Discover. Oncology最新文献

Background: Hepatocellular carcinoma (HCC) is a common and aggressive form of cancer. There is an interplay between ferroptosis and lipid metabolism in various types of cancer. The objective of this research is to examine the functions of genes related to ferroptosis and lipid metabolism (FLMRGs) in HCC, develop prognostic models, and analyze their significance for immunotherapy and treatment response.

Methods: RNA-seq data from TCGA and GEO databases were analyzed. Differential expression analysis was used to identify FLMRGs in HCC. Consensus clustering categorized patients into distinct clusters. Prognostic models were built using Cox regression and machine learning. Immune infiltration, mutation, and drug response analyses were conducted to assess the tumor microenvironment and therapeutic potential.

Results: We identified 14 differentially expressed FLMRGs between HCC and normal samples, of which 12 genes were correlated with the prognosis of HCC patients. HCC patients were categorized into two FLMRG clusters. These clusters were associated with HCC patient survival and immune cell infiltration. We identified differentially expressed genes between the two FLMRG clusters, and prognosis-related DEGs were utilized to construct a FLMRG prognostic signature (risk score). HCC patients with a high-risk score exhibited poor prognosis. Furthermore, the risk score model was correlated with immune cell infiltration, responses to immunotherapy, and drug sensitivity in HCC patients. High-risk patients demonstrated increased expression of immune checkpoints and higher tumor mutational burden (TMB). The signature genes displayed differential expression and prognostic value in HCC.

Conclusion: Our study highlights the significance of FLMRGs in HCC prognosis and may provide insights for personalized immunotherapy and chemotherapy strategies, contributing to the understanding of HCC pathogenesis and treatment.

Pheochromocytomas (PCCs) and paragangliomas (PGLs), collectively PPGLs, are rare tumors with significant molecular heterogeneity, which complicates prognosis and treatment. This study analyzed publicly available datasets (TCGA-PPGLs, GSE19422, GSE60459) to identify differentially expressed genes (DEGs) related to mitochondrial autophagy and ferroptosis in PPGLs. We identified 6,286 DEGs, including 31 mitochondrial ferroptosis-related DEGs (MFRDEGs). A prognostic model based on four genes (AMBRA1, EIF2S1, SRC, PHGDH) demonstrated high predictive accuracy (AUC > 0.9). Functional enrichment analysis highlighted key pathways, including mitophagy and Fc epsilon receptor I (FcεRI) signaling. Protein-protein interaction (PPI) and ceRNA network analyses revealed potential regulatory mechanisms. Calibration and decision curve analyses confirmed the model's clinical utility. These findings offer insights into PPGL molecular mechanisms, suggest prognostic biomarkers, and propose candidate therapeutic targets to improve risk stratification and personalized treatment. However, experimental validation is required to confirm their biological relevance before clinical application.

Purpose: The review aims to comprehensively examine the anticancer potential of diosgenin, a natural steroidal sapogenin, in the context of hepatocellular carcinoma (HCC). It highlights the underlying mechanisms of action, discusses diosgenin's limitations in bioavailability, and evaluates nanotechnology-based drug delivery systems designed to enhance its therapeutic efficacy.

Methods: This review was conducted through a structured and comprehensive literature screening process to ensure thorough coverage of relevant studies. Publications were retrieved from PubMed, Scopus, and Web of Science databases using keywords such as "diosgenin," "hepatocellular carcinoma," "liver cancer," "nanocarrier," "drug delivery," and "phytochemicals" applied individually and in various combinations. The search encompassed articles published between 2000 and 2025, with priority given to peer-reviewed English-language studies. Out of more than 300 records initially identified, approximately 125 studies met the inclusion criteria addressing diosgenin's pharmacodynamics, molecular mechanisms, and nanotechnology-based delivery systems.

Results: Diosgenin exerts anti-HCC effects through multiple pathways including PI3K/Akt, NF-κB/STAT3, MAPK, and mitochondrial apoptosis signaling. While its low solubility and poor bioavailability limit clinical application, nanocarriers have significantly improved drug stability, sustained release, and targeted tumor delivery. Among them, niosomes and carbon nanotubes showed notable efficacy, with diosgenin-loaded niosomes reducing HepG2 cell viability, and carbon nanotubes demonstrating synergistic tumor inhibition when co-loaded with ferulic acid. Diosgenin-based liposomes also enhanced the effect of doxorubicin, increasing apoptosis and reducing tumor burden in vivo.

Conclusion: Diosgenin represents a promising multi-targeted agent for liver cancer therapy, especially when combined with advanced drug delivery systems. These diosgenin loaded nanocarriers overcome pharmacokinetic limitations and significantly improve therapeutic outcomes through enhanced tumor targeting and synergistic effects with chemotherapeutic agents. However, successful clinical translation requires addressing key regulatory, ethical, and manufacturing challenges, including standardization of nanocarrier formulations, large-scale reproducibility, and long-term safety evaluation. Overall, diosgenin-based nanocarriers show promising potential for clinical translation, offering safer and more targeted therapeutic options for HCC.

Accurate classification of cancer subtypes is crucial for personalised therapies and targeted interventions. In this study, we propose BioGAT-LGG, a deep learning framework that integrates multi-omics data, including mRNA, miRNA, and DNA methylation, using a correlation-based Graph Attention Network version 2 (GATv2) for biomarker discovery and Lower-Grade Glioma (LGG) subtype classification. Unlike existing methodologies that rely on external biological priors, such as protein-protein interaction networks or reference graphs, BioGAT-LGG constructs gene-driven correlation graphs, enabling the model to learn biologically meaningful molecular interactions. To improve feature interpretability and reduce dimensionality, LASSO regression is performed during model training. The model achieved 98.03% accuracy, with precision (98.12%), recall (97.74%), and F1-score (97.87%) in a stratified 10-fold cross-validation. Extensive analysis and enrichment of known cancer-related pathways, including PI3K-Akt signalling, Small Cell Lung Cancer, and Transcriptional Misregulation in Cancer, identified the biomarkers hsa-mir-3936, MTCO1P40, and CCND2, which were subsequently validated. These results indicate that BioGAT-LGG effectively captures biologically validated mechanisms and can enable clinically significant subtype classification and biomarker-guided decision-making. This framework thus lays a scalable foundation for multi-omics integration in oncology, which can be further adopted in other tumour types.