Search Results (4,354)

Triple-negative breast cancer (TNBC) is a highly aggressive and therapeutically challenging subtype of breast cancer due to its lack of estrogen receptors, progesterone receptors, and HER2 (Human epidermal growth factor receptor 2) expression, which severely limits available treatment options. Recently, Simvastatin—a widely used HMG-CoA (3-hydroxy-3-methylglutaryl-coenzyme A) reductase inhibitor for hyperlipidemia—has garnered interest for its potential anticancer effects. This study investigates the therapeutic potential of Simvastatin in triple-negative breast cancer (TNBC). The results demonstrate that Simvastatin significantly inhibits the proliferation of TNBC cells, particularly MDA-MB-231, in a dose- and time-dependent manner. Mechanistically, Simvastatin primarily induces G1 phase cell cycle arrest to exert its antiproliferative effects, with no significant evidence of apoptosis or necrosis. These findings support the potential repositioning of Simvastatin as a therapeutic agent to suppress TNBC cell growth. Further analysis shows that Simvastatin downregulates cyclin-dependent kinase 4 (CDK4), a key regulator of the G1/S cell cycle transition and a known marker of poor prognosis in breast cancer. These findings highlight a novel, apoptosis-independent mechanism of Simvastatin’s anticancer action in TNBC. Importantly, given that many breast cancer patients also suffer from hyperlipidemia, Simvastatin offers dual therapeutic benefits—managing both lipid metabolism and tumor cell proliferation. Thus, Simvastatin holds promise as an adjunctive therapy in the treatment of TNBC and warrants further clinical investigation. Full article

Background/Objectives: Leukemia is associated with high recurrence rates and cancer vaccines are emerging as a promising immunotherapy against the disease. Here, we investigate the mechanism of action by which a personalized vaccine made from leukemia cells infected with an oncolytic virus (ICV) induces anti-tumor immunity. Methods: Using the L1210 murine model, leukemia cells were infected and irradiated to create the ICV. The CRISPR-Cas9 system was used to engineer knockout cells to test in treatment efficacy studies. Results: We found that pro-inflammatory interferons (IFNs) that are produced by infected vaccine cells induce the immunoproteasome (ImP), a specialized proteasome subtype that is found in immune cells. Interestingly, we show that while a vaccine using the oncolytic vesicular stomatitis virus (oVSV) completely protects against tumor challenge, the wild-type (wt) virus, which does not induce the ImP, is not as effective. To delineate the contribution of the ImP for vaccine efficacy, we generated ImP-knockout cell lines and found no differences in treatment efficacy compared to wild-type cells. Furthermore, an ICV using another murine leukemia model that expresses the ImP only when infected by an IFN gamma-encoding variant of the virus demonstrated similar efficacy as the parental virus. Conclusions: Taken together, our data show that ImP expression by vaccine cells was not required for the efficacy of leukemia ICVs. Full article

Background/Objectives: The two major subtypes of inflammatory bowel disease (IBD)—Crohn’s disease (CD) and ulcerative colitis (UC)—are known to increase the likelihood of developing colorectal cancer (CRC). While this relationship has been well studied in Western populations, evidence from East Asia remains limited and inconsistent. Using nationwide cohort data, this study explored the potential connection between IBD and CRC in a large Korean population. Methods: We conducted a retrospective cohort study using data from the Korean National Health Insurance Service–National Sample Cohort from 2005 to 2019. A total of 9920 CRC patients were matched 1:4 with 39,680 controls using propensity scores based on age, sex, income, and region. Overlap weighting and multivariable logistic regression were used to evaluate the association between IBD and CRC. Subgroup analyses were conducted to assess effect modification by demographic and clinical factors. Results: IBD markedly increased the likelihood of developing CRC (adjusted odds ratio (aOR) = 1.38; 95% confidence interval (CI): 1.20–1.58; p < 0.001), with the association primarily driven by UC (aOR = 1.52; 95% CI: 1.27–1.83). CD appeared unrelated to heightened CRC risk overall, though a significant association was observed among low-income CD patients (aOR = 1.58; 95% CI: 1.15–2.16). The UC–CRC association persisted across all subgroups, including patients without comorbidities. Conclusions: Our findings support an independent association between IBD—particularly UC—and increased CRC risk in Korea. These results underscore the need for personalized CRC surveillance strategies that account for disease subtype, comorbidity burden, and socioeconomic status, especially in vulnerable subpopulations. Full article

Breast cancer (BC) progression appears to be significantly influenced by the diabetic microenvironment, characterised by hyperglycaemia and hyperinsulinemia, though the exact cellular mechanisms remain partly unclear. This study investigated the effects of exposure to supra-physiological levels of glucose and insulin on two distinct BC cell models: hormone-responsive MCF-7 cells and triple-negative MDA-MB-231 cells. To evaluate the effects triggered by high insulin level in different BC cell subtypes, we analysed the activation status of PI3K/AKT and MAPK pathways, cell proliferation, cell distribution in cell cycle phases and cell migration. High insulin level significantly activates the insulin metabolic pathway via AKT phosphorylation in both cell lines while inducing pro-proliferative stimulus and modulation of cell distribution in cell cycle phases only in the hormone-responsive MCF-7 cell line. On the contrary, high-glucose containing medium alone did not modulate proliferation nor further increased it when combined with high insulin level in both the investigated cell lines. However, following insulin treatment, the MAPK pathway remained unaffected, suggesting that the proliferation effects in the MCF-7 cell line are mediated by AKT activation. This linkage was also demonstrated by AKT phosphorylation blockade, driven by the AKT inhibitor MK-2206, which negated the proliferative stimulus. Interestingly, while MDA-MB-231 cells, following chronic hyperinsulinemia exposure, did not exhibit enhanced proliferation, they displayed a marked increase in migratory behaviour. These findings suggest that chronic hyperinsulinemia, but not hyperglycaemia, exerts subtype-specific effects in BC, highlighting the potential of targeting insulin pathways for therapeutic intervention. Full article

Tongue cancer, the most aggressive subtype of oral cancer, presents critical challenges due to the limited number of specialists available and the time-consuming nature of conventional histopathological diagnosis. To address these issues, we developed an intelligent diagnostic system integrating Mueller matrix microscopy with deep learning to enhance diagnostic accuracy and efficiency. Through Mueller matrix polar decomposition and transformation, micro-polarization feature parameter images were extracted from tongue cancer tissues, and purity parameter images were generated by calculating the purity of the Mueller matrices. A multi-stage feature dataset of Mueller matrix parameter images was constructed using histopathological samples of tongue cancer tissues with varying stages. Based on this dataset, the clinical potential of Mueller matrix microscopy was preliminarily validated for histopathological diagnosis of tongue cancer. Four mainstream medical image classification networks—AlexNet, ResNet50, DenseNet121 and VGGNet16—were employed to quantitatively evaluate the classification performance for tongue cancer stages. DenseNet121 achieved the highest classification accuracy of 98.48%, demonstrating its potential as a robust framework for rapid and accurate intelligent diagnosis of tongue cancer. Full article

Human colorectal cancer (CRC) encompasses tumors affecting a segment of the large intestine (colon) and rectum. It is the third most commonly diagnosed malignancy and the second leading cause of cancer deaths worldwide. It is a multifactorial disease, whose carcinogenesis process involves genetic and epigenetic alterations in oncogenes and tumor suppressor genes, including genes related to DNA repair. The pathogenic mechanisms are described based on the pathways of chromosomal instability, microsatellite instability, and CpG island methylator phenotype. When detected early, CRC is potentially curable, and its treatment is based on the pathological characteristics of the tumor and factors related to the patient, as well as on drug efficacy and toxicity studies. Therefore, the aim of this study was to review the pathogenesis and molecular subtypes of CRC and to describe the main targets of disease-directed therapy used in patients refractory to current treatments. Full article

Background: Pleural mesothelioma (PM) is a type of cancer that is difficult to diagnose and treat. Patients may have vastly varying prognoses, and prognostic factors may help guide the clinical approach. As a recently identified biomarker, the pan-Immune-Inflammation-Value (PIV) is a simple, comprehensive, and peripheral blood cell-based biomarker. Methods: The present study represents a retrospective observational analysis carried out within a single-center setting. Ninety-five patients with PM stages I–IV were enrolled in the study. We analyzed the correlation between patients’ demographic characteristics, clinicopathological factors such as histological subtypes, surgery status, tumor thickness, blood-based parameters, and treatment options with their prognoses. PIV was calculated by the following formula: (neutrophil count × monocyte count × platelet count)/lymphocyte count. Additionally, blood-based parameters were used to calculate the platelet-to-lymphocyte ratio (PLR), neutrophil-to-lymphocyte ratio (NLR), and systemic immune inflammation index (SII). Results: We categorized the patients into two groups, low PIV group (PIV ≤ 732.3) and high PIV group (PIV > 732.3) according to the determined cut-off value, which was defined as the median. It was revealed that high PIV was associated with poor survival outcomes. The median follow-up period was 15.8 months (interquartile range, IQR, 7.1 to 29.8 months). The median overall survival (OS) was significantly longer in patients in the low PIV group (median 29.8 months, 95% confidence interval (CI), 15.6 to 44) than the high PIV group (median 14.7 months, 95% CI, 10.8 to 18.6 p < 0.001). Furthermore, the study revealed that patients with low PIV, NLR, and SII values were more likely to be eligible for surgery and were diagnosed at earlier stages. Additionally, these markers were identified as potential predictors of disease-free survival (DFS) in the surgical cohort and of treatment response across the entire patient population. Conclusions: In addition to well-established clinical factors such as stage, histologic subtype, resectability, and Eastern Cooperative Oncology Group (ECOG) performance status (PS), PIV emerged as an independent and significant prognostic factor of overall survival (OS) in patients with PM. Moreover, PIV also demonstrated a remarkable independent prognostic value for disease-free survival (DFS) in this patient population. Additionally, some clues are provided for conditions such as treatment responses, staging, and suitability for surgery. As such, in this cohort, it has outperformed the other blood-based markers based on our findings. Given its ease of calculation and cost-effectiveness, PIV represents a promising and practical prognostic tool in the clinical management of pleural mesothelioma. It can be easily calculated using routinely available laboratory parameters for every cancer patient, requiring no additional cost or complex procedures, thus facilitating its integration into everyday clinical practice. Full article

Feline mammary carcinoma (FMC) exhibits aggressive behavior, with limited treatment options. Given the relevance of the PD-1/PD-L1/PD-L2 axis in human breast cancer immunotherapy, this study assessed PD-1 and its ligands in rare FMC histotypes (n = 48) using immunohistochemistry on tumor cells (TCs), intratumoral lymphocytes (iTILs), and stromal tumor-infiltrating lymphocytes (sTILs). PD-1 was expressed in 13% of TCs, 85% of iTILs, and 94% of sTILs, while PD-L1 was observed in 46% of TCs, 96% of iTILs, and 100% of sTILs. PD-L2 was expressed in 79% of TCs and 100% of both iTILs and sTILs, with PD-L1/PD-L2 co-expression in 42% of TCs. Higher PD-1 IHC scores in TCs were associated with a less aggressive biological behavior; PD-L1 in iTILs was linked to skin ulceration, whereas PD-L2 in TCs was associated with its absence. Our findings highlight the relevance of the PD-1/PD-L1/PD-L2 immune checkpoint in rare FMC subtypes and support further investigation into checkpoint-blockade therapies. Full article

Pediatric high-grade glioma (pHGG) is a devastating group of childhood cancers associated with poor outcomes. Traditionally, diagnosis was based on histologic and immunohistochemical characteristics, including high mitotic activity, presence of necrosis, and presence of glial cell markers (e.g., GFAP). With advances in molecular tumor profiling, these tumors have been recategorized based on specific molecular findings that better lend themselves to prediction of treatment response and prognosis. pHGG is now categorized into four subtypes: H3K27-altered, H3G34-mutant, H3/IDH-WT, and infant-type high-grade glioma (iHGG). Molecular profiling has not only increased the specificity of diagnosis but also improved prognostication. Additionally, these molecular findings provide novel targets for individual tumor-directed therapy. While these therapies are largely still under investigation, continued investigation of distinct molecular markers in these tumors is imperative to extending event-free survival (EFS) and overall survival (OS) for patients with pHGG. Full article

Organoid and spheroid technologies have rapidly become pivotal in thyroid cancer research, offering models that are more physiologically relevant than traditional two-dimensional culture. In the study of papillary and anaplastic thyroid carcinomas, two subtypes that differ both histologically and clinically, three-dimensional (3D) models offer unparalleled insights into tumor biology, therapeutic vulnerabilities, and resistance mechanisms. These models maintain essential tumor characteristics such as cellular diversity, spatial structure, and interactions with the microenvironment, making them extremely valuable for disease modeling and drug testing. This review emphasizes recent progress in the development and use of thyroid cancer organoids and spheroids, focusing on their role in replicating disease features, evaluating targeted therapies, and investigating epithelial–mesenchymal transition (EMT), cancer stem cell behavior, and treatment resistance. Patient-derived organoids have shown potential in capturing individualized drug responses, supporting precision oncology strategies for both differentiated and aggressive subtypes. Additionally, new platforms, such as thyroid organoid-on-a-chip systems, provide dynamic, high-fidelity models for functional studies and assessments of endocrine disruption. Despite ongoing challenges, such as standardization, limited inclusion of immune and stromal components, and culture reproducibility, advancements in microfluidics, biomaterials, and machine learning have enhanced the clinical and translational potential of these systems. Organoids and spheroids are expected to become essential in the future of thyroid cancer research, particularly in bridging the gap between laboratory discoveries and patient-focused therapies. Full article

Triple-negative breast cancer (TNBC) is a clinically and molecularly heterogeneous subtype defined by the absence of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) expression. In this study, tumor specimens from 104 TNBC patients were analyzed to characterize molecular and clinicopathological features and to assess the expression and therapeutic potential of four key surface markers: epidermal growth factor receptor (EGFR), epithelial cell adhesion molecule (EpCAM), tissue factor (TF), and trophoblast cell surface antigen (TROP2). Multiplex immunofluorescence (mIF) demonstrated elevated EGFR and TROP2 expression in the majority of samples. Significant positive correlations were observed between EGFR and TF, as well as between TROP2 and both TF and EpCAM. Expression analyses revealed increased EGFR and TF levels with advancing tumor stage, whereas EpCAM expression declined in advanced-stage tumors. TROP2 and TF expression were significantly elevated in higher-grade tumors. Additionally, EGFR and EpCAM levels were significantly higher in patients with elevated Ki-67 indices. Binding specificity assays using single-chain variable fragment (scFv-SNAP) fusion proteins confirmed robust targeting efficacy, particularly for EGFR and TROP2. These findings underscore the therapeutic relevance of EGFR and TROP2 as potential biomarkers and targets in TNBC. Full article

High-grade serous ovarian cancer (HGSOC) is the most common and aggressive subtype of ovarian cancer, accounting for approximately 70% of cases. This study investigates genetic mutations and their associations with overall survival (OS), complete cytoreduction (R0), and platinum response in patients undergoing either primary debulking surgery followed by adjuvant chemotherapy (PDS) or neoadjuvant chemotherapy followed by interval debulking surgery (NACT). Genetic analysis was performed on 43 primary HGSOC tumor samples using targeted massive parallel sequencing via next-generation sequencing (NGS). Clinical and molecular data were evaluated collectively and through subgroup comparisons between PDS and NACT cohorts. All analyzed samples harbored genetic alterations. Univariate survival analysis revealed that the total number of mutations (p = 0.0035), as well as mutations in HRAS (p = 0.044), FLT3 (p = 0.023), TP53 (p = 0.03), and ERBB4 (p = 0.007), were significantly associated with poorer OS. Multivariate Cox regression integrating clinical and molecular data confirmed that ERBB4 mutations are independently associated with adverse outcomes. These findings reveal a distinctive mutational landscape between the PDS and NACT groups and suggest that ERBB4 alterations may define a particularly aggressive tumor phenotype. This study contributes to a deeper understanding of HGSOC biology and may support the development of novel therapeutic targets and personalized treatment strategies in the context of precision oncology. Full article

Mitomycin C (MMC) is a widely employed chemotherapeutic agent, particularly in non-muscle invasive bladder cancer (NMIBC), where it functions by inducing DNA cross-linking and promoting tumor cell apoptosis. However, the tumor microenvironment (TME) significantly influences the therapeutic efficacy of MMC. Among the key regulators within the TME, the complement system and the coagulation pathway play a crucial role in modulating immune responses to cancer therapies, including MMC. This article explores the interaction between platinum nanoparticles (PtNPs) with human serum (HS) of NMIBC patients (T1 and Ta subtypes) at three different points: before the chemotherapy instillation of MMC (t₀) and three (t₃) and six months (t₆) after the treatment with MMC. This novel nanoproteomic strategy allowed the identification of a TME proteomic signature associated with the response to MMC treatment. Importantly, two proteins involved in the immune response were found to be deregulated across all patients (T1 and Ta subtypes) during MMC treatment: prothrombin (F2) downregulated and complement component C7 (C7) upregulated. By understanding how these biomarker proteins interact with MMC treatment, novel therapeutic strategies can be developed to enhance treatment outcomes and overcome resistance in NMIBC. Full article

Cancer metabolic reprogramming plays a critical role in tumor progression and therapeutic resistance, underscoring the need for advanced analytical strategies. Metabolomics, leveraging mass spectrometry and nuclear magnetic resonance (NMR) spectroscopy, offers a comprehensive and functional readout of tumor biochemistry. By enabling both targeted metabolite quantification and untargeted profiling, metabolomics captures the dynamic metabolic alterations associated with cancer. The integration of metabolomics with machine learning (ML) approaches further enhances the interpretation of these complex, high-dimensional datasets, providing powerful insights into cancer biology from biomarker discovery to therapeutic targeting. This review systematically examines the transformative role of ML in cancer metabolomics. We discuss how various ML methodologies—including supervised algorithms (e.g., Support Vector Machine, Random Forest), unsupervised techniques (e.g., Principal Component Analysis, t-SNE), and deep learning frameworks—are advancing cancer research. Specifically, we highlight three major applications of ML–metabolomics integration: (1) cancer subtyping, exemplified by the use of Similarity Network Fusion (SNF) and LASSO regression to classify triple-negative breast cancer into subtypes with distinct survival outcomes; (2) biomarker discovery, where Random Forest and Partial Least Squares Discriminant Analysis (PLS-DA) models have achieved >90% accuracy in detecting breast and colorectal cancers through biofluid metabolomics; and (3) prognostic modeling, demonstrated by the identification of race-specific metabolic signatures in breast cancer and the prediction of clinical outcomes in lung and ovarian cancers. Beyond these areas, we explore applications across prostate, thyroid, and pancreatic cancers, where ML-driven metabolomics is contributing to earlier detection, improved risk stratification, and personalized treatment planning. We also address critical challenges, including issues of data quality (e.g., batch effects, missing values), model interpretability, and barriers to clinical translation. Emerging solutions, such as explainable artificial intelligence (XAI) approaches and standardized multi-omics integration pipelines, are discussed as pathways to overcome these hurdles. By synthesizing recent advances, this review illustrates how ML-enhanced metabolomics bridges the gap between fundamental cancer metabolism research and clinical application, offering new avenues for precision oncology through improved diagnosis, prognosis, and tailored therapeutic strategies. Full article

Hepatocellular carcinoma (HCC) is the most prevalent subtype of liver cancer with an increasing incidence worldwide. Single nucleotide polymorphisms (SNPs) may influence disease risk and serve as predictive markers. This study aimed to evaluate the association of PNPLA3 (rs738409 and rs2294918), GCKR (rs780094), MBOAT7 (rs641738), NCAN (rs2228603), and TM6SF2 (rs58542926) SNPs with the risk of developing HCC in a Mexican population. A case-control study was conducted in unrelated Mexican individuals. Cases were 173 adults with biopsy-confirmed HCC and 346 were healthy controls. Genotyping was performed using TaqMan allelic discrimination assay. Logistic regression was applied to evaluate associations under codominant, dominant, and recessive inheritance models. p-values were corrected using the Bonferroni test (pC). Haplotype and gene–gene interaction were also analyzed. The GG homozygous of rs738409 and rs2294918 of PNPLA3, TT, and TC genotypes of GCKR, as well as the TT genotype of MBOAT7, were associated with a significant increased risk to HCC under different inheritance models (~Two folds in all cases). The genotypes of NCAN and TM6SF2 did not show differences. The haplotype G-G of rs738409 and rs2294918 of PNPLA3 was associated with an increased risk of HCC [OR (95% CI) = 2.2 (1.7–2.9)]. There was a significant gene–gene interaction between PNPLA3 (rs738409), GCKR (rs780094), and MBOAT7 (rs641738) (Cross-validation consistency (CVC): 10/10; Testing accuracy = 0.6084). This study demonstrates for the first time that PNPLA3 (rs738409 and rs2294918), GCKR (rs780094), and MBOAT7 (rs641738) are associated with an increased risk of developing HCC from multiple etiologies in Mexican patients. Full article