Discover. Oncology最新文献

The glutathione (GSH) redox system plays a central role in maintaining cellular homeostasis, but its dysregulation in cancer contributes to tumor progression, therapy resistance, and metabolic adaptation. Elevated intracellular GSH levels represent both a barrier to conventional therapies and an opportunity to design redox-responsive drug delivery systems. In recent years, GSH has emerged as a promising therapeutic trigger and biomarker, driving the development of nanotechnology-enabled platforms for controlled drug release, imaging, and theranostics. This review provides a critical and translational analysis of GSH-responsive nanomedicine, highlighting chemical strategies such as disulfide/diselenide linkages, transition metal systems, and GSH-activated prodrugs. Unlike prior reviews, which often present descriptive overviews, this article emphasizes comparative evaluation of design principles, biological mechanisms, and translational hurdles, including biosafety, tumor heterogeneity, and large-scale manufacturability. We further outline future perspectives such as hybrid multifunctional nanoplatforms, patient-specific redox profiling, and clinical pathways for regulatory approval. By integrating insights from redox biology and nanotechnology, this review offers a timely and original perspective on the opportunities and challenges of exploiting tumor redox imbalance for precision drug delivery and cancer therapy.

Background: Emerging evidence highlights the significant role of Neutrophil Extracellular Traps (NETs) in hepatocellular carcinoma (HCC), but the underlying molecular mechanisms involving NETs formation remain incompletely understood. This study aims to identify key biomarkers related to NETs in HCC through bioinformatic analysis.

Methods: We obtained RNA sequencing data of hepatocellular carcinoma tissues and adjacent normal liver tissues from the Gene Expression Omnibus (GEO) databases, followed by data correction, integration, and annotation. Subsequently, differentially expressed genes (DEGs) were identified, and Weighted Gene Co-expression Network Analysis (WGCNA) was used to screen for disease-related genes. The intersection with NETs-related genes (NRGs) yielded differentially expressed NET-related genes (DENRGs), which were subjected to single-sample Gene Set Enrichment Analysis (ssGSEA). Three machine learning models (LASSO, SVM-RFE, and RF) were further employed to screen for key biomarkers. Receiver Operating Characteristic (ROC) curves and a nomogram model were used to validate the diagnostic and predictive efficacy of those key biomarkers, with further validation using an external dataset. Unsupervised clustering and Gene Set Variation Analysis (GSVA) were performed on the key biomarkers.

Results: We conducted a comprehensive bioinformatic analysis on 223 HCC samples and 127 normal liver tissue samples from 5 datasets. Transcriptomic analysis identified 826 DEGs. WGCNA revealed key gene modules associated with HCC, including 362 genes. By intersecting these with 627 NRGs, we identified 18 DENRGs. The results of ssGSEA showed that most of immune cells were significantly downregulated in HCC. Machine learning models (LASSO, SVM-RFE, and RF) identified three downregulated biomarkers (ECM1, DNASE1L3, JUN). A nomogram and ROC curves confirmed the diagnostic accuracy of these biomarkers. Cluster analysis revealed two distinct HCC subtypes with different immune microenvironment characteristics. Drug-gene interaction analysis identified potential inhibitors targeting DNASE1L3 and JUN.

Conclusion: This study identified NET-related key biomarkers (ECM1, DNASE1L3, JUN) as reliable diagnostic tools for HCC, highlighting their diagnostic and therapeutic potential, and providing insights for HCC diagnostic tools and immunotherapy strategies.

Introduction: Hürthle cell carcinoma (HCC) -recently known as oncocytic carcinoma- is a rare type of differentiated thyroid cancer that presents a diagnostic and therapeutic challenge because of its morphological heterogeneity and uncertain biological behavior.

Methods: This retrospective single-center cohort study included all the patients with HCC who underwent surgical treatment in our center from January 2009 to May 2024. The epidemiological, clinical, and oncological data of the included patients were analyzed.

Results: This study included nineteen cases of HCC (9 males and 10 females). The average age at diagnosis was 54.8 ± 12.2 years. Preoperative fine needle aspiration cytology (FNAC) classified 2 tumors as Bethesda I, 7 as Bethesda III, 6 as Bethesda IV, and 4 as Bethesda V. A variety of surgical procedures were used, including hemithyroidectomy in 3 patients and total thyroidectomy in 12 patients. Two patients underwent neck dissection. The median tumor size was 6.7 cm. Pathological evaluation identified 9 patients with unifocal lesions and 10 with multifocal lesions. Only one patient showed positive lymph node involvement. The median times to death, distant metastasis, and locoregional recurrence were 4, 13, and 6 years, respectively. For locoregional recurrence, the restricted mean survival time (RMST) at five years was 4.4 years (95% CI 3.9-4.9), 4.6 years (95% CI 4.1-5.0) for distant metastasis, and 4.1 years (95% CI 3.6-4.5) for overall survival. There was a trend towards worse prognosis in females, younger age, and those with primary surgery outside the center. These differences did not achieve statistical significance, at least partly due to the small sample size.

Conclusion: Diagnosing HCC remains challenging due to its overlapping features with other thyroid conditions, making fine-needle aspiration cytology less definitive. Surgical treatment remains the preferred therapeutic option. Age, gender, and the volume of the surgical center for the initial procedure can influence patient outcomes, particularly recurrence and survival rates.

Background: Resilience is the dynamic ability to adapt to adversity using personal and social resources. Childhood cancer represents a major family stressor, and grandparents often provide emotional, practical, and financial support. Yet, their psychosocial outcomes and resilience remain poorly understood. We aimed to: (1) identify resilience trajectories (2), examine their association with post-traumatic stress symptoms, and (3) determine factors influencing resilience.

Methods: This multicenter cohort study included grandparents of children recently diagnosed with cancer and treated at one of eight participating pediatric oncology centers in Switzerland. Eligible grandparents were recruited and completed questionnaires at 3-, 6-, 12-, and 24- months post-diagnosis. Resilience (CD-RISC 10), post-traumatic stress symptoms (IES-R), information needs, health literacy (HLS-EU-Q12), partnership quality, and social support (MSPSS) were measured. We used group-based trajectory modeling to identify resilience trajectories (Aim 1), linear mixed models to examine associations of resilience trajectories with post-traumatic stress symptoms (Aim 2), and linear mixed-effects models to identify the internal and external resources for resilience (Aim 3).

Results: We included data of 41 grandparents of 20 children with cancer. Mean age was 67.6 years; most were grandmothers (n = 25, 61%), unemployed or retired (n = 23, 59%), and partnered (n = 35, 90%). Two resilience trajectories emerged within two years after diagnosis: low-stable (n = 17, 43%) and high-declining (n = 23, 57%). Grandparents in the low-stable group reported significantly higher post-traumatic stress symptoms (β: -19.8, 90% CI -29.2, -10.4, p < 0.001). The following internal resources were positively associated with resilience: higher health literacy (β: 0.31, 90% CI 0.20, 0.42, p < 0.001), more information received (β: 1.53, 90% CI 1.27, 1.79, p < 0.001), and having income that meets needs (β: 7.56, 90% CI 1.86, 13.26, p = 0.029). No external resources showed significant associations.

Conclusion: Timely, clear, and tailored information may help strengthen grandparents' resilience and reduce stress.

Objective: This study aims to explore shared key genes between head and neck neoplasm (HNN) and aging.

Methods: Using single-cell RNA sequencing data of peripheral blood from HNSCC patients, aging individuals, and healthy controls, we identified cross-group co-expressed, downregulated cell subpopulations as core targets. Integrated pseudotime trajectory analysis and intercellular communication modeling were employed to investigate the dynamic evolution and functional interaction patterns of these subpopulations. Differentially expressed genes were identified, followed by Mendelian randomization (MR) analysis to assess their causal associations with HNN. Co-localization analysis was performed using GWAS data for HNN and expression quantitative trait loci (eQTL) datasets. Key genes were further subjected to metabolic pathway enrichment analysis.

Results: T cell subsets were found to be represented in both HNN and aging. Among them, CD4_naive T cells were down-regulated in both groups, leading to the identification of 24 differentially expressed genes. MR studies have shown that CCR, LEF1, NOSIP and FHIT have causal relationships with HNN. In the validation phase, however, only FHIT was retained, for which co-localization analysis revealed limited evidence of a shared causal variant between the GWAS and eQTL signals (H4 = 0.01). The metabolic enrichment highlighted metabolic pathways associated with these genes.

Conclusions: This study identified CD4_naive T cells down-regulation as a shared feature of HNN and aging and highlighted FHIT as potential molecular links. These findings may provide novel insights into the intersection of aging and tumorigenesis based on MR and single-cell analysis, offering potential targets for combined therapeutic strategies.

Background: Approximately 10% of the patients with infantile hemangioma (IH) may exhibit resistance to propranolol (PRN) therapy, and thus alternative strategies are required. Our previous studies reported that oxymatrine (OMT) could inhibit the growth of hemangiomas, however the underlying pharmacological actions have not been fully addressed.

Methods: In this study, a murine IH model was constructed by implantation of EOMA cells into nude mice. OMT was administrated (50 mg/kg; i.p) for 21 days. Metabolic changes were examined by proteomics and metabolomics, followed by in vitro experimental validation using EOMA cells.

Results: OMT significantly suppressed the growth of hemangioma in vivo without significant adverse effects. A total of 869 differentially expressed proteins and 38 metabolites were identified. In addition to canonical apoptosis regulation, OMT also caused significant metabolic disturbances, particularly in purine and pyrimidine metabolism. Furthermore, ferroptosis may be involved in the therapeutic effect of OMT. In the validation experiments in vitro, we found that OMT dose-dependently reduced the viability of EOMA cells, concomitant with increased production of lipid reactive oxygen species (ROS) and Fe2 + accumulation.

Conclusions: In conclusion, these findings suggested that treatment with OMT could suppress the growth of hemangiomas through metabolic disturbances and inducing ferroptosis, which may provide new insights to the management of IH.