Comprehensive Analysis of Prognostic Alternative Splicing Signatures in Endometrial Cancer

Abstract

Background: Alternative splicing (AS) is one of the critical post-transcriptional regulatory mechanisms of various cancers and also plays a crucial role in the development of cancers, including endometrial cancer (EC).

Methods: The splicing data and gene expression profiles of EC were obtained from The Cancer Genome Atlas. The corresponding clinical data were extracted from TCGA-CDR. With univariate Cox regression analysis, least absolute shrinkage and selection operator model, and multivariate Cox regression analysis, the survival-related AS events were selected. Functional enrichment analysis was also performed to investigate the functions of these AS events. Splicing factors and AS regulation network were constructed to understand the correlation among these AS events.

Result: A total of 1826 AS events were identified as survival-related events. Functional enrichment analysis showed that these AS events were associated with several immune system-related processes. Then, the prognostic signatures were developed based on these survival-related events and acted as an independent prognostic factor for EC. Splicing factors and AS regulation network were also constructed to understand the regulatory mechanisms of AS events in EC.

Conclusion: This study systematically analyzed the role of AS events in EC and developed the prognostic model for EC.

Keywords: TCGA; alternative splicing events; endometrial cancer; precision medicine; prognostic signature; splicing factors.

FIGURE 1

Summary of AS events of endometrial carcinoma. (A) Counts of AS events and correlation genes. (B) Counts of survival-associated AS events and correlation genes.

FIGURE 2

Top 20 significant AS events of endometrial carcinoma. (A) The UpSet intersection diagram of seven types of AS events. (B) The UpSet intersection diagram of survival-associated AS events. (C) The volcano plot of survival-related AS events (red dots). The blue dots indicate AS events that aren’t related to survival. (B–H) The top 20 survival-related AS events based on AA (D), AD (E), AP (F), AT (G), ES (H), ME (I), and RI (J).

FIGURE 3

Functional enrichment analysis of corresponding genes of survival-related AS events. The network (A) and bar plot (B) of enrich terms of correlation genes of survival-related AS events. The dot in (A) represents every corresponding genes of top significant survival-related AS events. The depth of the color of the bar plot (B) indicates the significance of p-value. Those enrichment terms including more node were more significant. (C) Protein–protein interaction network of top significant survival-related AS events. (D) Top MCODE of the PPI network.

FIGURE 4

Prognostic signatures of survival-related AS events constructed based on LASSO COX analysis. (A–H) Represent the result of AA, AD, AP, AT, ES, ME, RI, and all seven types of AS events.

FIGURE 5

Kaplan–Meier and ROC curves of prognostic predictors. (A–G) The Kaplan–Meier curves indicate the survival probability of high-risk group patients (red line) and low-risk group patients (blue line) based on seven types of AS events, respectively. (H) The Kaplan–Meier curves represent the survival probability of high- and low-risk group patients based on all seven types. (I) The ROC curves of all prognostic predictors.

FIGURE 6

Forest plots for all prognostic predictors and clinical features based on univariate Cox analysis (A) and multivariate Cox analysis (B).

FIGURE 7

Splicing correlation network in endometrial cancer. Red dots represent the AS events whose PSI values are positively correlated with survival times. Green dots represent those whose PSI values are negatively correlated with survival times. Survival-associated factors are drawn in blue dots. The relationship between the PSI values of AS events and the expression of splicing factors were represented by a red line (positive correlation) and a green line (negative correlation).