Diagnostic and Prognostic Biomarkers in Renal Clear Cell Carcinoma

被引:17
|
作者
Weaver, Chaston [1 ]
Bin Satter, Khaled [1 ]
Richardson, Katherine P. [1 ,2 ]
Tran, Lynn K. H. [3 ]
Tran, Paul M. H. [4 ]
Purohit, Sharad [1 ,2 ,5 ,6 ]
机构
[1] Augusta Univ, Med Coll Georgia, Ctr Biotechnol & Genom Med, 1120 15th St, Augusta, GA 30912 USA
[2] Augusta Univ, Med Coll Georgia, Dept Interdisciplinary Hlth Sci, 1120 15th St, Augusta, GA 30912 USA
[3] Baylor Coll Med, Dept Urol, Houston, TX 76798 USA
[4] Yale Univ, Yale Sch Med, Dept Internal Med, New Haven, CT 06510 USA
[5] Augusta Univ, Dept Undergrad Hlth Profess, Coll Allied Hlth Sci, 1120 15th St, Augusta, GA 30912 USA
[6] Augusta Univ, Dept Obstet & Gynecol, Med Coll Georgia, 1120 15th St, Augusta, GA 30912 USA
关键词
clear cell carcinoma; molecular pathology; biomarkers; gene and protein signatures; machine learning; treatment decision; EXPRESSION SIGNATURE; KIDNEY CANCER; PROMOTER HYPERMETHYLATION; SERUM DNA; TUMOR; DISCOVERY; URINE; IDENTIFICATION; HETEROGENEITY; RECURRENCE;
D O I
10.3390/biomedicines10112953
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
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页数:14
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