Abstract
BackgroundAlternative splicing (AS) is a molecular event that drives protein diversity through the generation of multiple mRNA isoforms. Growing evidence demonstrates that dysregulation of AS is associated with tumorigenesis. However, an integrated analysis in identifying the AS biomarkers attributed to esophageal carcinoma (ESCA) is largely unexplored.MethodsAS percent-splice-in (PSI) data were obtained from the TCGA SpliceSeq database. Univariate and multivariate Cox regression analysis was successively performed to identify the overall survival (OS)-associated AS events, followed by the construction of AS predictor through different splicing patterns. Then, a nomogram that combines the final AS predictor and clinicopathological characteristics was established. Finally, a splicing regulatory network was created according to the correlation between the AS events and the splicing factors (SF).ResultsWe identified a total of 2389 AS events with the potential to be used as prognostic markers that are associated with the OS of ESCA patients. Based on splicing patterns, we then built eight AS predictors that are highly capable in distinguishing high- and low-risk patients, and in predicting ESCA prognosis. Notably, the area under curve (AUC) value for the exon skip (ES) prognostic predictor was shown to reach a score of 0.885, indicating that ES has the highest prediction strength in predicting ESCA prognosis. In addition, a nomogram that comprises the pathological stage and risk group was shown to be highly efficient in predicting the survival possibility of ESCA patients. Lastly, the splicing correlation network analysis revealed the opposite roles of splicing factors (SFs) in ESCA.ConclusionIn this study, the AS events may provide reliable biomarkers for the prognosis of ESCA. The splicing correlation networks could provide new insights in the identification of potential regulatory mechanisms during the ESCA development.
Highlights
Being the seventh most frequently occurring tumor in humans, esophageal carcinoma (ESCA) ranks the sixth in causing fatalities worldwide
The RNA sequencing (RNA-seq) data and clinical information of the The Cancer Genome Atlas (TCGA) ESCA cohort were obtained from the TCGA data portal1; while the Percent-splice-in (PSI) data of Alternative splicing (AS) events for ESCA were obtained from the TCGA SpliceSep2, a data portal that provides AS profiles across 33 tumors based on the TCGA RNA-seq data
The clinical information of ESCA patients was downloaded from the TCGA database
Summary
Being the seventh most frequently occurring tumor in humans, esophageal carcinoma (ESCA) ranks the sixth in causing fatalities worldwide. Due to the high morbidity and mortality rates of ESCA, there is an urgent call for the development of a highly efficient prognostic method. Over the past few decades, a great deal of effort has been made to identify prognostic biomarkers and therapeutic targets for ESCA. The studies showed some promising results, the research only focused on aspects such as mutation-driving factors and transcriptional levels (Zhu J. et al, 2018), thereby neglecting the diversity of RNA isoforms driven by posttranslational modifications. Alternative splicing (AS) is a molecular event that drives protein diversity through the generation of multiple mRNA isoforms. An integrated analysis in identifying the AS biomarkers attributed to esophageal carcinoma (ESCA) is largely unexplored
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