4.7 Article

Circulating RNAs in prostate cancer patients

Journal

CANCER LETTERS
Volume 524, Issue -, Pages 57-69

Publisher

ELSEVIER IRELAND LTD
DOI: 10.1016/j.canlet.2021.10.011

Keywords

Prostate cancer; exRNA; EV-RNA; Liquid biopsy; Transcriptomics; Computational biology; Biomarkers

Categories

Funding

  1. Fondazione AIRC per la Ricerca sul Cancro (AIRC)
  2. Cancer Research UK (CRUK) [22792, A26822]
  3. Fondazione CARITRO, Prostate Cancer SPORE grant [P50 CA211024-01A1]

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The identification of prostate cancer biomarkers in patients' blood and urine can significantly improve PCa diagnosis and monitoring. Using EV-RNAs as biomarkers in PCa shows great potential, but faces challenges of EV heterogeneity and complexity.
Growing bodies of evidence have demonstrated that the identification of prostate cancer (PCa) biomarkers in the patients' blood and urine may remarkably improve PCa diagnosis and progression monitoring. Among diverse cancer-derived circulating materials, extracellular RNA molecules (exRNAs) represent a compelling component to investigate cancer-related alterations. Once outside the intracellular environment, exRNAs circulate in biofluids either in association with protein complexes or encapsulated inside extracellular vesicles (EVs). Notably, EV-associated RNAs (EV-RNAs) were used for the development of several assays (such as the FDA-approved Progensa Prostate Cancer Antigen 3 (PCA3 test) aiming at improving early PCa detection. EV-RNAs encompass a mixture of species, including small non-coding RNAs (e.g. miRNA and circRNA), lncRNAs and mRNAs. Several methods have been proposed to isolate EVs and relevant RNAs, and to perform RNA-Seq studies to identify potential cancer biomarkers. However, EVs in the circulation of a cancer patient include a multitude of diverse populations that are released by both cancer and normal cells from different tissues, thereby leading to a heterogeneous EV-RNA-associated transcriptional signal. Decrypting the complexity of such a composite signal is nowadays the major challenge faced in the identification of specific tumor-associated RNAs. Multiple deconvolution algorithms have been proposed so far to infer the enrichment of cancer-specific signals from gene expression data. However, novel strategies for EVs sorting and sequencing of RNA associated to single EVs populations will remarkably facilitate the identification of cancer-related molecules. Altogether, the studies summarized here demonstrate the high potential of using EV-RNA biomarkers in PCa and highlight the urgent need of improving technologies and computational approaches to characterize specific EVs populations and their relevant RNA cargo.

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