Dataset Information

Tumor Endothelial Cell-A Biological Tool for Translational Cancer Research.

ABSTRACT: Going from bench to bedside is a simplified description of translational research, with the ultimate goal being to improve the health status of mankind. Tumor endothelial cells (TECs) perform angiogenesis to support the growth, establishment, and dissemination of tumors to distant organs. TECs have various features that distinguish them from normal endothelial cells, which include alterations in gene expression patterns, higher angiogenic and metabolic activities, and drug resistance tendencies. The special characteristics of TECs enhance the vulnerability of tumor blood vessels toward antiangiogenic therapeutic strategies. Therefore, apart from being a viable therapeutic target, TECs would act as a better mediator between the bench (i.e., angiogenesis research) and the bedside (i.e., clinical application of drugs discovered through research). Exploitation of TEC characteristics could reveal unidentified strategies of enhancing and monitoring antiangiogenic therapy in the treatment of cancer, which are discussed in this review.

SUBMITTER: Annan DA

PROVIDER: S-EPMC7247330 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Tumor Endothelial Cell-A Biological Tool for Translational Cancer Research.

Annan Dorcas Akuba-Muhyia DA Kikuchi Hiroshi H Maishi Nako N Hida Yasuhiro Y Hida Kyoko K

International journal of molecular sciences 20200503 9

Going from <i>bench to bedside</i> is a simplified description of translational research, with the ultimate goal being to improve the health status of mankind. Tumor endothelial cells (TECs) perform angiogenesis to support the growth, establishment, and dissemination of tumors to distant organs. TECs have various features that distinguish them from normal endothelial cells, which include alterations in gene expression patterns, higher angiogenic and metabolic activities, and drug resistance tend ...[more]

PMID: 32375250

Similar Datasets

Project description:BackgroundBioinformatics and medical informatics are two research fields that serve the needs of different but related communities. Both domains share the common goal of providing new algorithms, methods and technological solutions to biomedical research, and contributing to the treatment and cure of diseases. Although different microarray techniques have been successfully used to investigate useful information for cancer diagnosis at the gene expression level, the true integration of existing methods into day-to-day clinical practice is still a long way off. Within this context, case-based reasoning emerges as a suitable paradigm specially intended for the development of biomedical informatics applications and decision support systems, given the support and collaboration involved in such a translational development. With the goals of removing barriers against multi-disciplinary collaboration and facilitating the dissemination and transfer of knowledge to real practice, case-based reasoning systems have the potential to be applied to translational research mainly because their computational reasoning paradigm is similar to the way clinicians gather, analyze and process information in their own practice of clinical medicine.ResultsIn addressing the issue of bridging the existing gap between biomedical researchers and clinicians who work in the domain of cancer diagnosis, prognosis and treatment, we have developed and made accessible a common interactive framework. Our geneCBR system implements a freely available software tool that allows the use of combined techniques that can be applied to gene selection, clustering, knowledge extraction and prediction for aiding diagnosis in cancer research. For biomedical researches, geneCBR expert mode offers a core workbench for designing and testing new techniques and experiments. For pathologists or oncologists, geneCBR diagnostic mode implements an effective and reliable system that can diagnose cancer subtypes based on the analysis of microarray data using a CBR architecture. For programmers, geneCBR programming mode includes an advanced edition module for run-time modification of previous coded techniques.ConclusiongeneCBR is a new translational tool that can effectively support the integrative work of programmers, biomedical researches and clinicians working together in a common framework. The code is freely available under the GPL license and can be obtained at http://www.genecbr.org.

Dataset Information

Tumor Endothelial Cell-A Biological Tool for Translational Cancer Research.

Publications

Tumor Endothelial Cell-A Biological Tool for Translational Cancer Research.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets