Dataset Information

Characterization of the alternative splicing landscape in lung adenocarcinoma reveals novel prognosis signature associated with B cells.

ABSTRACT:

Background

Lung cancer is the second most commonly diagnosed cancer and the leading cause of cancer-related death. Malignant pleural effusion (MPE) is a special microenvironment for lung cancer metastasis. Alternative splicing, which is regulated by splicing factors, affects the expression of most genes and influences carcinogenesis and metastasis.

Methods

mRNA-seq data and alternative splicing events in lung adenocarcinoma (LUAD) were obtained from The Cancer Genome Atlas (TCGA). A risk model was generated by Cox regression analyses and LASSO regression. Cell isolation and flow cytometry were used to identify B cells.

Results

We systematically analyzed the splicing factors, alternative splicing events, clinical characteristics, and immunologic features of LUAD in the TCGA cohort. A risk signature based on 23 alternative splicing events was established and identified as an independent prognosis factor in LUAD. Among all patients, the risk signature showed a better prognostic value in metastatic patients. By single-sample gene set enrichment analysis, we found that among tumor-infiltrating lymphocytes, B cells were most significantly correlated to the risk score. Furthermore, we investigated the classification and function of B cells in MPE, a metastatic microenvironment of LUAD, and found that regulatory B cells might participate in the regulation of the immune microenvironment of MPE through antigen presentation and promotion of regulatory T cell differentiation.

Conclusions

We evaluated the prognostic value of alternative splicing events in LUAD and metastatic LUAD. We found that regulatory B cells had the function of antigen presentation, inhibited naïve T cells from differentiating into Th1 cells, and promoted Treg differentiation in LUAD patients with MPE.

SUBMITTER: Shao MM

PROVIDER: S-EPMC10335703 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Characterization of the alternative splicing landscape in lung adenocarcinoma reveals novel prognosis signature associated with B cells.

Shao Ming-Ming MM Zhai Kan K Huang Zhong-Yin ZY Yi Feng-Shuang FS Zheng Sheng-Cai SC Liu Ya-Lan YL Qiao Xin X Chen Qing-Yu QY Wang Zhen Z Shi Huan-Zhong HZ

PloS one 20230711 7

<h4>Background</h4>Lung cancer is the second most commonly diagnosed cancer and the leading cause of cancer-related death. Malignant pleural effusion (MPE) is a special microenvironment for lung cancer metastasis. Alternative splicing, which is regulated by splicing factors, affects the expression of most genes and influences carcinogenesis and metastasis.<h4>Methods</h4>mRNA-seq data and alternative splicing events in lung adenocarcinoma (LUAD) were obtained from The Cancer Genome Atlas (TCGA). ...[more]

PMID: 37432957

Similar Datasets

Project description:Background Cuproptosis, a recently discovered type of programmed cell death (PCD), paves a new avenue for cancer treatment. It has been revealed that PCD-related lncRNAs play a critical role in various biological processes of lung adenocarcinoma (LUAD). However, the role of cuproptosis-related lncRNA (CuRLs) remains unclear. This study aimed to identify and validate a CuRLs-based signature for the prognostic prediction of patients with LUAD. Methods RNA sequencing data and clinical information of LUAD were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Pearson correlation analysis was used to identify CuRLs. Univariate Cox regression analysis, Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, and stepwise multivariate Cox analysis were applied to construct a novel prognostic CuRLs signature. A nomogram was developed for the prediction of patient survival outcomes. Gene set variation analysis (GSVA), gene set enrichment analysis (GSEA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were utilized to explore potential functions underlying the CuRLs signature. Patients were divided into low- and high-risk groups. Several algorithms, such as tumor immune estimation resource (TIMER), cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), and QuanTIseq, were combined to comprehensively investigate the differences in immune landscape between different risk groups. Sensitivity to common anticancer drugs was analyzed using the pRRophetic algorithm. Results We constructed a novel prognostic signature based on 10 CuRLs, including CARD8-AS1, RUNDC3A-AS1, TMPO-AS1, MIR31HG, SEPSECS-AS1, DLGAP1-AS1, LINC01137, ZSCAN16-AS1, APTR, and ELOA-AS1. This 10-CuRLs risk signature showed great diagnostic accuracy combined with traditional clinical risk factors, and a nomogram was constructed for potential clinical translation. The tumor immune microenvironment was significantly different between different risk groups. Among drugs commonly used in the treatment of lung cancer, the sensitivity of cisplatin, docetaxel, gemcitabine, gefitinib, and paclitaxel was higher in low-risk patients, and patients in the low-risk group may benefit more from imatinib. Conclusions These results revealed the outstanding contribution of the CuRLs signature to the evaluation of prognosis and treatment modalities for patients with LUAD. The differences in characteristics between different risk groups provide an opportunity for better patient stratification and to explore novel drugs in different risk groups.

Project description:BackgroundAlternative splicing (AS) is a critical mechanism of post-transcriptional regulation and has been widely reported to be associated with the tumor progression and tumor microenvironment (TME) formation. However, the role of AS in lung adenocarcinoma (LUAD) has not been clearly elucidated. This study presents a comprehensive analysis exploring the impact of AS on prognosis and TME in LUAD.MethodsThe gene expression transcriptome profiles and survival data were obtained from The Cancer Genome Atlas (TCGA) database, and the splicing profiles were obtained from the TCGA SpliceSeq database. Base on prognostic AS events, a prognostic signature was constructed using Least Absolute Shrinkage and Selection Operator (LASSO) regression followed by multivariate Cox regression analysis. Survival outcomes was analyzed using the Kaplan-Meier method and the predictive performance of the signature was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, the landscape of the TME was assessed by ESTIMATE, Microenvironment Cell Population (MCP)-counter, and single-sample Gene-Set Enrichment Analysis (ssGSEA) algorithms.ResultsA total of 127 prognostic AS events with P value <0.001 from 89 genes in LUAD were confirmed. A prognostic signature was constructed based on 20 prognostic AS events. Kaplan-Meier survival analysis demonstrated that higher risk scores were associated with poorer overall survival (OS). The area under the ROC curve of risk scores predicting the 1-, 3-, and 5-year survival probability were 0.791, 0.847, and 0.832, respectively. Furthermore, significant relationship was observed between the prognostic signature and the landscape of the TME. High-risk patients had lower stromal/immune scores, higher tumor purity, and significantly decreased abundance of majority immune cells, and immune-related signatures (P<0.05). Finally, a potential regulatory mechanism of the AS events is displayed in a regulatory network.ConclusionsThis research highlights the prognostic value of AS events for patients with LUAD and provide new insight into the regulation of the TME by AS. Notably, AS may affect the patient's prognosis by altering the TME. Our findings provide important guidance for the development of novel biomarkers and therapeutic targets in patients with LUAD.

Project description: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.

Project description: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.

Project description:BackgroundLung adenocarcinoma (LUAD) is the most common form of lung cancer, accounting for 30% of all cases and 40% of all non-small-cell lung cancer cases. Immune-related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironment. The present study was aimed at finding an immune-related gene signature for predicting LUAD patient outcomes.MethodsFirst, we chose the TCGA-LUAD project in the TCGA database as the training cohort for model training. For model validating, we found the datasets of GSE72094 and GSE68465 in the GEO database and took them as the candidate cohorts. We obtained 1793 immune-related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests-variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature. The Kaplan-Meier, Cox model, and ROC curve were deployed to examine the gene signature's prognosis in both cohorts. We conducted GSEA analysis to study further the mechanisms and pathways that involved the gene signature. Finally, we performed integrating analyses about the 22 TICs, fully interpreted the relationship between our signature and each TIC, and highlighted some TICs playing vital roles in the signature's prognostic ability.ResultsA nine-gene signature was produced from the data of the training cohort. The Kaplan-Meier estimator, Cox proportional hazard model, and ROC curve confirmed the independence and predictive ability of the signature, using the data from the validation cohort. The GSEA analysis results illustrated the gene signature's mechanism and emphasized the importance of immune-related pathways for the gene signature. 22 TICs immune infiltration analysis revealed resting mast cells' key roles in contributing to gene signature's prognostic ability.ConclusionsThis study discovered a novel immune-related nine-gene signature (BTK, CCR6, S100A10, SEMA3C, GPI, SCG2, TNFRSF11A, CCL20, and DKK1) that predicts LUAD prognosis precisely and associates with resting mast cells strongly.

Dataset Information

Characterization of the alternative splicing landscape in lung adenocarcinoma reveals novel prognosis signature associated with B cells.

Background

Methods

Results

Conclusions

Publications

Characterization of the alternative splicing landscape in lung adenocarcinoma reveals novel prognosis signature associated with B cells.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets