The Roles of Alternative Splicing in Tumor-immune Cell Interactions

Wang, Yue; Zhang, Honglei; Jiao, Baowei; Nie, Jianyun; Li, Xi-Yin; Wang, Wenhuan; Wang, Hairui

doi:10.2174/1568009620666200619123725

Cited by 10 publications

(12 citation statements)

References 113 publications

(123 reference statements)

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“…Previous studies have shown that alternative splicing plays an important role in cancer immunity. 48 , 49 , 50 , 51 The immunogenic epitope in tumor cells is constitutively spliced out, which made the tumor cells cloud not be recognized by T‐cell receptor engineered T cells. 7 Therefore, whether the risk score could complement with immune score to better evaluate the efficacy and prognosis of immunotherapy still needs more in‐depth analysis.…”

Section: Discussionmentioning

confidence: 99%

Novel prognostic model predicts overall survival in colon cancer based on RNA splicing regulation gene expression

Luo

Chen

et al. 2022

Cancer Science

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Colon cancer is the third most common cancer and the second leading cause of cancer‐related death worldwide. Dysregulated RNA splicing factors have been reported to be associated with tumorigenesis and development in colon cancer. In this study, we interrogated clinical and RNA expression data of colon cancer patients from The Cancer Genome Atlas (TCGA) dataset and the Gene Expression Omnibus (GEO) database. Genes regulating RNA splicing correlated with survival in colon cancer were identified and a risk score model was constructed using Cox regression analyses. In the risk model, RNA splicing factor peroxisome proliferator‐activated receptor‐γ coactivator‐1α (PPARGC1) is correlated with a good survival outcome, whereas Cdc2‐like kinase 1(CLK1), CLK2, and A‐kinase anchor protein 8‐like (AKAP8L) with a bad survival outcome. The risk model has a good performance for clinical prognostic prediction both in the TCGA cohort and the other two validation cohorts. In the tumor microenvironment (TME) analysis, the immune score was higher in the low‐risk group, and TME‐related pathway gene expression was also higher in low‐risk group. We further verified the mRNA and protein expression levels of these four genes in the adjacent nontumor, tumor, and liver metastasis tissues of colon cancer patients, which were consistent with bioinformatics analysis. In addition, knockdown of AKAP8L can suppress the proliferation and migration of colon cancer cells. Animal studies have also shown that AKAP8L knockdown can inhibit tumor growth in colon cancer in vivo. We established a prognostic risk model for colon cancer based on genes related to RNA splicing regulation and uncovered the role of AKAP8L in promoting colon cancer progression.

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Section: Discussionmentioning

confidence: 99%

Novel prognostic model predicts overall survival in colon cancer based on RNA splicing regulation gene expression

Luo

Chen

et al. 2022

Cancer Science

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“…Studies have reported that aberrant AS events are involved in a variety of tumor processes, including immune destruction [ 49 , 50 ]. Neoepitopes derived from aberrant AS events are recognized by T lymphocytes to induce antitumor immune responses [ 51 ].…”

Section: Discussionmentioning

confidence: 99%

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

Lai

Yang

2021

BMC Cancer

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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.

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“…AS regulates immune responses across a variety of conditions ( 17 ). The AS events of specific genes could influence tumor growth between tumor-immune cell interactions ( 18 ). Studies have investigated how AS serves to modulate lymphocyte activity ( 19 ).…”

Section: Discussionmentioning

confidence: 99%

A Prediction Model Using Alternative Splicing Events and the Immune Microenvironment Signature in Lung Adenocarcinoma

Zhu

Wang²,

Sun

et al. 2021

Front. Oncol.

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BackgroundAlternative splicing (AS) is a gene regulatory mechanism that drives protein diversity. Dysregulation of AS is thought to play an essential role in cancer initiation and development. This study aimed to construct a prognostic signature based on AS and explore the role in the tumor immune microenvironment (TIME) in lung adenocarcinoma.MethodsWe analyzed transcriptome profiling and clinical lung adenocarcinoma data from The Cancer Genome Atlas (TCGA) database and lists of AS-related and immune-related signatures from the SpliceSeq. Prognosis-related AS events were analyzed by univariate Cox regression analysis. Gene set enrichment analyses (GSEA) were performed for functional annotation. Prognostic signatures were identified and validated using univariate and multivariate Cox regression, LASSO regression, Kaplan–Meier survival analyses, and proportional hazards model. The context of TIME in lung adenocarcinoma was also analyzed. Gene and protein expression data of Cyclin-Dependent Kinase Inhibitor 2A (CDKN2A) were obtained from ONCOMINE and Human Protein Atlas. Splicing factor (SF) regulatory networks were visualized.ResultsA total of 19,054 survival-related AS events in lung adenocarcinoma were screened in 1,323 genes. Exon skip (ES) and mutually exclusive exons (ME) exhibited the most and fewest AS events, respectively. Based on AS subtypes, eight AS prognostic signatures were constructed. Patients with high-risk scores were associated with poor overall survival. A nomogram with good validity in prognostic prediction was generated. AUCs of risk scores at 1, 2, and 3 years were 0.775, 0.736, and 0.759, respectively. Furthermore, the prognostic signatures were significantly correlated with TIME diversity and immune checkpoint inhibitor (ICI)-related genes. Low-risk patients had a higher StromalScore, ImmuneScore, and ESTIMATEScore. AS-based risk score signature was positively associated with CD8+ T cells. CDKN2A was also found to be a prognostic factor in lung adenocarcinoma. Finally, potential functions of SFs were determined by regulatory networks.ConclusionTaken together, our findings show a clear association between AS and immune cell infiltration events and patient outcome, which could provide a basis for the identification of novel markers and therapeutic targets for lung adenocarcinoma. SF networks provide information of regulatory mechanisms.

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The Roles of Alternative Splicing in Tumor-immune Cell Interactions

Cited by 10 publications

References 113 publications

Novel prognostic model predicts overall survival in colon cancer based on RNA splicing regulation gene expression

Novel prognostic model predicts overall survival in colon cancer based on RNA splicing regulation gene expression

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

A Prediction Model Using Alternative Splicing Events and the Immune Microenvironment Signature in Lung Adenocarcinoma

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