An alternative splicing signature model for predicting hepatocellular carcinoma-specific survival

Background: Alternative splicing (AS) is a transcriptional regulation mechanism, which can expand the coding ability of genome and contribute to the occurrence and development of cancer. A systematic analysis of AS in hepatocellular carcinoma (HCC) is lacking and urgently needed.

Methods: Univariate and multivariate Cox regression analyses were used to distinguish survival-related AS events and to calculate the risk score. Kaplan-Meier analysis and receiver operating characteristic (ROC) curves were used to evaluate the AS events' clinical significance to build a risk model in HCC.

Results: Data of AS events was obtained from the Splice-Seq database. The corresponding clinical information of HCC was downloaded from The Cancer Genome Atlas (TCGA) data portal. We analyzed 78,878 AS events from 13,045 genes in HCC patients. A total of 2,440 and 2,888 AS events were significantly related to HCC patients' disease-free survival (DFS) and overall survival (OS). The two prognostic models (DFS and OS) were constructed based on a total of seven AS types from survival-related AS events above. The area under the curve (AUC) of the ROC curves was 0.769 in the DFS cohort and 0.886 in the OS cohort.

Conclusions: The prognostic model constructed by AS events can be used to predict the prognosis of HCC patients and provide potential therapeutic targets for further validation.