Systemic characterization of alternative splicing related to prognosis and immune infiltration in malignant mesothelioma

Abstract

Background: Malignant mesothelioma (MM) is a relatively rare and highly lethal tumor with few treatment options. Thus, it is important to identify prognostic markers that can help clinicians diagnose mesothelioma earlier and assess disease activity more accurately. Alternative splicing (AS) events have been recognized as critical signatures for tumor diagnosis and treatment in multiple cancers, including MM.

Methods: We systematically examined the AS events and clinical information of 83 MM samples from TCGA database. Univariate Cox regression analysis was used to identify AS events associated with overall survival. LASSO analyses followed by multivariate Cox regression analyses were conducted to construct the prognostic signatures and assess the accuracy of these prognostic signatures by receiver operating characteristic (ROC) curve and Kaplan-Meier survival analyses. The ImmuCellAI and ssGSEA algorithms were used to assess the degrees of immune cell infiltration in MM samples. The survival-related splicing regulatory network was established based on the correlation between survival-related AS events and splicing factors (SFs).

Results: A total of 3976 AS events associated with overall survival were identified by univariate Cox regression analysis, and ES events accounted for the greatest proportion. We constructed prognostic signatures based on survival-related AS events. The prognostic signatures proved to be an efficient predictor with an area under the curve (AUC) greater than 0.9. Additionally, the risk score based on 6 key AS events proved to be an independent prognostic factor, and a nomogram composed of 6 key AS events was established. We found that the risk score was significantly decreased in patients with the epithelioid subtype. In addition, unsupervised clustering clearly showed that the risk score was associated with immune cell infiltration. The abundances of cytotoxic T (Tc) cells, natural killer (NK) cells and T-helper 17 (Th17) cells were higher in the high-risk group, whereas the abundances of induced regulatory T (iTreg) cells were lower in the high-risk group. Finally, we identified 3 SFs (HSPB1, INTS1 and LUC7L2) that were significantly associated with MM patient survival and then constructed a regulatory network between the 3 SFs and survival-related AS to reveal potential regulatory mechanisms in MM.

Conclusion: Our study provided a prognostic signature based on 6 key events, representing a better effective tumor-specific diagnostic and prognostic marker than the TNM staging system. AS events that are correlated with the immune system may be potential therapeutic targets for MM.

Keywords: Alternative splicing; Immune infiltration; Malignant mesothelioma; Prognostic signature; Risk score.

Fig. 1

Experimental design and analyses presented in our work

Fig. 2

Overview of AS profiling and MM-specific exon skipped genes. A Number of AS events and parent genes for each AS event type in 83 MM patients. B UpSet plot of interactions among the seven types of AS events. C KEGG pathway analysis of 103 mutation-associated ES events from 80 genes in MM. The dot size represents the number of enriched genes, and adjusted p values are indicated by the color scale on the side. D Interaction network of 80 ES genes in MM. AA, alternate acceptor; AD, alternate donor; AP, alternate promoter; AT, alternate terminator; ES, exon skip; ME, mutually exclusive exon; RI, retained intron

Fig. 3

Overview of survival-related AS events and functional enrichment analyses of survival-related ES events genes. A Number of survival-related AS events and parent genes for each AS type. B UpSet plot showing the interactions among the seven types of survival-related AS events. C GO analysis of parental genes with survival-associated ES events. D KEGG pathway analysis of parental genes with survival-associated ES events

Fig. 4

Kaplan-Meier plots and ROC curves of prognostic signatures for each AS type. A Kaplan-Meier survival analysis of OS between the low-risk group and the high-risk group in MM patients based on survival-related AS events. The red line indicates the high-risk group, whereas the blue line indicates the low-risk group. B Time-dependent ROC curves to evaluate the predictive performance of each prognostic signature at 3 years and 5 years

Fig. 5

Assessment of the predictive ability of prognostic signatures. A MM patients were divided into the high-risk and low-risk groups based on the risk score. Distributions of the risk score, survival time and the expression heatmap of the candidate AS events of the seven significant prognostic signatures with AUCs > 0.9. B Calculation of the C-index of each AS prognostic signature

Fig. 6

Construction of a prognostic model based on all AS events. A MM patients were divided into high-risk and low-risk groups according to the risk score model. B Kaplan-Meier survival analysis of MM patients in the low-risk and high-risk groups. C Time-dependent ROC curves to evaluate the predictive performance of the prognostic signature at 3 years and 5 years. D The nomogram was constructed based on AS events of the prognostic signature. E Calibration curves for predicting the three- and five-year survival probability of MM patients. F Univariate and multivariable Cox proportional hazards regression models were applied to evaluate the independence of the prognostic signature

Fig. 7

Relationship between risk scores and tumor-infiltrating immune cells in the tumor microenvironment. A Distribution of risk scores in the low- and high-immune infiltration subtypes. B Unsupervised clustering heat map showing the association between the risk score and the immune infiltration subtype of MM patients. C The PSI of 6 key AS events in two immune infiltration subtypes. D The PSI of 6 key AS events between the high-risk score group and the low-risk score group

Fig. 8

Risk scores are correlated with immune cells in the tumor microenvironment. A Estimation of the relative abundances of the 24 tumor-infiltrating immune cells by ImmuCellAI. B Comparisons of the abundances of cytotoxic T (Tc) cells, natural killer (NK) cells, T helper 17 (Th17) cells and induced regulatory T (iTreg) cells between the high-risk and low-risk groups. C Correlation analyses of 24 types of tumor-infiltrating immune cells in MM patients. D Survival analysis of patients with different infiltration of Th17 cells and cytotoxic T cells

Fig. 9

Prognostic SFs and the splicing regulation network. A Kaplan-Meier survival curves of significant prognosis-related SFs, including HSPB1, INTS1 and LUC7L2. B) Construction of the interaction network of survival-related SFs and survival-related AS events. The positive/negative correlations between the expression of SFs and PSI values for AS events are represented with red/green lines