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Diagnostic and Prognostic Biomarkers in Renal Clear Cell Carcinoma

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BIOMEDICINES
卷 10, 期 11, 页码 -

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MDPI
DOI: 10.3390/biomedicines10112953

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clear cell carcinoma; molecular pathology; biomarkers; gene and protein signatures; machine learning; treatment decision

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This review focuses on the molecular changes associated with the development of clear cell renal carcinoma (ccRCC) and the gene expression and protein signatures, highlighting the importance of utilizing these molecular profiles in clinical practice. Machine learning and precision medicine may help overcome the barriers in incorporating tumor molecular profiles into the diagnosis and treatment of ccRCC.
Renal clear cell carcinoma (ccRCC) comprises over 75% of all renal tumors and arises in the epithelial cells of the proximal convoluted tubule. Molecularly ccRCC is characterized by copy number alterations (CNAs) such as the loss of chromosome 3p and VHL inactivation. Additional driver mutations (SETD2, PBRM1, BAP1, and others) promote genomic instability and tumor cell metastasis through the dysregulation of various metabolic and immune-response pathways. Many researchers identified mutation, gene expression, and proteomic signatures for early diagnosis and prognostics for ccRCC. Despite a tremendous influx of data regarding DNA alterations, gene expression, and protein expression, the incorporation of these analyses for diagnosis and prognosis of RCC into the clinical application has not been implemented yet. In this review, we focused on the molecular changes associated with ccRCC development, along with gene expression and protein signatures, to emphasize the utilization of these molecular profiles in clinical practice. These findings, in the context of machine learning and precision medicine, may help to overcome some of the barriers encountered for implementing molecular profiles of tumors into the diagnosis and treatment of ccRCC.

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