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Prognostic alternative splicing signature reveals the landscape of immune infiltration in Pancreatic Cancer.

ABSTRACT: Background: Pancreatic cancer (PC) is an aggressive cancer with worse survival in the world. Emerging evidence suggested that the imbalance of alternative splicing (AS) is a hallmark of cancer and indicated poor prognosis of patients. Genes-derived splicing events can produce neoepitopes for immunotherapy. However, the profound study of splicing profiling in PC is still elusive. We aimed to identification of novel prognostic signature across a comprehensive splicing landscape and reveal their relationship with tumor-infiltrating immune cells in pancreatic cancer microenvironment. Methods: Based on integrated analysis of splicing profiling and clinical data, differentially splicing events were filtered out. Then, stepwise Cox regression analysis was applied to identify survival-related splicing events and construct prognostic signature. Functional enrichment analysis was performed to explore biology function. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were performed to validate the predictive effect of predictive signature. We also verified the clinical value of prognostic signature under the influence of different clinical parameters. For deeper analysis, we evaluated the correlation between prognostic signature and infiltrating immune cells by CIBERSORT. Results: According to systematic analyzing, a final six splicing events were identified and validated the good prognostic capability in entire TCGA dataset, validation set 1 and validation set 2 by Kaplan-Meier curves (P < 0.0001). The area under the curve (AUC) of ROC curves were also confirmed the high predictive efficiency of the prognostic signature in these three cohorts (AUC = 0.857, 0.895 and 0.788). In order to validate whether prognostic signature highlights a correlation between AS and immune contexture, CIBERSORT was performed to analyze the proportion of tumor-infiltrating immune cells in PC. Based on prognostic signature, we identified survival-related immune cells including CD8 T cells (P = 0.0111), activated CD4 memory T cells (P = 0.0329) and resting mast cells (P = 0.0352). Conclusion: In conclusion, our study contribute to provide a promising prognostic signature based on six splicing events and revealed prognosis-related immune cells which indeed represented novel tumor drivers and provide potential targets for personalized therapeutic.

SUBMITTER: Wang L

PROVIDER: S-EPMC7545682 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe incidence of thyroid cancer (THCA) continues to increase in recent decades. Accumulating evidence showed that the unbalanced alternative splicing (AS) promotes the occurrence of cancers and leads to poor prognosis of patients. However, the research on alternative splicing events in THCA is lacking, and its underlying mechanism is not fully understood. This study identifies a novel prognostic signature based on AS events to reveal the relationship of AS with tumor immune microenvironment.MethodsBased on the AS data, transcriptional data, and clinical information, the differentially expressed alternative splicings (DEASs) were screened out. Least absolute shrinkage and selection operator (LASSO) regression and multi-Cox regression analyses were employed to identify prognostic results related to AS events and establish a prognostic signature. The predictive ability of the signature was assessed by Kaplan-Meier (K-M) survival curve, risk plots, and receiver operating characteristic (ROC) curves. Furthermore, correlations between tumor-infiltrating immune cells, immune checkpoints, immune score and prognostic signature were analyzed.ResultsAccording to the LASSO regression analysis, a total of five AS events were selected to construct the signature. K-M survival curve showed that the higher the risk score, the worse the OS of the patients. Risk plots further confirmed this result. ROC curves indicated the high predictive efficiency of the prognostic signature. As for tumor immune microenvironment, patients in the high-risk group had a higher proportion of immune cells, including plasma cell, CD8+ T cell, macrophages (M0 and M2), and activated dendritic cell. Immune checkpoint proteins, such as PDCD1LG2, HAVCR2, CD274, etc., were significantly higher in the high-risk group. We also found that the ESTIMATE score, stromal score, and immune score were lower in the high-risk group, while the result of tumor purity was the opposite.ConclusionsCollectively, a prognostic signature consisting of five AS events in THCA was established. Furthermore, there was an inextricable correlation between immune cell infiltration, immune checkpoint proteins, and AS events. This study will provide a basis for THCA immunotherapy in the future.

Project description:Background: The tumor microenvironment (TME) mainly comprises tumor cells and tumor-infiltrating immune cells mixed with stromal components. Latestresearch hasdisplayed that tumor immune cell infiltration (ICI) is associated with the clinical outcome of patients with osteosarcoma (OS). This work aimed to build a gene signature according to ICI in OS for predicting patient outcomes. Methods: The TARGET-OS dataset was used for model training, while the GSE21257 dataset was taken forvalidation. Unsupervised clustering was performed on the training cohort based on the ICI profiles. The Kaplan-Meier estimator and univariate Cox proportional hazards models were used to identify the differentially expressed genes between clusters to preliminarily screen for potential prognostic genes. We incorporated these potential prognostic genes into a LASSO regression analysis and produced a gene signature, which was next assessed with the Kaplan-Meier estimator, Cox proportional hazards models, ROC curves, IAUC, and IBS in the training and validation cohorts. In addition, we compared our signature to previous models. GSEAswere deployed to further study the functional mechanism of the signature. We conducted an analysis of 22 TICsfor identifying the role of TICs in the gene signature's prognosis ability. Results: Data from the training cohort were used to generate a nine-gene signature. The Kaplan-Meier estimator, Cox proportional hazards models, ROC curves, IAUC, and IBS validated the signature's capacity and independence in predicting the outcomes of OS patients in the validation cohort. A comparison with previous studies confirmed the superiority of our signature regarding its prognostic ability. Annotation analysis revealed the mechanism related to the gene signature specifically. The immune-infiltration analysis uncoveredkey roles for activated mast cells in the prognosis of OS. Conclusion: We identified a robust nine-gene signature (ZFP90, UHRF2, SELPLG, PLD3, PLCB4, IFNGR1, DLEU2, ATP6V1E1, and ANXA5) that can predict OS outcome precisely and is strongly linked to activated mast cells.

Project description:BackgroundGlioma 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 clinical significance are still lacking in glioma.MethodsAS events and transcriptome data of 653 glioma patients were downloaded online. ssGSEA was performed on transcriptome data of 653 patients to divided them into low, medium and high immune cell infiltration groups. Immune-related AS events were filtrated based on this grouping. Then lasso Cox regression analysis and multivariate Cox regression analysis were done to achieve an immune-related AS events prognostic signature for glioma. Kaplan-Meier analysis, ROC analyses, univariate Cox regression and multivariate Cox regression were performed to reveal the independent prognostic role of this signature. Meanwhile, a nomogram was constructed to achieved better prognostic value for glioma patients. Besides, functional enrichment analyses and correlation analyses with immune cells infiltration were used to validated the immune-related characteristic of this signature.Results36 immune-related AS events were achieved based on the grouping mentioned above. A nine-immune-related alternative splicing event signature was built for glioma patients. This signature showed an independent prognostic value and a nomogram containing gender, age, Karnofsky performance score, grade, IDH status, MGMT promoter status and risk score derived from the signature was constructed with a higher predictive ability for overall survival. Association with the infiltration of immune cell subtypes was validated and functional enrichment analysis found that the signature was mainly enriched in immune-related and pro-tumor functions.ConclusionOur research presented all immune-related AS events in glioma, identified an immune-related prognostic AS events risk model and a nomogram was constructed to predict the prognosis individually and more precisely. Tight connection was verified between this signature and clinical characteristics. Also, immune cells infiltration and immune checkpoints expression level were proved to link to risk scores, which enhanced the understanding of relationship between AS events and glioma immune microenvironment, firstly revealing the potential role of AS in immunotherapy of glioma.

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Prognostic alternative splicing signature reveals the landscape of immune infiltration in Pancreatic Cancer.

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