Identification of Tumor Microenvironment-Related Alternative Splicing Events to Predict the Prognosis of Endometrial Cancer

Abstract

Background: Endometrial cancer (EC) is one of the most common female malignant tumors. The immunity is believed to be associated with EC patients' survival, and growing studies have shown that aberrant alternative splicing (AS) might contribute to the progression of cancers.

Methods: We downloaded the clinical information and mRNA expression profiles of 542 tumor tissues and 23 normal tissues from The Cancer Genome Atlas (TCGA) database. ESTIMATE algorithm was carried out on each EC sample, and the OS-related different expressed AS (DEAS) events were identified by comparing the high and low stromal/immune scores groups. Next, we constructed a risk score model to predict the prognosis of EC patients. Finally, we used unsupervised cluster analysis to compare the relationship between prognosis and tumor immune microenvironment.

Results: The prognostic risk score model was constructed based on 16 OS-related DEAS events finally identified, and then we found that compared with high-risk group the OS in the low-risk group was notably better. Furthermore, according to the results of unsupervised cluster analysis, we found that the better the prognosis, the higher the patient's ESTIMATE score and the higher the infiltration of immune cells.

Conclusions: We used bioinformatics to construct a gene signature to predict the prognosis of patients with EC. The gene signature was combined with tumor microenvironment (TME) and AS events, which allowed a deeper understanding of the immune status of EC patients, and also provided new insights for clinical patients with EC.

Keywords: alternative splicing; endometrial cancer; gene signature; prognosis; tumor microenvironment.

Figure 1

Flow chart of the bioinformatic analysis.

Figure 2

The K-M survival curves of high and low stromal/immune scores groups. (A) Stromal scores. (B) Immune scores.

Figure 3

DEAS events between high and low stromal/immune scores groups. (A) The UpSet plot of intersections between AS events and the corresponding gene intersections. The volcano plots (B, D) and heatmaps (C, E) of DEAS events between the high and low stromal/immune scores groups. The up-regulated (F) or down-regulated (G) DEAS events in both stromal score and immune scores groups by Venn diagram.

Figure 4

GO and KEGG pathway enrichment analysis of DEAS events. (A) GO analysis. (B) KEGG pathway enrichment analysis.

Figure 5

The 16 OS-related DEAS events signatures associated with risk score predicts EC patients’ OS. (A) K-M survival curve to test the predictive effect of the gene signature. (B) ROC curve analysis to evaluate the sensitivity and specificity of the gene signature.

Figure 6

Stratified analysis for the prognostic risk model in the different subgroups according to TCGA molecular classifications. (A) MSI, (B) POLE, (C) CN-HIGH, (D) CN-LOW.

Figure 7

The immune microenvironment was closely related to the prognosis of EC patients. (A) TCGA EC cohort was clustered into three subgroups by unsupervised cluster analysis. (B) K-M survival curves of three clusters. (C) The comparison of stromal scores, immune scores, and ESTIMATE scores between three clusters. (D) Box plots for comparison of immune cell infiltration between three clusters. ****, P < 0.0001; NS, not significant.

Figure 8

Regulatory network between SFs and AS events. Thirty-nine SFs (blue) were significantly related to 68 survival-associated AS events consisting of 37 adverse AS events (green) and 31 favorable AS events (red). The majority of adverse AS events were positively correlated with SF expression (green lines) and the most favorable AS events were negatively correlated with SF expression (red lines).