Alternative Splicing Events in Immune Infiltration of Lung Adenocarcinoma.
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ABSTRACT:
SUBMITTER: Huang T
PROVIDER: S-EPMC8656114 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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.
| S-EPMC8728792 | biostudies-literature
Project description:Lung adenocarcinoma (LUAD) is the leading cause of cancer deaths worldwide due to the lack of early diagnostic markers and specific drugs. Previous studies have shown the association of LUAD growth with aberrant alternative splicing (AS). Herein, clinical data of 535 tumor tissues and 59 normal tissues were extracted from The Cancer Genome Atlas (TCGA) database. Each sample was analyzed using the ESTIMATE algorithm; a comparison between higher and lower score groups (stromal or immune) was made to determine the overall- and progression-free survival-related differentially expressed AS (DEAS) events. We then performed unsupervised clustering of these DEASs, followed by determining their relationship with survival rate, immune cells, and the tumor microenvironment (TME). Next, two prognostic signatures were developed using bioinformatics tools to explore the prognosis of cases with LUAD. Five OS- and six PFS-associated DEAS events were implemented to establish a prognostic risk score model. When compared to the high-risk group (HRG), the PFS and OS of the low-risk group (LRG) were found to be considerable. Additionally, a better prognosis was found considerably associated with the ESTIMATE score of the patients as well as immune cells infiltration. Our analysis of AS events in LUAD not only helps to clarify the tumorigenesis mechanism of AS but also provides ideas for revealing potential prognostic biomarkers and therapeutic targets.
| S-EPMC8647402 | biostudies-literature
Project description:BackgroundRecent studies identified correlations between splicing factors (SFs) and tumor progression and therapy. However, the potential roles of SFs in immune regulation and the tumor microenvironment (TME) remain unknown.MethodsWe used UpSet plots to screen for prognostic-related alternative splicing (AS) events. We evaluated SF patterns in specific immune landscapes. Single sample gene set enrichment analysis (ssGSEA) algorithms were used to quantify relative infiltration levels in immune cell subsets. Principal component analysis (PCA) algorithm-based SFscore were used to evaluate SF patterns in individual tumors with an immune response.ResultsFrom prognosis-related AS events, 16 prognosis-related SFs were selected to construct three SF patterns. Further TME analyses showed these patterns were highly consistent with immune-inflamed, immune-excluded, and immune-desert landscapes. Based on SFscore constructed using differentially expressed genes (DEGs) between SF patterns, patients were classified into two immune-subtypes associated with differential pharmacogenomic landscapes and cell features. A low SFscore was associated with high immune cell infiltration, high tumor mutation burden (TMB), and elevated expression of immune check points (ICPs), indicating a better immune response.ConclusionsSFs are significantly associated with TME remodeling. Evaluating different SF patterns enhances our understanding of the TME and improves effective immunotherapy strategies.
| S-EPMC9467413 | biostudies-literature
Project description:PurposePancreatic adenocarcinoma (PAAD) is characterized by low antitumour immune cell infiltration in an immunosuppressive microenvironment. This study aimed to systematically explore the impact on prognostic alternative splicing events (ASs) of tumour immune microenvironment (TIME) in PAAD.MethodsThe ESTIMATE algorithm was implemented to compute the stromal/immune-related scores of each PAAD patient, followed by Kaplan-Meier (KM) survival analysis of patients with different scores grouped by X-tile software. TIME-related differentially expressed ASs (DEASs) were determined and evaluated through functional annotation analysis. In addition, Cox analyses were implemented to construct a TIME-related signature and an AS clinical nomogram. Moreover, comprehensive analyses, including gene set enrichment analysis (GSEA), immune infiltration, immune checkpoint gene expression, and tumour mutation were performed between the two risk groups to understand the potential mechanisms. Finally, Cytoscape was implemented to illuminate the AS-splicing factor (SF) regulatory network.ResultsA total of 437 TIME-related DEASs significantly related to PAAD tumorigenesis and the formation of the TIME were identified. Additionally, a robust TIME-related prognostic signature based on seven DEASs was generated, and an AS clinical nomogram combining the signature and four clinical predictors also exhibited prominent discrimination by ROC (0.762 ~ 0.804) and calibration curves. More importantly, the fractions of CD8 T cells, regulatory T cells and activated memory CD4 T cells were lower, and the expression of four immune checkpoints-PD-L1, CD47, CD276, and PVR-was obviously higher in high-risk patients. Finally, functional analysis and tumour mutations revealed that aberrant immune signatures and activated carcinogenic pathways in high-risk patients may be the cause of the poor prognosis.ConclusionWe extracted a list of DEASs associated with the TIME through the ESTIMATE algorithm and constructed a prognostic signature on the basis of seven DEASs to predict the prognosis of PAAD patients, which may guide advanced decision-making for personalized precision intervention.
| S-EPMC8590242 | biostudies-literature
Project description:Alternative splicing (AS), an essential process for the maturation of mRNAs, is involved in tumorigenesis and tumor progression, including angiogenesis, apoptosis, and metastasis. AS changes can be frequently observed in different tumors, especially in geriatric lung adenocarcinoma (GLAD). Previous studies have reported an association between AS events and tumorigenesis but have lacked a systematic analysis of its underlying mechanisms. In the present study, we obtained splicing event information from SpliceSeq and clinical information regarding GLAD from The Cancer Genome Atlas. Survival-associated AS events were selected to construct eight prognostic index (PI) models. We also constructed a correlation network between splicing factors (SFs) and survival-related AS events to identify a potential molecular mechanism involved in regulating AS-related events in GLAD. Our study findings confirm that AS has a strong prognostic value for GLAD and sheds light on the clinical significance of targeting SFs in the treatment of GLAD.
| S-EPMC7569206 | biostudies-literature
Project description:Alternative splicing (AS), as an effective and universal mechanism of transcriptional regulation, is involved in the development and progression of cancer. Therefore, systematic analysis of alternative splicing in pancreatic adenocarcinoma (PAAD) is warranted. The corresponding clinical information of the RNA-Seq data and PAAD cohort was downloaded from the TCGA data portal. Then, a java application, SpliceSeq, was used to evaluate the RNA splicing pattern and calculate the splicing percentage index (PSI). Differentially expressed AS events (DEAS) were identified based on PSI values between PAAD cancer samples and normal samples of adjacent tissues. Kaplan-Meier and Cox regression analyses were used to assess the association between DEAS and patient clinical characteristics. Unsupervised cluster analysis used to reveal four clusters with different survival patterns. At the same time, GEO and TCGA combined with GTEx to verify the differential expression of AS gene and splicing factor. After rigorous filtering, a total of 45,313 AS events were identified, 1,546 of which were differentially expressed AS events. Nineteen DEAS were found to be associated with OS with a five-year overall survival rate of 0.946. And the subtype clusters results indicate that there are differences in the nature of individual AS that affect clinical outcomes. Results also identified 15 splicing factors associated with the prognosis of PAAD. And the splicing factors ESRP1 and RBM5 played an important role in the PAAD-associated AS events. The PAAD-associated AS events, splicing networks, and clusters identified in this study are valuable for deciphering the underlying mechanisms of AS in PAAD and may facilitate the establishment of therapeutic goals for further validation.
| S-EPMC8100795 | biostudies-literature
Project description:BackgroundAlternative splicing (AS), a pivotal post-transcriptional process across more than 95% of human transcripts, is involved in transcript structural variations and protein complexity. Clinical implications of AS events and their interaction with tumor immunity were systematically analyzed in lung adenocarcinoma (LUAD).MethodsTranscriptome profiling as well as AS data of LUAD were retrospectively curated. Then, the network of the overall survival (OS)-relevant AS events with splicing factors was established. After screening OS-relevant AS events, a LASSO prognostic model was conducted and evaluated with ROC curves. A nomogram that integrated independent prognostic indicators was created. Immune response and immune cell infiltration were estimated with ESTIMATE, CIBERSORT, and ssGSEA algorithms. Drug sensitivity was inferred with pRRophetic package.ResultsIn total, 2415 OS-relevant AS events were identified across LUAD patients. The interaction network of splicing factors with OS-relevant AS events uncovered the underlying regulatory mechanisms of AS events in LUAD. Thereafter, a prognostic model containing 12 AS events was developed, which acted as a reliable and independent prognostic indicator following verification. A nomogram that constituted stage and risk score displayed great effectiveness in evaluating the survival likelihood. Moreover, the AS-based prognostic model was in relation to immune response and immune cell infiltration. Patients with a high-risk score displayed therapeutic superiority to cisplatin, erlotinib, gefitinib, and gemcitabine. Finally, three AS-relevant genes (CDKN2A, TTC39C, and PKIB) were identified as prognostic markers.ConclusionCollectively, our findings developed an AS event signature with powerful prognostic predictive efficacy in LUAD.
| S-EPMC9271776 | biostudies-literature
Project description:Background: Osteosarcoma typically occurs in adolescents, and the survival rate of patients with metastatic and recurrent osteosarcoma remains low. Abnormal regulation of alternative splicing is associated with the development of osteosarcoma. However, there is no genome-wide analysis of the function and regulatory mechanisms of aberrant alternative splicing associated with osteosarcoma. Methods: Published transcriptome data on osteosarcoma (GSE126209) derived from osteosarcoma patient tissue were downloaded. Gene expression profiling by high-throughput sequencing was performed on 9 normal samples and 10 tumor samples for genome-wide identification of osteosarcoma-related alternative splicing events. The potential function of osteosarcoma-associated alternative splicing events was examined by immune infiltration and correlation analysis. Regulation of aberrantly expressed RNA-binding proteins (RBPs) related to alternative splicing in osteosarcoma was clarified by co-expression analysis. Results: A total of 63 alternative splicing events, which are highly credible and dominant, were identified. GO enrichment analysis indicated that alternative splicing may be closely related to the immune response process. Immune infiltration analysis showed significant changes in the percentages of CD8 T cells, resting memory CD4 T cells, activated memory CD4 T cells, monocytes, resting dendritic cells, and activated mast cells in tumors compared to normal tissues, indicating the involvement of these immune cell types in the occurrence of osteosarcoma. Moreover, the analysis identified alternative splicing events that were co-altered with resting memory CD4 T cells, resting dendritic cells, and activated mast cells, events that may be associated with regulation of the osteosarcoma immune microenvironment. In addition, a co-regulatory network (RBP-RAS-immune) of osteosarcoma-associated RBPs with aberrant alternative splicing and altered immune cells was established. These RBPs include NOP58, FAM120C, DYNC1H1, TRAP1, and LMNA, which may serve as molecular targets for osteosarcoma immune regulation. Conclusion: These findings allow us to further understand the causes of osteosarcoma development and provide a new research direction for osteosarcoma immunotherapy or targeted therapy.
| S-EPMC10149916 | biostudies-literature
Project description:BackgroundAlternative splicing (AS) is believed to play a vital role in tumor development. Therefore, comprehensive investigation of AS and its biological function in prostate cancer (PCa) is crucial.MethodsThe AS profiling of 489 patients with PCa was obtained from The Cancer Genome Atlas (TCGA) SpliceSeq database. Bioinformatics tools were used to describe splicing associations and build prognostic models. Unsupervised clustering of the determined prognostic AS events and the relationship with immune characteristics were also explored.ResultsIn total, 20,723 AS events were detected and 2,805 were identified in PCa. In the regulatory networks, the data suggested a significant correlation between splicing factor (SF) expression and AS events. To stratify the progression risk of PCa patients, prognostic models were constructed using splicing patterns. Six AS events were screened out as independent prognostic factors for progression-free survival. Based on the gene features, we constructed the combined prognostic predictors model, and the receiver operating characteristic (ROC) curve for this model reached a high area under the ROC curve (AUC) of 0.729793, indicating a favorable ability to predict patient outcomes. Through unsupervised clustering analysis, the correlations between AS-based clusters and prognosis as well as immune characteristics were revealed. The correlation analysis on TIMER revealed the relationship between gene expression and immune cell infiltration.ConclusionsThis in-depth genome-wide analysis of the AS profiling in PCa revealed unique AS events associated with cancer progression and the infiltration of immune cells, with potential for predicting outcomes and therapeutic responses.
| S-EPMC8350246 | biostudies-literature
Project description:Analysis of The Cancer Genome Atlas data revealed that alternative splicing (AS) events could serve as prognostic biomarkers in various cancer types. This study examined lung adenocarcinoma (LUAD) tissues for AS and assessed AS events as potential indicators of prognosis in our cohort. RNA sequencing and bioinformatics analysis were performed. We used SUPPA2 to analyze the AS profiles. Using univariate Cox regression analysis, overall survival (OS)-related AS events were identified. Genes relating to the OS-related AS events were imported into Cytoscape, and the CytoHubba application was run. OS-related splicing factors (SFs) were explored using the log-rank test. The relationship between the percent spliced-in value of the OS-related AS events and SF expression was identified by Spearman correlation analysis. We found 1957 OS-related AS events in 1151 genes, and most were protective factors. Alternative first exon splicing was the most frequent type of splicing event. The hub genes in the gene network of the OS-related AS events were FBXW11, FBXL5, KCTD7, UBB and CDC27. The area under the curve of the MIX prediction model was 0.847 for 5-year survival based on seven OS-related AS events. Overexpression of SFs CELF2 and SRSF5 was associated with better OS. We constructed a correlation network between SFs and OS-related AS events. In conclusion, we identified prognostic predictors using AS events that stratified LUAD patients into high- and low-risk groups. The discovery of the splicing networks in this study provides an insight into the underlying mechanisms.
| S-EPMC7693837 | biostudies-literature