Personalized Medicine in Urologic Oncology

Lotan, Yair; Schalken, Jack; Lawrentschuk, Nathan

doi:10.48083/yvnw6070

Cited by 1 publication

(2 citation statements)

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“…It can also predict splice variants, gene fusions, and epigenetic changes, which are missed in the DNA analysis [41]. Low sample numbers and lack of validation are major obstacles to the clinical transition [42]. The gene expression signatures provided in Table 3 incorporate diagnostic, prognostic, and predictive response outcomes, representing the current state of expression signature analyses.…”

Section: Gene Expression Signaturesmentioning

confidence: 99%

“…This gene signature has been shown to identify patients who would benefit from surveillance versus adjuvant therapy following surgery [45]. Gene expression signatures can also be used to differentiate tumor and normal tissue [42]. Therefore, targeting driver mutation-specific expression profiles is a logical strategy to detect early oncogenic changes in pre-neoplastic cells as well as to supplement diagnosis by tumor biopsy and guide treatment decisions [46,47].…”

Section: Gene Expression Signaturesmentioning

confidence: 99%

See 1 more Smart Citation

Diagnostic and Prognostic Biomarkers in Renal Clear Cell Carcinoma

Weaver¹,

Satter²,

Richardson³

et al. 2022

Preprint

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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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Section: Gene Expression Signaturesmentioning

confidence: 99%