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Review
. 2022 Nov 17;10(11):2953.
doi: 10.3390/biomedicines10112953.

Diagnostic and Prognostic Biomarkers in Renal Clear Cell Carcinoma

Chaston Weaver  1 , Khaled Bin Satter  1 , Katherine P Richardson  1   2 , Lynn K H Tran  3 , Paul M H Tran  4 , Sharad Purohit  1   2   5   6
Affiliations
Review

Diagnostic and Prognostic Biomarkers in Renal Clear Cell Carcinoma

Chaston Weaver et al. Biomedicines. .

Abstract

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.

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

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Implementation of biomarkers in Clear Cell Renal Carcinoma (ccRCC). DNA sequencing, microarray, and mass spectrometry with liquid chromatography identify the mutation, gene, and protein expression profiles. These profiles create big data, that, when analyzed by machine learning algorithms, can identify markers for diagnosis, prognosis, and therapeutic decisions for ccRCC. In terms of diagnosis, these biomarkers can help in distinguishing ccRCC patients from healthy individuals, as well as from other patients with benign or malignant renal masses. Disease progression and survival, as an outcome of therapy, can be monitored by prognostic biomarkers. Finally, these biomarkers can provide information, which can offer precision medicine for patients.
See this image and copyright information in PMC

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