Translational bioinformatics is becoming a driven force and a new scientific paradigm for cancer research in the era of big data. To promote the cross-disciplinary communication and research, we take cholangiocarcinoma as an example to review the present status and the future perspectives of the bioinformatics models applied in cancer study. We first summarize the present application of computational methods to the study of cholangiocarcinoma ranged from pattern recognition of biological data, knowledge based data annotation to systems biological level modeling and clinical translation. Then the future opportunities and challenges about database or knowledge base building, novel model developing and molecular mechanism exploring as well as the intelligent decision supporting system construction for the precision diagnosis, prognosis and treatment of cholangiocarcinoma are discussed.
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http://dx.doi.org/10.7150/ijbs.24622 | DOI Listing |
Genet Epidemiol
January 2025
Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, Massachusetts, USA.
Large-scale gene-environment interaction (GxE) discovery efforts often involve analytical compromises for the sake of data harmonization and statistical power. Refinement of exposures, covariates, outcomes, and population subsets may be helpful to establish often-elusive replication and evaluate potential clinical utility. Here, we used additional datasets, an expanded set of statistical models, and interrogation of lipoprotein metabolism via nuclear magnetic resonance (NMR)-based lipoprotein subfractions to refine a previously discovered GxE modifying the relationship between physical activity (PA) and HDL-cholesterol (HDL-C).
View Article and Find Full Text PDFClin Transl Med
January 2025
Frazer Institute, Faculty of Medicine, The University of Queensland, Woolloongabba, Queensland, Australia.
Background: Paediatric sarcomas, including rhabdomyosarcoma, Ewing sarcoma and osteosarcoma, represent a group of malignancies that significantly contribute to cancer-related morbidity and mortality in children and young adults. These cancers share common challenges, including high rates of metastasis, recurrence or treatment resistance, leading to a 5-year survival rate of approximately 20% for patients with advanced disease stages. Despite the critical need, therapeutic advancements have been limited over the past three decades.
View Article and Find Full Text PDFClin Transl Med
January 2025
Department of Cell Biology, Histology and Embryology, Gottfried Schatz Research Center, Medical University of Graz, Graz, Austria.
The editorial, "Clinical and translational mode of single-cell measurements: An artificial intelligent single-cell," introduces the innovative clinical artificial intelligence single-cell (caiSC) system, which merges AI with single-cell informatics to advance real-time diagnostics, disease monitoring, and treatment prediction. By combining clinical data and multimodal molecular inputs, caiSC facilitates personalized medicine, promising enhanced diagnostic precision and tailored therapeutic approaches. Despite its potential, caiSC lacks comprehensive data coverage across cell types and diseases, presenting challenges in data quality and model robustness.
View Article and Find Full Text PDFSci Rep
January 2025
School of BioSciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, 632014, India.
The proliferation-specific oncogenic transcription factor, FOXM1 is overexpressed in primary and recurrent breast tumors across all breast cancer (BC) subtypes. Intriguingly, FOXM1 overexpression was found to be highest in Triple-negative breast cancer (TNBC), the most aggressive BC with the worst prognosis. However, FOXM1-mediated TNBC pathogenesis is not completely elucidated.
View Article and Find Full Text PDFNat Commun
January 2025
School of Food and Biological Engineering, Engineering Research Center of Bio-process, Ministry of Education, Key Laboratory of Animal Source of Anhui Province, Hefei University of Technology, Hefei, 230009, China.
Dissection of the physiological interactomes of histone post-translational modifications (hPTMs) is crucial for understanding epigenetic regulatory pathways. Peptide- or protein-based histone photoaffinity tools expanded the ability to probe the epigenetic interactome, but in situ profiling in native cells remains challenging. Here, we develop a nucleus-targeting histone-tail-based photoaffinity probe capable of profiling the hPTM-mediated interactomes in native cells, by integrating cell-permeable and nuclear localization peptide modules into an hPTM peptide equipped with a photoreactive moiety.
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