Extracellular Vesicles—A New Potential Player in the Immunology of Renal Cell Carcinoma

Exosomes as novel tools for renal cell carcinoma therapy, diagnosis, and prognosis

Background

Renal Cell Carcinoma (RCC) stands as a formidable challenge within the field of oncology, despite considerable research endeavors. The advanced stages of this malignancy present formidable barriers to effective treatment and management.

Objective

This review aims to explore the potential of exosomes in addressing the diagnostic and therapeutic challenges associated with RCC. Specifically, it investigates the role of exosomes as biomarkers and therapeutic vehicles in the context of RCC management.

Methods

For this review article, a comprehensive literature search was conducted using databases such as PubMed, employing relevant keywords to identify research articles pertinent to the objectives of the review. Initially, 200 articles were identified, which underwent screening to remove duplicates and assess relevance based on titles and abstracts, followed by a detailed examination of full texts. From the selected articles, relevant data were extracted and synthesized to address the review's objectives. The conclusions were drawn based on a thorough analysis of the findings. The quality was ensured through independent review and resolution of discrepancies among multiple reviewers.

Results

Exosomes demonstrate potential as diagnostic tools for early detection, prognosis, and treatment monitoring in RCC. Their ability to deliver various therapeutic agents, such as small interfering RNAs, lncRNAs, chemotherapeutic drugs, and immune-stimulating agents, allows for a personalized approach to RCC management. By leveraging exosome-based technologies, precision and efficacy in treatment strategies can be significantly enhanced.

Conclusion

Despite the promising advancements enabled by exosomes in the management of RCC, further research is necessary to refine exosome-based technologies and validate their efficacy, safety, and long-term benefits through rigorous clinical trials. Embracing exosomes as integral components of RCC diagnosis and treatment represents a significant step towards improving patient outcomes and addressing the persistent challenges posed by this malignancy in the field of oncology.

Identification of novel snoRNA-based biomarkers for clear cell renal cell carcinoma from urine-derived extracellular vesicles

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC with high rates of metastasis. Targeted therapies such as tyrosine kinase and checkpoint inhibitors have improved treatment success, but therapy-related side effects and tumor recurrence remain a challenge. As a result, ccRCC still have a high mortality rate. Early detection before metastasis has great potential to improve outcomes, but no suitable biomarker specific for ccRCC is available so far. Therefore, molecular biomarkers derived from body fluids have been investigated over the past decade. Among them, RNAs from urine-derived extracellular vesicles (EVs) are very promising.

METHODS

RNA was extracted from urine-derived EVs from a cohort of 78 subjects (54 ccRCC patients, 24 urolithiasis controls). RNA-seq was performed on the discovery cohort, a subset of the whole cohort (47 ccRCC, 16 urolithiasis). Reads were then mapped to the genome, and expression was quantified based on 100 nt long contiguous genomic regions. Cluster analysis and differential region expression analysis were performed with adjustment for age and gender. The candidate biomarkers were validated by qPCR in the entire cohort. Receiver operating characteristic, area under the curve and odds ratios were used to evaluate the diagnostic potential of the models.

RESULTS

An initial cluster analysis of RNA-seq expression data showed separation by the subjects' gender, but not by tumor status. Therefore, the following analyses were done, adjusting for gender and age. The regions differentially expressed between ccRCC and urolithiasis patients mainly overlapped with small nucleolar RNAs (snoRNAs). The differential expression of four snoRNAs (SNORD99, SNORD22, SNORD26, SNORA50C) was validated by quantitative PCR. Confounder-adjusted regression models were then used to classify the validation cohort into ccRCC and tumor-free subjects. Corresponding accuracies ranged from 0.654 to 0.744. Models combining multiple genes and the risk factors obesity and hypertension showed improved diagnostic performance with an accuracy of up to 0.811 for SNORD99 and SNORA50C (p = 0.0091).

CONCLUSIONS

Our study uncovered four previously unrecognized snoRNA biomarkers from urine-derived EVs, advancing the search for a robust, easy-to-use ccRCC screening method.