miREV: An Online Database and Tool to Uncover Potential Reference RNAs and Biomarkers in Small-RNA Sequencing Data Sets from Extracellular Vesicles Enriched Samples

Urinary extracellular vesicles: Assessment of pre‐analytical variables and development of a quality control with focus on transcriptomic biomarker research

“…groups of more variable miRNAs to better assess the concordance of uEV isolation methods for miRNA‐seq. We also want to point out that the robustness is a positive sign that a common stable transcriptome, “biofluid ‐specific housekeeping exRNA signature”, exists and seems feasible to be defined in future efforts of uEV method standardization including those of the extracellular RNA consortium or miREV (Hildebrandt et al., 2021 ).…”

Exploration of Extracellular Vesicle miRNAs, Targeted mRNAs and Pathways in Prostate Cancer: Relation to Disease Status and Progression

“…While the minute quantities of RNA in EV pose a challenge for miRNAseq, the qPCR validation problem may be due to lack of standard reference miRNAs for uEV and PCa studies. Thus, even if we strived to identify stable miRNAs in our and previous datasets, currently accumulating knowledge of uEV miRNAs hopefully leads to identification of better, widely accepted reference miRNAs in the future [20,21].…”

“…Technically, RNA research into uEV is challenging. The best practices in EV pipelines are currently still developing, and they include pre-analytics (sample collection, processing and storage), EV isolation, RNA detection and data normalization/analysis [5,7,20,21]. The International Society of Extracellular Vesicles (ISEV) has launched several standardization efforts to expedite the development, including, e.g., Minimal Information for Studies of Extracellular Vesicles (MISEV) guidelines, and targeted ISEV position papers [5,22].…”

Molecular Biology in Treatment Decision Processes—Neuro-Oncology Edition

“…There is significant potential in examination of tumor as well as normal tissue response with respect to gene expression profiling, lymphocyte assays, radiogenomics with structural variations including single nucleotide polymorphisms (SNPs), copy number variations (CNVs), gene expression (mRNA, miRNA, lncRNA) [33,34]. In this context the challenge of leveraging existing molecular data that is increasingly curated in large scale public data bases (Figure 2C) including The Cancer Genome Atlas Program (TCGA) [93][94][95], Gene Expression Omnibus (GEO) [96], the growing presence of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) [97] lies in both merging the acquisition of further data towards robust clinical endpoints as well as merging dose volume histogram and pattern of failure data present currently in distinct silos with exisiting results [29,[98][99][100][101]. These efforts are gaining ground at the trascriptome [99,100,102] and proteome [97] levels with paralleled progress in the identification of differentially expressed genes (DEG) (Figure 2C).…”

“…In this context the challenge of leveraging existing molecular data that is increasingly curated in large scale public data bases (Figure 2C) including The Cancer Genome Atlas Program (TCGA) [93][94][95], Gene Expression Omnibus (GEO) [96], the growing presence of the Clinical Proteomic Tumor Analysis Consortium (CPTAC) [97] lies in both merging the acquisition of further data towards robust clinical endpoints as well as merging dose volume histogram and pattern of failure data present currently in distinct silos with exisiting results [29,[98][99][100][101]. These efforts are gaining ground at the trascriptome [99,100,102] and proteome [97] levels with paralleled progress in the identification of differentially expressed genes (DEG) (Figure 2C). However, further advancement will require robust frameworks and workflows in the clinical space that allow for continual linking to the research space (Figure 3) [55].…”