TY - JOUR
T1 - Diagnostic and Prognostic Biomarkers in Renal Clear Cell Carcinoma
AU - Weaver, Chaston
AU - Bin Satter, Khaled
AU - Richardson, Katherine P.
AU - Tran, Lynn K.H.
AU - Tran, Paul M.H.
AU - Purohit, Sharad
N1 - Publisher Copyright:
© 2022 by the authors.
PY - 2022/11
Y1 - 2022/11
N2 - 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.
AB - 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.
KW - biomarkers
KW - clear cell carcinoma
KW - gene and protein signatures
KW - machine learning
KW - molecular pathology
KW - treatment decision
UR - http://www.scopus.com/inward/record.url?scp=85147316048&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85147316048&partnerID=8YFLogxK
U2 - 10.3390/biomedicines10112953
DO - 10.3390/biomedicines10112953
M3 - Review article
AN - SCOPUS:85147316048
SN - 2227-9059
VL - 10
JO - Biomedicines
JF - Biomedicines
IS - 11
M1 - 2953
ER -