Identification of prognostic alternative splicing events in sarcoma

Abstract

Sarcoma is a rare malignancy with unfavorable prognoses. Accumulating evidence indicates that aberrant alternative splicing (AS) events are generally involved in cancer pathogenesis. The aim of this study was to identify the prognostic value of AS-related survival genes as potential biomarkers, and highlight the functional roles of AS events in sarcoma. RNA-sequencing and AS-event datasets were downloaded from The Cancer Genome Atlas (TCGA) sarcoma cohort and TCGA SpliceSeq, respectively. Survival-related AS events were further assessed using a univariate analysis. A multivariate Cox regression analysis was also performed to establish a survival-gene signature to predict patient survival, and the area-under-the-curve method was used to evaluate prognostic reliability. KOBAS 3.0 and Cytoscape were used to functionally annotate AS-related genes and to assess their network interactions. We detected 9674 AS events in 40,184 genes from 236 sarcoma samples, and the 15 most significant genes were then used to construct a survival regression model. We further validated the involvement of ten potential survival-related genes (TUBB3, TRIM69, ZNFX1, VAV1, KCNN2, VGLL3, AK7, ARMC4, LRRC1, and CRIP1) in the occurrence and development of sarcoma. Multivariate survival model analyses were also performed, and validated that a model using these ten genes provided good classifications for predicting patient outcomes. The present study has increased our understanding of AS events in sarcoma, and the gene-based model using AS-related events may serve as a potential predictor to determine the survival of sarcoma patients.

Figure 1

An overview of AS events in sarcoma. (A) The seven different subtypes of AS classification include ES, RI, AP, AT, AD, AA, and ME. (B) The relationship between AS events and their distribution among the seven subtypes. The ES events occurred in 15,311 genes, whereas the AT and AP events occurred in 8287 and 7837 genes, respectively. (C) Histogram showing the distribution among the seven types of splicing events that were significantly associated with overall survival prognoses. The ES, AT, and AP events accounted for the majority of the splicing events associated with overall survival prognoses. (D) Histogram showing the distribution among the seven types of splicing events that were associated with survival-related genes. The ES, AT and AP events presented a large amount of survival related genes. (E) Venn diagram showing the intersection between survival-related AS and survival-related genes. In total, we identified 267 genes related to survival.

Figure 2

AS events are closely related to sarcoma prognoses. (A) The landscape of the seven subtypes of AS-associated genes that were significantly related to overall survival. (B) Distribution among the seven subtypes of AS-associated genes that were significantly related to survival genes. (C) AS-subtype area-under-the-curve (AUC) analyses for the classification of the top 15 AS-associated genes based on prognoses. (D) AS-subtype AUC analyses for prognosis classifications of the top 15 AS-associated genes based on multivariate modeling.

Figure 3

The gene interaction network significantly related to overall survival. The different colors correspond to different AS-event subtypes, where yellow represents ES events and red represents ES events, according to the annotation on diagram. The ES event related genes, such as VEGFA, NME1, PTK2, and RFC5, showed significant interactions with the 267 genes associated with overall survival prognoses.

Figure 4

Functional annotations of AS events. (A) A KEGG enrichment analysis of the genes that were significantly related to overall survival. The AS events, such as ES, AP, RI, and ME, were the top 4 AS events that showed significant correlation with genes related to overall survival prognoses. (B) The KEGG enrichment results for the AS genes with the highest values for gene significance. AS events are closed linked to various biological functions. The results indicated that the ES and ME events are involved in complex pathway interactions, such as those related to nuclear, cancer, and immune cell pathways, among others.

Figure 5

AS gene-related risk scores and survival analyses. (A) Risk-score analysis of the sarcoma samples. (B) The gene expression heat map for the ten hub genes. Gene expression differences were significant between the high- and low-risk groups. (C) Relationship between survival time and survival status for each sarcoma sample. (D) Kaplan–Meier curves for overall survival and transcriptome gene expression levels. (E) Area-under-the-curve (AUC) multifactorial survival analysis related to overall patient survival based on gene expression at the transcriptome level. (F) Kaplan–Meier curves for overall survival and AS genes. (G) AUC multifactorial analysis of AS gene overall survival related to patient survival.

Figure 6

The association between the expression levels of the ten hub genes and overall survival of patients with sarcoma. (A–G) The upregulated expression levels of KCNN2, CRIP1, AK7, ZNFX1, VAV1, TRIM69, and VGLL3 were significantly associated with a better overall patient survival for sarcoma. (H–J) The upregulated expressions of LRRC1, ARMC4, and TUBB3 were significantly associated with poor prognoses in sarcoma patients.