Cholangiocarcinoma (CCA) is a highly aggressive and poorly prognostic tumor. Due to the lack of early symptoms, diagnosing CCA remains challenging, often occurring at an advanced stage. Therefore, exploring the underlying mechanisms of CCA development and identifying potential biomarkers and therapeutic targets is crucial. Recently, metabolic reprogramming in cancer cells has emerged as a hallmark of the disease. Glycolysis has been identified as a central component of metabolic reprogramming in CCA, with multiple signaling pathways and key enzymes playing significant roles. Additionally, non-coding RNAs (ncRNAs) and post-translational modifications of proteins are also involved in regulating glycolysis in CCA. In this review, we provide a comprehensive summary of the alterations in cancer metabolism and the diverse signaling pathways involved, as they might exert an impact on the development of CCA. Overall, targeting glycolysis holds considerable promise as a crucial strategy for enhancing the therapeutic outcomes of CCA. In addition, we performed a bioinformatic analysis of the relationship between CCA and glycolysis to identify and investigate potential targets. The purpose of this study is to provide a theoretical basis for the development of CCA targets.
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http://dx.doi.org/10.2147/JIR.S497551 | DOI Listing |
EMBO Rep
January 2025
Killer Cell Biology Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
Cytotoxic lymphocytes are crucial to our immune system, primarily eliminating virus-infected or cancerous cells via perforin/granzyme killing. Perforin forms transmembrane pores in the plasma membrane, allowing granzymes to enter the target cell cytosol and trigger apoptosis. The prowess of cytotoxic lymphocytes to efficiently eradicate target cells has been widely harnessed in immunotherapies against haematological cancers.
View Article and Find Full Text PDFNeuropsychopharmacology
January 2025
Neurocognition and Emotion in Affective Disorders (NEAD) Centre, Psychiatric Centre Copenhagen, Mental Health Services, Capital Region of Denmark, Frederiksberg, Denmark.
Individuals with bipolar disorder (BD) show heterogeneity in clinical, cognitive, and daily functioning characteristics, which challenges accurate diagnostics and optimal treatment. A key goal is to identify brain-based biomarkers that inform patient stratification and serve as treatment targets. The objective of the present study was to apply a data-driven, multivariate approach to quantify the relationship between multimodal imaging features and behavioral phenotypes in BD.
View Article and Find Full Text PDFMagn Reson Imaging
January 2025
Background: Huai-he Hospital of Henan University, Kaifeng, China. Electronic address:
Objective: To explore the application value of MRI-based imaging histology and deep learning model in the identification and classification of breast phyllodes tumors.
Methods: Seventy-seven patients diagnosed as breast phyllodes tumors and fibroadenomas by pathological examination were retrospectively analyzed, and traditional radiomics features, subregion radiomics features, and deep learning features were extracted from MRI images, respectively. The features were screened and modeled using variance selection method, statistical test, random forest importance ranking method, Spearman correlation analysis, least absolute shrinkage and selection operator (LASSO).
PLoS One
January 2025
Kirby Institute, University of New South Wales, Sydney, NSW, Australia.
Background: Risk of anal cancer is high in certain populations and screening involves collection of anal swabs for HPV DNA and/or cytology testing. However, barriers exist, such as the need for an intimate examination, and stigma around HIV status, sexual orientation, and sexual practices. Self-collected anal swabs (SCA) are a proposed alternative to clinician-collected swabs (CCA) to overcome these barriers.
View Article and Find Full Text PDFBackground: Understanding the relationship between genetic variations and brain imaging phenotypes is an important issue in Alzheimer's disease (AD) research. As an alternative to GWAS univariate analyses, canonical correlation analysis (CCA) and its deep learning extension (DCCA) are widely used to identify associations between multiple genetic variants such as SNPs and multiple imaging traits such as brain ROIs from PET/MRI. However, with the recent availability of numerous genetic variants from genotyping and whole genome sequencing data for AD, these approaches often suffer from severe overfitting when dealing with 'fat' genetics data, e.
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