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19 pages, 6330 KiB  
Article
Characterisation of Castration-Resistant Cell-Derived Exosomes and Their Effect on the Metastatic Phenotype
by Jorge Recio-Aldavero, Lorena Parra-Gutiérrez, Laura Muñoz-Moreno, Irene D. Román, María Isabel Arenas and Ana M. Bajo
Cancers 2025, 17(1), 141; https://doi.org/10.3390/cancers17010141 (registering DOI) - 4 Jan 2025
Viewed by 236
Abstract
Background/Objectives: Prostate cancer (PCa) is characterised by its progression to a metastatic and castration-resistant phase. Prostate tumour cells release small extracellular vesicles or exosomes which are taken up by target cells and can potentially facilitate tumour growth and metastasis. The present work studies [...] Read more.
Background/Objectives: Prostate cancer (PCa) is characterised by its progression to a metastatic and castration-resistant phase. Prostate tumour cells release small extracellular vesicles or exosomes which are taken up by target cells and can potentially facilitate tumour growth and metastasis. The present work studies the effect of exosomes from cell lines that are representative of the different stages of the disease on the tumoral phenotype of PC3 cells. Methods: Exosomes were isolated by ultracentrifugation from human prostate epithelial cells (RWPE-1) and androgen-dependent PCa cells (LNCaP) and castration-resistant PCa cells (CRPC) with moderate (DU145) or high (PC3) metastatic capacity. The biophysical and biochemical properties of the exosomes were characterised as well as their effects on PC3 cell viability and migration. Results: The study of the exosomes of prostate cell lines shows heterogeneity in their size, presenting in some of them two types of populations; in both populations, a larger size in those derived from PC3 cells and a smaller size in those derived from non-tumourigenic prostate cells were detected. Differences were found in the physical properties of those derived from healthy and PCa cells, as well as between cells representative of the most aggressive stages of the disease. The highest gamma-glutamyl transferase (GGT) activity was observed in androgen-dependent cells and differences in the pro-metalloproteinases (MMP) activity were detected in healthy cells and in castration-resistant cells with moderate metastatic capacity with respect to PC3 cells. The treatment of PC3 cells with their own exosomes increased PC3 cell viability and migration. Conclusion: Exosomes represent a promising field of research in the diagnosis, prognosis, and treatment of prostate cancer. Full article
(This article belongs to the Special Issue Exosomes in Cancer Metastasis)
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<p>Biochemical identification of prostate cell lines-derived exosomes. (<b>a</b>) Immunodetection of CD9, CD63, LAMP2, PSMA, GGT, MMP9, and MMP2 in exosomes isolated from PC3, LNCaP, and RWPE-1 cell lines. Representative experiments are shown. (<b>b</b>) Transmission electron microscopy (TEM) of exosomes isolated from PC3, DU145, LNCaP, and RWPE-1 cell lines labelled with CD9 (top microphotographs) or CD63 (lower microphotographs). Immuno-gold labelled exosome with uranyl acetate staining. Scale bar = 100 nm.</p>
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<p>Polydispersity index, size and electrical properties of PC3-, DU145-, LNCaP- and RWPE-1-derived exosomes. (<b>a</b>) Exosomes polydispersity index and exosomes/microexosomes size (nm) isolated from culture medium of PC3, DU145, LNCaP and RWPE-1 cell lines. (<b>b</b>) Zeta potential (mV), (<b>c</b>) conductivity (mS/cm), and (<b>d</b>) mobility (µm × cm/V × s) of exosomes isolated from culture medium of PC3, DU145, LNCaP and RWPE-1 cell lines. Data represent mean ± SEM. *, <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 PC3. ###, <span class="html-italic">p</span> &lt; 0.001 compared to DU145. †††, <span class="html-italic">p</span> &lt; 0.001 compared to LNCaP.</p>
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<p>Multivariate analysis by PCA (<b>left</b> panels) and cluster analysis by PCA-LDA (<b>right</b> panels) of intact (<b>a</b>) and lysed (<b>b</b>) exosomes from castration-resistant PCa cells with moderate (DU145) or high (PC3) metastatic capacity. In both intact and lysed exosomes, the percentage accuracy according to PCA-LDA was 100%. Fifty samples were analysed: 21 from DU145 and 29 from PC3. PCA-LDA: Principal Component Analysis coupled with Linear Discrimination Analysis.</p>
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<p>Multivariate analysis by PCA (<b>left</b> panels) and cluster analysis by PCA-LDA (<b>right</b> panels) of intact (<b>a</b>) and lysed (<b>b</b>) exosomes from RWPE (cells of human prostate epithelial), LNCaP (cells representative of castration sensible PCa) and PC3 (cells representative of metastatic castration-resistant PCa) cell lines. In intact and lysed exosomes, accuracy percentage according to PCA-LDA was 76% and 84%, respectively. A total of 72 samples were analysed: 29 from PC3, 26 from LNCaP and 17 from RWPE. PCA-LDA: Principal Component Analysis coupled with Linear Discrimination Analysis.</p>
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<p>Gamma-glutamyl transpeptidase (GGT) activity in exosomes isolated from culture medium of PC3, DU145, LNCaP and RWPE-1 cell lines. A highly significant almost 3-fold increase in activity of exosomes isolated from the LNCaP cell line versus PC3 is shown. Data represent mean ± SEM. **, <span class="html-italic">p</span> &lt; 0.01; ****, <span class="html-italic">p</span> &lt; 0.0001 compared to PC3. ###, <span class="html-italic">p</span> &lt; 0.001 compared to DU145. ††††, <span class="html-italic">p</span> &lt; 0.0001 compared to LNCaP.</p>
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<p>Gelatine zymography of matrix metalloproteinases 9 and 2 from prostate cell lines-derived exosomes. (<b>a</b>) Zymographs were performed in PC3 and DU145, LNCaP, or RWPE-1 cell lines. Very slight bands of MMP9 (84 kDa) and high levels of pro-MMP9 (92 kDa) and pro-MMP2 (74 kDa) were detected in all cell lines. Each zymogram is representative of six independent experiments. (<b>b</b>) Densitometric analysis of zymography gels from six separate experiments, showing the percentage of gelatinase activity of isolated exosomes from DU145, LNCaP, or RWPE-1 cell lines with respect to PC3 gelatinase activity. Data represent mean ± SEM. *, <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; ****, <span class="html-italic">p</span> &lt; 0.0001. (<b>c</b>) N-cadherin expression in exosomes isolated from culture medium of PC3, DU145 and RWPE-1 cell lines. Data represent mean ± SEM of N-cadherin expression percentage with respect to PC3. **, <span class="html-italic">p</span> &lt; 0.01. Representative experiments are shown.</p>
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<p>PC3 cells viability treated with exosomes isolated from culture medium of prostate cell lines. PC3 cells were treated for 8 h (<b>a</b>) or 24 h (<b>b</b>) with 0 µg, 5 µg, 10 µg, 15 µg, 20 µg, or 25 µg of RWPE1-, LNCaP, or PC3-derived exosomes. Data represent mean ± SEM of cell viability percentage with respect to 0 μg exosomes. *, <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 0 µg exosomes.</p>
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<p>Effect of PC3-derived exosomes on PC3 cell migration. PC3 cells were treated for 0 h, 8 h or 24 h with 0 µg, 5 µg, 10 µg, or 15 µg of PC3-derived exosomes. (<b>a</b>) Wound-healing assay was performed to detect the migration of cells. Representative images are shown from three independent experiments. (<b>b</b>) The percentage of wound closure at different times (0 h, 8 h and 24 h) compared to the initial wound (0 h) is shown. Data represent mean ± SEM. ****, <span class="html-italic">p</span> &lt; 0.0001 compared to 0 h. #, <span class="html-italic">p</span> &lt; 0.05 compared to 0 µg exosomes.</p>
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23 pages, 2695 KiB  
Review
Lipidic and Inorganic Nanoparticles for Targeted Glioblastoma Multiforme Therapy: Advances and Strategies
by Ewelina Musielak and Violetta Krajka-Kuźniak
Micro 2025, 5(1), 2; https://doi.org/10.3390/micro5010002 - 3 Jan 2025
Viewed by 355
Abstract
Due to their biocompatibility, nontoxicity, and surface conjugation properties, nanomaterials are effective nanocarriers capable of encapsulating chemotherapeutic drugs and facilitating targeted delivery across the blood–brain barrier (BBB). Although research on nanoparticles for brain cancer treatment is still in its early stages, these systems [...] Read more.
Due to their biocompatibility, nontoxicity, and surface conjugation properties, nanomaterials are effective nanocarriers capable of encapsulating chemotherapeutic drugs and facilitating targeted delivery across the blood–brain barrier (BBB). Although research on nanoparticles for brain cancer treatment is still in its early stages, these systems hold great potential to revolutionize drug delivery. Glioblastoma multiforme (GBM) is one of the most common and lethal brain tumors, and its heterogeneous and aggressive nature complicates current treatments, which primarily rely on surgery. One of the significant obstacles to effective treatment is the poor penetration of drugs across the BBB. Moreover, GBM is often referred to as a “cold” tumor, characterized by an immunosuppressive tumor microenvironment (TME) and minimal immune cell infiltration, which limits the effectiveness of immunotherapies. Therefore, developing novel, more effective treatments is critical to improving the survival rate of GBM patients. Current strategies for enhancing treatment outcomes focus on the controlled, targeted delivery of chemotherapeutic agents to GBM cells across the BBB using nanoparticles. These therapies must be designed to engage specialized transport systems, allowing for efficient BBB penetration, improved therapeutic efficacy, and reduced systemic toxicity and drug degradation. Lipid and inorganic nanoparticles can enhance brain delivery while minimizing side effects. These formulations may include epitopes—small antigen fragments that bind directly to free antibodies, B cell receptors, or T cell receptors—that interact with transport systems and enable BBB crossing, thereby boosting therapeutic efficacy. Lipid-based nanoparticles (LNPs), such as liposomes, niosomes, solid lipid nanoparticles (SLNs), and nanostructured lipid carriers (NLCs), are among the most promising delivery systems due to their unique properties, including their size, surface modification capabilities, and proven biosafety. Additionally, inorganic nanoparticles such as gold nanoparticles, mesoporous silica, superparamagnetic iron oxide nanoparticles, and dendrimers offer promising alternatives. Inorganic nanoparticles (INPs) can be easily engineered, and their surfaces can be modified with various elements or biological ligands to enhance BBB penetration, targeted delivery, and biocompatibility. Strategies such as surface engineering and functionalization have been employed to ensure biocompatibility and reduce cytotoxicity, making these nanoparticles safer for clinical applications. The use of INPs in GBM treatment has shown promise in improving the efficacy of traditional therapies like chemotherapy, radiotherapy, and gene therapy, as well as advancing newer treatment strategies, including immunotherapy, photothermal and photodynamic therapies, and magnetic hyperthermia. This article reviews the latest research on lipid and inorganic nanoparticles in treating GBM, focusing on active and passive targeting approaches. Full article
(This article belongs to the Section Microscale Biology and Medicines)
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<p>Diagram of the structure of the human brain [<a href="#B9-micro-05-00002" class="html-bibr">9</a>].</p>
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31 pages, 977 KiB  
Review
Advances in Therapy for Urothelial and Non-Urothelial Subtype Histologies of Advanced Bladder Cancer: From Etiology to Current Development
by Whi-An Kwon, Ho Kyung Seo, Geehyun Song, Min-Kyung Lee and Weon Seo Park
Biomedicines 2025, 13(1), 86; https://doi.org/10.3390/biomedicines13010086 - 1 Jan 2025
Viewed by 563
Abstract
Urothelial carcinoma (UC) is the most common histological subtype of bladder tumors; however, bladder cancer represents a heterogeneous group of diseases with at least 40 distinct histological subtypes. Among these, the 2022 World Health Organization classification of urinary tract tumors identifies a range [...] Read more.
Urothelial carcinoma (UC) is the most common histological subtype of bladder tumors; however, bladder cancer represents a heterogeneous group of diseases with at least 40 distinct histological subtypes. Among these, the 2022 World Health Organization classification of urinary tract tumors identifies a range of less common subtypes of invasive UC, formerly known as variants, which are considered high-grade tumors, including squamous cell, small-cell, sarcomatoid urothelial, micropapillary, plasmacytoid, and urachal carcinomas, and adenocarcinoma. Their accurate histological diagnosis is critical for risk stratification and therapeutic decision-making, as most subtype histologies are associated with poorer outcomes than conventional UC. Despite the importance of a precise diagnosis, high-quality evidence on optimal treatments for subtype histologies remains limited owing to their rarity. In particular, neoadjuvant and adjuvant chemotherapy have not been well characterized, and prospective data are scarce. For advanced-stage diseases, clinical trial participation is strongly recommended to address the lack of robust evidence. Advances in molecular pathology and the development of targeted therapies and immunotherapies have reshaped our understanding and classification of bladder cancer subtypes, spurring efforts to identify predictive biomarkers to guide personalized treatment strategies. Nevertheless, the management of rare bladder cancer subgroups remains challenging because they are frequently excluded from clinical trials. For localized disease, curative options such as surgical resection or radiotherapy are available; however, treatment options become more limited in recurrence or metastasis, where systemic therapy is primarily used to control disease progression and palliate symptoms. Herein, we present recent advances in the management of urothelial and non-urothelial bladder cancer subtypes and also explore the current evidence guiding their treatment and emphasize the challenges and perspectives of future therapeutic strategies. Full article
(This article belongs to the Section Cancer Biology and Oncology)
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<p>(<b>A</b>) Micropapillary subtype of UC (H&amp;E); multiple small nests of tumor cells with surrounding lacunar (empty) space are a classic feature and may be the most helpful feature in making the diagnosis. (<b>B</b>) Plasmacytoid subtype of UC (H&amp;E); discohesive single cells with eccentrically placed nuclei and abundant eosinophilic cytoplasm, which are often deeply infiltrative but with minimal stromal reaction. (<b>C</b>) Sarcomatoid urothelial carcinoma (H&amp;E); malignant spindled cells with a nonspecific morphologic appearance and a mesenchymal-like growth pattern. The most common component is an undifferentiated high-grade spindle cell sarcoma, as in this figure. (<b>D</b>) Bladder squamous cell carcinoma (H&amp;E); keratinization and intercellular bridges, features consistent with squamous differentiation. (<b>E</b>) Neuroendocrine bladder cancer (H&amp;E); small, round cells with a high nuclear-to-cytoplasmic ratio, hyperchromatic nuclei, and fine chromatin, typical of neuroendocrine differentiation. (<b>F</b>) Bladder adenocarcinoma (H&amp;E); prominent glandular formation of columnar cells and potential mucin production, raising the differential diagnosis of spread from the gastrointestinal tract or other primary sites. Abbreviations: UC, urothelial carcinoma; H&amp;E, hematoxylin and eosin.</p>
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<p>(<b>A</b>) Micropapillary subtype of UC (H&amp;E); multiple small nests of tumor cells with surrounding lacunar (empty) space are a classic feature and may be the most helpful feature in making the diagnosis. (<b>B</b>) Plasmacytoid subtype of UC (H&amp;E); discohesive single cells with eccentrically placed nuclei and abundant eosinophilic cytoplasm, which are often deeply infiltrative but with minimal stromal reaction. (<b>C</b>) Sarcomatoid urothelial carcinoma (H&amp;E); malignant spindled cells with a nonspecific morphologic appearance and a mesenchymal-like growth pattern. The most common component is an undifferentiated high-grade spindle cell sarcoma, as in this figure. (<b>D</b>) Bladder squamous cell carcinoma (H&amp;E); keratinization and intercellular bridges, features consistent with squamous differentiation. (<b>E</b>) Neuroendocrine bladder cancer (H&amp;E); small, round cells with a high nuclear-to-cytoplasmic ratio, hyperchromatic nuclei, and fine chromatin, typical of neuroendocrine differentiation. (<b>F</b>) Bladder adenocarcinoma (H&amp;E); prominent glandular formation of columnar cells and potential mucin production, raising the differential diagnosis of spread from the gastrointestinal tract or other primary sites. Abbreviations: UC, urothelial carcinoma; H&amp;E, hematoxylin and eosin.</p>
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19 pages, 1551 KiB  
Review
The Role of Exosomes in Cancer Progression and Therapy
by Shynggys Sergazy, Roza Seydahmetova, Alexandr Gulyayev, Zarina Shulgau and Mohamad Aljofan
Biology 2025, 14(1), 27; https://doi.org/10.3390/biology14010027 - 1 Jan 2025
Viewed by 267
Abstract
Exosomes are small extracellular vesicles and are crucial in intercellular communication. Interestingly, tumor-derived exosomes carry oncogenic molecules, such as proteins and microRNAs, which can reprogram recipient cells, promote angiogenesis, and stimulate cancer pre-metastatic niche, supporting cancer growth and metastasis. On the other hand, [...] Read more.
Exosomes are small extracellular vesicles and are crucial in intercellular communication. Interestingly, tumor-derived exosomes carry oncogenic molecules, such as proteins and microRNAs, which can reprogram recipient cells, promote angiogenesis, and stimulate cancer pre-metastatic niche, supporting cancer growth and metastasis. On the other hand, their biocompatibility, stability, and ability to cross biological barriers make them attractive candidates for drug delivery. Recent advances have shown the potential for exosomes to be used in early disease detection and in targeted drug therapy by delivering therapeutic agents specifically to tumor sites. Despite the promising applications, a number of challenges remain, including exosome isolation and characterization, as well as their inherent heterogeneity. Thus, the current review aims to describe the roles of exosomes in health and disease, and discuss the challenges that hinder their development into becoming useful medical tools. Full article
(This article belongs to the Collection Extracellular Vesicles: From Biomarkers to Therapeutic Tools)
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<p>Molecular cargo of tumor-derived exosomes.</p>
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<p>Exosomal roles in cancer growth and progression.</p>
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<p>Therapeutic potential of exosomes. Exosomes can be utilized in immune therapies by delivering tumor antigens to dendritic cells, thus initiating a T-cell-dependent antitumor response.</p>
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21 pages, 17082 KiB  
Article
Single-Cell and Bulk Transcriptomics Reveal the Immunosenescence Signature for Prognosis and Immunotherapy in Lung Cancer
by Yakun Zhang, Jiajun Zhou, Yitong Jin, Chenyu Liu, Hanxiao Zhou, Yue Sun, Han Jiang, Jing Gan, Caiyu Zhang, Qianyi Lu, Yetong Chang, Yunpeng Zhang, Xia Li and Shangwei Ning
Cancers 2025, 17(1), 85; https://doi.org/10.3390/cancers17010085 - 30 Dec 2024
Viewed by 390
Abstract
Background: Immunosenescence is the aging of the immune system, which is closely related to the development and prognosis of lung cancer. Targeting immunosenescence is considered a promising therapeutic approach. Methods: We defined an immunosenescence gene set (ISGS) and examined it across 33 TCGA [...] Read more.
Background: Immunosenescence is the aging of the immune system, which is closely related to the development and prognosis of lung cancer. Targeting immunosenescence is considered a promising therapeutic approach. Methods: We defined an immunosenescence gene set (ISGS) and examined it across 33 TCGA tumor types and 29 GTEx normal tissues. We explored the 46,993 single cells of two lung cancer datasets. The immunosenescence risk model (ISRM) was constructed in TCGA LUAD by network analysis, immune infiltration analysis, and lasso regression and validated by survival analysis, cox regression, and nomogram in four lung cancer cohorts. The predictive ability of ISRM for drug response and immunotherapy was detected by the oncopredict algorithm and XGBoost model. Results: We found that senescent lung tissues were significantly enriched in ISGS and revealed the heterogeneity of immunosenescence in pan-cancer. Single-cell and bulk transcriptomics characterized the distinct immune microenvironment between old and young lung cancer. The ISGS network revealed the crucial function modules and transcription factors. Multiplatform analysis revealed specific associations between immunosenescence and the tumor progression of lung cancer. The ISRM consisted of five risk genes (CD40LG, IL7, CX3CR1, TLR3, and TLR2), which improved the prognostic stratification of lung cancer across multiple datasets. The ISRM showed robustness in immunotherapy and anti-tumor therapy. We found that lung cancer patients with a high-risk score showed worse survival and lower expression of immune checkpoints, which were resistant to immunotherapy. Conclusions: Our study performed a comprehensive framework for assessing immunosenescence levels and provided insights into the role of immunosenescence in cancer prognosis and biomarker discovery. Full article
(This article belongs to the Section Cancer Informatics and Big Data)
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<p>The complete workflow of this study. Our study describes immunosenescence in human transcriptomes, including three sections: function analysis of the immunosenescence gene set in bulk and single-cell transcriptomes, candidate features for immunosenescence prognostic models in lung cancer, and development and validation of the immunosenescence risk model in lung cancer.</p>
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<p>ISGS functional properties associated with aging in TCGA and GTEX. (<b>A</b>) The landscape for differential expression and GSEA enrichment scores between age groups in TCGA (left) and GTEX (right). NES represents normalized enrichment score, red means NES &gt; 0, blue means NES &lt; 0; <span class="html-italic">p</span> value represents the significance of gene set enrichment; Up represented the number of upregulated genes in old groups; Common represents the number of genes shared by ISGS genes and upregulated genes in the old group. (<b>B</b>) Based on GSEA enrichment curves, the ISGS is significantly enriched during the aging process in lung tumors and lung tissues (<span class="html-italic">p</span> &lt; 0.05, NES &gt; 1). Red bar means old group; blue bar means young group. (<b>C</b>) Radar charts show the log2 (fold change) of ISGS genes in TCGA cancer types (top) and in GTEX normal tissues (bottom). (<b>D</b>) Bar plot represents the difference in ssgsea score of the ISGS between TCGA tumors and normal tissues.</p>
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<p>Four ISGS-related cell clusters were identified in single-cell data of lung cancer. (<b>A</b>) The tSNE plot of single-cell clustering analysis of GSE144945. (<b>B</b>) The number of overlapping genes of senescence-related gene sets. (<b>C</b>) To assess whether gene sets were enriched in cell subsets, we scored individual cells using four gene set enrichment methods and then calculated the differentially expressed gene sets for each cell subset. Finally, we used the robust rank aggregation (RRA) algorithm to screen out the gene sets that were significantly enriched in most gene set enrichment analysis methods. (<b>D</b>) The top 10 expression (left) and enriched (right) pathways of the markers of immunosenescence clusters. Red Hallmark represents the upregulated pathway, blue Hallmark represents the downregulated pathway. (<b>E</b>) The dot plot shows the average expression of cell molecules in the immunosenescence clusters. (<b>F</b>) The pseudotime trajectory of immunosenescence clusters annotated by states (top), clusters (middle), and pseudotime (bottom). (<b>G</b>) The expression of the top 1 marker of immunosenesence clusters during the pseudotime. (<b>H</b>) The different regulation of switch genes between two branches (state1–2 and state1–3).</p>
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<p>Immunosenescence-associated characteristics in tumor immune microenvironments. (<b>A</b>) Uniform manifold approximation and projection (UMAP) plot showing the main cell types in single-cell datasets of lung cancer patients (top). Proportions of cell clusters, with the numbers in parentheses indicating the number of cells (bottom). (<b>B</b>) The enrichment score (ES) for the ISGS within cell clusters, represented in the UMAP plot. (<b>C</b>) The distribution of ISGS enrichment scores of cell clusters. (<b>D</b>) The number of shared marker genes among the top 5 ISGS enriched cells. (<b>E</b>) KEGG pathways that were significantly enriched by the markers of the top 5 ISGS enriched cells. (<b>F</b>) Box plot of ES between the old and young groups. (<b>G</b>) The proportions of cell sub-populations between age groups. (<b>H</b>) The proportions of cell sub-populations among samples. (<b>I</b>) The inferred interaction number (top) and strength (bottom) between old and young groups. (<b>J</b>) Inferred cell–cell interactions among cell clusters in groups. (<b>K</b>) The crosstalk of the tumor-infiltrating lymphocyte cells (cytotoxic CD8 + T cells, CD4+ T cells, and B cells). The numbers represent the relative interaction strength as the sum of interaction weights. Edge weights are proportional to interaction strength; a thicker line refers to a stronger signal. (<b>L</b>) Dot plot for LRIs between B cells and other cells comparing old and young groups.</p>
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<p>ISGS activity was significantly correlated with the immune infiltration in lung adenocarcinoma. (<b>A</b>) Fraction of immune cell infiltration between old and young samples in the TCGA LUAD cohort. Green represents the old group; yellow represents the young group. (<b>B</b>) The overall activity of the ISGS is positively correlated with immune cells (<span class="html-italic">p</span> &lt; 0.05, R &gt; 6.0). The scatter represents the correlation coefficient. (<b>C</b>) The heat map shows that the expression of ISGS genes was significantly upregulated in old samples. Green represents the old group; yellow represents the young group. (<b>D</b>) Functional annotation of upregulated ISGS genes. GO terms show the biological process (BP). Red bar means log2 (fold change). (<b>E</b>) Correlation between upregulated ISGS genes and immune cell infiltration. (<b>F</b>) Scatter plots between ISGS genes and immune factors. (<b>G</b>) GSVA scores for the ISGS differed significantly between old and young groups (<span class="html-italic">p</span> &lt; 0.05, Wilcoxon rank sum test). (<b>H</b>) Scatter plots of GSVA scores in age groups. (<b>I</b>) Pearson correlation between the immune infiltration score and the GSVA score of the ISGS. GSVA, gene set variation analysis.</p>
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<p>Protein-protein interaction (PPI) network and TF-target network associated with the ISGS. (<b>A</b>) The PPI network consists of ISGS genes via string analysis. (<b>B</b>) Overall expression levels of ISGS genes between young and old groups (<span class="html-italic">t</span>-test, <span class="html-italic">p</span> &lt; 0.05). (<b>C</b>) The lollipop chart shows the degrees of nodes in the PPI network. (<b>D</b>) Hub genes significantly enriched in GO terms (BP, biological process; CC, cellular component; MF, molecular function). (<b>E</b>) Boxplots of the expressions of TFs between old and young groups. (<b>F</b>) The TF-target network consists of ISGS genes and TFs.</p>
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<p>Construction and validation of the immunosenescence risk model (ISRM) in lung cancer cohorts. (<b>A</b>) Key ISGS genes were selected as candidate features for the model. (<b>B</b>) KEGG pathways are enriched by feature genes, showing the top ten pathways. (<b>C</b>) LASSO regression analysis identified 5 risk genes for the ISRM. (<b>D</b>) IHC staining of risk genes (CD40LG, CX3CR1, IL7, TLR3) in LUAD. (<b>E</b>) Kaplan–Meier plots of overall survival grouped by the median of the risk scores. Blue represents the high-risk group; red represents the low-risk group. (<b>F</b>) Kaplan–Meier plots of overall survival grouped by the median of the GSVA scores. Yellow represents the high-score group; light blue represents the low-score group. (<b>G</b>) ROC curves for one-year survival rate in TCGA LUAD patients. Red means the IRSM prediction model, blue means the GSVA prediction model. (<b>H</b>) Univariate Cox regression analysis for the ISRM and clinical factors. (<b>I</b>) Multivariate Cox regression analysis for the ISRM and clinical factors. ***: <span class="html-italic">p</span> &lt; 0.001. (<b>J</b>) A constructed nomogram for prognostic prediction of a patient with LUAD. The importance of each variable was ranked according to the standard deviation along nomogram scales. ***: <span class="html-italic">p</span> &lt; 0.001. (<b>K</b>) Kaplan–Meier curves for overall survival grouped by the risk scores in GSE68465 and GSE72094 (<span class="html-italic">p</span> &lt; 0.5, log-rank test). (<b>L</b>) ROC curves for one-year survival rate in GSE68465, GSE72094, GSE26939, and GSE68571. TCGA, The Cancer Genome Atlas; ROC, receiver operating characteristic; LUAD, lung adenocarcinoma; AUC, the area under the ROC curve.</p>
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<p>Application of the ISRM in anti-tumor therapy and immunotherapy of lung cancer. (<b>A</b>) Density plots and boxplots of high-risk group-specific anti-tumor drugs predicted by the ISRM model. Blue represents the high-risk group; orange represents the low-risk group. (<b>B</b>) Anti-tumor drugs (left) with significant IC50 differences between risk groups, targets (middle), and pathways (right). (<b>C</b>) Boxplots of immune checkpoint molecules grouped by risk scores. Blue represents the high-risk group, orange represents the low-risk group. (<b>D</b>) Heatmap showing the expression of the risk genes and PDCD1 in the GSE93157 cohort of 35 patients. Blue represents the high-risk group; orange represents the low-risk group. (<b>E</b>) Violin plots of risk genes and PDCD1 grouped by the ISRM prediction model. Blue represents the high-risk group; orange represents the low-risk group. (<b>F</b>) Violin plots of risk genes and PDCD1 grouped by the response to anti-PD-1 immunotherapy. Blue means responder (NPD), and yellow means non-responder (PD). (<b>G</b>) XGBoost evaluated the predictive ability of ISRM on immunotherapy. IC50, the half maximal inhibitory concentration.</p>
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18 pages, 1567 KiB  
Review
Rising Concern About the Carcinogenetic Role of Micro-Nanoplastics
by Lorenzo Ruggieri, Ottavia Amato, Cristina Marrazzo, Manuela Nebuloni, Davide Dalu, Maria Silvia Cona, Anna Gambaro, Eliana Rulli and Nicla La Verde
Int. J. Mol. Sci. 2025, 26(1), 215; https://doi.org/10.3390/ijms26010215 - 30 Dec 2024
Viewed by 253
Abstract
In recent years, awareness regarding micro-nanoplastics’ (MNPs) potential effects on human health has progressively increased. Despite a large body of evidence regarding the origin and distribution of MNPs in the environment, their impact on human health remains to be determined. In this context, [...] Read more.
In recent years, awareness regarding micro-nanoplastics’ (MNPs) potential effects on human health has progressively increased. Despite a large body of evidence regarding the origin and distribution of MNPs in the environment, their impact on human health remains to be determined. In this context, there is a major need to address their potential carcinogenic risks, since MNPs could hypothetically mediate direct and indirect carcinogenic effects, the latter mediated by particle-linked chemical carcinogens. Currently, evidence in this field is scarce and heterogeneous, but the reported increased incidence of malignant tumors among younger populations, together with the ubiquitous environmental abundance of MNPs, are rising a global concern regarding the possible role of MNPs in the development and progression of cancer. In this review, we provide an overview of the currently available evidence in eco-toxicology, as well as methods for the identification and characterization of environmental MNP particulates and their health-associated risks, with a focus on cancer. In addition, we suggest possible routes for future research in order to unravel the carcinogenetic potential of MNP exposure and to understand prognostic and preventive implications of intratumoral MNPs. Full article
(This article belongs to the Section Molecular Nanoscience)
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<p>MNP environmental recirculation. Once emitted into the environment, MNPs have the capability to continuously recirculate through air suspension, soil penetration and sea emission, entering the water cycle. Sea natant MNPs can be ingested by marine organisms, persisting in the marine food chain. In addition, airborne MNPs can precipitate within rain, penetrating in soil and reaching underwater, rivers and lakes.</p>
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<p>Hallmarks of cancer related to MNPs. Current evidence regarding the role of MNPs in cancer initiation and promotion is linked mainly to the capability of MNPs to induce metabolic stress through the induction of ROS, fostering immune infiltration and chronic inflammation since their persistence in cancer cells and macrophages. The inability of intracellular lytic enzymes of mononuclear phagocytes to process MNPs induces a “frustrated” phenotype that can cause uncontrolled cell death, further sustaining inflammatory processes. Moreover, MNP-exposed cancer cells showe an augmented capacity of invasion and metastatization in preclinical models. Finally, genomic instability could be the result of intricated cytotoxic and genotoxic damage triggered by the presence of MNPs, which needs to be further elucidated. Faded sections indicate that poor or no evidence is available in demonstrating a potential effect in those specific hallmarks.</p>
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22 pages, 2957 KiB  
Review
Research Progress on Glioma Microenvironment and Invasiveness Utilizing Advanced Multi-Parametric Quantitative MRI
by Dandan Song, Guoguang Fan and Miao Chang
Cancers 2025, 17(1), 74; https://doi.org/10.3390/cancers17010074 - 29 Dec 2024
Viewed by 360
Abstract
Magnetic resonance imaging (MRI) currently serves as the primary diagnostic method for glioma detection and monitoring. The integration of neurosurgery, radiation therapy, pathology, and radiology in a multi-disciplinary approach has significantly advanced its diagnosis and treatment. However, the prognosis remains unfavorable due to [...] Read more.
Magnetic resonance imaging (MRI) currently serves as the primary diagnostic method for glioma detection and monitoring. The integration of neurosurgery, radiation therapy, pathology, and radiology in a multi-disciplinary approach has significantly advanced its diagnosis and treatment. However, the prognosis remains unfavorable due to treatment resistance, inconsistent response rates, and high recurrence rates after surgery. These factors are closely associated with the complex molecular characteristics of the tumors, the internal heterogeneity, and the relevant external microenvironment. The complete removal of gliomas presents challenges due to their infiltrative growth pattern along the white matter fibers and perivascular space. Therefore, it is crucial to comprehensively understand the molecular features of gliomas and analyze the internal tumor heterogeneity in order to accurately characterize and quantify the tumor invasion range. The multi-parameter quantitative MRI technique provides an opportunity to investigate the microenvironment and aggressiveness of glioma tumors at the cellular, blood perfusion, and cerebrovascular response levels. Therefore, this review examines the current applications of advanced multi-parameter quantitative MRI in glioma research and explores the prospects for future development. Full article
(This article belongs to the Section Tumor Microenvironment)
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<p>A framework diagram exhibiting the available applications of advanced multi-parametric quantitative MRI in exploring the glioma microenvironment and invasiveness. Abbreviations: MRI, magnetic resonance imaging. PWI, perfusion-weighted imaging. TDS, time-dependent diffusion-weighted imaging. fMRI, functional magnetic resonance imaging (reprinted with permission from Ref. [<a href="#B16-cancers-17-00074" class="html-bibr">16</a>]. 2018, Englander ZK et al.). CEST, chemical exchange saturation transfer (reprinted with permission from Ref. [<a href="#B17-cancers-17-00074" class="html-bibr">17</a>]. 2018, Paech D et al.). IVIM, intra-voxel incoherent motion (reprinted with permission from Ref. [<a href="#B18-cancers-17-00074" class="html-bibr">18</a>]. 2023, Guo, D et al.). MRF, magnetic resonance fingerprinting. TME, tumor microenvironment. PVS, perivascular space. WM, white matter. BBB, blood–brain barrier. rCBV, relative cerebral blood volume. rCBF, relative cerebral blood flow.</p>
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<p>Representative cases utilizing MRF imaging across gliomas with different grades and IDH mutation statuses. Abbreviations: MRF, magnetic resonance fingerprinting. IDH, isocitrate dehydrogenase. WHO, World Health Organization. MWF, myelin water fraction.</p>
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<p>Representative cases utilizing multiple diffusion model imaging across gliomas with different grades and IDH mutation statuses. Abbreviations: IDH, isocitrate dehydrogenase. WHO, World Health Organization. DTI, diffusion tensor imaging. FA, fractional anisotropy. DKI, diffuse kurtosis imaging. RK, radial kurtosis. NODDI, neurite orientation dispersion and density imaging. ICVF, intracellular volume fraction. ODI, orientation dispersion index. MAP, mean apparent propagator. NGRad, radial non-Gaussianity.</p>
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<p>Representative cases utilizing TDS across gliomas with different grades and IDH mutation statuses. Abbreviations: TDS, time-dependent diffusion-weighted imaging. IDH, isocitrate dehydrogenase. WHO, World Health Organization. <span class="html-italic">d</span>, diameter. Dex, extracellular diffusion coefficient.</p>
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30 pages, 1775 KiB  
Systematic Review
The Immunoexpression and Prognostic Significance of Stem Cell Markers in Malignant Salivary Gland Tumors: A Systematic Review and Meta-Analysis
by Eleni-Marina Kalogirou, Athina Tosiou, Stavros Vrachnos, Vasileios L. Zogopoulos, Ioannis Michalopoulos, Theodora Tzanavari and Konstantinos I. Tosios
Genes 2025, 16(1), 37; https://doi.org/10.3390/genes16010037 - 29 Dec 2024
Viewed by 378
Abstract
Background/Objectives: Salivary gland carcinomas encompass a broad group of malignant lesions characterized by varied prognoses. Stem cells have been associated with the potential for self-renewal and differentiation to various subpopulations, resulting in histopathological variability and diverse biological behavior, features that characterize salivary gland [...] Read more.
Background/Objectives: Salivary gland carcinomas encompass a broad group of malignant lesions characterized by varied prognoses. Stem cells have been associated with the potential for self-renewal and differentiation to various subpopulations, resulting in histopathological variability and diverse biological behavior, features that characterize salivary gland carcinomas. This study aims to provide a thorough systematic review of immunohistochemical studies regarding the expression and prognostic significance of stem cell markers between different malignant salivary gland tumors (MSGTs). Methods: The English literature was searched via the databases MEDLINE/PubMed, EMBASE via OVID, Web of Science, Scopus, and CINHAL via EBSCO. The Joanna Briggs Institute Critical Appraisal Tool was used for risk of bias (RoB) assessment. Meta-analysis was conducted for markers evaluated in the same pair of diseases in at least two studies. Results: Fifty-four studies reported the expression of stem cell markers, e.g., c-KIT, CD44, CD133, CD24, ALDH1, BMI1, SOX2, OCT4, and NANOG, in various MSGTs. Low, moderate, and high RoB was observed in twenty-five, eleven, and eighteen studies, respectively. Meta-analysis revealed an outstanding discriminative ability of c-KIT for adenoid cystic carcinoma (AdCC) over polymorphous adenocarcinoma [P(LG)A] but did not confirm the prognostic significance of stem cell markers in MSGTs. Conclusions: This study indicated a possible link between stem cells and the histopathological heterogeneity and diverse biological behavior that characterize the MSGTs. c-KIT might be of diagnostic value in discriminating between AdCC and P(LG)A. Full article
(This article belongs to the Section Human Genomics and Genetic Diseases)
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<p>The PRISMA [<a href="#B25-genes-16-00037" class="html-bibr">25</a>] flow diagram presenting the search strategy.</p>
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<p>RoB evaluation with the JBI Critical Appraisal Tool [<a href="#B30-genes-16-00037" class="html-bibr">30</a>].</p>
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<p>Forest plots of the pooled Sensitivity (<b>A</b>), Specificity (<b>B</b>), LR+ (<b>C</b>), LR- (<b>D</b>), DOR (<b>E</b>), and Summary Receiver Operating Characteristic (SROC) curve (<b>F</b>) of the eight studies involving the c-KIT for the AdCC and P(LG)A pair [<a href="#B60-genes-16-00037" class="html-bibr">60</a>,<a href="#B65-genes-16-00037" class="html-bibr">65</a>,<a href="#B72-genes-16-00037" class="html-bibr">72</a>,<a href="#B75-genes-16-00037" class="html-bibr">75</a>,<a href="#B76-genes-16-00037" class="html-bibr">76</a>,<a href="#B78-genes-16-00037" class="html-bibr">78</a>,<a href="#B79-genes-16-00037" class="html-bibr">79</a>,<a href="#B80-genes-16-00037" class="html-bibr">80</a>].</p>
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<p>Forest plots of the pooled DOR involving c-KIT for the AdCC vs. MEC (<b>A</b>), ACC (<b>B</b>), CXPA (<b>C</b>), NOS (<b>D</b>), SDC (<b>E</b>), MYOC (<b>F</b>), and SCC (<b>G</b>) pairs [<a href="#B35-genes-16-00037" class="html-bibr">35</a>,<a href="#B36-genes-16-00037" class="html-bibr">36</a>,<a href="#B60-genes-16-00037" class="html-bibr">60</a>,<a href="#B69-genes-16-00037" class="html-bibr">69</a>,<a href="#B72-genes-16-00037" class="html-bibr">72</a>,<a href="#B75-genes-16-00037" class="html-bibr">75</a>,<a href="#B80-genes-16-00037" class="html-bibr">80</a>].</p>
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<p>Forest plots of the pooled DOR involving CD133 for the AdCC vs. MEC (<b>A</b>), ACC vs. MEC (<b>B</b>), MEC vs. PA (<b>C</b>), and ACC vs. PA (<b>D</b>) pairs [<a href="#B58-genes-16-00037" class="html-bibr">58</a>].</p>
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16 pages, 1390 KiB  
Article
Efficacy and Safety of Three Cycles of TIP and Sequential High Dose Chemotherapy in Patients with Testicular Non-Seminomatous Germ Cell Tumors
by Musa Baris Aykan, Gulsema Yildiran Keskin, İsmail Erturk, Ramazan Acar, Ahmet Fatih Kose and Nuri Karadurmus
J. Clin. Med. 2025, 14(1), 131; https://doi.org/10.3390/jcm14010131 - 29 Dec 2024
Viewed by 381
Abstract
Background: Salvage treatment options have not been validated in relapsed or refractory germ cell tumors. Moreover, the study populations including these patients have different heterogeneities. This study aimed to evaluate the efficacy and safety of three cycles of TIP sequential high-dose chemotherapy [...] Read more.
Background: Salvage treatment options have not been validated in relapsed or refractory germ cell tumors. Moreover, the study populations including these patients have different heterogeneities. This study aimed to evaluate the efficacy and safety of three cycles of TIP sequential high-dose chemotherapy in patients with testicular non-seminomatous germ cell tumors who relapsed or had a refractory course after first-line platinum-based chemotherapy. Methods: Data of 141 patients who underwent three cycles of TIP followed by HDCT due to relapsed/refractory gonadal NSGCTs after first-line cisplatin-based chemotherapy (BEP/EP) at Gulhane School of Medicine Hospital Medical Oncology Department between January 2017 and May 2024 were evaluated retrospectively. Patients underwent a treatment regimen consisting of two phases. Initially, they received three cycles of induction therapy using a combination known as TIP, which includes paclitaxel, ifosfomide, and cisplatin. Following this, they were given a single cycle of high-dose chemotherapy. Demographic and clinicopathological features of patients and treatment-related complications and survival outcomes were recorded. Results: Median follow-up for all patients was 35.2 (95% CI, 29.45 to 41.07) months. Complete Response (CR) or marker negative Partial Response (PR) after HDCT was achieved in 84 (59.6%) patients. Median time for PFS not reached (NR) (95% CI, NR) in the entire group. The 2-year PFS rate was 51.8%. Median time for OS not reached (95% CI, NR) and the 2-year OS rate was 72.3%. The most common myelotoxicity observed after HDCT until engraftment was grade 4 neutropenia (100%) and grade 4 thrombocytopenia (96.5%). Transplantation-related mortality occurred in 7.1% of patients. Variables that remained statistically significant in multivariable analysis and were associated with poor prognosis for overall survival were platinum refractory disease and AFP and/or beta HCG elevation. Conclusions: Significant survival can be achieved after three cycles of TIP consecutive HDCT, while treatment-related mortality was found to be low. Full article
(This article belongs to the Section Oncology)
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<p>Progression-free survival graph.</p>
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<p>Overall survival graph.</p>
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<p>PFS by SII status.</p>
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<p>OS by SII status.</p>
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36 pages, 2799 KiB  
Review
Molecular Sentinels: Unveiling the Role of Sirtuins in Prostate Cancer Progression
by Surbhi Chouhan, Naoshad Muhammad, Darksha Usmani, Tabish H. Khan and Anil Kumar
Int. J. Mol. Sci. 2025, 26(1), 183; https://doi.org/10.3390/ijms26010183 - 28 Dec 2024
Viewed by 623
Abstract
Prostate cancer (PCa) remains a critical global health challenge, with high mortality rates and significant heterogeneity, particularly in advanced stages. While early-stage PCa is often manageable with conventional treatments, metastatic PCa is notoriously resistant, highlighting an urgent need for precise biomarkers and innovative [...] Read more.
Prostate cancer (PCa) remains a critical global health challenge, with high mortality rates and significant heterogeneity, particularly in advanced stages. While early-stage PCa is often manageable with conventional treatments, metastatic PCa is notoriously resistant, highlighting an urgent need for precise biomarkers and innovative therapeutic strategies. This review focuses on the dualistic roles of sirtuins, a family of NAD+-dependent histone deacetylases, dissecting their unique contributions to tumor suppression or progression in PCa depending on the cellular context. It reveals their multifaceted impact on hallmark cancer processes, including sustaining proliferative signaling, evading growth suppressors, activating invasion and metastasis, resisting cell death, inducing angiogenesis, and enabling replicative immortality. SIRT1, for example, fosters chemoresistance and castration-resistant prostate cancer through metabolic reprogramming, immune modulation, androgen receptor signaling, and enhanced DNA repair. SIRT3 and SIRT4 suppress oncogenic pathways by regulating cancer metabolism, while SIRT2 and SIRT6 influence tumor aggressiveness and androgen receptor sensitivity, with SIRT6 promoting metastatic potential. Notably, SIRT5 oscillates between oncogenic and tumor-suppressive roles by regulating key metabolic enzymes; whereas, SIRT7 drives PCa proliferation and metabolic stress adaptation through its chromatin and nucleolar regulatory functions. Furthermore, we provide a comprehensive summary of the roles of individual sirtuins, highlighting their potential as biomarkers in PCa and exploring their therapeutic implications. By examining each of these specific mechanisms through which sirtuins impact PCa, this review underscores the potential of sirtuin modulation to address gaps in managing advanced PCa. Understanding sirtuins’ regulatory effects could redefine therapeutic approaches, promoting precision strategies that enhance treatment efficacy and improve outcomes for patients with aggressive disease. Full article
(This article belongs to the Special Issue Gene Regulation in Endocrine Disease)
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<p>Structural features of sirtuins. All sirtuins share a conserved catalytic core, comprising a Rossmann fold domain, a Zn<sup>2</sup><sup>+</sup>-binding domain, and a catalytic histidine, which are critical for their enzymatic functions. Despite this shared core, human sirtuins feature unique N-terminal and C-terminal domains, which vary in length and sequence, contributing to their diverse roles.</p>
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<p>Categorization of sirtuins in prostate cancer based on their tumor-modulating roles. Sirtuins that promote prostate tumor progression include SIRT1, SIRT2, SIRT6, and SIRT7 (highlighted in red). Those with tumor-suppressive functions are SIRT3 and SIRT4 (highlighted in blue). SIRT5 exhibits a Janus-faced role, acting as both a tumor suppressor and promoter (highlighted in purple).</p>
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<p>Multifaceted role of SIRT1 in PCa. SIRT1 maintains cellular homeostasis, mitigating PIN development by facilitating autophagosome maturation. SIRT1 preserves mitochondrial integrity by reducing ROS levels through the regulation of SOD2 acetylation and promotes antioxidant defense via the ERG-PGC1α pathway. Additionally, SIRT1 is involved in NED by activating the Akt and AMPK-SIRT1 pathways, particularly in response to oxidative stress and inflammatory signaling from ADT. Epigenetically, SIRT1 modulates gene expression by controlling IGFBP2 through histone acetylation and contributes to chromatin remodeling via the PRC4 complex, promoting oncogenic transformation. In terms of metabolism, SIRT1 drives mitochondrial biogenesis and de novo lipogenesis, supporting lipid synthesis critical for cancer cell growth. It also modulates immune responses, facilitating immune evasion, while recruiting NK cells and macrophages in MSCs to counter tumor proliferation. Under hypoxia, SIRT1 enhances cellular adhesion and invasiveness through the leptin-HIF-1α pathway, crucial for tumor spread. Moreover, SIRT1 regulates apoptosis by deacetylating FOXO transcription factors, stabilizing MMP2 for EMT and supporting therapeutic resistance through KU70 interactions. Its role as an AR corepressor aids in resistance to ADT, particularly in castration-resistant cases. Lastly, SIRT1’s interaction with TLX and β-catenin impacts cell survival and metabolism, highlighting its complex regulatory impact in PCa progression and treatment resistance.</p>
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<p>Role of SIRT2 in prostate cancer progression. In PCa, SIRT2 exhibits a complex role, with its expression levels and function shifting from tumor-suppressive in early stages to potentially oncogenic in advanced stages, like CRPC and NEPC. Initially, SIRT2 helps maintain epigenetic stability by deacetylating histones, particularly countering the hyperacetylation of H3K18 seen in aggressive tumors. This reduction in SIRT2 activity in CRPC correlates with increased acetylation by p300, contributing to oncogenic gene expression and AR signaling resistance. SIRT2 also influences key signaling molecules, such as by deacetylating the LIFR to suppress oncogenic signaling through the PDPK1-AKT pathway. Furthermore, SIRT2 modulates transcription factors, like FOXO3, accelerating its degradation and, thus, reducing cell cycle arrest and apoptosis, particularly in CRPC and NEPC. Additionally, SIRT2 supports metabolic adaptations by promoting the production of lactosylceramide, which enhances cancer cell invasiveness.</p>
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<p>Diverse roles of SIRT6 in prostate cancer. SIRT6 is frequently overexpressed in PCa, correlating with aggressive disease traits, such as high Gleason scores and metastasis. Knockdown studies show that silencing SIRT6 reduces cell viability, induces apoptosis, and increases DNA damage, underscoring its role in tumor progression. SIRT6 promotes cancer cell proliferation, migration, and invasion, potentially by activating the Wnt/β-catenin pathway, a driver of EMT and metastasis. Furthermore, SIRT6’s modulation of necroptosis impacts immune cell recruitment within the tumor microenvironment, enhancing inflammatory responses upon SIRT6 inhibition. SIRT6 also contributes to lineage plasticity in neuroendocrine differentiation, particularly through ADORA2A-driven metabolic rewiring and regulates glycolytic activity via E2F1, which suppresses SIRT6 expression.</p>
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<p>SIRT7 as modulator of prostate cancer progression. Elevated expression of SIRT7 in tumor tissues correlates positively with cancer grade and is linked to increased migration and invasion in androgen-independent PCa cell lines, while its silencing reduces these aggressive traits. Notably, SIRT7 overexpression in less aggressive cell lines enhances resistance to the chemotherapeutic agent docetaxel, underscoring its role in promoting treatment resistance. SIRT7 is also involved in epigenetic reprogramming associated with EMT, contributing to metastatic potential and poor patient prognosis. The interconnection of SIRT7 with critical signaling pathways, particularly the p53 pathway, highlights its influence on various oncogenic processes. Additionally, SIRT7’s regulation of AR signaling suggests its potential as a prognostic marker and therapeutic target, especially in treatment-resistant PCa cases.</p>
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<p>Tumor-suppressive role of SIRT3 in prostate cancer. SIRT3 inhibits the acetylation of mitochondrial ACO2, enhancing its activity and promoting citrate synthesis, which favors aggressive cancer phenotypes. The suppression of SIRT3 by the AR and its co-regulator SRC-2 leads to increased ACO2 activity, while SIRT3 overexpression reduces metastasis, particularly to bone, highlighting the therapeutic potential of targeting this AR-SRC-2-SIRT3 axis. Furthermore, SIRT3 interacts with the steroidogenic enzyme HSD17B4, preventing its acetylation and subsequent degradation, thus supporting its oncogenic activity in PCa. SIRT3 also inhibits EMT by suppressing the Wnt/β-catenin signaling pathway, thereby promoting FOXO3A expression, which correlates with reduced cell migration and invasion. Additionally, SIRT3 inhibits the PI3K/Akt pathway, leading to c-MYC degradation, further establishing its tumor-suppressive role. Moreover, SIRT3’s regulation of necroptosis and its impact on the metabolic interplay between CAFs and PCa cells suggest that it influences cancer cell survival and growth.</p>
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<p>Role of SIRT4 in prostate cancer. SIRT4 expression is significantly reduced in PCa tissues compared to non-cancerous counterparts, with lower levels correlating with more aggressive tumor characteristics, such as higher Gleason scores. Functional assays reveal that SIRT4 inhibits the migration, invasion, and proliferation of PCa cells, while promoting apoptosis, primarily by disrupting glutamine metabolism, which is crucial for tumor growth. Mechanistically, SIRT4 hinders GDH1, limiting metabolic pathways vital for tumor cell proliferation. Additionally, SIRT4 influences cell cycle progression by impeding AKT phosphorylation, thereby enhancing the nuclear retention of the cell cycle inhibitor p21, which leads to cell cycle arrest. The interplay between SIRT4 and PAK6 further complicates its role; while SIRT4 deacetylates ANT2 to promote its degradation and regulate apoptosis, PAK6 destabilizes SIRT4, creating a regulatory feedback loop that favors tumor survival.</p>
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<p>Dual role of SIRT5 in prostate cancer. SIRT5 levels are markedly decreased in aggressive stages of PCa, correlating with reduced patient survival. Its desuccinylation activity is particularly critical, as it targets LDHA, where increased succinylation at lysine 118 enhances LDH activity, promoting the migration and invasion of cancer cells. Additionally, SIRT5 regulates the MAPK pathway by desuccinylating ACAT1, thereby inhibiting downstream targets, like matrix MMP9 and cyclin D1, both vital for metastatic potential. Furthermore, SIRT5 influences the PI3K/AKT/NF-ĸB signaling pathway, where its loss leads to increased pro-inflammatory cytokines and enhanced tumor cell survival, contributing to metastasis beyond the bone.</p>
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23 pages, 12776 KiB  
Review
Understanding the Immune System and Biospecimen-Based Response in Glioblastoma: A Practical Guide to Utilizing Signal Redundancy for Biomarker and Immune Signature Discovery
by Luke R. Jackson, Anna Erickson, Kevin Camphausen and Andra V. Krauze
Curr. Oncol. 2025, 32(1), 16; https://doi.org/10.3390/curroncol32010016 - 28 Dec 2024
Viewed by 319
Abstract
Glioblastoma (GBM) is a primary central nervous system malignancy with a median survival of 15–20 months. The presence of both intra- and intertumoral heterogeneity limits understanding of biological mechanisms leading to tumor resistance, including immune escape. An attractive field of research to examine [...] Read more.
Glioblastoma (GBM) is a primary central nervous system malignancy with a median survival of 15–20 months. The presence of both intra- and intertumoral heterogeneity limits understanding of biological mechanisms leading to tumor resistance, including immune escape. An attractive field of research to examine treatment resistance are immune signatures composed of cluster of differentiation (CD) markers and cytokines. CD markers are surface markers expressed on various cells throughout the body, often associated with immune cells. Cytokines are the effector molecules of the immune system. Together, CD markers and cytokines can serve as useful biomarkers to reflect immune status in patients with GBM. However, there are gaps in the understanding of the intricate interactions between GBM and the peripheral immune system and how these interactions change with standard and immune-modulating treatments. The key to understanding the true nature of these interactions is through multi-omic analysis of tumor progression and treatment response. This review aims to identify potential non-invasive blood-based biomarkers that can contribute to an immune signature through multi-omic approaches, leading to a better understanding of immune involvement in GBM. Full article
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<p>The brain as a site of active and passive immunity illustrating known linkages to cell types and their immune functions. Immune cells in the brain and their location (<b>top panel</b>), legend of immune cell types, and their respective markers (<b>lower panel</b>). While other immune cells, like mast and plasma cells, are known to play a role in CNS immune surveillance, there is limited data available demonstrating involvement in glioma, and their cell makers are redundant with the other cells listed here. Immune maker signatures were based on data established in previously published reviews [<a href="#B25-curroncol-32-00016" class="html-bibr">25</a>,<a href="#B26-curroncol-32-00016" class="html-bibr">26</a>,<a href="#B27-curroncol-32-00016" class="html-bibr">27</a>,<a href="#B28-curroncol-32-00016" class="html-bibr">28</a>,<a href="#B29-curroncol-32-00016" class="html-bibr">29</a>,<a href="#B30-curroncol-32-00016" class="html-bibr">30</a>,<a href="#B31-curroncol-32-00016" class="html-bibr">31</a>]. Illustrations in this figure were created with <a href="http://Biorender.com" target="_blank">Biorender.com</a> (accessed on 23 September 2024).</p>
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<p>Glioblastoma development leads to ambivalent alteration in immune function with ensuing evolution of immune marker profiles as consequences of tumor progression, biological aggressiveness, and subsequent management. These are reflected in immune signatures of varying degrees in tissue, CSF, and blood. Illustrations in this figure were created with Biorender.com and PowerPoint.</p>
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<p>(<b>A</b>) Immune cell markers, clusters of differentiation (CD), and (<b>B</b>) cytokine markers illustrating expression in healthy brain tissue as compared to blood based on highest expression in the brain and concentration in the blood, respectively. Average blood concentration values (left Y-axis) were plotted next to the brain tissue-specific maximum transcript levels (right Y-axis) for each marker with data obtained from the Human Protein Atlas (HPA) [<a href="#B82-curroncol-32-00016" class="html-bibr">82</a>] showing the differential expression of blood and tissues. The Human Protein Atlas data for both blood and brain tissue specimens are representative of patients without GBM to help identify which immune blood-based biomarkers may be able to reflect changes in brain tissue. The Human Protein Atlas. Available online: <a href="https://www.proteinatlas.org/" target="_blank">https://www.proteinatlas.org/</a> (accessed 23 September 2024).</p>
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<p>Overview of neuro-inflammation pathways. (<b>A</b>). Prominent signals associated with pro-inflammatory signaling (CCL2, IL6, TNF, IL10, and IL4) are illustrated downstream from NF-κβ in neuroinflammation pathways as identified in microglia. (<b>B</b>). Signals present in astrocytes in neuroinflammation leading to neurogenesis, Treg and T cell recruitment, microglial activation (adapted from Ingenuity Pathway Analysis (IPA)) [<a href="#B95-curroncol-32-00016" class="html-bibr">95</a>].</p>
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<p>Overview of molecular pathways and immune signatures involved in PMT. Tumor cells produce cytokines, growth factors, and other relevant biomarkers (yellow) that increase invasive properties and have immunomodulating capacity, encouraging PMT. Effects of tumor immunomodulation (purple) and their impacts on tumor phenotypic behavior (peach) are linked above as well. Additional biomarkers are shown in the figure as transmembrane receptors and channels (green) and metabolites (blue) with their overall connections to immunomodulation and development of PMT (adapted from Ingenuity Pathway Analysis (IPA)) [<a href="#B95-curroncol-32-00016" class="html-bibr">95</a>].</p>
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<p>PD-1 immunotherapy illustrating dendritic cell markers (yellow) and the interplay between M2 macrophages, T cells, and tumor cells highlighting significant signaling pathways in GBM (magenta) in tumor cells and activated CD4+ and CD8+ T lymphocytes. Figure adapted from Ingenuity Pathway Analysis (IPA) [<a href="#B95-curroncol-32-00016" class="html-bibr">95</a>].</p>
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<p>Approach to the immune signal redundancy problem in GBM. Signal redundancy is multifactorial and evolves in stages from the normal CNS to GBM development/progression/management (left to right), and with radiation and chemotherapy to encompass a balance of immune suppression, evasion, and response as well as immune system exhaustion (upper panel, left to right). The redundancy cannot be modified; thus, emphasis is placed on enhanced utilization of clinically available data (step 1), linkage of multi-channel data across all types of biospecimens (step 2), and comparison with the normal CNS. Computational approaches can then be employed to normalize data and select the most important clinically relevant features (step 3), followed by validation aimed at the most promising signals and the use of novel therapies (step 4).</p>
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18 pages, 967 KiB  
Review
Application of Spatial Transcriptomics in Digestive System Tumors
by Bowen Huang, Yingjia Chen and Shuqiang Yuan
Biomolecules 2025, 15(1), 21; https://doi.org/10.3390/biom15010021 - 27 Dec 2024
Viewed by 319
Abstract
In the field of digestive system tumor research, spatial transcriptomics technologies are used to delve into the spatial structure and the spatial heterogeneity of tumors and to analyze the tumor microenvironment (TME) and the inter-cellular interactions within it by revealing gene expression in [...] Read more.
In the field of digestive system tumor research, spatial transcriptomics technologies are used to delve into the spatial structure and the spatial heterogeneity of tumors and to analyze the tumor microenvironment (TME) and the inter-cellular interactions within it by revealing gene expression in tumors. These technologies are also instrumental in the diagnosis, prognosis, and treatment of digestive system tumors. This review provides a concise introduction to spatial transcriptomics and summarizes recent advances, application prospects, and technical challenges of these technologies in digestive system tumor research. This review also discusses the importance of combining spatial transcriptomics with single-cell RNA sequencing (scRNA-seq), artificial intelligence, and machine learning in digestive system cancer research. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Medicine)
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<p>Schematic diagram of three different spatial transcriptomics methods. (<b>A</b>) In situ hybridization methods sequence the transcripts within the tissue after rolling circle amplification (RCA), while in situ sequencing methods identify the transcripts by hybridizing with fluorescent probes. (<b>B</b>) In sequencing-based methods, transcripts within the tissue are captured by poly(dT) on spatially barcoded microarray slides, after which transcripts are reverse transcribed and sequenced by next-generation sequencing (NGS).</p>
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<p>Considerations for selecting a suitable spatial transcriptomics method. When selecting a suitable spatial transcriptomics method, the experiment objective, the capture efficiency and spatial resolution, the sample tissue area, the quality of mRNAs, and the sensitivity and detection efficiency should be taken into consideration.</p>
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48 pages, 13373 KiB  
Review
Non-Coding RNAs in Breast Cancer: Diagnostic and Therapeutic Implications
by Roman Beňačka, Daniela Szabóová, Zuzana Guľašová and Zdenka Hertelyová
Int. J. Mol. Sci. 2025, 26(1), 127; https://doi.org/10.3390/ijms26010127 - 26 Dec 2024
Viewed by 293
Abstract
Breast cancer (BC) is one of the most prevalent forms of cancer globally, and has recently become the leading cause of cancer-related mortality in women. BC is a heterogeneous disease comprising various histopathological and molecular subtypes with differing levels of malignancy, and each [...] Read more.
Breast cancer (BC) is one of the most prevalent forms of cancer globally, and has recently become the leading cause of cancer-related mortality in women. BC is a heterogeneous disease comprising various histopathological and molecular subtypes with differing levels of malignancy, and each patient has an individual prognosis. Etiology and pathogenesis are complex and involve a considerable number of genetic alterations and dozens of alterations in non-coding RNA expression. Non-coding RNAs are part of an abundant family of single-stranded RNA molecules acting as key regulators in DNA replication, mRNA processing and translation, cell differentiation, growth, and overall genomic stability. In the context of breast cancer, non-coding RNAs are involved in cell cycle control and tumor cell migration and invasion, as well as treatment resistance. Alterations in non-coding RNA expression may contribute to the development and progression of breast cancer, making them promising biomarkers and targets for novel therapeutic approaches. Currently, the use of non-coding RNAs has not yet been applied to routine practice; however, their potential has been very well studied. The present review is a literature overview of current knowledge and its objective is to delineate the function of diverse classes of non-coding RNAs in breast cancer, with a particular emphasis on their potential utility as diagnostic and prognostic markers or as therapeutic targets and tools. Full article
(This article belongs to the Special Issue The Role of RNAs in Cancers: Recent Advances)
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Figure 1

Figure 1
<p>Main histological forms of breast cancer and their molecular classification. On <span class="html-italic">the left</span> is a schematic representation of various cell masses in the breast, including benign tumor and malignant tumor subtypes (red). Molecular subtypes of breast cancer with essential markers, histological grade, therapeutic outline, and prognosis are shown on <span class="html-italic">the right</span>.</p>
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<p>Biogenesis of long non-coding RNAs (LncRNAs) and their role in breast cancer. (<b>A</b>) (1) Intergenic RNAs (LincRNAs) are transcripts of dsDNA between two protein-coding genes. (2) Antisense LncRNAs (asLncRNAs) are transcribed from complementary strands, either within the intronic or exonic region of protein-coding genes. (3) Intronic LncRNAs are transcripts of dsDNA from the intronic region of a protein-coding gene. Enhancer LncRNAs (eLnc RNAs) are transcribed from the dsDNA of enhancer regions of genes. (4) Bidirectional LncRNAs (biLncRNAs) originate from the bidirectional transcription of protein-coding genes. LncRNAs mediate the positioning of transcription factors in the promoters of protein-coding genes. (<b>B</b>) LncRNAs associated with different cellular processes that are important in cancerogenesis. (<b>C</b>) LncRNAs associated with each molecular subtype of breast cancer. Data are based on several sources [<a href="#B47-ijms-26-00127" class="html-bibr">47</a>,<a href="#B50-ijms-26-00127" class="html-bibr">50</a>,<a href="#B51-ijms-26-00127" class="html-bibr">51</a>,<a href="#B52-ijms-26-00127" class="html-bibr">52</a>,<a href="#B61-ijms-26-00127" class="html-bibr">61</a>,<a href="#B62-ijms-26-00127" class="html-bibr">62</a>].</p>
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<p>Biosynthesis and use of microRNA in breast cancer. (<b>A</b>) The primary transcript of microRNA (pri-miRNA) is processed in the nucleus into pre-miRNA by the RNAse III and self-assembles into double-stranded RNA with a small loop (hairpin shape). miRNA is exported from the nucleus into the cytosol (exportin 5), where a hairpin loop is cut off by Dicer endoribonuclease (RNA helicase), while the rest of the miRNA assembles into a complex called RNA-induced silencing complex (RISC) together with the Argonaute family of nucleoproteins (Ago). RISC binds to complementary motifs in the mRNA to cause post-transcriptional mRNA silencing by blocking the translation of the mRNA or degrading the mRNA into fragments. (<b>B</b>) Examples of upregulated (red) or downregulated (blue) miRNAs in ER+ and ER- breast cancer. (<b>C</b>) Regulatory role of miRNA in gene expression can be achieved by i) binding to mRNA and preventing translation or ii) binding to sponges made by lncRNA and/or circRNA. The synthesis of miRNAs can be epigenetically regulated by hyper- or hypomethylation. (<b>D</b>) Upregulated (red) and downregulated (blue) miRNAs in breast cancer, further subdivided according to their function in different cellular processes. Schematic visualization of acquired data on different miRNAs is based on the sources mentioned in the text and <a href="#ijms-26-00127-t001" class="html-table">Table 1</a>.</p>
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<p>Biogenesis and dysregulation of circRNAs in breast cancer. (<b>A</b>) CircRNAs can originate from intronic (i-circRNA), exonic (e-circRNA), or both intronic and exonic (ei-circRNA) transcripts of protein-coding genes. These transcripts can undergo either direct back-splitting (e.g., in e-circRNA formation) or the debranching of resistant intron variants (e.g., i-circRNA formation), intron-pairing-driven circularization, or exon skipping (e.g., ei-circRNA formation). CircRNA can act as an miRNA sponge by binding and suspending RNA-BPs (RNA binding proteins), or by interfering with mRNA (messenger RNA) translation. (<b>B</b>) CircRNAs associated with pro-oncogenic activity (<span class="html-italic">red</span>) and tumor suppressor activity (<span class="html-italic">blue</span>) and their role in breast cancer pathogenesis. The same circRNA can fall into several categories. (<b>C</b>) Distribution of circRNAs dysregulated in different BC subtypes (luminal A/BHR (+), hormone-positive, HER2+, and TNBC types). Image of breast tumor adapted from Servier Medical Art under license CC-BY-3.0.</p>
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<p>Biosynthesis of piRNAs and their alterations in breast cancer. (<b>A</b>) Formation of piRNA (PIWI-interacting RNA) occurs within and outside of the nucleus. Following transcription from genomic loci that contain transposon fragments, cluster transcripts are spliced into piRNA precursors (pre-piRNAs). The DNA loci responsible for producing piRNA precursors yield either single- or double-stranded molecules (sense and antisense transcripts). In the subsequent phase of piRNA biosynthesis (only the antisense precursor is illustrated in the figure), pre-piRNAs are transported to the perinuclear space (nuage) in close proximity to the mitochondria, where they are processed by the RNA helicase Armitage (Armi). Following despiralisation, the 5′ end of the precursor molecule is cleaved by the endonuclease Zucchini (Zuc), with the resulting 5′ fragment incorporated into PIWI proteins. The 3′ to 5′ exonuclease Nibbler (Nbr) then trims the piRNA to its final length. Concurrently, the small RNA 2′-O-methyltransferase Hen1 methylates the 2′-hydroxy group at the 3′ end. This process represents the primary biogenesis of piRNA (in the figure, this process is shown in red). The secondary biogenesis of piRNAs is called the ping-pong cycle and allows for amplification (arrows with dashed line; the DNA sequence with highlighted yellow background). The protein Aubergine (Aub) binds to antisense piRNAs and the complex cleaves sense piRNA precursors to give rise to sense piRNAs, which then form a complex with Ago3 (Argonaute3). The Ago3/piRNA complexes, in turn, cleave antisense piRNA precursors into pieces that form a complex with Aub. This cycle produces a large number of piRNAs in a short period of time. The piRNA-PIWI complexes return to the nucleus. The piRNA-PIWI complexes carry out their transposon-active activity with the help of DMTs (DNA methyl transferases) and HDACs (histone deacetylases). (<b>B</b>) Upregulated (<span class="html-italic">red</span>) and downregulated (<span class="html-italic">blue</span>) piRNAs in BC. Breast cancer image was adapted from Servier Medical Art under CC-BY-3.0 license. Data adapted from [<a href="#B222-ijms-26-00127" class="html-bibr">222</a>,<a href="#B228-ijms-26-00127" class="html-bibr">228</a>,<a href="#B230-ijms-26-00127" class="html-bibr">230</a>].</p>
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<p>Synthesis and use of siRNAs in breast cancer. (<b>A</b>) The precursors of siRNAs are long pieces of ds RNA or hairpin ss-RNA called pri-siRNAs. In cytosol, the molecule is cleaved by the Dicer enzyme into double-stranded siRNA (ds-siRNA) and, later, single-stranded siRNA (ss-siRNA), which form a RISC complex (RNA-induced silencing complex) together with RNPs (ribonucleoproteins) and Ago protein (Argonaute family). Inside the complex, siRNA binds to complementary motifs of the target mRNA to process the fragmentation. (<b>B</b>) Nanoparticles (NPs) (types in blue box) protect siRNAs from degradation, improve targeting and increasing the accumulation of s-siRNA in the tumor cells. Receptor-mediated endocytosis is initiated by the targeting of ligands or cationic components of NP. Following an endosomal escape, s-siRNA binds to the RISC, which enables the identification and degradation of complementary mRNA targets. (<b>C</b>) Experimental use of s-siRNA to target and inhibit mRNAs of the selected genes. For an explanation, see the text. Data adapted from Ngamcherdtrakul and Yantasee [<a href="#B247-ijms-26-00127" class="html-bibr">247</a>] and other resources [<a href="#B246-ijms-26-00127" class="html-bibr">246</a>,<a href="#B247-ijms-26-00127" class="html-bibr">247</a>,<a href="#B248-ijms-26-00127" class="html-bibr">248</a>,<a href="#B249-ijms-26-00127" class="html-bibr">249</a>,<a href="#B250-ijms-26-00127" class="html-bibr">250</a>,<a href="#B251-ijms-26-00127" class="html-bibr">251</a>,<a href="#B252-ijms-26-00127" class="html-bibr">252</a>,<a href="#B253-ijms-26-00127" class="html-bibr">253</a>,<a href="#B254-ijms-26-00127" class="html-bibr">254</a>,<a href="#B255-ijms-26-00127" class="html-bibr">255</a>,<a href="#B256-ijms-26-00127" class="html-bibr">256</a>,<a href="#B257-ijms-26-00127" class="html-bibr">257</a>,<a href="#B258-ijms-26-00127" class="html-bibr">258</a>,<a href="#B259-ijms-26-00127" class="html-bibr">259</a>,<a href="#B260-ijms-26-00127" class="html-bibr">260</a>,<a href="#B261-ijms-26-00127" class="html-bibr">261</a>,<a href="#B262-ijms-26-00127" class="html-bibr">262</a>]. <span class="html-italic">Abb.</span> CCR2, C Motif Chemokine Receptor 2; CXCR4, CXC Chemokine Receptor 4; DOPC, 1,2-dioleoyl-sn-glycero-3-phosphocholine; DANCR, Differentiation Antagonizing Non-Protein Coding RNA; DODAP-1,2-dioleoyl-3-dimethylammonium-propane; infMo, inflammatory monocytes; Lcn-2, Lipocalin-2; MIF, macrophage migration inhibitory factor; MSNP, mesoporous silica nanoparticle; mRNA, messenger RN; MTDH, metadherin; NP, nanoparticle; PAGA, Polyaminolated glycidyl methacrylate; PDPA, poly(2-(diisopropyl amino) ethyl methacrylate; PEG, Polyethyleneglycol; PEI, polyethyleneimine; Pgp, P-glycoprotein; PIGF, placental growth factor; PLK, Polo-like kinase; PLGA, poly (lactic-co-glycolic acid); PTPN, protein tyrosine phosphatase non-receptor; TAM, tumor-associated macrophage; T-Ly, T-cell; TME, tumor microenvironment; VEGF, vascular endothelial growth factor. Picture of breast cancer used from Servier Medical Art under CC-BY-3.0 license.</p>
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<p>Small nuclear (snRNA) and small nucleolar (sncRNA) molecules in breast cancer (BC). snRNA is a class of small nuclear RNA molecules which complex with small nuclear ribonucleoproteins (snRNP) to form various types of spliceosomes. These are involved in the post-transcriptional slicing of introns from the pre-messenger RNA (<span class="html-italic">pre-mRNA</span>) to form a mature mRNA (<span class="html-italic">left panel</span>). Certain components of snRNP and snRNA, e.g., U1, U2, U4, U5, and U6 (illustrated in color), were found to be overexpressed (<span class="html-italic">red</span>) in BC. The <span class="html-italic">right panel</span> shows sncRNAs that are upregulated (<span class="html-italic">red</span>) or downregulated (<span class="html-italic">blue</span>) in different types of breast cancer. <span class="html-italic">Abb. PRPF4</span>, core component of U4/U6 snRNP; <span class="html-italic">PRPF8</span>, core component of U4/U6-U5 tri-snRNP spliceosome complex; <span class="html-italic">SNRPC</span>, small nuclear ribonucleoprotein polypeptide C; <span class="html-italic">SNRNP200</span>, U5 small nuclear ribonucleoprotein. See the text for further explanation.</p>
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20 pages, 5310 KiB  
Article
Breed-Associated Differences in Differential Gene Expression Following Immunotherapy-Based Treatment of Canine High-Grade Glioma
by Susan A. Arnold, Walter C. Low and Grace Elizabeth Pluhar
Animals 2025, 15(1), 28; https://doi.org/10.3390/ani15010028 - 26 Dec 2024
Viewed by 318
Abstract
Canine high-grade glioma (HGG) is among the deadliest and most treatment-resistant forms of canine cancer. Successful, widespread treatment is challenged by heterogeneity in tumor cells and the tumor microenvironment and tumor evolution following treatment. Immunotherapy is theoretically a strong novel therapy, since HGG-generated [...] Read more.
Canine high-grade glioma (HGG) is among the deadliest and most treatment-resistant forms of canine cancer. Successful, widespread treatment is challenged by heterogeneity in tumor cells and the tumor microenvironment and tumor evolution following treatment. Immunotherapy is theoretically a strong novel therapy, since HGG-generated immunosuppression is a substantial malignancy mechanism. Immunotherapy has improved survival times overall, but has been associated with extremely poor outcomes in French bulldogs. Given this breed-specific observation, we hypothesized that within the French bulldog breed, there are key transcriptomic differences when compared to other breeds, and that their tumors change differently in response to immunotherapy. Using bulk RNA sequencing, French bulldog tumors were confirmed to differ substantially from boxer and Boston terrier tumors, with only 15.9% overlap in significant differentially expressed genes (DEGs). In upregulated DEGs, the magnitude of changes in expression post-treatment compared to pre-treatment was markedly greater in French bulldogs. Gene set enrichment analysis confirmed that following treatment, French bulldog tumors showed enrichment of key immune-associated pathways previously correlated with poor prognosis. Overall, this study confirmed that French bulldog HGG transcriptomes differ from boxer and Boston terrier transcriptomes, further refining description of the canine glioma transcriptome and providing important information to guide novel therapy development, both for specific dog breeds and for possible correlative variants of human glioblastoma. Full article
(This article belongs to the Special Issue Cancer Immunotherapy Research in Veterinary Medicine)
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Graphical abstract

Graphical abstract
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<p><b>Top left:</b> Heatmap showing significant DEGs with absolute log2-fold changes &gt; 1 in French bulldogs; <b>Top right:</b> heatmap showing significant DEGs with absolute log2-fold changes &gt; 1 in boxers and Boston terriers; <b>Bottom left:</b> heatmap showing expression patterns of the significant DEGs of French bulldogs in boxers and Boston terriers; <b>Bottom right:</b> heatmap showing expression patterns of the significant DEGs of boxers and Boston terriers in French bulldogs.</p>
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<p>Principal component analysis showing merged RNASeq analyses of French bulldogs and boxers/Boston terriers. Samples clustered by treatment timepoint and breed.</p>
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<p>Visualization of DEGs in French bulldogs compared to Boxers and Boston Terriers. (<b>Left</b>): Venn Diagram showing DEGs exclusive to either breed group or shared between groups; (<b>Right</b>): scatterplot of the log2-fold changes in French bulldog DEGs versus boxer/Boston terrier DEGs.</p>
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<p>The top 25 upregulated and downregulated DEGs by breed group. For each analysis, the displayed DEGs were significant with an absolute log2-fold change &gt; 1 in the breed group of interest. The expression of the alternate breed group for each DEG is provided for comparison. <b>Top left</b>: Top significantly upregulated DEGs in French bulldogs; <b>Top right</b>: top significantly downregulated DEGs in French bulldogs; <b>Bottom left</b>: top significantly upregulated DEGs in boxers and Boston terriers; <b>Bottom right</b>: top significantly downregulated DEGs in boxers and Boston terriers.</p>
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<p>Violin plots for the top 25 upregulated (<b>Top</b>) and top 25 downregulated (<b>Bottom</b>) DEGs in French bulldogs.</p>
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<p>Violin plots for the top 25 upregulated (<b>Top</b>) and top 25 downregulated (<b>Bottom</b>) DEGs in boxers and Boston terriers.</p>
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<p>Barplot with error bars of the top 25 significantly upregulated DEGs in French bulldogs that were not significant in boxers and Boston terriers.</p>
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<p>The top 50 DEGs with the largest difference in expression following treatment between French bulldogs and boxers/Boston terriers represented as a bar plot (<b>Top</b>) and violin plots (<b>Bottom</b>). Note that for some DEGs, the direction of log2-fold change post-treatment compared to pre-treatment was opposite in French bulldogs versus boxers and Boston terriers.</p>
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<p>Gene set enrichment analyses. (<b>Left</b>): In French bulldogs; (<b>Right</b>): In boxers and Boston terriers.</p>
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21 pages, 2950 KiB  
Article
In Vitro Effect of Estrogen and Progesterone on Cytogenetic Profile of Uterine Leiomyomas
by Alla S. Koltsova, Anna A. Pendina, Olga V. Malysheva, Ekaterina D. Trusova, Dmitrii A. Staroverov, Maria I. Yarmolinskaya, Nikolai I. Polenov, Andrey S. Glotov, Igor Yu. Kogan and Olga A. Efimova
Int. J. Mol. Sci. 2025, 26(1), 96; https://doi.org/10.3390/ijms26010096 - 26 Dec 2024
Viewed by 211
Abstract
In the present study, we aimed to investigate intratumoral karyotype diversity as well as the estrogen/progesterone effect on the cytogenetic profile of uterine leiomyomas (ULs). A total of 15 UL samples obtained from 15 patients were cultured in the media supplemented with estrogen [...] Read more.
In the present study, we aimed to investigate intratumoral karyotype diversity as well as the estrogen/progesterone effect on the cytogenetic profile of uterine leiomyomas (ULs). A total of 15 UL samples obtained from 15 patients were cultured in the media supplemented with estrogen and/or progesterone and without adding hormones. Conventional cytogenetic analysis of culture samples revealed clonal chromosomal abnormalities in 11 out of 15 ULs. Cytogenetic findings were presented by simple and complex chromosomal rearrangements (64% and 36% of cases, respectively) verified through FISH and aCGH. In most ULs with complex chromosomal rearrangements, the breakpoints did not feature clusterization on a single chromosome but were evenly distributed across rearranged chromosomes. The number of breakpoints showed a strong positive correlation with the number of rearranged chromosomes. Moreover, both abovementioned parameters were in a linear dependency from the number of karyotypically different clones per UL. This suggests that complex chromosomal rearrangements in ULs predominantly originate through sequential events rather than one hit. The results of UL cytogenetic analysis depended on the presence of estrogen and/or progesterone in the culture medium. The greatest variety of cytogenetically different cell clones was detected in the samples cultured without hormone supplementation. Their counterparts cultured with progesterone supplementation showed a sharp decrease in clone number, whereas such a decrease induced by estrogen or estrogen–progesterone supplementation was insignificant. These findings suggest that estrogen–progesterone balance is crucial for forming a UL cytogenetic profile, which, in turn, may underlie the unique response of the every karyotypically abnormal UL to medications. Full article
(This article belongs to the Special Issue Molecular Research in Gynecological Diseases—2nd Edition)
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Figure 1
<p>Cytogenetic profile of UL15 revealed through conventional karyotyping. Karyograms (<b>a</b>–<b>e</b>) demonstrate karyotypes detected in UL cells: 46,XX (<b>a</b>); 46,XX,inv(7)(p24p13),t(3;<span class="underline">3</span>;7)(q13;<span class="underline">q26</span>;q32),t(10;13)(p12;q22) (<b>b</b>); 46,XX,del(16)(q12.1q23.3) (<b>c</b>); 46,XX,del(16)(q12.1q23.3),del(3)(q21) (<b>d</b>); 46,XX,del(3)(q27) (<b>e</b>). A schematic explanation of the possible origin of detected cytogenetic clones (<b>f</b>). Most probably, three karyotypically abnormal clones (C1, C2, C3) originated independently from 46,XX UL cells (C0) and one clone (C2.1) originated as a subclone of C2 by acquiring additional chromosomal abnormality.</p>
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