Figure - available from: Frontiers in Oncology
This content is subject to copyright.
Identification of immune-related alternative splicing events. (A) Differential AS events between low and medium immune cell infiltration groups. (B) Differential AS events between medium and high immune cell infiltration groups. (C) Venn plot for the ultimate immune-related AS events.
Source publication
Background
Glioma is the most common malignant brain tumor in adults, with its tumor-promoting immune microenvironment always being intricate to handle with. Amounts of evidence has accumulated to suggest that alternative splicing (AS) is related to tumor immune microenvironment. However, comprehensive analysis of immune-related AS events and their...
Similar publications
Due to limitations of sensitive biomarkers, the clinical prognosis of patients with head and neck squamous cell carcinoma (HNSCC) remains poor. Alternative splicing (AS) is the basis of both transcriptome and proteome richness, so more and more evidence indicates an important relationship between AS and tumor progression. The aim of this study was...
Background
Accumulating evidences indicate that the specific alternative splicing (AS) events are linked to the occurrence and prognosis of gastric cancer (GC). Nevertheless, the impact of AS is still unclear and needed to further elucidation.
Methods
The expression profile of GC and normal samples were downloaded from TCGA. AS events were achieve...
Background: N6-methyladenosine (m6A) RNA methylation is an important epigenetic modification affecting alternative splicing (AS) patterns of genes to regulate gene expression. AS drives protein diversity and its imbalance may be an important factor in tumorigenesis. However, the clinical significance of m6A RNA methylation regulator-related AS in t...
Alternative splicing (AS) is a gene regulatory mechanism that drives protein diversity and dysregulation of AS plays a significant role in tumorigenesis. This study aimed to develop a prognostic signature based on AS and elucidate the role in tumor immune microenvironment (TIME) in clear cell renal cell carcinoma (ccRCC). The prognosis-related AS e...
Citations
... AS is the primary driving force behind generating diverse proteins, which is the basis for the remarkable and complex functional regulation seen in eukaryotic cells (Xie et al., 2019). Genome-wide studies showed that 90-95% of human genes undergo some level of AS, and almost one-third of them were proved to be generated multiple protein isoforms (Kim et al., 2014;Wang et al., 2021). These processes usually show an extreme complexity in brain tissues and can play an important role in the progression of many CNS diseases (Merkin et al., 2012;Galarza-Munoz et al., 2017;Consortium 2020). ...
Glioma is a primary high malignant intracranial tumor with poorly understood molecular mechanisms. Previous studies found that both DNA methylation modification and gene alternative splicing (AS) play a key role in tumorigenesis of glioma, and there is an obvious regulatory relationship between them. However, to date, no comprehensive study has been performed to analyze the influence of DNA methylation level on gene AS in glioma on a genome-wide scale. Here, we performed this study by integrating DNA methylation, gene expression, AS, disease risk methylation at position, and clinical data from 537 low-grade glioma (LGG) and glioblastoma (GBM) individuals. We first conducted a differential analysis of AS events and DNA methylation positions between LGG and GBM subjects, respectively. Then, we evaluated the influence of differential methylation positions on differential AS events. Further, Fisher’s exact test was used to verify our findings and identify potential key genes in glioma. Finally, we performed a series of analyses to investigate influence of these genes on the clinical prognosis of glioma. In total, we identified 130 glioma-related genes whose AS significantly affected by DNA methylation level. Eleven of them play an important role in glioma prognosis. In short, these results will help to better understand the pathogenesis of glioma.
... These studies demonstrate that AS events can play a very important role in the TIME. What's more, the relationship between AS events and TIME has been con rmed in a variety of tumor diseases, such as hepatocellular carcinoma [46], esophageal squamous cell carcinoma [47], gastric carcinoma [48], endometrial carcinoma [49], triple negative breast carcinoma [50] and glioma [51]. However, this relationship has not been studied inccRCC. ...
Background
Although extensive researches related alternative splicing (AS) as the prognostic markers of patients in cancers, which remains unknown in clear cell renal cell carcinoma (ccRCC), especially in immunotherapy. Therefore, a novel survival-associated AS signature was established to predict prognosis of patients and explored its correlation with immune cell infiltration or immune checkpoint expression to explain the phenomenon of resistance to immunotherapy in ccRCC.Methodsccording to AS data, clinical information and gene expression data in ccRCC, overall survival-related AS events was identified and further the AS-related prognostic risk model established by LASSO regression and multi-Cox regression analysis was evaluated by Kaplan-Meier survival analysis, the ROC curves and a nomogram model. Then we clarified the biological processes and pathways by GSEA, and further measured the immune cell infiltration by ESTIMATE, CIBERSORT and ssGSEA. Finally we analysed the clinical features and immune features of different parental genes, and quested the splicing factors regulating riskScore-related AS events by Spearman correlation analysis.ResultsWe obtained the most significant 5 AS events, including C4orf19|69001|AT, UACA|31438|AP, FAM120C|89237|AT, TRIM16L|39629|AP and SEC31A|100881|ES, to establish the prognostic risk model, and further illustrated the stability and importance of the riskScore prognostic signatures. Then we found that in high risk group, most of the top 10 GO enrichments and the KEGG pathway were closely related to the immunity, and the higher immune cell infiltration, and higher expression of classic immune checkpoints such as PD1 and CTLA4. In addition, 6 different parental genes were obtained, including C4orf19, ARHGAP24 DNASE1L3, P4HA1, SLC39A14 and TAF1D. These 6 genes could not be the independent prognostic signatures, but the expression of these genes was closely related to immune cells infiltration and the expression of immune checkpoints. Finally, we got aberrant 52 splicing factors regulating riskScore-related AS events.Conclusion
Our study discovered that overall survival-related AS events mediated by aberrant splicing factors can be constructed a prognostic risk model to predict prognosis of patiens and utilized to index the situation of immune cell infiltration and immune checkpoint expression that impact tumor immune microenvironment in ccRCC.
Background
Immune microenvironment is involved in tumor initiation and progression, and its effect on glioblastoma (GBM) is still unknown.
Object
We sought to investigate the association between immune status and GBM.
Methods
Transcriptome data and the relevant clinical data were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases, and we identified two immune subtypes based on 29 immune-associated gene sets.
Results
Through single-sample gene set enrichment analysis (ssGSEA), we found that the high-immunity subtype had the most tumor-infiltrating immune cells and immune checkpoint molecules in GBM patients. Furthermore, we could more effectively identify immune signature pathways in GBM.
Conclusion
After validation with the GEO dataset, we conclude that the identified GBM high-immune subtypes may be amenable to the application of novel immune therapy for GBM.
Background
Immune-related lncRNA is involved in tumor initiation and progression, while its effect in glioblastoma (GBM) is still unknown.
Objective
We sought to investigate the association between immune-related lncRNA (ir-lncRNA) and GBM.
Methods
Transcriptomic and clinical data were obtained from the TCGA dataset, and we found 2008 ir-lncRNA differentially expressed between GBM and adjacent brain tissues.
Results
Appling the univariate Cox and Lasso regression model, we found 30 prognosis-related ir-lncRNA pairs to construct a Cox regression risk model to associate the outcome of GBM patients. Furthermore, with this risk model, we can identify the tumor immune infiltration status, the expression of immunosuppressive biomarkers, and chemical sensitivity in GBM patients.
Conclusions
We constructed an immunologic risk model with lncRNA to associate the survival outcome of GBM patients, which can provide useful biomarkers.
Alternative splicing controls gene expression at the transcriptional level, producing structurally and functionally distinct protein heterodimers. Aberrant alternative splicing greatly affects cell development and plays an important role in the invasion and metastasis of many types of cancer. Recently, it has been shown that alternative splicing can alter the tumor microenvironment and regulate processes such as remodeling, immunity, and inflammation in the tumor microenvironment. However, there is no comprehensive literature review of the complex relationship between alternative splicing and the tumor microenvironment. Therefore, this review aims to collect all the latest data on this topic and provide a new perspective on the therapeutic and potential prognostic markers of cancer.
Background: ZCCHC24 is one of alternative splicing factors which affect immune cell infiltration, but the mechanisms whereby it drives immune infiltration in cancer remain uncertain.
Methods: ZCCHC24 expression was analyzed via the Oncomine database and Tumor Immune Estimation Resource (TIMER) site. We evaluated the influence of ZCCHC24 on clinical prognosis using Kaplan-Meier plotter, the PrognoScan database and Gene Expression Profiling Interactive Analysis (GEPIA). The correlations between ZCCHC24 and cancer immune infiltrates was investigated via TIMER. In addition, correlations between ZCCHC24 expression and gene marker sets of immune infiltrates were analyzed by TIMER and GEPIA.
Results: ZCCHC24 significantly impacts the prognosis of diverse cancers via The Cancer Genome Atlas (TCGA). elevated ZCCHC24 expression was significantly correlated with poor OS and progression-free survival (PFS) in gastric cancers (OS HR = 2.11, P = 6.3e-12; PFS HR = 2.03, P = 3.1e-11). Moreover, ZCCHC24 significantly impacts the prognosis of diverse cancers via The Cancer Genome Atlas (TCGA). Specifically, high ZCCHC24 expression was correlated with worse OS and PFS in Stage, T stage, N stage, M stage, Lauren classification and differentiation of gastric cancer patients. ZCCHC24 expression was positively correlated with infiltrating levels of CD4+ T，CD8+ T cells, Macrophages, Neutrophils and dendritic cells (DCs) in stomach adenocarcinoma (STAD) and lung squamous cell carcinoma (LUSC). ZCCHC24 expression showed strong correlations with diverse immune marker sets in STAD and LUSC.
Conclusions: These findings suggest that ZCCHC24 is a key factor which governs immune cell recruitment to gastric cancer and lung squamous cell carcinoma, potentially playing a vital role in governing immune cell infiltration and thus representing a valuable prognostic biomarker in gastric cancer and lung squamous cell carcinoma patients.
Background: Head and neck squamous cell carcinoma (HNSC) is a prevalent and heterogeneous malignancy with poor prognosis and high mortality rates. There is significant evidence of alternative splicing (AS) contributing to tumor development, suggesting its potential in predicting prognosis and therapeutic efficacy. This study aims to establish an AS-based prognostic signature in HNSC patients.
Methods: The expression profiles and clinical information of 486 HNSC patients were downloaded from the TCGA database, and the AS data were downloaded from the TCGA SpliceSeq database. The survival-associated AS events were identified by conducting a Cox regression analysis and utilized to develop a prognostic signature by fitting into a LASSO-regularized Cox regression model. Survival analysis, univariate and multivariate Cox regression analysis, and receiver operating characteristic (ROC) curve analysis were performed to evaluate the signature and an independent cohort was used for validation. The immune cell function and infiltration were analyzed by CIBERSORT and the ssGSEA algorithm.
Results: Univariate Cox regression analysis identified 2726 survival-associated AS events from 1714 genes. The correlation network reported DDX39B, PRPF39, and ARGLU1 as key splicing factors (SF) regulating these AS events. Eight survival-associated AS events were selected and validated by LASSO regression to develop a prognostic signature. It was confirmed that this signature could predict HNSC outcomes independent of other variables via multivariate Cox regression analysis. The risk score AUC was more than 0.75 for 3 years, highlighting the signature’s prediction capability. Immune infiltration analysis reported different immune cell distributions between the two risk groups. The immune cell content was higher in the high-risk group than in the low-risk group. The correlation analysis revealed a significant correlation between risk score, immune cell subsets, and immune checkpoint expression.
Conclusion: The prognostic signature developed from survival-associated AS events could predict the prognosis of HNSC patients and their clinical response to immunotherapy. However, this signature requires further research and validation in larger cohort studies.
Background: N6-methyladenosine (m6A) RNA methylation is an important epigenetic modification affecting alternative splicing (AS) patterns of genes to regulate gene expression. AS drives protein diversity and its imbalance may be an important factor in tumorigenesis. However, the clinical significance of m6A RNA methylation regulator-related AS in the tumor microenvironment has not been investigated in low-grade glioma (LGG).
Methods: We used 12 m6A methylation modulatory genes (WTAP, FTO, HNRNPC, YTHDF2, YTHDF1, YTHDC2, ALKBH5, YTHDC1, ZC3H13, RBM15, METTL14, and METTL3) from The Cancer Genome Atlas (TCGA) database as well as the TCGA-LGG (n = 502) dataset of AS events and transcriptome data. These data were downloaded and subjected to machine learning, bioinformatics, and statistical analyses, including gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Univariate Cox, the Least Absolute Shrinkage and Selection Operator (LASSO), and multivariable Cox regression were used to develop prognostic characteristics. Prognostic values were validated using Kaplan-Maier survival analysis, proportional risk models, ROC curves, and nomograms. The ESTIMATE package, TIMER database, CIBERSORT method, and ssGSEA algorithm in the R package were utilized to explore the role of the immune microenvironment in LGG. Lastly, an AS-splicing factor (SF) regulatory network was examined in the case of considering the role of SFs in regulating AS events.
Results: An aggregate of 3,272 m6A regulator-related AS events in patients with LGG were screened using six machine learning algorithms. We developed eight AS prognostic characteristics based on splice subtypes, which showed an excellent prognostic prediction performance. Furthermore, quantitative prognostic nomograms were developed and showed strong validity in prognostic prediction. In addition, prognostic signatures were substantially associated with tumor immune microenvironment diversity, ICB-related genes, and infiltration status of immune cell subtypes. Specifically, UGP2 has better promise as a prognostic factor for LGG. Finally, splicing regulatory networks revealed the potential functions of SFs.
Conclusion: The present research offers a novel perspective on the role of AS in m6A methylation. We reveal that m6A methylation regulator-related AS events can mediate tumor progression through the immune-microenvironment, which could serve as a viable biological marker for clinical stratification of patients with LGG so as to optimize treatment regimens.
Glioblastoma (GBM) is the most malignant form of brain cancer in the world. Nevertheless, the survival rate of patients with GBM is extremely low. N6-methyladenosine (m6A) and long noncoding RNAs (lncRNAs) conduct important biological functions in patients' survival status and the immunotherapeutic response. Here, m6A-related lncRNAs were identified by a co-expression method. Univariate and multivariate Cox regression together with LASSO were applied to establish the risk model. Kaplan-Meier and ROC analysis were applied to evaluate the prediction power of this risk model. Finally, the related immune profiling and chemical sensitivity targets were also investigated. The risk model holding three m6A-related lncRNAs was confirmed as an independent predictor for the prognosis. Furthermore, we found the risk model based on m6A-related lncRNAs is associated with the immune status, immunosuppressive biomarkers, and chemo-sensitivity in GBM patients. The RP11-552D4.1 is found to facilitate neuronal proliferation. This risk model consisted of m6A-related lncRNAs may be available for the clinical interventions in GBM patients.
The median survival of patients with gliomas is relatively short. To investigate the epigenetic mechanisms associated with poor survival, we analyzed publicly available datasets from patients with glioma. This analysis revealed 12 prognosis-related m6A regulatory genes that may be responsible for poor prognosis. These genes may be involved in genomic changes inherent to oxidative phosphorylation, adipogenesis, hedgehog signaling, and Myc signaling. We reconstructed a risk model with univariate and multivariate Cox analyses and identified older age and the m6A risk score as independent risk factors for predicting the prognosis of glioma patients, which is associated with glioma immune infiltration. In conclusion, m6A regulatory genes may serve as both reliable biomarkers and potential targets to increase the chance of survival of patients with glioma.
