Currently there is a lack of targeted therapies that lead to long-term attenuation or regression of disease in patients with advanced clear cell renal cell carcinoma (ccRCC). Our group has implemented a high-throughput genetic analysis coupled with a high-throughput proliferative screen in order to investigate the genetic contributions of a large cohort of overexpressed genes at the functional level in an effort to better understand factors involved in tumor initiation and progression. Patient gene array analysis identified transcripts that are consistently elevated in patient ccRCC as compared to matched normal renal tissues. This was followed by a high-throughput lentivirus screen, independently targeting 195 overexpressed transcripts identified in the gene array in four ccRCC cell lines. This revealed 31 'hits' that contribute to ccRCC cell proliferation. Many of the hits identified are not only presented in the context of ccRCC for the first time, but several have not been previously linked to cancer. We further characterize the function of a group of hits in tumor cell invasion. Taken together these findings reveal pathways that may be critical in ccRCC tumorigenicity, and identifies novel candidate factors that could serve as targets for therapeutic intervention or diagnostic/prognostic biomarkers for patients with advanced ccRCC.
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http://dx.doi.org/10.18632/oncotarget.2097 | DOI Listing |
ACS Infect Dis
January 2025
Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado 80523, United States.
Developing new classes of drugs that are active against infections caused by is a priority for treating and managing this deadly disease. Here, we describe screening a small library of 20 DNA gyrase inhibitors and identifying new lead compounds. Three structurally diverse analogues were identified with minimal inhibitory concentrations of 0.
View Article and Find Full Text PDFHum Reprod
January 2025
Institute of Genomics, Estonian Genome Centre, University of Tartu, Tartu, Estonia.
Study Question: Do polycystic ovary syndrome (PCOS), menstrual cycle phases, and ovulatory status affect reproductive tract (RT) microbiome profiles?
Summary Answer: We identified microbial features associated with menstrual cycle phases in the upper and lower RT microbiome, but only two specific differences in the upper RT according to PCOS status.
What Is Known Already: The vaginal and uterine microbiome profiles vary throughout the menstrual cycle. Studies have reported alterations in the vaginal microbiome among women diagnosed with PCOS.
Eur Heart J Cardiovasc Imaging
January 2025
Vall d'Hebron Research Institute (VHIR), Barcelona, Spain.
Background: Cardiac magnetic resonance (CMR) is essential for diagnosing cardiomyopathy, serving as the gold standard for assessing heart chamber volumes and tissue characterization. Hemodynamic forces (HDF) analysis, a novel approach using standard cine CMR images, estimates energy exchange between the left ventricular (LV) wall and blood. While prior research has focused on peak or mean longitudinal HDF values, this study aims to investigate whether unsupervised clustering of HDF curves can identify clinically significant patterns and stratify cardiovascular risk in non-ischemic LV cardiomyopathy (NILVC).
View Article and Find Full Text PDFPLoS One
January 2025
Foot and Mouth Disease Department, National Veterinary Research Institute, Vom, Plateau State, Nigeria.
The global public health risk posed by Salmonella Kentucky (S. Kentucky) is rising, particularly due to the dissemination of antimicrobial resistance genes in human and animal populations. This serovar, widespread in Africa, has emerged as a notable cause of non-typhoidal gastroenteritis in humans.
View Article and Find Full Text PDFPLoS One
January 2025
Institute of Ocean Engineering, Ningbo University, Ningbo, Zhejiang, China.
Hydrological prediction in ungauged basins often relies on the parameter transplant method, which incurs high labor costs due to its dependence on expert input. To address these issues, we propose a novel hydrological prediction model named STH-Trans, which leverages multiple spatiotemporal views to enhance its predictive capabilities. Firstly, we utilize existing geographic and topographic indicators to identify and select watersheds that exhibit similarities.
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