Transfer RNA fragments (tRFs) have gene silencing effects similarly to miRNAs, can be sorted into extracellular vesicles (EVs) and are emerging as potential circulating biomarkers for cancer diagnosis. We aimed at analyzing the expression of tRFs in gastric cancer (GC) and understanding their potential as biomarkers. We explored miRNA datasets from gastric tumors and normal adjacent tissues (NAT) from TCGA repository, and proprietary 3D-cultured GC cell lines and corresponding EVs, in order to identify differentially represented tRFs using MINTmap and R/Bioconductor packages. Selected tRFs were validated in patient-derived EVs. We found 613 Differentially Expressed (DE)-tRFs in the TCGA dataset, of which 19 were concomitantly upregulated in TCGA gastric tumors and present in 3D-cells and EVs, but barely expressed in NAT. Moreover, 20 tRFs were expressed in 3D-cells and EVs and downregulated in TCGA gastric tumors. Of these 39 DE-tRFs, 9 tRFs were also detected in patient-derived EVs. Interestingly, the targets of these 9 tRFs affect neutrophil activation and degranulation, cadherin binding, focal adhesion and cell-substrate junction, highlighting these pathways as major targets of EV-mediated crosstalk with the tumor microenvironment. Furthermore, as they are present in four distinct GC datasets and can be detected even in low quality patient-EV derived samples, they hold promise as GC biomarkers. By repurposing already available NGS data, we could identify and cross-validate a set of tRFs holding potential as GC diagnosis biomarkers.
Transfer RNA fragments (tRFs) have gene silencing effects similarly to miRNAs, can be sorted into extracellular vesicles (EVs) and are emerging as potential circulating biomarkers for cancer diagnoses. We aimed at analyzing the expression of tRFs in gastric cancer (GC) and understanding their potential as biomarkers. We explored miRNA datasets from gastric tumors and normal adjacent tissues (NATs) from TCGA repository, as well as proprietary 3D-cultured GC cell lines and corresponding EVs, in order to identify differentially represented tRFs using MINTmap and R/Bioconductor packages. Selected tRFs were validated in patient-derived EVs. We found 613 Differentially Expressed (DE)-tRFs in the TCGA dataset, of which 19 were concomitantly upregulated in TCGA gastric tumors and present in 3D cells and EVs, but barely expressed in NATs. Moreover, 20 tRFs were expressed in 3D cells and EVs and downregulated in TCGA gastric tumors. Of these 39 DE-tRFs, 9 tRFs were also detected in patient-derived EVs. Interestingly, the targets of these 9 tRFs affect neutrophil activation and degranulation, cadherin binding, focal adhesion and the cell–substrate junction, highlighting these pathways as major targets of EV-mediated crosstalk with the tumor microenvironment. Furthermore, as they are present in four distinct GC datasets and can be detected even in low quality patient-derived EV samples, they hold promise as GC biomarkers. By repurposing already available NGS data, we could identify and cross-validate a set of tRFs holding potential as GC diagnosis biomarkers.
Small RNA (sRNA) profiling of Extracellular Vesicles (EVs) by Next-Generation Sequencing (NGS) often delivers poor outcomes, independently of reagents, platforms or pipelines used, which contributes to poor reproducibility of studies. Here we analysed pre/post-sequencing quality controls (QC) to predict issues potentially biasing biological sRNA-sequencing results from purified human milk EVs, human and mouse EV-enriched plasma and human paraffin-embedded tissues. Although different RNA isolation protocols and NGS platforms were used in these experiments, all datasets had samples characterized by a marked removal of reads after pre-processing. The extent of read loss between individual samples within a dataset did not correlate with isolated RNA quantity or sequenced base quality. Rather, cDNA electropherograms revealed the presence of a constant peak whose intensity correlated with the degree of read loss and, remarkably, with the percentage of adapter dimers, which were found to be overrepresented sequences in high read-loss samples. The analysis through a QC pipeline, which allowed us to monitor quality parameters in a stepby-step manner, provided compelling evidence that adapter dimer contamination was the main factor causing batch effects. We concluded this study by summarising peer-reviewed published workflows that perform consistently well in avoiding adapter dimer contamination towards a greater likelihood of sequencing success.
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