3.1 Overview of AS events in TCGA-LUSC
Integrated AS events were analyzed in 487 TCGA-LUSC data set (Figure 1A). A total of 31,345 AS events from 9,633 genes were detected including 12,416 ES in 5,554 genes, 6,073 AP in 3369 genes, 5,579 AT in 3,167 genes, 2,675 AA in 1,996 genes, 2,323 AD in 1,722 genes, 2,129 RI in 1,474 genes, 150 ME in 144 genes (Figure 1B). Among them, ES events was the most common type, accounting for more than one-third of all events, followed by AP and AT events, while ME was the least. Notably, the amount of AS events far exceeded the number of genes. Furthermore, a subset of overlapping AS events among the seven types of AS in LUSC were illustrated by UpSet plot diagram (Figure 1C).
3.2 Identification of prognosis-related AS events in LUSC
First of all, we conducted a univariate Cox analysis of the 31,345 AS events in 487 LUSC patients to evaluate the relationship between AS events and overall survival (OS) status in LUSC. Consequently, 1,996 AS events within 1,409 genes were obviously related to overall survival of LUSC patients (Figure 2A). As shown in Figure 2B-H, the top 20 AS events were significantly related to OS among seven types of AS events. Interestingly, some of survival associated AS gens underwent multiple types of AS events. For example, AA, AD, RI and ES of ATXN2L and AA, AP, RI of NPIPB4 were conspicuously related to OS of LUSC patients.
3.3 Establishment of prognostic AS signatures for LUSC patients
The important prognostic related AS events in all AS events in univariate Cox analysis were selected as candidates to select the most significant AS events by LASSO Cox regression model analysis (Figure 3). Further, several prediction signatures based on the prognostic associated AS events were constructed by multivariate Cox analyses. Eventually, a combined prognostic model was built integrated from different types of AS events (Table S2). As shown in Figure 4A-H, Kaplan–Meier curves showed that LUSC patients in high-risk group had appreciably shorter OS than patients in low-risk group, demonstrated that these AS signatures could be powerful biomarkers to distinguish patients' prognosis. Obviously, the combined prognostic indicator showed better performance than single type of AS events (Figure 4H). Then, ROC curve was performed to appraise the prognostic efficiency of prognostic AS models. The results showed that all signatures had a robust predictive property with AUC values from 0.837 to 0.978 (Figure 4I). Conceivably, the combined model with all types of AS events had highest efficiency with 0.978 (AUC) than single prognostic models. The distribution of patients' risk score, survival status and expression profiles of all AS models were shown in Figure 5.
3.4 A network of prognosis-related AS genes and SFs
More importantly, extensive dysregulated AS events in many types of cancers are easily programmed by some specific SFs. Hence, an important issue is that whether several key SFs could potentially regulate these prognosis-associated AS events in LUSC. To determine those specific SFs which had closely connection with the prognosis-associated AS events in LUSC, univariate Cox analysis of SFs were implemented according to gene expression level of LUSC patients. Consequently, there were 25 SFs obviously related to OS of LUSC patients shown in Table S3. Furthermore, correlations between SFs and prognostic AS events were tested in LUSC using Spearman's test (Figure 6A). In correlation networks, 22 SFs (purple dots) were obviously related to 546 prognosis-related AS events, including 202 favorable AS events (green dots) and 344 adverse AS events (red dots). Interestingly, there was a positive correlation (red lines) between majority of poor prognostic AS events (red dots) and SFs (purple dots), while there was a negative correlation (green lines) between majority of favorable prognostic AS events (green dots) and SFs. For example, splicing factors SNRNP48 and DDX39B had adverse survival for LUSC patients (Figures 6B-C). ES of NADSYN1 and AP of TMEM25 were adverse factors, whereas AT of TNFRSF1A and AT of FBXL12 were related to favorable prognosis. Correlation between SNRNP48 and AT of TNFRSF1A or AP of TMEM25 were shown in dot plots, suggesting high expression of SNRNP48 had positive association with poor overall survival (Figure 6D-E). Similarly, correlation between DDX39B and ES of NADSYN1 or AT of FBXL12 were shown in dot plots, implicating high expression of DDX39B had negative association with favorable prognosis (Figure 6F-G).