Faculty, Staff and Student Publications

Publication Date

3-17-2025

Journal

Clinical Cancer Research

DOI

10.1158/1078-0432.CCR-24-3324

PMID

39836407

PMCID

PMC11913570

PubMedCentral® Posted Date

9-17-2025

PubMedCentral® Full Text Version

Author MSS

Abstract

Purpose: Renal medullary carcinoma (RMC) is a highly aggressive malignancy defined by the loss of the SMARCB1 tumor suppressor. It mainly affects young individuals of African descent with sickle cell trait, and it is resistant to conventional therapies used for other renal cell carcinomas. This study aimed to identify potential biomarkers for early detection and disease monitoring of RMC.

Experimental design: Integrated profiling of primary untreated RMC tumor tissues and paired adjacent kidney controls was performed using RNA sequencing and histone chromatin immunoprecipitation sequencing. The expression of serum cancer antigen 125 (CA-125), was prospectively evaluated in 47 patients with RMC. Functional studies were conducted in RMC cell lines to assess the effects of SMARCB1 reexpression.

Results: MUC16, encoding for CA-125, was identified as one of the top upregulated genes in RMC tissues, with concomitant enrichment of active histone marks H3K4me3 and H3K27ac at its promoter. Elevated serum CA-125 levels were found in 31 of 47 (66%) patients with RMC and correlated significantly with metastatic tumor burden (P = 0.03). Functional studies in RMC cell lines demonstrated that SMARCB1 reexpression significantly reduced MUC16 expression.

Conclusions: The correlation between serum CA-125 levels and metastatic burden suggests that CA-125 is a clinically relevant biomarker for RMC. These findings support further exploration of CA-125 for disease monitoring and targeted therapeutics in RMC.

Keywords

Humans, Biomarkers, Tumor, Female, Male, Middle Aged, Kidney Neoplasms, Prospective Studies, Adult, CA-125 Antigen, Aged, Carcinoma, Medullary, SMARCB1 Protein, Gene Expression Regulation, Neoplastic, Cell Line, Tumor, Carcinoma, Renal Cell, Gene Expression Profiling

Published Open-Access

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