Alternative splicing perturbation landscape identifies RNA binding proteins as potential therapeutic targets in cancer

Abstract

Alternative splicing (AS) plays an important role in gene regulation, and AS perturbations are frequently observed in cancer. RNA binding protein (RBP) is one of the molecular determinants of AS, and perturbations in RBP-gene network activity are causally associated with cancer development. Here, we performed a systematic analysis to characterize the perturbations in AS events across 18 cancer types. We showed that AS alterations were prevalent in cancer and involved in cancer-related pathways. Given that the extent of AS perturbation was associated with disease severity, we proposed a computational pipeline to identify RBP regulators. Pan-cancer analysis identified a number of conserved RBP regulators, which play important roles in regulating AS of genes involved in cancer hallmark pathways. Our application analysis revealed that the expression of 68 RBP regulators helped in cancer subtyping. Specifically, we identified four subtypes of kidney cancer with differences in cancer hallmark pathway activities and prognosis. Finally, we identified the small molecules that can potentially target the RBP genes and suggested potential candidates for cancer therapy. In summary, our comprehensive AS perturbation landscape analysis identified RBPs as potential therapeutic targets in cancer and provided novel insights into the regulatory functions of RBPs in cancer.

Keywords: RNA binding protein; alterative splicing; cancer; regulatory network; therapeutic targets.

Graphical abstract

Figure 1

Perturbation of AS events across cancer types (A) A total of 18 cancer types were analyzed. (B) The proportion of AS events for different types across cancers. Seven types of AS events were analyzed. (C) The enrichment of different subtypes of AS events. Top: the number of upregulated AS and downregulated AS events in 18 cancer types. Bottom: the odds ratio for up- and downregulated AS events. Right: the total number of differential AS events in 18 cancer types. Red: upregulated AS; green: downregulated AS.

Figure 2

Alternative splicing perturbation similarity of cancer (A) t-SNE plot showing the similarity of cancers. Each dot represents one cancer patient, and the same dots belong to one cancer type. (B) Clustering of cancers based on the similarity of perturbed AS events. (C) Bar plots show the number of differential AS event-involved genes observed in different numbers of cancer types. The genes involved in pan-cancer differential AS events are shown in the box. (D) The enriched functions by the pan-cancer differential AS event-involved genes. Green, biological process in Gene Ontology; blue, molecular functions in Gene Ontology; orange, tissue-specific expressed genes.

Figure 3

AS perturbation correlated with disease severity (A) The flowchart for identifying RBP regulators in cancer. (B) The proportion of curated RBP genes in different classes. (C) The cartoon shows the correlation between AS perturbed severity and survival rates. (D) Kaplan-Meier plots indicating survival of cancer patients with high and low AS activity scores. Top: SA and SR in KIRC. Bottom: SA and SR in LIHC.

Figure 4

Identifying RBP regulators of AS events in cancer (A) Number of dysregulated RBPs across cancer types. Red: upregulated RBPs; green: downregulated RBPs. (B) Number of RBPs observed in different cancers. Cancer-specific RBPs are enlarged at top right. (C and D) Circos plots showing the active and repressive RBP regulators across cancer types. Each circle represents one cancer type, the color indicating the Spearman correlation coefficient, for active RBPs (C) and for repressive RBPs (D).

Figure 5

Function of pan-cancer RBP regulators (A) Number of RBP regulators identified in different number of cancer types. Top: active RBPs. Bottom: repressive RBPs. Three types of RBPs were defined, including cancer-specific, moderate, and pan-cancer RBPs. (B) Heatmap shows whether the corresponding RBPs (columns) were identified in specific cancers (rows). Top: bar plots show the number of AS events in different subtypes. Bottom: river plots show the link between genes and AS types. (C) Network showing the RBP-pathways in cancer. The numbers adjacent to the pathways show the number of RBPs that correlated with each pathway.

Figure 6

Kidney cancer subtyping based on RBP regulators (A) Venn plot showing the overlap of RBPs in KIRC and KIRP. The heatmap on the right shows the expression of 68 RBPs across 826 kidney cancer patients. (B) The heatmap shows the similarity of cancer patients. (C) Distribution of age at diagnosis for patients in different subtypes. (D) Proportion of patients in high versus low stages in four subtypes. (E) Kaplan-Meier plots indicating survival of cancer patients in different subtypes.

Figure 7

Pathway activities across patients in different subtypes (A) Heatmap showing the cancer hallmark-related pathway activities across patients. (B) Boxplots showing the distribution of complement pathway activities for patients in different subtypes. (C) Boxplots showing the distribution of bile acid metabolism pathway activities for patients in different subtypes.

Figure 8

The correlation between expression of RBPs and drug activities (A) The number of drugs or RBPs identified in each cancer type. (B) Network showing the drug-RBP correlations in KIRC. The size of nodes corresponds to the number of correlations. Red lines, positive correlations; blue lines, negative correlations. (C) Sub-network showing the drugs correlated with RBPs used for subtyping. (D and E) Kaplan-Meier plots indicating overall survival (D) and disease-free survival (E) of cancer patients with high and low PPIH expression.