808 research outputs found
Beitrag zur Äthiologie der "reversiblen hepatischen Dysfunktion" (Stauffer-Syndrom) bei Nierentumoren
The role of obesity in macrophage-mediated mechanisms promoting early-onset colon cancer.
Early-onset colon cancer (EOCC) is a leading cause of cancer death among people younger than 50 years of age in the United States and is associated with metabolic dysfunction and obesity. Anti-inflammatory tumor-associated macrophages (TAM) and low Peroxisome Proliferator Activated Receptor Gamma (PPARγ) gene expression in colon cancer (CC) tissue promote tumor progression and decreased patient survival. Obesity-related hormones, such as leptin and adiponectin, have the potential to affect gene expression in TAM to promote CC progression and thereby link obesity and EOCC. The aim of this project was to identify target genes in human CC and to investigate the effects of leptin, adiponectin and the inflammatory macrophage metabolite itaconate on age- and obesity-related gene expression
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DNA methylation-based classification of central nervous system tumours.
Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology
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