Impact Factor ISSN: 1837-9664
Research Paper The scPagwas analysis of single-cell sequencing combined with Bayesian deconvolution with SMR Reveals Tumor Heterogeneity and Predicts Immunotherapy Response in Neuroblastoma Full text PDF
Research Paper LH2 Promotes Tumor Progression in Oral Squamous Cell Carcinoma via EPHA7-AKT-VEGF Signaling Full text PDF
Research Paper EfficientNetB5-Based Deep Learning for Automated Cancer Detection in Tissue Microarray Images Full text PDF
Review Molecular mechanism and potential role of mitophagy in breast cancer Full text PDF
Review A candidate PPARγ to GPX4 axis in ferroptosis suppression and platinum resistance in bladder cancer: evidence appraisal and a testable research agenda Full text PDF
Research Paper Exosomes from Cancer-associated Fibroblasts Orchestrate Lung Metastasis of Breast Cancer via LncRNA LMAEP Full text PDF
Issue 10; 2026
Indexed in PubMed and SCI expanded; full texts in Europe PMC. ...
EfficientNetB5-Based Deep Learning for Automated Cancer Detection in Tissue Microarray Images
Background: Tissue microarray (TMA) technology is pivotal in cancer research, enabling simultaneous analysis of multiple samples on a single slide. Manual analysis of TMA images remains time-consuming and subjective. This study aimed to develop a robust deep learning model to automate TMA image analysis, enhance accuracy, and support cancer diagnosis. Methods: We utilized the EfficientNetB5 deep learning architecture to classify TMA images from the ...
LH2 Promotes Tumor Progression in Oral Squamous Cell Carcinoma via EPHA7-AKT-VEGF Signaling
Background: Lysyl hydroxylase 2 (LH2) specifically hydroxylates lysine residues in type I collagen telopeptides. Aberrant LH2 expression has been implicated as a driver of aggressive phenotypes in multiple cancers, including oral squamous cell carcinoma (OSCC). However, the intracellular signaling mechanisms activated by LH2 overexpression remain unclear. Methods: In this study, we performed a comprehensive expression analysis to clarify LH2-driven ...