 patients.</p>
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22 pages, 7977 KiB  
Article
Construction of T-Cell-Related Prognostic Risk Models and Prediction of Tumor Immune Microenvironment Regulation in Pancreatic Adenocarcinoma via Integrated Analysis of Single-Cell RNA-Seq and Bulk RNA-Seq
by Dingya Sun, Yijie Hu, Jun Peng and Shan Wang
Int. J. Mol. Sci. 2025, 26(6), 2384; https://doi.org/10.3390/ijms26062384 - 7 Mar 2025
Viewed by 194
Abstract
Pancreatic adenocarcinoma (PAAD) is a fatal malignant tumor of the digestive system, and immunotherapy has currently emerged as a key therapeutic approach for treating PAAD, with its efficacy closely linked to T-cell subsets and the tumor immune microenvironment. However, reliable predictive markers to [...] Read more.
Pancreatic adenocarcinoma (PAAD) is a fatal malignant tumor of the digestive system, and immunotherapy has currently emerged as a key therapeutic approach for treating PAAD, with its efficacy closely linked to T-cell subsets and the tumor immune microenvironment. However, reliable predictive markers to guide clinical immunotherapy for PAAD are not available. We analyzed the single-cell RNA sequencing (scRNA-seq) data focused on PAAD from the GeneExpressionOmnibus (GEO) database. Then, the information from the Cancer Genome Atlas (TCGA) database was integrated to develop and validate a prognostic risk model derived from T-cell marker genes. Subsequently, the correlation between these risk models and the effectiveness of immunotherapy was explored. Analysis of scRNA-seq data uncovered six T-cell subtypes and 1837 T-cell differentially expressed genes (DEGs). Combining these data with the TCGA dataset, we constructed a T-cell prognostic risk model containing 16 DEGs, which can effectively predict patient survival and immunotherapy outcomes. We have found that patients in the low-risk group had better prognostic outcomes, increased immune cell infiltration, and signs of immune activation compared to those in the high-risk group. Additionally, analysis of tumor mutation burden showed higher mutation rates in patients with PAAD in the high-risk group. Risk scores with immune checkpoint gene expression and drug sensitivity analysis provide patients with multiple therapeutic targets and drug options. Our study constructed a prognostic risk model for PAAD patients based on T-cell marker genes, providing valuable insights into predicting patient prognosis and the effectiveness of immunotherapy. Full article
(This article belongs to the Section Molecular Biology)
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<p>Workflow of the present study.</p>
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<p>Integration and clustering of PAAD scRNA-Seq data. (<b>A</b>) Violin plot after quality control. (<b>B</b>) Volcano plot of DEGs. (<b>C</b>) T-cell type annotations. (<b>D</b>) Heat map showing top ten marker genes in 6 cell types.</p>
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<p>Analysis of T-cell trajectory and intercellular communication in pancreatic ductal adenocarcinoma (PAAD). (<b>A</b>,<b>B</b>) Quantification of interactions and strengths in communication networks between cells. (<b>C</b>,<b>D</b>) Diagram illustrating distinct T-cell clusters present in PAAD. (<b>E</b>) Heatmap displaying levels of dynamic gene expression over pseudo-time and the most significant Gene Ontology Biological Process term for each group.</p>
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<p>Bubble chart indicating the activity of signaling pathways across various cell populations.</p>
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<p>KM analysis of PAAD patients based on 17 T-cell subsets (based on best value).</p>
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<p>PAAD cohort was used for constructing the T-cell marker gene risk mode. (<b>A</b>,<b>B</b>) LASSO regression was performed to identify 16 DEGs with the most significant prognostic value. (<b>C</b>) Distribution of training cohort risk scores and survival status. (<b>D</b>) Survival analysis KM curves for TCGA PAAD patients based on the risk score in the training cohort. (<b>E</b>) Heatmap displaying the 16 DEGs included in the risk signature. (<b>F</b>) ROC analysis validated the predictive accuracy of the risk signature.</p>
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<p>Validation of the signature risk genes associated with 16 genes. (<b>A</b>) Testing cohort distribution of risk scores and survival status. (<b>B</b>) Survival analysis KM curves in TCGA-PAAD patients based on risk score in the testing cohort. (<b>C</b>,<b>F</b>) A prognostic risk model of 16 genes associated with T-cell. (<b>D</b>) Heatmap displaying prognostic DEGs in the testing cohort. (<b>E</b>) Validation of the predictive accuracy of the risk signature through ROC analysis.</p>
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<p>Correlation of the T-cell prognostic model genes with immune cell infiltration and immune checkpoints in the TCGA-PAAD cohort. (<b>A</b>) A comparison of the infiltration levels of 22 different immune cells between high-risk and low-risk groups was conducted. (<b>B</b>) TIMER was used to evaluate the relative abundance of six major immune cell subtypes. (<b>C</b>) The ssGSEA algorithm was employed to estimate the abundance of 24 distinct immune cell populations. (****, <span class="html-italic">p</span> &lt; 0.0001; ***, <span class="html-italic">p</span> &lt; 0.001; **, 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01; *, 0.01 &lt; <span class="html-italic">p</span> &lt; 0.05; ns, <span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Analysis of T-cell prognostic model genes’ correlation with immune checkpoints in the TCGA cohort and assessment of tumor mutations and IC50 in the two risk categories. (<b>A</b>,<b>B</b>) Graph illustrating risk scores and TMB. (<b>C</b>–<b>F</b>) Box plots of IC50 predicted values for four drugs. (<b>G</b>) Evaluation of immune checkpoint levels in the two risk groups. (****, <span class="html-italic">p</span> &lt; 0.0001; ***, <span class="html-italic">p</span> &lt; 0.001; **, 0.001 &lt; <span class="html-italic">p</span> &lt; 0.01; *, 0.01 &lt; <span class="html-italic">p</span> &lt; 0.05; ns, <span class="html-italic">p</span> &gt; 0.05).</p>
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24 pages, 3480 KiB  
Article
Biological Effects of Polysaccharides from Bovistella utriformis as Cytotoxic, Antioxidant, and Antihyperglycemic Agents: In Vitro and In Vivo Studies
by Aya Maaloul, Claudia Pérez Manríquez, Juan Decara, Manuel Marí-Beffa, Daniel Álvarez-Torres, Sofía Latorre Redoli, Borja Martínez-Albardonedo, Marisel Araya-Rojas, Víctor Fajardo and Roberto T. Abdala Díaz
Pharmaceutics 2025, 17(3), 335; https://doi.org/10.3390/pharmaceutics17030335 - 5 Mar 2025
Viewed by 217
Abstract
Background/Objectives: This study explores the bioactive potential of Bovistella utriformis biomass and its polysaccharides (PsBu) through comprehensive biochemical and bioactivity analyses, focusing on their antioxidant, cytotoxic, and antihyperglycemic properties. Methods: Elemental analysis determined the biomass’s chemical composition. Antioxidant activity was assessed [...] Read more.
Background/Objectives: This study explores the bioactive potential of Bovistella utriformis biomass and its polysaccharides (PsBu) through comprehensive biochemical and bioactivity analyses, focusing on their antioxidant, cytotoxic, and antihyperglycemic properties. Methods: Elemental analysis determined the biomass’s chemical composition. Antioxidant activity was assessed using ABTS and DPPH assays. Monosaccharide composition was analyzed via gas chromatography-mass spectrometry (GC-MS). In vitro cytotoxicity assays were conducted on cancer and normal cell lines to determine IC50 values and selectivity indices (SI). Zebrafish embryo toxicity was evaluated for teratogenic effects, and an in vivo antihyperglycemic study was performed in diabetic rat models. Results: The biomass exhibited high carbon content (around 41%) and nitrogen levels, with a balanced C/N ratio nearing 5. Protein content exceeded 50%, alongside significant carbohydrate, fiber, and ash levels. Antioxidant assays revealed inhibition rates of approximately 89% (ABTS) and 64% (DPPH). GC-MS analysis identified glucose as the predominant sugar (>80%), followed by galactose and mannose. Additionally, HPLC detected a photoprotective compound, potentially a mycosporin-like amino acid. Cytotoxicity assays demonstrated PsBu’s selective activity against colon, lung, and melanoma cancer cell lines (IC50: 100–500 µg·mL−1), while effects on normal cell lines were lower (IC50 > 1300 µg·mL−1 for HaCaT, >2500 µg·mL−1 for HGF-1), with SI values approaching 27, supporting PsBu’s potential as a targeted anticancer agent. Zebrafish embryo assays yielded LC50 values ranging from 1.4 to 1.8 mg·mL−1. In vivo, PsBu reduced fasting blood glucose levels in hyperglycemic rats (approximately 210 mg·dL−1 vs. 230 mg·dL−1 in controls) and preserved pancreatic β-cell integrity (around 80% vs. 65% in controls). Conclusions: These findings suggest that B. utriformis biomass and PsBu exhibit strong antioxidant activity, selective cytotoxicity against cancer cells, and antihyperglycemic potential, making them promising candidates for further biomedical applications. Full article
(This article belongs to the Section Drug Targeting and Design)
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<p><span class="html-italic">Bovistella utriformis</span> fungus collected in its natural habitat for the study.</p>
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<p>Chromatogram obtained by HPLC of <span class="html-italic">B. utriformis</span>.</p>
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<p>HPLC absorption spectrum of <span class="html-italic">B. utriformis</span>.</p>
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<p>Fourier transform infrared spectroscopy (FT-IR) of polysaccharides obtained from <span class="html-italic">B. utriformis</span> polysaccharides (PsBu).</p>
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<p>Qualitative analysis of Ps-Bu by gas chromatography coupled to mass spectrometry.</p>
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<p>Survival rate of cell lines treated with PsBu. (<b>A</b>) HCT-116 (Colon Cancer), (<b>B</b>) 1064Sk (Fibroblasts), (<b>C</b>) G-361 (Melanoma), (<b>D</b>) NCI-H460 (Lung Cancer), (<b>E</b>) HGF-1 (Gingival Fibroblasts), and (<b>F</b>) HaCaT (Keratinocytes).</p>
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<p>LC<sub>50</sub> estimation following PsBu treatment of zebrafish embryos during 48 hpf. Plots show the linear relationship between viability (<b>A</b>) or the logarithm of the viability/mortality ratio (<b>B</b>) and the logarithm of concentration at 48 hpf. Intersections of regression lines with 0,5 viability index (<b>A</b>) and abscissa (<b>B</b>) are log (LC<sub>50</sub>) estimations. Linear adjustments are y = −1.591x + 5.6606 (R<sup>2</sup> = 0.6511; <span class="html-italic">p</span> ≈ 0.0000) (<b>A</b>) and y = −3.1702x + 10.0968 (R2 = 0.7422; <span class="html-italic">p</span> ≈ 0.0000) (<b>B</b>) (Excel, Microsoft Office). LC50 values are in the text.</p>
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<p>Body size reduction of 48 hpf zebrafish embryos induced by PsBu. (<b>A</b>–<b>C</b>). General morphology of E3 medium control (<b>A</b>) and PsBu-treated (<b>B</b>,<b>C</b>) larvae. Treating solutions were 0.75 (<b>B</b>) and (<b>C</b>) 1 mg mL<sup>−1</sup> PsBu in the E3 medium. (<b>D</b>). Linear regressions of standard length (empty circles) with PsBu concentration (y = −0.4x + 3.4809; R<sup>2</sup> = 0.4809; <span class="html-italic">p</span> ≈ 0.000). *** means comparison with E3 control data (t-student <span class="html-italic">p</span> &lt; 0.001).</p>
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<p>Teratogenic and toxic effects of PsBu over zebrafish embryos treated for 48 h. (<b>A</b>). Pericardial edema shown after 0.5 mg mL<sup>−1</sup> PsBu treatment. (<b>B</b>). Size reduction, slight kyphosis and pericardial edema shown after 0.5 mg mL<sup>−1</sup> PsBu treatment. Bar represents 1 mm.</p>
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<p>Glucose tolerance and insulin sensitivity. The effect of acute treatment with polysaccharides extracted from <span class="html-italic">B. utriformis</span> (PsBu) (200 mg kg<sup>−1</sup>) on the oral glucose tolerance test (OGTT) (<b>A</b>) and insulin sensitivity test (IST) (<b>B</b>) in male Wistar rats. Blood glucose levels were evaluated before (0 min) and after (0, 5, 10, 15, 30, 45, 60, and 120 min) glucose overload (2 mg kg<sup>−1</sup>) or insulin administration (1 IU kg<sup>−1</sup>). Points indicate the mean ± SEM (n ± 8 animals/group). Two-way ANOVA and Bonferroni post hoc test results were denoted as follows: (*) <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; 0.001 vs. vehicle group.</p>
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15 pages, 2899 KiB  
Article
ECM Stiffness-Induced Redox Signaling Enhances Stearoyl Gemcitabine Efficacy in Pancreatic Cancer
by Shuqing Zhao, Edward Agyare, Xueyou Zhu, Jose Trevino, Sherise Rogers, Enrique Velazquez-Villarreal, Jason Brant, Payam Eliahoo, Jonathan Barajas, Ba Xuan Hoang and Bo Han
Cancers 2025, 17(5), 870; https://doi.org/10.3390/cancers17050870 - 3 Mar 2025
Viewed by 231
Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, largely due to its dense fibrotic stroma that promotes drug resistance and tumor progression. While patient-derived organoids (PDOs) have emerged as promising tools for modeling PDAC and evaluating therapeutic responses, the [...] Read more.
Background: Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, largely due to its dense fibrotic stroma that promotes drug resistance and tumor progression. While patient-derived organoids (PDOs) have emerged as promising tools for modeling PDAC and evaluating therapeutic responses, the current PDO models grown in soft matrices fail to replicate the tumor’s stiff extracellular matrix (ECM), limiting their predictive value for advanced disease. Methods: We developed a biomimetic model using gelatin-based matrices of varying stiffness, achieved through modulated transglutaminase crosslinking rates, to better simulate the desmoplastic PDAC microenvironment. Using this platform, we investigated organoid morphology, proliferation, and chemoresistance to gemcitabine (Gem) and its lipophilic derivative, 4-N-stearoyl gemcitabine (Gem-S). Mechanistic studies focused on the interplay between ECM stiffness, hypoxia-inducible factor (HIF) expression, and the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway in drug resistance. Results: PDAC organoids in stiffer matrices demonstrated enhanced stemness features, including rounded morphology and elevated cancer stem cell (CSC) marker expression. Matrix stiffness-induced gemcitabine resistance correlated with the upregulation of ABC transporters and oxidative stress adaptive responses. While gemcitabine activated Nrf2 expression, promoting oxidative stress mitigation, Gem-S suppressed Nrf2 levels and induced oxidative stress, leading to increased reactive oxygen species (ROS) and enhanced cell death. Both compounds reduced HIF expression, with gemcitabine showing greater efficacy. Conclusions: Our study reveals ECM stiffness as a critical mediator of PDAC chemoresistance through the promotion of stemness and modulation of Nrf2 and HIF pathways. Gem-S demonstrates promise in overcoming gemcitabine resistance by disrupting Nrf2-mediated adaptive responses and inducing oxidative stress. These findings underscore the importance of biomechanically accurate tumor models and suggest that dual targeting of mechanical and oxidative stress pathways may improve PDAC treatment outcomes. Full article
(This article belongs to the Section Cancer Drug Development)
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<p>Matrix mechanics govern PDAC organoid architecture and growth dynamics. (<b>A</b>) Non-invasive rheological characterization of cell-embedded collagen-transglutaminase (Col-Tgel) matrices. Ultrasound transducers measured matrix deformation under 440 kPa shear stress over time. Representative stress–strain curves demonstrate distinct viscoelastic properties between matrix formulations. (<b>B</b>) Representative phase contrast micrographs of seven patient-derived PDAC organoids cultured in stiff (6A) vs. soft (6B) matrices for 6–8 days. Semi-dome 3D configuration enabled the visualization of matrix-dependent morphological adaptations. Scale bars: 1000 µm (low magnification) and 100 µm (high magnification). (<b>C</b>) Quantitative analysis of organoid size using ImageJ software (version 1.5.4). Measurements represent organoid diameters from G68-derived cells after 8 days of culture in matrices 6A and 6B. Data presented as mean ± SD; * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001 (Student’s <span class="html-italic">t</span>-test). (<b>D</b>) Comparative analysis of cell proliferation kinetics across culture conditions. Doubling times were calculated following collagenase-mediated matrix digestion and automated cell counting (Beckman Coulter). Data shown as mean ± SD from three independent experiments.</p>
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<p>Matrix-dependent transcriptional reprogramming in PDAC organoids. (<b>A</b>) Multidimensional scaling (MDS) plot of RNA sequencing data from three patient-derived PDAC lines (G43, VG59, and LM-1) cultured under different conditions (2D monolayer, soft matrix 6B, and stiff matrix 6A). Analysis based on the top 1000 most variable genes demonstrates distinct transcriptional clustering by culture condition. Each point represents an individual sample, with distances reflecting relative transcriptional similarities. (<b>B</b>) Mean difference plots comparing gene expression between culture conditions. Each point represents an individual gene, with red dots indicating upregulated genes (fold change &gt; 2) and blue dots indicating downregulated genes (fold change &lt; −2) relative to the 2D culture. Gray dots represent genes with less than two-fold change in expression. Results shown for three pairwise comparisons: 3D soft matrix (6B) vs. 2D culture, 3D stiff matrix (6A) vs. 2D culture, and 3D stiff matrix (6A) vs. 3D soft matrix (6B). RNA sequencing performed with &gt;20 million reads per sample. Data analyzed using edgeR following TMM normalization. <span class="html-italic">n</span> = 3 biological replicates per condition.</p>
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<p>Matrix stiffness orchestrates drug resistance programs in PDAC patient-derived organoids. (<b>A</b>) Schematic representation of the experimental design. PDAC PDOs were established in soft (6B) and stiff (6A) matrices for 6 days prior to gemcitabine (Gem) treatment. Cell viability was quantified via the CellTiterGlo luminescence assay following 72 h drug exposure. (<b>B</b>) Dose–response curves for G43-derived organoids treated with gemcitabine. The IC<sub>50</sub> values demonstrate progressive resistance from the 2D monolayer to soft and stiff 3D cultures (4–12-fold increase in IC<sub>50</sub>). Data presented as mean ± SD from three independent experiments. (<b>C</b>,<b>D</b>) Quantitative assessment of cell death using the Live/Dead fluorescence assay following 1 µM gemcitabine treatment. Representative images and quantification showing reduced apoptosis in 3D cultures compared to 2D conditions. Scale bar = 100 µm. (<b>E</b>) Immunochemical analysis of key cellular markers across culture conditions. Representative images showing the differential expression of HIF-1α (hypoxia), Ki67 (proliferation), and vimentin (EMT) in 2D, soft 3D, and stiff 3D environments. Scale bar = 100 µm. (<b>F</b>) Quantification of representative IHC images; the values represent the total number of cells counted from one representative section per condition. (<b>G</b>–<b>L</b>) RT-qPCR analysis of mechanosensitive gene expression in G68-derived organoids. Data normalized to the 2D culture and ACTB expression, showing matrix-dependent regulation of stemness regulators CD44 (<b>G</b>) and PTK2 (<b>H</b>), drug efflux transporters ABCC2 (<b>I</b>) and ABCC1 (<b>J</b>), and stress response factors NRF2 (<b>K</b>) and HIF-1α (<b>L</b>). Data represent mean ± SEM from three biological replicates. * <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; 0.001 by one-way ANOVA with Tukey’s post hoc test.</p>
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<p>Matrix stiffness differentially modulates the efficacy of stearoyl-modified gemcitabine in PDAC organoids. (<b>A</b>) Chemical structure and schematic representation of gemcitabine modification, showing stearoyl conjugation at the 4N position to generate Gem-S. (<b>B</b>,<b>C</b>) Comparative drug response analysis in G68 cells across matrix conditions. Dose–response curves demonstrate differential sensitivity to Gem and Gem-S in soft (6B) vs. stiff (6A) matrices. The IC<sub>50</sub> values reveal significantly enhanced Gem-S potency in stiff matrices (1.291 × 10<sup>−7</sup> M vs. 1.339 × 10<sup>−8</sup> M, <span class="html-italic">p</span> &lt; 0.001). Data are presented as mean ± SD from three independent experiments. (<b>D</b>,<b>E</b>) Matrix-dependent drug sensitivity across seven patient-derived organoid lines. Cell viability assessed after 72 h drug exposure in soft (6B) and stiff (6A) matrices. Similar drug responses were observed in soft matrices, while stiff matrices show enhanced sensitivity to Gem-S compared to unmodified gemcitabine. Data were normalized to the vehicle controls and presented as mean ± SEM. Statistical significance was determined by two-sided unpaired Student’s <span class="html-italic">t</span>-test (ns <span class="html-italic">p</span> &gt; 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>Oxidative stress mediates enhanced Gem-S efficacy in stiff matrix environments. (<b>A</b>) Visualization of reactive oxygen species (ROS) in PDOs cultured in stiff (6A) matrices following 48 h drug exposure. Representative MitoROX fluorescence images from four independent PDO lines captured across quarter-sections of 3D droplets. Scale bar = 1000 µm. (<b>B</b>) Quantitative analysis of the ROS levels. ImageJ (version 1.5.4)-based fluorescence intensity measurements demonstrating significantly elevated ROS production in Gem-S-treated organoids compared to Gem treatment (mean ± SD, *** <span class="html-italic">p</span> &lt; 0.0001, Student’s <span class="html-italic">t</span>-test). (<b>C</b>–<b>E</b>) Differential regulation of stress response pathways and drug resistance mechanisms. RT-qPCR analysis showing drug-specific effects on HIF-1α expression (hypoxic response), NRF2 expression (antioxidant defense), and ABCC2 expression (drug efflux). Data were normalized to the vehicle control and presented as mean ± SEM from three independent experiments. (<b>F</b>,<b>G</b>) Antioxidant rescue experiments. (<b>F</b>) Representative Live/Dead fluorescence images following Gem-S treatment with or without N-acetylcysteine (NAC) co-treatment. (<b>G</b>) Quantification of the mean fluorescence intensity (MFI) demonstrating the partial rescue of cell viability by NAC in Gem-S-treated organoids. Data are presented as mean ± SD, * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 by one-way ANOVA with Tukey’s post hoc test.</p>
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11 pages, 2319 KiB  
Article
Real-Time Blood Flow Assessment Using ICG Fluorescence Imaging During Hepatobiliary and Pancreatic Surgery with Consideration of Vascular Reconstruction
by Hiroyuki Fujimoto, Masahiko Kinoshita, Changgi Ahn, Takuto Yasuda, Kosuke Hatta, Mizuki Yoshida, Koichi Nakanishi, Takahito Kawaguchi, Naoki Tani, Takuma Okada, Genki Watanabe, Ryota Tanaka, Shigeaki Kurihara, Kohei Nishio, Hiroji Shinkawa, Kenjiro Kimura and Takeaki Ishizawa
Cancers 2025, 17(5), 868; https://doi.org/10.3390/cancers17050868 - 3 Mar 2025
Viewed by 206
Abstract
Background/Objectives: Indocyanine green (ICG) fluorescence imaging is widely utilized for visualizing hepatic tumors, hepatic segmentation, and biliary anatomy, improving the safety and curability of cancer surgery. However, its application for perfusion assessment in hepatobiliary and pancreatic (HBP) surgery has been less explored. Methods: [...] Read more.
Background/Objectives: Indocyanine green (ICG) fluorescence imaging is widely utilized for visualizing hepatic tumors, hepatic segmentation, and biliary anatomy, improving the safety and curability of cancer surgery. However, its application for perfusion assessment in hepatobiliary and pancreatic (HBP) surgery has been less explored. Methods: This study evaluated outcomes of patients undergoing HBP surgery with vascular reconstruction from April 2022 to August 2024. During surgery, ICG (1.25–5 mg/body) was administered intravenously to assess the need and quality of vascular reconstruction via fluorescence imaging. Results: Among 30 patients undergoing hepatectomies and/or pancreatectomies, ICG fluorescence imaging was used in 16 cases (53%) to evaluate organ and vascular perfusion. In two hepatectomy cases with consideration of reconstruction of the middle hepatic veins, sufficient fluorescence intensities in drainage areas led to the avoidance of middle hepatic vein reconstruction. In 14 cases requiring vascular reconstruction, fluorescence imaging visualized smooth blood flow through anastomotic sites in 11 cases, while insufficient signals were observed in 3 cases. Despite this, re-do anastomoses were not indicated because the fluorescence signals in the targeted organs were adequate. Postoperative contrast-enhanced computed tomography confirmed satisfactory blood perfusion in all cases. Conclusions: Real-time blood flow assessment using ICG fluorescence imaging provides valuable information for intraoperative decision-making in HBP surgeries that require vascular reconstruction of major vessels, such as hepatic arteries, veins, and the portal system. Full article
(This article belongs to the Special Issue Clinical Surgery for Hepato-Pancreato-Biliary (HPB) Cancer)
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<p>A case of reconstruction avoidance, using LIGHTVISION® (Patient no. 2). The metastatic liver tumor is located near the MHV ((<b>A</b>), arrow). Mild hepatic congestion was observed during ICG fluorescence imaging (<b>B</b>), but no hepatic congestion was seen in the contrast-enhanced CT after surgery (<b>C</b>). <a href="#app1-cancers-17-00868" class="html-app">Supplementary Video S1</a> demonstrated the operative movie in patient no. 2.</p>
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<p>A case of insufficient fluorescence imaging, using LIGHTVISION® (Patient no. 5). Hepatic congestion was observed before the reconstruction ((<b>A</b>), circled area). The fluorescence signal at the reconstructed vessel site was insufficient ((<b>B</b>), arrow). Hepatic congestion improved after reconstruction ((<b>C</b>), circled area). <a href="#app1-cancers-17-00868" class="html-app">Supplementary Video S2</a> demonstrated the operative movie in patient no. 5.</p>
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<p>A case of sufficient and insufficient fluorescence imaging, using LIGHTVISION® (Patient no. 12). The time-series changes in ICG fluorescence signals were observed before ICG administration and at 10, 15, 20, and 30 s after administration (<b>A</b>). The ICG fluorescence signal in the reconstructed vessels was sufficient in the SMV ((<b>A</b>), arrow) but insufficient in the SpV ((<b>A</b>), short arrow). Blood flow in the SMV was clearly observed on contrast-enhanced CT ((<b>B</b>), circled area), whereas no blood flow was observed in the SpV ((<b>C</b>), arrow).</p>
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<p>A case of sufficient and insufficient fluorescence imaging, using LIGHTVISION® (Patient no. 12). The time-series changes in ICG fluorescence signals were observed before ICG administration and at 10, 15, 20, and 30 s after administration (<b>A</b>). The ICG fluorescence signal in the reconstructed vessels was sufficient in the SMV ((<b>A</b>), arrow) but insufficient in the SpV ((<b>A</b>), short arrow). Blood flow in the SMV was clearly observed on contrast-enhanced CT ((<b>B</b>), circled area), whereas no blood flow was observed in the SpV ((<b>C</b>), arrow).</p>
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<p>A case of sufficient fluorescence imaging with postoperative thrombosis, using LIGHTVISION® (Patient no. 15)<b>.</b> The ICG fluorescence signal in the reconstructed vessel was sufficient (<b>A</b>). Blood flow was observed in the reconstructed vessel on contrast-enhanced CT on postoperative day 3 ((<b>B</b>), circled area), but no blood flow was observed on postoperative day 19 ((<b>C</b>), circled area).</p>
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12 pages, 480 KiB  
Review
Neuroimmune Interactions in Pancreatic Cancer
by Jun Cheng, Rui Wang and Yonghua Chen
Biomedicines 2025, 13(3), 609; https://doi.org/10.3390/biomedicines13030609 - 2 Mar 2025
Viewed by 270
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive primary malignancy, and recent technological advances in surgery have opened up more possibilities for surgical treatment. Emerging evidence highlights the critical roles of diverse immune and neural components in driving the aggressive behavior of PDAC. [...] Read more.
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive primary malignancy, and recent technological advances in surgery have opened up more possibilities for surgical treatment. Emerging evidence highlights the critical roles of diverse immune and neural components in driving the aggressive behavior of PDAC. Recent studies have demonstrated that neural invasion, neural plasticity, and altered autonomic innervation contribute to pancreatic neuropathy in PDAC patients, while also elucidating the functional architecture of nerves innervating pancreatic draining lymph nodes. Research into the pathogenesis and therapeutic strategies for PDAC, particularly from the perspective of neuroimmune network interactions, represents a cutting-edge area of investigation. This review focuses on neuroimmune interactions, emphasizing the current understanding and future challenges in deciphering the reciprocal relationship between the nervous and immune systems in PDAC. Despite significant progress, key challenges remain, including the precise molecular mechanisms underlying neuroimmune crosstalk, the functional heterogeneity of neural and immune cell populations, and the development of targeted therapies that exploit these interactions. Understanding the molecular events governing pancreatic neuroimmune signaling axes will not only advance our knowledge of PDAC pathophysiology but also provide novel therapeutic targets. Translational efforts to bridge these findings into clinical applications, such as immunomodulatory therapies and neural-targeted interventions, hold promise for improving patient outcomes. This review underscores the need for further research to address unresolved questions and translate these insights into effective therapeutic strategies for PDAC. Full article
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<p>The schematic illustrates the complex interplay between sympathetic nerves, parasympathetic nerves, sensory nerves, and immune cells within the tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC). Key interactions include: (1) Sympathetic nerve signaling via adrenoceptor beta 2 (ADRB2) and its role in tumor progression [<a href="#B10-biomedicines-13-00609" class="html-bibr">10</a>,<a href="#B47-biomedicines-13-00609" class="html-bibr">47</a>]; (2) Parasympathetic nerve signaling via acetylcholine (Ach) and its impact on cancer stem cell (CSC) activity [<a href="#B12-biomedicines-13-00609" class="html-bibr">12</a>,<a href="#B48-biomedicines-13-00609" class="html-bibr">48</a>]; (3) Sensory nerve involvement through substance P (SP) and neurokinin-1 receptor (NK-1R) pathways, which promote tumor migration and immune modulation [<a href="#B25-biomedicines-13-00609" class="html-bibr">25</a>]. Additionally, the figure highlights potential therapeutic interventions, such as CXCR3 antagonists and anti-CCL21 strategies, targeting neuroimmune crosstalk in PDAC [<a href="#B49-biomedicines-13-00609" class="html-bibr">49</a>]. Ach, acetylcholine; IFN-γ, interferon-γ; TAMs, tumor-associated macrophages; TNF-α, tumor necrosis factor-α; NGF, nerve growth factor.</p>
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21 pages, 30064 KiB  
Article
Spatial Transcriptomics Reveals Novel Mechanisms Involved in Perineural Invasion in Pancreatic Ductal Adenocarcinomas
by Vanessa Lakis, Noni L Chan, Ruth Lyons, Nicola Blackburn, Tam Hong Nguyen, Crystal Chang, Andrew Masel, Nicholas P. West, Glen M. Boyle, Ann-Marie Patch, Anthony J. Gill and Katia Nones
Cancers 2025, 17(5), 852; https://doi.org/10.3390/cancers17050852 - 1 Mar 2025
Viewed by 253
Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) has a high incidence of perineural invasion (PNI), a pathological feature of the cancer invasion of nerves. PNI is associated with a poor prognosis, local recurrence and cancer pain. It has been suggested that interactions between nerves and [...] Read more.
Background: Pancreatic ductal adenocarcinoma (PDAC) has a high incidence of perineural invasion (PNI), a pathological feature of the cancer invasion of nerves. PNI is associated with a poor prognosis, local recurrence and cancer pain. It has been suggested that interactions between nerves and the tumor microenvironment (TME) play a role in PDAC tumorigenesis. Methods: Here, we used Nanostring GeoMx Digital Spatial Profiler to analyze the whole transcriptome of both cancer and nerve cells in the microenvironment of PNI and non-PNI foci from 13 PDAC patients. Conclusions: We identified previously reported pathways involved in PNI, including Axonal Guidance and ROBO-SLIT Signaling. Spatial transcriptomics highlighted the role of PNI foci in influencing the immune landscape of the TME and similarities between PNI and nerve injury response. This study revealed that endocannabinoid and polyamine metabolism may contribute to PNI, cancer growth and cancer pain. Key members of these pathways can be targeted, offering potential novel research avenues for exploring new cancer treatment and/or pain management options in PDAC. Full article
(This article belongs to the Special Issue Tumor Microenvironment: Intercellular Communication)
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<p>Representative images of regions collected in this study. (<b>a</b>) H&amp;E image of sample used in this study with circled regions containing PNI and cancer or nerves (non-PNI). (<b>b</b>–<b>d</b>) Black squares in specimen indicate regions selected for GeoMx. (<b>e</b>) Zoomed image of region selected in H&amp;E in (<b>b</b>) (PNI region). (<b>f</b>) Same region presented in (<b>e</b>), showing GeoMx DSP ROI and segmented AOIs in terms of PanCK and PGP9.5 positivity for collection of tags. Cancer (arrowhead) and nerve (arrow) compartments in PNI focus. (<b>g</b>) H&amp;E of cancer compartment (non-PNI) indicated in (<b>c</b>) (square), cancer away from any visible nerve. (<b>h</b>) GeoMx DSP ROI showing segmented AOI in terms of PanCK positivity (purple) for collection of tags. (<b>i</b>) H&amp;E image of uninvolved nerve (non-PNI) away from tumor microenvironment indicated in (<b>d</b>) (square), with no visible cancer invasion. (<b>j</b>) GeoMx DSP of collected ROI. Regions (<b>e</b>–<b>j</b>) are examples of regions where gene expression was measured; selection of regions was based on visual inspection of adjacent H&amp;E and PanCK or PGP9.5 positivity for collection of expression tags.</p>
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<p>Expression measured in 74 regions collected by DSP GeoMx. (<b>a</b>) Principal component analysis (PCA) of normalized expression of regions profiled in cancer and nerve compartments that pass QC (n = 74). Gene counts showed separations of cancer and nerve regions. Each dot represents expression of region profiled and is colored by location in PNI or non-PNI foci. Circles indicate 95th percentile of expression. (<b>b</b>) PCA, same data presented in (<b>a</b>), colored by patient from whom samples were collected. (<b>c</b>) Normalized expression of full ROIs collected from nerve compartments (n = 17) in PNI or non-PNI foci. PCA separated nerves with PNI from non-PNI (without visual signs of invasion). (<b>d</b>) Same data presented in (<b>c</b>), colored by patient from whom samples were collected. (<b>e</b>) Normalized expression of AOIs (areas segmented by PanCK positivity) collected from cancer compartments (n = 40) in PNI or non-PNI foci. (<b>f</b>) Same data presented in (<b>e</b>), colored by patient from whom samples were collected.</p>
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<p>Differentially expressed genes and pathways enriched in cancer compartment of PNI foci. (<b>a</b>) Volcano plot showing differentially expressed genes in PNI cancer compartment compared with non-PNI foci. Each dot represents gene, with red dots indicating genes significantly up-regulated (adjusted <span class="html-italic">p</span>-value &lt; 0.20 and fold change &gt; 1.4), dark blue dots indicating genes that are significantly down-regulated (adjusted <span class="html-italic">p</span>-value &lt; 0.20 and fold change &lt; −1.4) and black dots indicating genes differentially expressed (adjusted <span class="html-italic">p</span>-value &lt; 0.20) with fold change between −1.4 and 1.4. Pink and light blue dots are genes with fold change &gt;1.4 and fold change &lt; −1.4, respectively, but are not statistically significant (adjusted <span class="html-italic">p</span>-value &gt; 0.20). Dotted lines represent threshold for statistical significance (adjusted <span class="html-italic">p</span>-value &lt; 0.20) and threshold for fold change (fold change &gt; 1.4 or &lt;−1.4). Gene symbols for some significantly differentially expressed genes are shown (for complete list, see <a href="#app1-cancers-17-00852" class="html-app">Table S3</a>). (<b>b</b>) Pathways predicted to be activated or inhibited by up-regulated genes in PNI cancer compartment (n = 288), (for full list of pathways, see <a href="#app1-cancers-17-00852" class="html-app">Table S4</a>). Pathways were obtained using IPA and up-regulated genes. (<b>c</b>) MGLL gene expression in cancer AOIs (PanCK-positive) in PNI and non-PNI regions (adjusted <span class="html-italic">p</span> = 0.091). (<b>d</b>) SAT1 gene expression in cancer AOIs (PanCK-positive) in PNI and non-PNI regions (adjusted <span class="html-italic">p</span> = 0.035).</p>
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<p>Immunohistochemistry (IHC) for MGLL. (<b>a</b>) Representative histology (H&amp;E staining) images of regions selected to measure immunoreactivity of MGLL in cancer cells in PNI and non-PNI foci. Arrow indicates nerve. IHC double immunofluorescence of morphological regions selected with PanCK and MGLL staining. (<b>b</b>) Mean intensity of MGLL in PanCK-positive regions (PNI and non-PNI foci). (<b>c</b>) Number of cells detected/region. (<b>d</b>) Percentage of cells with positive expression of MGLL. IHC was performed in 8 PDAC samples and quantification of MGLL immunoreactivity performed in 59 regions (31 PNI and 28 non-PNI). Indicated <span class="html-italic">p</span>-values from T-test.</p>
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<p>Differentially expressed genes and pathways enriched in nerve compartment of PNI foci. (<b>a</b>) Volcano plot showing differentially expressed genes in PNI nerve compartment compared with non-PNI foci. Each dot represents gene, with red dots indicating genes that are significantly up-regulated (adjusted <span class="html-italic">p</span>-value &lt; 0.20 and fold change &gt; 1.4), dark blue dots indicating genes that are significantly down-regulated (adjusted <span class="html-italic">p</span>-value &lt; 0.20 and fold change &lt; −1.4) and black dots indicating genes that are significantly differentially expressed (adjusted <span class="html-italic">p</span>-value &lt; 0.20) with fold change between −1.4 and 1.4. Pink and light blue dots are genes with fold change &gt; 1.4 and fold change &lt; −1.4, respectively, but are not statistically significant (adjusted <span class="html-italic">p</span>-value &gt; 0.20). Dotted lines represent threshold for statistical significance (adjusted <span class="html-italic">p</span>-value &lt; 0.20) and threshold for fold change (fold change &gt; 1.4 or &lt;−1.4). Gene symbols for significantly differentially expressed genes are shown (for complete list, see <a href="#app1-cancers-17-00852" class="html-app">Table S5</a>). (<b>b</b>) Pathways predicted to be activated by up-regulated genes (n = 178) (for full list of pathways, see <a href="#app1-cancers-17-00852" class="html-app">Table S6</a>). Pathways were obtained using IPA. (<b>c</b>) Normalized gene expression of Nestin (NES) (adjusted <span class="html-italic">p</span> = 0.009). (<b>d</b>) GAP43’s normalized expression in nerves with PNI and non-PNI evidence (adjusted <span class="html-italic">p</span> = 0.025).</p>
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<p>Immunohistochemistry (IHC) of Nestin. (<b>a</b>) Representative histology (H&amp;E staining) images of regions selected to measure immunoreactivity of Nestin in nerves with PNI and non-PNI foci. Arrow indicates nerve fibers. IHC double immunofluorescence of morphological regions selected in H&amp;E with Nestin and S100 staining. (<b>b</b>) Mean intensity of Nestin in S100-positive regions in nerve areas in PNI and non-PNI foci. (<b>c</b>) Number of cells detected/region. (<b>d</b>) Percentage of cells with positive expression of Nestin. IHC was performed in 7 PDAC samples and quantification performed in 41 regions (25 PNI and 16 non-PNI). Indicated <span class="html-italic">p</span>-values from T-test.</p>
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<p>Receptor–ligand expression in cancer and nerve compartments in PNI and non-PNI foci. Scatterplots of normalized gene expression of significant differentially expressed ligand–receptor pathways (LPRs), obtained using BulkSignalR (adjusted <span class="html-italic">p</span> value &lt; 0.20) with superimposed linear model line. (<b>a</b>) EPHA2 (receptor) and EFNA1 (ligand) expression in cancer compartment of PNI (purple) and non-PNI (green). (<b>b</b>) BMPR2 (receptor) and GDF7 (ligand) expression in nerve compartment of PNI (purple) and non-PNI (green). (<b>c</b>) CD47 (receptor) and THBS2 (ligand) expression in nerve compartment of PNI (purple) and non-PNI (green).</p>
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27 pages, 2937 KiB  
Article
Inflammatory Stimuli and Fecal Microbiota Transplantation Accelerate Pancreatic Carcinogenesis in Transgenic Mice, Accompanied by Changes in the Microbiota Composition
by Agnieszka Świdnicka-Siergiejko, Jarosław Daniluk, Katarzyna Miniewska, Urszula Daniluk, Katarzyna Guzińska-Ustymowicz, Anna Pryczynicz, Milena Dąbrowska, Małgorzata Rusak, Michał Ciborowski and Andrzej Dąbrowski
Cells 2025, 14(5), 361; https://doi.org/10.3390/cells14050361 - 28 Feb 2025
Viewed by 195
Abstract
An association between gut microbiota and the development of pancreatic ductal adenocarcinoma (PDAC) has been previously described. To better understand the bacterial microbiota changes accompanying PDAC promotion and progression stimulated by inflammation and fecal microbiota transplantation (FMT), we investigated stool and pancreatic microbiota [...] Read more.
An association between gut microbiota and the development of pancreatic ductal adenocarcinoma (PDAC) has been previously described. To better understand the bacterial microbiota changes accompanying PDAC promotion and progression stimulated by inflammation and fecal microbiota transplantation (FMT), we investigated stool and pancreatic microbiota by 16s RNA-based metagenomic analysis in mice with inducible acinar transgenic expressions of KrasG12D, and age- and sex-matched control mice that were exposed to inflammatory stimuli and fecal microbiota obtained from mice with PDAC. Time- and inflammatory-dependent stool and pancreatic bacterial composition alterations and stool alpha microbiota diversity reduction were observed only in mice with a Kras mutation that developed advanced pancreatic changes. Stool Actinobacteriota abundance and pancreatic Actinobacteriota and Bifidobacterium abundances increased. In contrast, stool abundance of Firmicutes, Verrucomicrobiota, Spirochaetota, Desulfobacterota, Butyricicoccus, Roseburia, Lachnospiraceae A2, Lachnospiraceae unclassified, and Oscillospiraceae unclassified decreased, and pancreatic detection of Alloprevotella and Oscillospiraceae uncultured was not observed. Furthermore, FMT accelerated tumorigenesis, gradually decreased the stool alpha diversity, and changed the pancreatic and stool microbial composition in mice with a Kras mutation. Specifically, the abundance of Actinobacteriota, Bifidobacterium and Faecalibaculum increased, while the abundance of genera such as Lachnospiraceace A2 and ASF356, Desulfovibrionaceace uncultured, and Roseburia has decreased. In conclusion, pancreatic carcinogenesis in the presence of an oncogenic Kras mutation stimulated by chronic inflammation and FMT dynamically changes the stool and pancreas microbiota. In particular, a decrease in stool microbiota diversity and abundance of bacteria known to be involved in short-fatty acids production were observed. PDAC mouse model can be used for further research on microbiota–PDAC interactions and towards more personalized and effective cancer therapies. Full article
(This article belongs to the Section Tissues and Organs)
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<p>Abundance of bacterial phyla in inflammation-induced pancreatic carcinogenesis. Relative abundance (%) of most common phyla in pancreas and stool samples (<b>a</b>). The differences between Kras/Cre mice and Cre mice 30 days and 120 days after saline and cerulein injections in the abundance of <span class="html-italic">Actinobacteriota</span> in pancreas samples (<b>b</b>), <span class="html-italic">Actinobacteriota</span> in stool (<b>c</b>), <span class="html-italic">Verrucomicrobiota</span> in stool (<b>d</b>), <span class="html-italic">Desulfobacterota</span> in stool (<b>e</b>), <span class="html-italic">Firmicutes</span> in stool (<b>f</b>). The median abundance (%) is indicated by a black line. The box represents the interquartile range. The whiskers extend to the upper adjacent value and the lower adjacent value and dots represent outliers. Statistically significant differences (<span class="html-italic">p</span> value &lt; 0.05) between mice are marked * (for details see <a href="#app1-cells-14-00361" class="html-app">Table S4</a>). Sal—saline, CER—cerulein, Kras/Cre—mice with Kras mutation, Cre—mice without Kras mutation.</p>
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<p>Abundance of bacterial genera in inflammation-induced pancreatic carcinogenesis. Relative abundance of most common phyla in pancreas and stool samples (<b>a</b>). The differences between Kras/Cre mice and Cre mice 30 days and 120 days after saline and cerulein injections in abundance of <span class="html-italic">Bifidobacterium</span> in pancreas samples (<b>b</b>), <span class="html-italic">Butyricicoccus</span> in stool (<b>c</b>), <span class="html-italic">Clostridia UCG-014</span> in stool (<b>d</b>), <span class="html-italic">Lachnospiraceae unclassified</span> in stool (<b>e</b>), <span class="html-italic">Lachnospiraceae A2</span> in stool (<b>f</b>), <span class="html-italic">Oscillospiraceae unclassified</span> in stool (<b>g</b>), <span class="html-italic">Roseburia</span> in stool (<b>h</b>), <span class="html-italic">Erysipelotrichaceae uncultured</span> in stool (<b>i</b>), <span class="html-italic">Lachnoclostridium</span> in stool (<b>j</b>) and <span class="html-italic">Lachnospiraceae UCG-006</span> in stool (<b>k</b>). The median abundance (%) is indicated by a black line. The box represents the interquartile range. The whiskers extend to the upper adjacent value and the lower adjacent value and dots represent outliers. Statistically significant differences (<span class="html-italic">p</span> value &lt; 0.05) between mice are marked * (for details: see <a href="#app1-cells-14-00361" class="html-app">Table S7</a>). Sal—saline, CER—cerulein, Kras/Cre—mice with Kras mutation, Cre—mice without Kras mutation.</p>
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<p>The microbiota diversity in pancreas and stool samples in inflammation-induced carcinogenesis. Shannon index—pancreas (<b>a</b>), Shannon index—stool (<b>b</b>), Principal coordinates analysis (PCoA)—stool phyla (<b>c</b>), Principal coordinates analysis (PCoA)—stool genera (<b>d</b>), Principal coordinates analysis (PCoA)—pancreas phyla (<b>e</b>), Principal coordinates analysis (PCoA)—pancreas genera (<b>f</b>). (<b>a</b>,<b>b</b>): The median alpha diversity (Shannon index) is indicated by a black line. The box represents the interquartile range. The whiskers extend to the upper adjacent value and the lower adjacent value and dots represent outliers. Statistically significant differences (<span class="html-italic">p</span> value &lt; 0.05) between mice are marked * (for details see <a href="#app1-cells-14-00361" class="html-app">Table S11</a>). Sal—saline, CER—cerulein, Kras/Cre—mice with Kras mutation, Cre—mice without Kras mutation.</p>
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<p>Abundance of bacterial phyla after fecal microbiota transplantation associated pancreatic carcinogenesis. Relative abundance of the most common phyla in pancreas and stool samples in Kras/Cre mice and Cre mice after FMT and sham treatments (<b>a</b>). The differences between tested mice in abundance of <span class="html-italic">Actinobacteriota</span> in pancreas sample (<b>b</b>), <span class="html-italic">Actinobacteriota</span> in stool (<b>c</b>), <span class="html-italic">Cyanobacteria</span> in stool (<b>d</b>), <span class="html-italic">Deferribacterota</span> in stool (<b>e</b>), <span class="html-italic">Desulfobacterota</span> in stool (<b>f</b>), <span class="html-italic">Proteobacteria</span> in stool (<b>g</b>), and <span class="html-italic">Spirochaetota</span> in stool (<b>h</b>). The median abundance (%) is indicated by a black line. The box represents the interquartile range. The whiskers extend to the upper adjacent value and the lower adjacent value and dots represent outliers. Statistically significant differences (<span class="html-italic">p</span> value &lt; 0.05) between mice are marked * (for details see <a href="#app1-cells-14-00361" class="html-app">Table S14</a>). Kras/Cre—mice with Kras mutation, Cre—mice without Kras mutation, FMT—fecal microbiota transplantation.</p>
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<p>Abundance of bacterial genera after fecal microbiota transplantation associated pancreatic carcinogenesis. Relative abundance of the most common genera in pancreas and stool samples in Kras/Cre mice and Cre mice after FMT and sham treatments (<b>a</b>). The differences between tested mice in abundance in pancreatic tissue of <span class="html-italic">Bifidobacterium</span> (<b>b</b>), <span class="html-italic">Dubosiella</span> (<b>c</b>), <span class="html-italic">Faecalibaculum</span> (<b>d</b>), <span class="html-italic">Roseburia</span> (<b>e</b>), <span class="html-italic">Desulfovibrionaceae</span> (<b>f</b>), <span class="html-italic">Lachnospiraceae A2</span> (<b>g</b>), <span class="html-italic">Lachnospiraceae ASF356</span> (<b>h</b>), and <span class="html-italic">Lachnospiraceae NK4A136 group</span> (<b>i</b>). The median abundance (%) is indicated by a black line. The box represents the interquartile range. The whiskers extend to the upper adjacent value and the lower adjacent value and dots represent outliers. Statistically significant differences (<span class="html-italic">p</span> value &lt; 0.05) between mice are marked * (for details see <a href="#app1-cells-14-00361" class="html-app">Table S17</a>). Kras/Cre—mice with Kras mutation, Cre—mice without Kras mutation, FMT—fecal microbiota transplantation.</p>
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<p>Differences between Kras/Cre mice and Cre mice after fecal transplantation and sham treatments in abundance in stool of bacterial genera. Relative abundance of <span class="html-italic">Bifidobacterium</span> (<b>a</b>), <span class="html-italic">Faecalibaculum</span> (<b>b</b>), <span class="html-italic">Roseburia</span> (<b>c</b>), <span class="html-italic">Desulfovibrionaceae uncultured</span> (<b>d</b>), <span class="html-italic">Lachnospiraceae A2</span> (<b>e</b>), <span class="html-italic">Lachnospiraceae ASF356</span> (<b>f</b>). The median abundance (%) is indicated by a black line. The box represents the interquartile range. The whiskers extend to the upper adjacent value and the lower adjacent value and dots represent outliers. Statistically significant differences (<span class="html-italic">p</span> value &lt; 0.05) between mice are marked * (for details see <a href="#app1-cells-14-00361" class="html-app">Table S20</a>). Kras/Cre mice—mice with Kras mutation, Cre mice—mice without Kras mutation, FMT—fecal microbiota transplantation.</p>
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<p>Bacterial microbiota diversity in fecal microbiota transplantation associated pancreatic carcinogenesis. Alpha diversity index (Shannon index) of the microbiota in pancreas (<b>a</b>) and stool (<b>b</b>) samples in Kras/Cre mice and Cre mice after fecal microbiota transplantation and sham treatments. Principal coordinate analysis (PCoA) plot results based on Bray–Curtis dissimilarity distance at the genera level in Kras/Cre mice and Cre mice after fecal microbiota transplantation and sham treatments in pancreas (<b>c</b>) and stool (<b>d</b>) samples. Bacterial stool alpha diversity changes (Shannon index) over time after fecal microbiota transplantation in Cre mice (<b>e</b>) and Kras/Cre mice (<b>f</b>). (<b>a</b>,<b>b</b>,<b>e</b>,<b>f</b>): The median is indicated by a black line. The box represents the interquartile range. The whiskers extend to the upper adjacent value and the lower adjacent value and dots represent outliers. Statistically significant differences (<span class="html-italic">p</span> value &lt; 0.05) between mice are marked * (for detail see <a href="#app1-cells-14-00361" class="html-app">Table S22</a>). Kras/Cre—mice with Kras mutation, Cre—mice without Kras mutation, FMT—fecal microbiota transplantation.</p>
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19 pages, 4454 KiB  
Article
Reshaping [99mTc]Tc-DT11 to DT14D Tagged with Trivalent Radiometals for NTS1R-Positive Cancer Theranostics
by Panagiotis Kanellopoulos, Berthold A. Nock, Eric P. Krenning and Theodosia Maina
Pharmaceutics 2025, 17(3), 310; https://doi.org/10.3390/pharmaceutics17030310 - 28 Feb 2025
Viewed by 220
Abstract
Background/Objectives: Radiotheranostics of neurotensin subtype 1 receptor (NTS1R)-expressing tumors, like pancreatic, gastrointestinal, or prostate cancer, has attracted considerable attention in recent years. Still, the fast degradation of neurotensin (NT)-based radioligands, by angiotensin-converting enzyme (ACE), neprilysin (NEP), and other proteases, has [...] Read more.
Background/Objectives: Radiotheranostics of neurotensin subtype 1 receptor (NTS1R)-expressing tumors, like pancreatic, gastrointestinal, or prostate cancer, has attracted considerable attention in recent years. Still, the fast degradation of neurotensin (NT)-based radioligands, by angiotensin-converting enzyme (ACE), neprilysin (NEP), and other proteases, has considerably compromised their efficacy. The recently introduced [99mTc]Tc-DT11 (DT11, N4-Lys(MPBA-PEG4)-Arg-Arg-Pro-Tyr-Ile-Leu-OH; N4, 6-(carboxy)-1,4,8,11-tetraazaundecane) has displayed promising uptake in NTS1R-positive tumors in mice and enhanced resistance to both ACE and NEP by virtue of the lateral MPBA-PEG4 (MPBA, 4-(4-methylphenyl)butyric acid; PEG4, 14-amino-3,6,9,12-tetraoxatetradecan-1-oic acid) chain attached to the ε-NH2 of Lys7. We were next interested in investigating whether these qualities could be retained in DT14D, likewise modified at Lys7 but carrying the universal chelator DOTA (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid) via a (βAla)3 spacer at the α-NH2 of Lys7. This chelator switch enables the labeling of DT14D with a wide range of trivalent radiometals suitable for true theranostic applications, not restricted to the diagnostic imaging of NTS1R-positive lesions only by single-photon emission computed tomography (SPECT). Methods: DT14D was labeled with Ga-67 (a surrogate for the positron emission tomography radionuclide Ga-68), In-111 (for SPECT), and Lu-177 (applied in radiotherapy). The resulting radioligands were tested in NTS1R-expressing pancreatic cancer AsPC-1 cells and mice models. Results: [67Ga]Ga/[111In]In/[177Lu]Lu-DT14D displayed high affinity for human NTS1R and internalization in AsPC-1 cells. They remained >70% intact 5 min after entering the mice’s circulation, displaying NTS1R-specific uptake in AsPC-1 xenografts. Conclusions: Suitably side-chain modified NT analogs show enhanced metabolic stability and hence better prospects for radiotheranostic application in NTS1R-positive cancer. Full article
(This article belongs to the Section Pharmacokinetics and Pharmacodynamics)
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<p>Chemical structures of (<b>a</b>) parental DT11 (DT11, N<sub>4</sub>-Lys(MPBA-PEG4)-Arg-Arg-Pro-Tyr-Ile-Leu-OH; N<sub>4</sub>, 6-(carboxy)-1,4,8,11-tetraazaundecane; MPBA, 4-(4-methylphenyl)butyric acid; PEG4, 14-amino-3,6,9,12-tetraoxatetradecan-1-oic acid) suitable for labeling with Tc-99m and (<b>b</b>) reshaped to DT14D (DOTA-βAla-βAla-βAla-Lys(MPBA-PEG4)<sup>7</sup>-Arg-Arg-Pro-Tyr-Ile-Leu-OH; DOTA, 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid), thereby enabling labeling with theranostic trivalent radiometals.</p>
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<p>Displacement of [<sup>125</sup>I]I-Tyr<sup>3</sup>-NT from NTS<sub>1</sub>R binding sites in WiDr cell membranes with increasing concentrations of DT14D—grey dashed line (◇, IC<sub>50</sub> = 2.56 ± 0.26 nM, n = 3), [<sup>nat</sup>Ga]Ga-DT14D—red line (<span class="html-fig-inline" id="pharmaceutics-17-00310-i001"><img alt="Pharmaceutics 17 00310 i001" src="/pharmaceutics/pharmaceutics-17-00310/article_deploy/html/images/pharmaceutics-17-00310-i001.png"/></span>, IC<sub>50</sub> = 1.55 ± 0.14 nM, n = 3), [<sup>nat</sup>In]In-DT14D—blue line (<span class="html-fig-inline" id="pharmaceutics-17-00310-i002"><img alt="Pharmaceutics 17 00310 i002" src="/pharmaceutics/pharmaceutics-17-00310/article_deploy/html/images/pharmaceutics-17-00310-i002.png"/></span>, IC<sub>50</sub> = 1.37 ± 0.54 nM, n = 3), and [<sup>nat</sup>Lu]Lu-DT14D—violet line (<span class="html-fig-inline" id="pharmaceutics-17-00310-i003"><img alt="Pharmaceutics 17 00310 i003" src="/pharmaceutics/pharmaceutics-17-00310/article_deploy/html/images/pharmaceutics-17-00310-i003.png"/></span>, IC<sub>50</sub> = 0.99 ± 0.18 nM, n = 3); the results represent the mean IC<sub>50</sub> values ± sd, n = number of separate experiments in triplicate.</p>
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<p>(<b>a</b>) NTS<sub>1</sub>R-specific uptake (specific internalized (solid), + specific membrane-bound (checkered) = total specific cell-associated) of [<sup>67</sup>Ga]Ga-DT14D (red bars), [<sup>111</sup>In]In-DT14D (blue bars) and [<sup>177</sup>Lu]Lu-DT14D (violet bars) and (<b>b</b>) non-specific (ns) uptake (ns internalized (solid) + ns membrane-bound (checkered) = total ns cell-associated) of [<sup>67</sup>Ga]Ga-DT14D (light red bars), [<sup>111</sup>In]In-DT14D (light blue bars), and [<sup>177</sup>Lu]Lu-DT14D (light violet bars) during 1 h incubation at 37 °C with confluent monolayers of AsPC-1 cells. The results are expressed as average values ± sd from 3 independent experiments, each performed in triplicate.</p>
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<p>Representative radiochromatograms of HPLC analysis (system 2) of mouse blood samples collected 5 min pi of (<b>a</b>) radiolabeled DT14D (red for [<sup>67</sup>Ga]Ga-DT14D, blue for [<sup>111</sup>In]In-DT14D, and violet for [<sup>177</sup>Lu]Lu-DT14D) administered alone (dashed lines; controls) or (<b>b</b>) 30 min after oral gavage of Entresto<sup>®</sup> (solid lines; Entresto<sup>®</sup>); intact radioligand percentages are included in <a href="#pharmaceutics-17-00310-t001" class="html-table">Table 1</a>.</p>
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<p>Biodistribution of [<sup>67</sup>Ga]Ga-DT14D in SCID mice with AsPC-1 tumors at 1 h (red bars) and 4 h pi (red checkered bars—controls; and blocked (treated with excess NT)—light gray solid bars); (<b>a</b>) data represent average %IA/g values ± sd, n = 4 and (<b>b</b>) tumor-to-organ (T/O) ratios; Bl = blood, Li = liver, He = heart, Ki = kidneys, St = stomach, In = intestines, Sp = spleen, Mu = muscle, Lu = lung, Pa = pancreas, Fe = femur, and Tu = AsPC-1 xenografts.</p>
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<p>Biodistribution of [<sup>67</sup>Ga]Ga-DT14D in SCID mice with AsPC-1 tumors at 1 h (red bars) and 4 h pi (red checkered bars—controls; and blocked (treated with excess NT)—light gray solid bars); (<b>a</b>) data represent average %IA/g values ± sd, n = 4 and (<b>b</b>) tumor-to-organ (T/O) ratios; Bl = blood, Li = liver, He = heart, Ki = kidneys, St = stomach, In = intestines, Sp = spleen, Mu = muscle, Lu = lung, Pa = pancreas, Fe = femur, and Tu = AsPC-1 xenografts.</p>
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<p>Biodistribution of [<sup>111</sup>In]In-DT14D in SCID mice bearing AsPC-1 xenografts from left to right: 4 h block (1st light gray solid bars—treated with excess NT), 4 h controls (2nd light blue solid bars), 4 h Entresto<sup>®</sup>-treated mice (3rd darker blue solid bars), 24 h controls (light blue checkered bars), and 24 h pi Entresto<sup>®</sup>-treated mice (darker blue checkered bars); (<b>a</b>) data represent average %IA/g values ± sd, n = 4 and (<b>b</b>) tumor-to-organ (T/O) ratios (4 h block—1st light gray solid bars not included); Bl = blood, Li = liver, He = heart, Ki = kidneys, St = stomach, In = intestines, Sp = spleen, Mu = muscle, Lu = lung, Pa = pancreas, Fe = femur, and Tu = AsPC-1 xenografts.</p>
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<p>Biodistribution of [<sup>111</sup>In]In-DT14D in SCID mice bearing AsPC-1 xenografts from left to right: 4 h block (1st light gray solid bars—treated with excess NT), 4 h controls (2nd light blue solid bars), 4 h Entresto<sup>®</sup>-treated mice (3rd darker blue solid bars), 24 h controls (light blue checkered bars), and 24 h pi Entresto<sup>®</sup>-treated mice (darker blue checkered bars); (<b>a</b>) data represent average %IA/g values ± sd, n = 4 and (<b>b</b>) tumor-to-organ (T/O) ratios (4 h block—1st light gray solid bars not included); Bl = blood, Li = liver, He = heart, Ki = kidneys, St = stomach, In = intestines, Sp = spleen, Mu = muscle, Lu = lung, Pa = pancreas, Fe = femur, and Tu = AsPC-1 xenografts.</p>
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<p>Biodistribution of [<sup>177</sup>Lu]Lu-DT14D in SCID mice bearing AsPC-1 xenografts from left to right: 4 h block (1st light gray solid bars—treated with excess NT), 4 h (2nd dark violet solid bars), 24 h (3rd lighter violet solid bars), 48 h (4th very light violet solid bars), and 72 h pi (5th very light violet chequered bars). The 4–72 h pi values refer to controls (non-treated with Entresto<sup>®</sup>); (<b>a</b>) data represent average %IA/g values ± sd, n = 4 and (<b>b</b>) tumor-to-organ (T/O) ratios (4 h block—1st light gray solid bars not included); Bl = blood, Li = liver, He = heart, Ki = kidneys, St = stomach, In = intestines, Sp = spleen, Mu = muscle, Lu = lung, Pa = pancreas, Fe = femur, and Tu = AsPC-1 xenografts.</p>
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32 pages, 2200 KiB  
Systematic Review
Paraneoplastic Syndromes in Gallbladder Cancer: A Systematic Review
by Beth Shin Rei Lau, Nevin Yi Meng Chua, Wee Teck Ong, Harjeet Singh, Vor Luvira, Kyoichi Takaori and Vishal G. Shelat
Medicina 2025, 61(3), 417; https://doi.org/10.3390/medicina61030417 - 27 Feb 2025
Viewed by 205
Abstract
Background and Objectives: Gallbladder cancer (GBC) is a biologically aggressive malignancy characterised by poor survival outcomes often attributed to delayed diagnosis due to nonspecific clinical presentations. Paraneoplastic syndromes (PNSs), atypical symptoms caused by cancer itself, may serve as valuable indicators for timely [...] Read more.
Background and Objectives: Gallbladder cancer (GBC) is a biologically aggressive malignancy characterised by poor survival outcomes often attributed to delayed diagnosis due to nonspecific clinical presentations. Paraneoplastic syndromes (PNSs), atypical symptoms caused by cancer itself, may serve as valuable indicators for timely diagnosis, particularly in malignancies with nonspecific features. Understanding the manifestations of PNSs in GBC is, therefore, critical. This systematic review collates case studies documenting the association of PNS with GBC, including subsequent management and clinical outcomes. Materials and Methods: A comprehensive search of PubMed, Embase, CINAHL, Web of Science, and Cochrane Library databases yielded 49 relevant articles. Upon searching other information sources, two more relevant articles were identified via citation sources. Results: The paraneoplastic syndromes were classified according to haematological (leukocytosis), dermatological (inflammatory myositis like dermatomyositis and polymyositis, acanthosis nigricans, Sweet’s syndrome, exfoliative dermatitis), neurological, metabolic (hypercalcemia, hyponatremia), and others (chorea). The analysis included the age, sex, and country of origin of the patient, as well as the time of PNS diagnosis relative to GBC diagnosis. Furthermore, common presenting complaints, investigations, and effectiveness of treatment modalities using survival time were assessed. Conclusions: While PNS management can offer some benefits, oncologic outcomes of GBC are largely poor. The majority of PNS in GBC are reported in advanced stages, and, hence, PNS has a minimal role in early diagnosis. PNS management can improve a patient’s quality of life, and thus recognition and treatment are important considerations in the holistic management of GBC patients. Full article
(This article belongs to the Section Gastroenterology & Hepatology)
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<p>PRISMA chart showing extraction of studies. * Records were screened and identified by authors B.S.R.L. and W.T.O. Any conflicts were resolved by author N.Y.M.C.</p>
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<p>Paraneoplastic leukocytosis of gallbladder carcinomas entering the bloodstream through venous outflow.</p>
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<p>Flowchart showing postulated mechanisms linking paraneoplastic inflammatory myositis to malignancies.</p>
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<p>Proposed pathophysiology of paraneoplastic hypercalcaemia.</p>
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12 pages, 1907 KiB  
Article
Computer-Aided Decision Support and 3D Models in Pancreatic Cancer Surgery: A Pilot Study
by Diederik W. M. Rasenberg, Mark Ramaekers, Igor Jacobs, Jon R. Pluyter, Luc J. F. Geurts, Bin Yu, John C. P. van der Ven, Joost Nederend, Ignace H. J. T. de Hingh, Bert A. Bonsing, Alexander L. Vahrmeijer, Erwin van der Harst, Marcel den Dulk, Ronald M. van Dam, Bas Groot Koerkamp, Joris I. Erdmann, Freek Daams, Olivier R. Busch, Marc G. Besselink, Wouter W. te Riele, Rinze Reinhard, Frank Willem Jansen, Jenny Dankelman, J. Sven D. Mieog and Misha D. P. Luyeradd Show full author list remove Hide full author list
J. Clin. Med. 2025, 14(5), 1567; https://doi.org/10.3390/jcm14051567 - 26 Feb 2025
Viewed by 147
Abstract
Background: Preoperative planning of patients diagnosed with pancreatic head cancer is difficult and requires specific expertise. This pilot study assesses the added value of three-dimensional (3D) patient models and computer-aided detection (CAD) algorithms in determining the resectability of pancreatic head tumors. Methods: This [...] Read more.
Background: Preoperative planning of patients diagnosed with pancreatic head cancer is difficult and requires specific expertise. This pilot study assesses the added value of three-dimensional (3D) patient models and computer-aided detection (CAD) algorithms in determining the resectability of pancreatic head tumors. Methods: This study included 14 hepatopancreatobiliary experts from eight hospitals. The participants assessed three radiologically resectable and three radiologically borderline resectable cases in a simulated setting via crossover design. Groups were divided in controls (using a CT scan), a 3D group (using a CT scan and 3D models), and a CAD group (using a CT scan, 3D and CAD). For the perceived fulfillment of preoperative needs, the quality and confidence of clinical decision-making were evaluated. Results: A higher perceived ability to determine degrees and the length of tumor–vessel contact was reported in the CAD group compared to controls (p = 0.022 and p = 0.003, respectively). Lower degrees of tumor–vessel contact were predicted for radiologically borderline resectable tumors in the CAD group compared to controls (p = 0.037). Higher confidence levels were observed in predicting the need for vascular resection in the 3D group compared to controls (p = 0.033) for all cases combined. Conclusions: “CAD (including 3D) improved experts’ perceived ability to accurately assess vessel involvement and supports the development of evolving techniques that may enhance the diagnosis and treatment of pancreatic cancer”. Full article
(This article belongs to the Special Issue State of the Art in Hepato-Pancreato-Biliary Surgery)
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<p>The integrated medical imaging workstation. The integrated medical imaging workstation consisting of a DICOM viewer showing the CT scan, segmentations, and the Looking Glass holographic display. The segmentations including information on tumor vessel contact were reconstructed and displayed on the Looking Glass holographic display.</p>
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<p>Box plots of perceived fulfillment of clinical needs. Fulfillment of clinical needs were scored on a Likert scale (1 = completely disagree and 5 = completely agree). Blue lines = medians; light blue boxes = 25th and 75th percentile; red crosses = outlier values; grey line = range of values. * <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Box plot regarding degrees of tumor and PV–SMV involvement. Box plot regarding the degrees of contact between tumor and portal vein/superior mesenteric vein in radiologically borderline resectable tumors. Blue lines = medians; blue boxes = 25th and 75th percentile; red crosses = outlier values; grey line = range of values. * <span class="html-italic">p</span>–value &lt; 0.05.</p>
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14 pages, 933 KiB  
Systematic Review
Diagnostic Accuracy of Radiomics in the Early Detection of Pancreatic Cancer: A Systematic Review and Qualitative Assessment Using the Methodological Radiomics Score (METRICS)
by María Estefanía Renjifo-Correa, Salvatore Claudio Fanni, Luis A. Bustamante-Cristancho, Maria Emanuela Cuibari, Gayane Aghakhanyan, Lorenzo Faggioni, Emanuele Neri and Dania Cioni
Cancers 2025, 17(5), 803; https://doi.org/10.3390/cancers17050803 - 26 Feb 2025
Viewed by 198
Abstract
Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal malignancy with increasing incidence and low survival rate, primarily due to the late detection of the disease. Radiomics has demonstrated its utility in recognizing patterns and anomalies not perceptible to the human eye. [...] Read more.
Background/Objectives: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive and lethal malignancy with increasing incidence and low survival rate, primarily due to the late detection of the disease. Radiomics has demonstrated its utility in recognizing patterns and anomalies not perceptible to the human eye. This systematic literature review aims to assess the application of radiomics in the analysis of pancreatic parenchyma images to identify early indicators predictive of PDAC. Methods: A systematic search of original research papers was performed on three databases: PubMed, Embase, and Scopus. Two reviewers applied the inclusion and exclusion criteria, and one expert solved conflicts for selecting the articles. After extraction and analysis of the data, there was a quality assessment of these articles using the Methodological Radiomics Score (METRICS) tool. The METRICS assessment was carried out by two raters, and conflicts were solved by a third reviewer. Results: Ten articles for analysis were retrieved. CT scan was the diagnostic imaging used in all the articles. All the studies were retrospective and published between 2019 and 2024. The main objective of the articles was to generate radiomics-based machine learning models able to differentiate pancreatic tumors from healthy tissue. The reported diagnostic performance of the model chosen yielded very high results, with a diagnostic accuracy between 86.5% and 99.2%. Texture and shape features were the most frequently implemented. The METRICS scoring assessment demonstrated that three articles obtained a moderate quality, five a good quality, and, finally, two articles yielded excellent quality. The lack of external validation and available model, code, and data were the major limitations according to the qualitative assessment. Conclusions: There is high heterogeneity in the research question regarding radiomics and pancreatic cancer. The principal limitations of the studies were mainly due to the nature of the trials and the considerable heterogeneity of the radiomic features reported. Nonetheless, the work in this field is promising, and further studies are still required to adopt radiomics in the early detection of PDAC. Full article
(This article belongs to the Special Issue Multimodality Imaging for More Precise Radiotherapy)
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<p>Study selection process flowchart according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines [<a href="#B18-cancers-17-00803" class="html-bibr">18</a>].</p>
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<p>Distribution of METRICS quality categorization.</p>
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17 pages, 2006 KiB  
Review
Targeting NEK Kinases in Gastrointestinal Cancers: Insights into Gene Expression, Function, and Inhibitors
by Lei Chen, Heng Lu, Farah Ballout, Wael El-Rifai, Zheng Chen, Ravindran Caspa Gokulan, Oliver Gene McDonald and Dunfa Peng
Int. J. Mol. Sci. 2025, 26(5), 1992; https://doi.org/10.3390/ijms26051992 - 25 Feb 2025
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Abstract
Gastrointestinal (GI) cancers, which mainly include malignancies of the esophagus, stomach, intestine, pancreas, liver, gallbladder, and bile duct, pose a significant global health burden. Unfortunately, the prognosis for most GI cancers remains poor, particularly in advanced stages. Current treatment options, including targeted and [...] Read more.
Gastrointestinal (GI) cancers, which mainly include malignancies of the esophagus, stomach, intestine, pancreas, liver, gallbladder, and bile duct, pose a significant global health burden. Unfortunately, the prognosis for most GI cancers remains poor, particularly in advanced stages. Current treatment options, including targeted and immunotherapies, are less effective compared to those for other cancer types, highlighting an urgent need for novel molecular targets. NEK (NIMA related kinase) kinases are a group of serine/threonine kinases (NEK1-NEK11) that play a role in regulating cell cycle, mitosis, and various physiological processes. Recent studies suggest that several NEK members are overexpressed in human cancers, including gastrointestinal (GI) cancers, which can contribute to tumor progression and drug resistance. Among these, NEK2 stands out for its consistent overexpression in all types of GI cancer. Targeting NEK2 with specific inhibitors has shown promising results in preclinical studies, particularly for gastric and pancreatic cancers. The development and clinical evaluation of NEK2 inhibitors in human cancers have emerged as a promising therapeutic strategy. Specifically, an NEK2 inhibitor, T-1101 tosylate, is currently undergoing clinical trials. This review will focus on the gene expression and functional roles of NEKs in GI cancers, as well as the progress in developing NEK inhibitors. Full article
(This article belongs to the Special Issue Molecular Targets in Gastrointestinal Diseases)
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<p>A cartoon showing the protein domain structure of members of the NEK kinase family.</p>
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<p>The gene expression of NEK family members in GI cancers. The data are from TNMplot databases of colon, esophagus (Esoph), liver, pancreas, rectum, and stomach, including normal and tumor samples. The comparison of the gene expression of the NEKs between tumor and normal samples was performed using the online TNMplot tools. * <span class="html-italic">p</span> &lt; 0.05. The different colors are used to differentiate the tumor types; one color represents one type of GI cancer.</p>
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<p>Basic chemical information of NEK2 inhibitors. All information in this figure was retrieved from PubChem database (<a href="https://pubchem.ncbi.nlm.nih.gov" target="_blank">https://pubchem.ncbi.nlm.nih.gov</a>, PubChem) on 19 February 2025.</p>
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13 pages, 1586 KiB  
Article
Administration of FOLFIRINOX for Advanced Pancreatic Cancer: Physician Practice Patterns During Early Use
by Joanna Gotfrit, Horia Marginean, Yoo-Joung Ko, Akmal Ghafoor, Petr Kavan, Haji Chalchal, Shahid Ahmed, Karen Mulder, Patricia Tang and Rachel Goodwin
Curr. Oncol. 2025, 32(3), 128; https://doi.org/10.3390/curroncol32030128 - 25 Feb 2025
Viewed by 173
Abstract
Advanced pancreatic cancer results in high morbidity and mortality. The standard of care treatment in the advanced setting changed in 2011 with the introduction of FOLFIRINOX (FFX) chemotherapy. However, it was highly toxic with significant risk of complications. We assessed the practice patterns [...] Read more.
Advanced pancreatic cancer results in high morbidity and mortality. The standard of care treatment in the advanced setting changed in 2011 with the introduction of FOLFIRINOX (FFX) chemotherapy. However, it was highly toxic with significant risk of complications. We assessed the practice patterns of medical oncologists across Canada. Methods: We performed a retrospective study of consecutive patients with advanced pancreatic cancer treated with FFX at eight Canadian cancer centers. Demographic, treatment, and outcome data were collected and analyzed. Results: The median age of the patients was 61 (range 24–80), 43% were female, 96% had an ECOG PS of 0 or 1, and 50% had three or more metastatic sites. The median follow-up time was 20.8 months (95%CI 18.6–24.9). Physicians started FFX at the standard dose 31% of the time. Physicians prescribed GCSF for primary prophylaxis most when giving standard-dose FFX (30% of the time) in comparison to reduced dose with or without the 5-FU bolus. Dose reductions occurred in 78.1% of patients, while dose delay occurred in 65.2% of patients. Conclusions: Medical oncologists in Canada historically prescribed FFX to patients with advanced pancreatic cancer in a fashion that was not uniform, prior to the emergence of evidence for upfront dose reductions. Full article
(This article belongs to the Special Issue Gastrointestinal Cancers in Eastern Canada)
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<p>Use of primary and secondary GCSF by starting dose. Abbreviations: w/o, without; 5Fub, 5-fluorouracil bolus; GCSF, granulocyte colony stimulating factor.</p>
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<p>Dose adjustments due to toxicity.</p>
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<p>Reasons for FFX discontinuation. Abbreviations: MD, medical doctor; FFX, FOLFIRINOX.</p>
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<p>Toxicities leading to discontinuation of FFX among patients discontinuing treatment due to toxicity. Abbreviations: FFX, FOLFIRINOX.</p>
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