Dataset Information

Alternative splicing patterns reveal prognostic indicator in muscle-invasive bladder cancer.

ABSTRACT:

Background

Bladder cancer is one of the most lethal malignancy in urological system, and 20-25% of bladder cancer patients are muscle invasive with unfavorable prognosis. However, the role of alternative splicing (AS) in muscle-invasive bladder cancer (MIBC) remains to be elucidated.

Methods

Percent spliced in (PSI) data obtained from the Cancer Genome Atlas (TCGA) SpliceSeq database (n = 394) were utilized to evaluate the AS events in MIBC. Prognosis-associated AS events were screened out by univariate Cox regression. LASSO Cox regression was used to identify reliable prognostic patterns in a training set and further validated in a test set. Splicing regulatory networks were constructed by correlations between PSI of AS events and RNA expression of splicing factors.

Results

As a result, a total of 2589 prognosis-related AS events in MIBC were identified. Pathways of spliceosomal complex (FDR = 0.017), DNA-directed RNA polymerase II, core complex (FDR = 0.032), and base excision repair (FDR = 0.038) were observed to be significantly enriched. Additionally, we noticed that most of the prognosis-related AS events were favorable factors. According to the LASSO and multivariate Cox regression analyses, 15-AS-based signature was established with the area under curve (AUC) of 0.709, 0.823, and 0.857 at 1-, 3-, and 5- years, respectively. The MIBC patients were further divided into high- and low-risk groups based on median risk sores. Interestingly, we observed that the prevalence of FGFR3 with mutations and focal amplification was significantly higher in low-risk group. Functional and immune infiltration analysis suggested potential signaling pathways and distinct immune states between these two groups. Moreover, splicing correlation network displayed a regulatory mode of prognostic splicing factors (SF) in MIBC patients.

Conclusions

This study not only provided novel insights into deciphering the possible mechanism of tumorgenesis and pathogenesis but also help refine risk stratification systems and potential treatment of decision-making for MIBC.

SUBMITTER: AZhaTi B

PROVIDER: S-EPMC9277948 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Objectives: Bladder cancer is the fourth most common malignancy in men in the United States. Aberrant alternative splicing (AS) events are involved in the carcinogenesis, but the association between AS and bladder cancer remains unclear. This study aimed to construct an AS-based prognostic signature and elucidate the role of the tumor immune microenvironment (TIME) and the response to immunotherapy and chemotherapy in bladder cancer. Methods: Univariate Cox regression analysis was performed to detect prognosis-related AS events. The least absolute shrinkage and selection operator (LASSO) and multivariate Cox analyses were employed to build prognostic signatures. Kaplan-Meier survival analysis, multivariate Cox regression analysis, and receiver operating characteristic (ROC) curves were conducted to validate the prognostic signatures. Then, the Estimation of Stromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) and tumor immune estimation resource (TIMER) databases were searched and the single-sample gene set enrichment analysis (ssGSEA) algorithm and CIBERSORT method were performed to uncover the context of TIME in bladder cancer. The Tumor Immune Dysfunction and Exclusion (TIDE) web tool and pRRophetic algorithm were used to predict the response to immunotherapy and chemotherapy. Finally, we constructed a correlation network between splicing factors (SFs) and survival-related AS events. Results: A total of 4684 AS events were significantly associated with overall survival in patients with bladder cancer. Eight prognostic signatures of bladder cancer were established, and a clinical survival prediction model was built. In addition, the consolidated prognostic signature was closely related to immune infiltration and the response to immunotherapy and chemotherapy. Furthermore, the correlation identified EIF3A, DDX21, SDE2, TNPO1, and RNF40 as hub SFs, and function analysis found ubiquitin-mediated proteolysis is correlated most significantly with survival-associated AS events. Conclusion: Our findings highlight the prognostic value of AS for patients with bladder cancer and reveal pivotal players of AS events in the context of TIME and the response to immunotherapy and chemotherapy, which may be important for patient management and treatment.

Project description:BackgroundMuscle-invasive bladder cancer (MIBC) patients are subject to unfavorable treatment options and a high recurrence rate. The status of TP53 mutations played an essential role in the progression and the prognosis of MIBC. The present study proposed to investigate the association between TP53 mutations and immunophenotype in MIBC.ResultsWe established an immune prognostic model (IPM) ground on the immune-associated genes derived from variation analysis between wild-type TP53 and mutated TP53 TCGA-MIBC patients, and validated in another cohort from GEO database. Based on IPM, we divided MIBC patients into low and high risk subgroups. The high risk MIBC patients had higher proportions of macrophages M1, and lower proportions of T cells regulatory (Tregs) and activated dendritic cells than the low risk MIBC patients. Moreover, the expression of immune checkpoints genes (PD1, CTLA4, LAG3, HAVCR2 and TIGIT) was higher in the high risk patients than the low risk patients. In clinical application, IPM exhibited better survival prediction than conventional clinical characteristics.ConclusionsOur investigation presented practical prognostic significance for MIBC patients and displayed the overarching landscape of the immune response in the MIBC microenvironment.MethodsData were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) analysis between the TP53 mutated and wild-type MIBC patients was conducted. The CIBERSORT algorithm was performed to evaluate the proportion of immune cell types. Gene expression profiles from the TCGA and GEO were used as training and testing cohorts to build and validate an immune-related prognostic model (IPM). Genes in the IPM model were first screened by univariate Cox analysis, then filtered by the least absolute shrinkage and selection operator (LASSO) Cox regression. A nomogram was finally established and evaluated by combining both the IPM and other clinical factors.