Non-invasive diagnostics are crucial for the timely detection of renal cell carcinoma (RCC), significantly improving survival rates. Despite advancements, specific lipid markers for RCC remain unidentified. We aimed to discover and validate potent plasma markers and their association with dietary fats. Using lipid metabolite quantification, machine-learning algorithms, and marker validation, we identified RCC diagnostic markers in studies involving 60 RCC and 167 healthy controls (HC), as well as 27 RCC and 74 HC, by analyzing their correlation with dietary fats. RCC was associated with altered metabolism in amino acids, glycerophospholipids, and glutathione. We validated seven markers (l-tryptophan, various lysophosphatidylcholines [LysoPCs], decanoylcarnitine, and l-glutamic acid), achieving a 96.9% AUC, effectively distinguishing RCC from HC. Decreased decanoylcarnitine, due to reduced carnitine palmitoyltransferase 1 (CPT1) activity, was identified as affecting RCC risk. High intake of polyunsaturated fatty acids (PUFAs) was negatively correlated with LysoPC (18:1) and LysoPC (18:2), influencing RCC risk. We validated seven potential markers for RCC diagnosis, highlighting the influence of high PUFA intake on LysoPC levels and its impact on RCC occurrence via CPT1 downregulation. These insights support the efficient and accurate diagnosis of RCC, thereby facilitating risk mitigation and improving patient outcomes.
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http://dx.doi.org/10.3390/nu16091265 | DOI Listing |
Microbiome
December 2024
Department of Colorectal Surgery, Fudan University Shanghai Cancer Center, Shanghai, China.
Background: Studies have reported clinical heterogeneity between right-sided colon cancer (RCC) and left-sided colon cancer (LCC). However, none of these studies used multi-omics analysis combining genetic regulation, microbiota, and metabolites to explain the site-specific difference.
Methods: Here, 494 participants from a 16S rRNA gene sequencing cohort (50 RCC, 114 LCC, and 100 healthy controls) and a multi-omics cohort (63 RCC, 79 LCC, and 88 healthy controls) were analyzed.
Sci Rep
December 2024
IFOM ETS, The AIRC Institute of Molecular Oncology, Milan, Italy.
Targeting nuclear mechanics is emerging as a promising therapeutic strategy for sensitizing cancer cells to immunotherapy. Inhibition of the mechano-sensory kinase ATR leads to mechanical vulnerability of cancer cells, causing nuclear envelope softness and collapse and activation of the cGAS-STING-mediated innate immune response. Finding novel compounds that interfere with the non-canonical role of ATR in controlling nuclear mechanics presents an intriguing therapeutic opportunity.
View Article and Find Full Text PDFCancer Genomics Proteomics
December 2024
Department of Cell Pathology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan;
Background/aim: α1-Acid glycoprotein (AGP), also known as orosomucoid, is an acute-phase protein that has been found increased in plasma of cancer patients. This study investigates the role of AGP expression in clear cell renal cell carcinoma (ccRCC) and its association with clinical outcomes.
Materials And Methods: We investigated the correlation between AGP levels and the prognosis of ccRCC through an analysis of The Cancer Genome Atlas (TCGA) database.
Pituitary
December 2024
Department of Diabetes and Endocrinology, St Vincent's Hospital Sydney, Darlinghurst, NSW, Australia.
Purpose: Rathke's cleft cysts (RCC) are present in up to 20% of autopsy studies but only a minority necessitate surgical treatment. Inflammation of RCC is thought to be significant in three processes: the development of classical symptoms, a predisposition to rupture or apoplexy, and increasing the rate of RCC recurrence. We aim to characterize clinical presentation, histological and radiological findings in patients with surgically managed RCC.
View Article and Find Full Text PDFJ Imaging
December 2024
Process Analysis and Technology PA & T, Reutlingen University, Alteburgstraße 150, 72762 Reutlingen, Germany.
Ultraviolet (UV) hyperspectral imaging shows significant promise for the classification and quality assessment of raw cotton, a key material in the textile industry. This study evaluates the efficacy of UV hyperspectral imaging (225-408 nm) using two different light sources: xenon arc (XBO) and deuterium lamps, in comparison to NIR hyperspectral imaging. The aim is to determine which light source provides better differentiation between cotton types in UV hyperspectral imaging, as each interacts differently with the materials, potentially affecting imaging quality and classification accuracy.
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