Clear cell renal cell carcinoma (ccRCC) is a common pathological subtype of renal cancer. Although the recent application of molecular-targeted agents has modestly improved the prognosis of ccRCC patients, their outcome is still poor. It is therefore important to characterize the molecular and biological mechanisms responsible for the development of ccRCC. Approximately 25% ccRCC patients involves the loss of RNA-binding protein QKI at 6q26, but the role of QKI in ccRCC is unknown. Here, we found that QKI-5 was frequently downregulated in ccRCC patients and its down-regulation was significantly associated with clinical features including T status, M status, and differentiation grade, and poorer patient prognosis. Moreover, QKI-5 inhibited the proliferation of kidney cancer cells both in vitro and in vivo. The subsequent functional studies showed that QKI-5 stabilized RASA1 mRNA via directly binding to the QKI response element region of RASA1, which in turn prevented the activation of the Ras-MAPK signaling pathway, suppressed cellular proliferation and induced cell cycle arrest. Overall, our data demonstrate a suppressive role of QKI in ccRCC tumourigenesis that involves the QKI-mediated post-transcriptional regulation of the Ras-MAPK signaling pathway.
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http://dx.doi.org/10.1080/15384101.2016.1235103 | DOI Listing |
J Transl Med
January 2025
Medical School of Nanjing University, Nanjing, 210093, China.
Background: Clear cell renal cell carcinoma (ccRCC) has a high incidence rate and poor prognosis, and currently lacks effective therapies. Recently, peptide-based drugs have shown promise in cancer treatment. In this research, a new endogenous peptide called CBDP1 was discovered in ccRCC and its potential anti-cancer properties were examined.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Urology, Zhongnan Hospital of Wuhan University, Wuhan, China.
Clear cell renal cell carcinoma is a prevalent urological malignancy, imposing substantial burdens on both patients and society. In our study, we used bioinformatics methods to select four putative target genes associated with EMT and prognosis and developed a nomogram model which could accurately predicting 5-year patient survival rates. We further analyzed proteome and single-cell data and selected PLCG2 and TMEM38A for the following experiments.
View Article and Find Full Text PDFBiochim Biophys Acta Mol Basis Dis
January 2025
Department of Urology, Urology Research Institute, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China; Fujian Key Laboratory of Precision Medicine for Cancer, the First Affiliated Hospital, Fujian Medical University, Fuzhou 350005, China. Electronic address:
Objective: The aim of this work is to identify putative hub genes for the advancement of clear cell renal cell carcinoma (ccRCC) and determine the fundamental mechanisms.
Methods: We employed multiple bioinformatics techniques to screen hub genes. Key hub gene expression levels in ccRCC were assessed.
Objective: The objective of this research was to devise and authenticate a predictive model that employs CT radiomics and deep learning methodologies for the accurate prediction of synchronous distant metastasis (SDM) in clear cell renal cell carcinoma (ccRCC).
Methods: A total of 143 ccRCC patients were included in the training cohort, and 62 ccRCC patients were included in the validation cohort. The CT images from all patients were normalized, and the tumor regions were manually segmented via ITK-SNAP software.
Cancers (Basel)
January 2025
Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, 2130 W Holcombe Blvd., Houston, TX 77030, USA.
Predicting the behavior of clear cell renal cell carcinoma (ccRCC) is challenging using standard-of-care histopathologic examination. Indeed, pathologic RCC tumor grading, based on nuclear morphology, performs poorly in predicting outcomes of patients with International Society of Urological Pathology/World Health Organization grade 2 and 3 tumors, which account for most ccRCCs. We applied spatial point process modeling of H&E-stained images of patients with grade 2 and grade 3 ccRCCs ( = 72) to find optimum separation into two groups.
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