Dataset Information

Systematic profiling of alternative splicing signature reveals prognostic predictor for cervical cancer.

ABSTRACT:

Aim

Cervical cancer is a common malignant carcinoma of the gynecological tract with high morbidity and mortality. Therefore, it is crucial to elucidate the pathogenesis, prevention, diagnosis and prognosis of cervical cancer by searching for the involved key genes.

Method

In this study, the alternative splicing (AS) events of 253 patients with cervical cancer were analyzed, and 41,766 AS events were detected in 9961 genes. Univariate analysis was performed to screen prognostic AS events. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis was used to identify the pathways in which these AS events were involved.

Results

We found that exon skip (ES) is the main AS event in patients with cervical cancer. There was pronounced consistency between the genes involved in overall survival and those involved in recurrence. At the same time, we found that a gene may exhibit several different types of AS events, and these different AS events may be related to prognosis. Four characteristic genes, HSPA14, SDHAF2, CAMKK2 and TM9SF1, that can be used as prognostic markers for cervical cancer were selected.

Conclusion

The importance of AS events in the development of cervical cancer and prediction of prognosis was revealed by a large amount of data at the whole genome level, which may provide a potential target for cervical cancer treatment. We also provide a new method for exploring the pathogenesis of cervical cancer to determine clinical treatment and prognosis more accurately.

SUBMITTER: Hu YX

PROVIDER: S-EPMC6865056 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Systematic profiling of alternative splicing signature reveals prognostic predictor for cervical cancer.

Hu Yue-Xin YX Zheng Ming-Jun MJ Zhang Wen-Chao WC Li Xiao X Gou Rui R Nie Xin X Liu Qing Q Hao Ying-Ying YY Liu Juan-Juan JJ Lin Bei B

Journal of translational medicine 20191119 1

<h4>Aim</h4>Cervical cancer is a common malignant carcinoma of the gynecological tract with high morbidity and mortality. Therefore, it is crucial to elucidate the pathogenesis, prevention, diagnosis and prognosis of cervical cancer by searching for the involved key genes.<h4>Method</h4>In this study, the alternative splicing (AS) events of 253 patients with cervical cancer were analyzed, and 41,766 AS events were detected in 9961 genes. Univariate analysis was performed to screen prognostic AS ...[more]

PMID: 31744495

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Project description:Alternative splicing (AS) is a pervasive and vital mechanism involved in the progression of cancer by expanding genomic encoding capacity and increasing protein complexity. However, the systematic analysis of AS in hepatocellular carcinoma (HCC) is lacking and urgently required. In the present study, genome‑wide AS events with corresponding clinical information were profiled in 290 patients with HCC from the Cancer Genome Atlas and SpliceSeq software. Functional enrichment analyses revealed the pivotal biological process of AS regulation. Univariate Cox regression analyses were performed, followed by stepwise forward multivariate analysis to develop the prognostic signatures. Spearman's correlation analyses were also used to construct potential regulatory network between the AS events and aberrant splicing factors. A total of 34,163 AS events were detected, among which 1,805 AS events from 1,314 parent genes were significantly associated with the overall survival (OS) of patients with HCC, and their parent genes serve crucial roles in HCC‑related oncogenic processes, including the p53 signaling pathway, AMPK signaling pathway and HIF‑1 signaling pathway. A prognostic AS signature was established that was found to be an independent prognostic factor for OS in stratified cohorts, harboring a noteworthy ability to distinguish between the distinct prognoses of patients with HCC (high‑risk vs. low‑risk, 827 vs. 3,125 days, P<2e‑16). Time‑dependent receiver‑-operator characteristic curves confirmed its robustness and clinical efficacy, with the area under the curves maintained >0.9 for short‑term and long‑term prognosis prediction. The splicing correlation network suggested a trend in the interactions between splicing factors and prognostic AS events, further revealing the underlying mechanism of AS in the oncogenesis of HCC. In conclusion, the present study provides a comprehensive portrait of global splicing alterations involved in the progression and HCC in addition to valuable prognostic factors for patients, which may represent as underappreciated hallmark and provide novel clues of therapeutic targets in HCC.

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: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.

Dataset Information

Systematic profiling of alternative splicing signature reveals prognostic predictor for cervical cancer.

Aim

Method

Results

Conclusion

Publications

Systematic profiling of alternative splicing signature reveals prognostic predictor for cervical cancer.

Similar Datasets

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