ABSTRACT
Transfer RNA (tRNA) modifications have emerged as critical posttranscriptional regulators of gene expression affecting diverse biological and disease processes. While there is extensive knowledge about the enzymes installing the dozens of post-transcriptional tRNA modifications - the tRNA epitranscriptome - very little is known about how metabolic, signaling, and other networks integrate to regulate tRNA modification levels. Here we took a comprehensive first step at understanding epitranscriptome regulatory networks by developing a high-throughput tRNA isolation and mass spectrometry-based modification profiling platform and applying it to a Pseudomonas aeruginosa transposon insertion mutant library comprising 5,746 strains. Analysis of >200,000 tRNA modification data points validated the annotations of predicted tRNA modification genes, uncovered novel tRNA-modifying enzymes, and revealed tRNA modification regulatory networks in P. aeruginosa. Platform adaptation for RNA-seq library preparation would complement epitranscriptome studies, while application to human cell and mouse tissue demonstrates its utility for biomarker and drug discovery and development.
ABSTRACT
High-grade serous ovarian cancer (HGSOC) is one of the most common histologic types of ovarian cancer. The purpose of this study was to identify potential prognostic biomarkers in urine specimens from patients with HGSOC. First, 56 urine samples with information on relapse-free survival (RFS) months were collected and classified into good prognosis (RFS ≥ 12 months) and poor prognosis (RFS < 12 months) groups. Next, data-independent acquisition (DIA)-based mass spectrometry (MS) analysis was combined with MSFragger-DIA workflow to identify potential prognostic biomarkers in a discovery set (n = 31). With the aid of parallel reaction monitoring (PRM) analysis, four candidate biomarkers (ANXA1, G6PI, SPB3, and SPRR3) were finally validated in both the discovery set and an independent validation set (n = 25). Subsequent RFS and Cox regression analyses confirmed the utility of these candidate biomarkers as independent prognostic factors affecting RFS in patients with HGSOC. Regression models were constructed to predict the 12-month RFS rate, with area under the receiver operating characteristic curve (AUC) values ranging from 0.847 to 0.905. Overall, candidate prognostic biomarkers were identified in urine specimens from patients with HGSOC and prediction models for the 12-month RFS rate constructed. SIGNIFICANCE: OC is one of the leading causes of death due to gynecological malignancies. HGSOC constitutes one of the most common histologic types of OC with aggressive characteristics, accounting for the majority of advanced cases. In cases where patients with advanced HGSOC potentially face high risk of unfavorable prognosis or disease advancement within a 12-month period, intensive medical monitoring is necessary. In the era of precision cancer medicine, accurate prediction of prognosis or 12-month RFS rate is critical for distinguishing patient groups requiring heightened surveillance. Patients could significantly benefit from timely modifications to treatment regimens based on the outcomes of clinical monitoring. Urine is an ideal resource for disease surveillance purposes due to its easy accessibility. Furthermore, molecules excreted in urine are less complex and more stable than those in other liquid samples. In the current study, we identified candidate prognostic biomarkers in urine specimens from patients with HGSOC and constructed prediction models for the 12-month RFS rate.