Survival-associated alternative splicing signatures in esophageal carcinoma. (9th October 2018) - Mao, Shuangshuang; Li, Yuan; Lu, Zhiliang; Che, Yun; Sun, Shouguo; Huang, Jianbing; Lei, Yuanyuan; Wang, Xinfeng; Liu, Chengming; Zheng, Sufei; Zang, Ruochuan; Li, Ning; et al.
Abstract : These results provided new insights into gene posttranscriptional regulation mechanisms and indicated some potential drug targets which might be a promising approach to treating ESCA. Abstract: Alternative splicing (AS), a major mechanism for the enhancement of transcriptome and proteome diversity, has been widely demonstrated to be involved in the full spectrum of oncogenic processes. High-throughput sequencing technology and the rapid accumulation of clinical data sets have provided an opportunity to systemically analyze the association between messenger RNA AS variants and patient clinical outcomes. Here, we compared differentially spliced AS transcripts between esophageal carcinoma (ESCA) and non-tumor tissues, profiled genome-wide survival-associated AS events in 87 patients with esophageal adenocarcinoma (EAC) and 79 patients with esophageal squamous cell carcinoma (ESCC) using The Cancer Genome Atlas (TCGA) RNA-seq data set, and constructed predictive models as well as splicing regulation networks by integrated bioinformatic analysis. A total of 2326 AS events in 1738 genes and 1812 AS events in 1360 genes were determined to be significantly associated with overall survival (OS) of patients in the EAC and ESCC cohorts, respectively, including some essential participants in the oncogenic process. The predictive model of each splice type performed reasonably well in distinguishing good and poor outcomes of patients with esophageal cancer, and values for the area under curve reached 0.942 and 0.815 in the EAC exon skip predictive model and the ESCC alternate acceptor site predictive model, respectively. The splicing regulation networks revealed an interesting correlation between survival-associated splicing factors and prognostic AS genes. In summary, we created prognostic models for patients with esophageal cancer based on AS signatures and constructed novel splicing correlation networks.