scholarly journals Systematic Profiling of Alternative Splicing Events in Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Jia Liu ◽  
Dekang Lv ◽  
Xiaobin Wang ◽  
Ruicong Wang ◽  
Xiaodong Li
Keyword(s):  
Ovarian Cancer ◽  
Alternative Splicing ◽  
Prognostic Models ◽  
Splicing Factors ◽  
Prediction Ability ◽  
Kaplan Meier ◽  
Prognosis Model ◽  
Independent Risk Factors ◽  
Prognostic Prediction ◽  
Cox Analysis

Alternative splicing (AS) is significantly related to the development of tumor and the clinical outcome of patients. In this study, our aim was to systematically analyze the survival-related AS signal in ovarian serous cystadenocarcinoma (OV) and estimate its prognostic validity in 48,049 AS events out of 21,854 genes. We studied 1,429 AS events out of 1,125 genes, which were significantly related to the overall survival (OS) in patients with OV. We established alternative splicing features on the basis of seven AS events and constructed a new comprehensive prognostic model. Kaplan-Meier curve analysis showed that seven AS characteristics and comprehensive prognostic models could strongly stratify patients with ovarian cancer and make them distinctive prognosis. ROC analysis from 0.781 to 0.888 showed that these models were highly efficient in distinguishing patient survival. We also verified the prognostic characteristics of these models in a testing cohort. In addition, uni-variate and multivariate Cox analysis showed that these models were superior independent risk factors for OS in patients with OV. Interestingly, AS events and splicing factor (SFs) networks revealed an important link between these prognostic alternative splicing genes and splicing factors. We also found that the comprehensive prognosis model signature had higher prediction ability than the mRNA signature. In summary, our study provided a possible prognostic prediction model for patients with OV and revealed the splicing network between AS and SFs, which could be used as a potential predictor and therapeutic target for patients with OV.

scholarly journals Alternative splicing acts as an independent prognosticator in ovarian carcinoma

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yan Ouyang ◽  
Kaide Xia ◽  
Xue Yang ◽  
Shichao Zhang ◽  
Li Wang ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Regression Analysis ◽  
Alternative Splicing ◽  
Ovarian Carcinoma ◽  
Cancer Patients ◽  
Cox Regression ◽  
Excision Repair ◽  
Splicing Factors ◽  
Prognostic Signature ◽  
Cox Regression Analysis

AbstractAlternative splicing (AS) events associated with oncogenic processes present anomalous perturbations in many cancers, including ovarian carcinoma. There are no reliable features to predict survival outcomes for ovarian cancer patients. In this study, comprehensive profiling of AS events was conducted by integrating AS data and clinical information of ovarian serous cystadenocarcinoma (OV). Survival-related AS events were identified by Univariate Cox regression analysis. Then, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis were used to construct the prognostic signatures within each AS type. Furthermore, we established a splicing-related network to reveal the potential regulatory mechanisms between splicing factors and candidate AS events. A total of 730 AS events were identified as survival-associated splicing events, and the final prognostic signature based on all seven types of AS events could serve as an independent prognostic indicator and had powerful efficiency in distinguishing patient outcomes. In addition, survival-related AS events might be involved in tumor-related pathways including base excision repair and pyrimidine metabolism pathways, and some splicing factors might be correlated with prognosis-related AS events, including SPEN, SF3B5, RNPC3, LUC7L3, SRSF11 and PRPF38B. Our study constructs an independent prognostic signature for predicting ovarian cancer patients’ survival outcome and contributes to elucidating the underlying mechanism of AS in tumor development.

scholarly journals Development and Validation of an Immune-Related Gene-Pair Model of High-Grade Serous Ovarian Cancer After Platinum-Based Chemotherapy

2020 ◽  
Author(s):  
Jiaxing Lin ◽  
Dan Sun ◽  
Tianren Li
Keyword(s):  
Ovarian Cancer ◽  
High Risk ◽  
Risk Group ◽  
Low Risk ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Related Gene ◽  
Training Set ◽  
Kaplan Meier ◽  
Prognosis Model

Abstract Background: High-grade serous ovarian cancer (HGSOC) is a common cause of death from gynecological cancer, with an overall survival rate that has not significantly improved in decades. Reliable bio-markers are needed to identify high-risk HGSOC to assist in the selection and development of treatment options.Method: The study included ten HGSOC cohorts, which were merged into four separate cohorts including a total of 1526 samples. We used the relative expression of immune genes to construct the gene-pair matrix, and the Least absolute shrinkage and selection operator regression was performed to build the prognosis model using the training set. The prognosis of the model was verified in the training set (363 cases) and three validation sets (of 251, 354, and 558 cases). Finally, the differences in immune cell infiltration and gene enrichment pathways between high and low score groups were identified.Results: A prognosis model of HGSOC overall survival rate was constructed in the training set, and included data for 35 immune gene-related gene pairs and the regression coefficients. The risk stratification of HGSOC patients was successfully performed using the training set, with a p-value of Kaplan-Meier of < 0.001. A score from this model is an independent prognostic factor of HGSOC, and prognosis was evaluated in different clinical subgroups. This model was also successful for the other three validation sets, and the results of Kaplan-Meier analysis were statistically significant. The model can also predict patient progression-free survival with HGSOC to reflect tumor growth status. There were differences in some immune cells between the high-risk and low-risk groups as defined by the model. There was a lower infiltration level of M1 macrophages in the high-risk group compared to that in the low-risk group (p < 0.001). Finally, many of the immune-related pathways were enriched in the low-risk group, with antigen processing and presentation identified as the most enriched pathways.Conclusion: The prognostic model based on immune-related gene pairs developed is a potential prognostic marker for high-grade serous ovarian cancer treated with platinum. The model has robust prognostic ability and wide applicability. More prospective studies will be needed to assess the practical application of this model for precision therapy.

scholarly journals Immune Subtypes Based on Immune-Related lncRNA: Differential Prognostic Mechanism of Pancreatic Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Qiyao Zhang ◽  
Zhihui Wang ◽  
Xiao Yu ◽  
Menggang Zhang ◽  
Qingyuan Zheng ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Immune Response ◽  
Prognostic Model ◽  
Effective Means ◽  
Prognostic Models ◽  
Functional Differences ◽  
Degree Of Malignancy ◽  
Prognostic Prediction ◽  
Cox Analysis

Pancreatic cancer consists one of tumors with the highest degree of malignancy and the worst prognosis. To date, immunotherapy has become an effective means to improve the prognosis of patients with pancreatic cancer. Long non-coding RNAs (lncRNAs) have also been associated with the immune response. However, the role of immune-related lncRNAs in the immune response of pancreatic cancer remains unclear. In this study, we identified immune-related lncRNA pairs through a new combinatorial algorithm, and then clustered and deeply analyzed the immune characteristics and functional differences between subtypes. Subsequently, the prognostic model of 3 candidate lncRNA pairs was determined by multivariate COX analysis. The results showed significant prognostic differences between the C1 and C2 subtypes, which may be due to the differential infiltration of CTL and NK cells and the activation of tumor-related pathways. The prognostic model of the 3 lncRNA pairs (AC244035.1_vs._AC063926.1, AC066612.1_vs._AC090124.1, and AC244035.1_vs._LINC01885) was established, which exhibits stable and effective prognostic prediction performance. These 3 lncRNA pairs may regulate the anti-tumor effect of immune cells through ion channel pathways. In conclusion, our research demonstrated the panoramic differences in immune characteristics between subtypes and stable prognostic models, and identified new potential targets for immunotherapy.

scholarly journals Alternative splicing events implicated in carcinogenesis and prognosis of thyroid gland cancer

2020 ◽  
Author(s):  
zenghong wu ◽  
Yi Zhong ◽  
Fu-Cheng Cai
Keyword(s):  
Thyroid Cancer ◽  
Alternative Splicing ◽  
Practical Knowledge ◽  
Splice Variants ◽  
Expression Patterns ◽  
Prognostic Models ◽  
Clinicopathological Parameters ◽  
Oncology Research ◽  
Kaplan Meier ◽  
Alternative Splicing Events

Abstract Background: Alternative splicing events (ASEs), a critical post-transcriptional regulatory mechanism, expands gene expression patterns, resulting in increased protein diversity and more than 95% of human genes experience AS and encode splice variants in the regular physiological processes. While the role of AS in the thyroid cancer as yet missing, therefore, it was necessary to carry out this study to provide more information about the combination of splicing and clinical parameters, as well as potential mechanism of the survival-related splicing events in thyroid cancer. Materials and methods: Here, we draw all-around AS profiles of thyroid cancer by analyzing RNA-seq data. We also constructed prognostic models via combining splicing signatures and clinicopathological parameters. Splicing network was constructed as a way to offer functional insight into the full practical knowledge of AS in the initiation and development of thyroid cancer. Results: There were 10446 genes, and 45150 AS events in 506 TC patients, which indicates that ASEs are universal in TC. Moreover, 1819 AS signatures were identified to be significantly related to OS of TC patients and among the seven types of ASES, ES was the most common, followed by AP and AT. Kaplan-Meier survival curves results suggested that seven types of ASEs were related to bad prognosis in TC patients (P<0.05). In TC, AA (AUC: 0.937), AD (AUC: 0.965), AT (AUC: 0.964), ES (AUC: 0.999), ME (AUC: 0.999), RI (AUC: 0.837) all demonstrated an AUC over 0.6, of which ES and ME best predict the incidence of TC. We found that age and risk score (All) were risk factors for TC patients. As for ASEs is regulated by SFs, we study if the TC-ASEs were regulated by various SFs and the results demonstrated that the expression of 90 SFs was related to 469 ASEs OS in the TC cohort. Conclusions: In sum, the findings in the current study may provide a basis for spliceosomes in TC, and the methods used in this study could provide novel perspectives in other fields of tumor study to help shed light on future oncology research.

scholarly journals The effect of cytoreductive partial nephrectomy in elderly patients with metastatic renal cell carcinoma：a systematic review

2019 ◽  
Author(s):  
Wei Zhuang ◽  
Jiabi Chen ◽  
Yining Li ◽  
Xiaoping Su
Keyword(s):  
Partial Nephrectomy ◽  
Tumor Size ◽  
Renal Cell ◽  
Term Survival ◽  
Factors Affecting ◽  
Kaplan Meier ◽  
N Stage ◽  
Independent Risk Factors ◽  
Male Patients ◽  
Cox Analysis

Abstract Background To explore the survival value of cytoreductive partial nephrectomy (cPN) in elderly metastatic renal cell carcinoma (EmRCC).Methods RCC patients aged ≥65 years from 2010 to 2015 in The Surveillance, Epidemiology and End Result database (SEER) were analyzed using Kaplan-Meier (K-M) method and multivariate COX analysis. Propensity score matching (PSM) was performed to balance effects of confounding factors such as general features and pathological features. We were committed to study the long-term survival advantages of cPN patients, explore the appropriate population of cPN, and try to establish a Nomogram model to predict individual survival.Results In EmRCC patients, especially in male patients with tumors size ≦7cm, N0 stage, or isolated metastases, cPN brought a better survival than cytoreductive radical nephrectomy (cRN). Tumor size and N stage were independent risk factors affecting the survival of cPN patients, cPN in patients with tumor size >7cm or N1 stage may present a higher risk of death.Conclusions The implementation of cPN in EmRCC patients who meet specific clinical characteristics like tumors size ≦7cm, N0 stage, or isolated metastases seems to help improve the tumor outcomes.

scholarly journals Identification of an alternative splicing signature as an independent factor in colon cancer

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Haitao Chen ◽  
Jun Luo ◽  
Jianchun Guo
Keyword(s):  
Colon Cancer ◽  
Alternative Splicing ◽  
Cancer Survival ◽  
Cox Regression ◽  
The Cancer Genome Atlas ◽  
Regulatory Relationship ◽  
Predictive Values ◽  
Entire Cohort ◽  
Kaplan Meier ◽  
Cox Analysis

Abstract Background Colon cancer is a common malignant tumor with a poor prognosis. Abnormal alternative splicing (AS) events played a part in the occurrence and metastasis of the tumor. We aimed to develop a survival-associated AS signature in colon cancer. Methods The Percent Spliced In values of AS events were available in The Cancer Genome Atlas (TCGA) SpliceSeq database. Univariate Cox analysis was carried out to detect the prognosis-related AS events. We created a predictive model on account of the survival-associated AS events, which was further validated with a training-testing group design. Kaplan-Meier analysis was applied to assess patient survival. The area under curve (AUC) of receiver operating characteristic (ROC) was performed to evaluate the predictive values of this model. Meanwhile, the clinical relevance of the signature and its regulatory relationship with splicing factors (SFs) were also evaluated. Results In total, 2132 survival-related AS events were identified from colon cancer samples. We developed an eleven-AS signature, in which the 5-year AUC value was 0.911. Meanwhile, the AUC values at five years were 0.782 and 0.855 in the testing and entire cohort, respectively. Multivariate Cox regression displayed that the T category and the risk score of the signature were independent risk factors of colon cancer survival. Also, we constructed an SFs-AS network based on 11 SFs and 48 AS events. Conclusions We identified an eleven-AS signature of colon cancer. This signature could be treated as an independent prognostic factor.

scholarly journals Systematic Profiling of mRNA Splicing Reveals the Prognostic Predictor and Potential Therapeutic Target for Glioblastoma Multiforme

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Botao Zhang ◽  
Quanyou Wu ◽  
Shujun Cheng ◽  
Wenbin Li
Keyword(s):  
Alternative Splicing ◽  
Glioblastoma Multiforme ◽  
Cox Regression ◽  
Prognostic Models ◽  
Splicing Factors ◽  
Acceptor Site ◽  
Systematic Analysis ◽  
Regulation Network ◽  
Interesting Correlation ◽  
Exon Skip

Despite many changes in alternative splicing events (ASEs) are frequently involved in various cancers, prognosis-related ASEs and drug treatment targets in glioblastoma multiforme (GBM) have not been well explored. ASEs participate in many biological behaviors in the initiation and progression of tumors, the aberrant ASE has been considered another hallmark of cancer, and the systematic study of alternative splicing may provide potential biomarkers for malignancies. In this study, we carried out a systematic analysis to characterize the ASE signatures in GBM cohort. Through comparing GBM tissues and nontumor tissues, a total of 48,191 differently expressed ASEs from 10,727 genes were obtained, and these aberrant ASEs play an important role in the oncogenic process. Then, we identified 514 ASEs independently associated with patient survival in GBM by univariate and multivariate Cox regression, including exon skip in CD3D, alternate acceptor site in POLD2, and exon skip in DCN. Those prognostic models built on ASEs of each splice type can accurately predict the outcome of GBM patients, and values for the area under curve were 0.97 in the predictive model based on alternate acceptor site. In addition, the splicing-regulatory network revealed an interesting correlation between survival-associated splicing factors and prognostic ASE corresponding genes. Moreover, these three hub splicing factors in splicing regulation network are the potential targets of some drugs. In conclusion, a systematic analysis of ASE signatures in GBM could serve as an indicator for identifying novel prognostic biomarkers and guiding clinical treatment.

scholarly journals Development and Validation of an Immune-Related Gene-Pair Model of High-Grade Serous Ovarian Cancer After Platinum-Based Chemotherapy

2021 ◽  
Vol 10 ◽  
Author(s):  
Jiaxing Lin ◽  
Xiao Xu ◽  
Dan Sun ◽  
Tianren Li
Keyword(s):  
Ovarian Cancer ◽  
Risk Group ◽  
High Grade ◽  
Serous Ovarian Cancer ◽  
Overall Survival Rate ◽  
Related Gene ◽  
Training Set ◽  
Immune Related Gene ◽  
Kaplan Meier ◽  
Prognosis Model

BackgroundHigh-grade serous ovarian cancer (HGSOC) is a common cause of death from gynecological cancer, with an overall survival rate that has not significantly improved in decades. Reliable bio-markers are needed to identify high-risk HGSOC to assist in the selection and development of treatment options.MethodThe study included ten HGSOC cohorts, which were merged into four separate cohorts including a total of 1,526 samples. We used the relative expression of immune genes to construct the gene-pair matrix, and the least absolute shrinkage and selection operator regression was performed to build the prognosis model using the training set. The prognosis of the model was verified in the training set (363 cases) and three validation sets (of 251, 354, and 558 cases). Finally, the differences in immune cell infiltration and gene enrichment pathways between high and low score groups were identified.ResultsA prognosis model of HGSOC overall survival rate was constructed in the training set, and included data for 35 immune gene-related gene pairs and the regression coefficients. The risk stratification of HGSOC patients was successfully performed using the training set, with a p-value of Kaplan-Meier of &lt; 0.001. A score from this model is an independent prognostic factor of HGSOC, and prognosis was evaluated in different clinical subgroups. This model was also successful for the other three validation sets, and the results of Kaplan-Meier analysis were statistically significant (p &lt; 0.05). The model can also predict patient progression-free survival with HGSOC to reflect tumor growth status. There was a lower infiltration level of M1 macrophages in the high-risk group compared to that in the low-risk group (p &lt; 0.001). Finally, the immune-related pathways were enriched in the low-risk group.ConclusionThe prognostic model based on immune-related gene pairs developed is a potential prognostic marker for high-grade serous ovarian cancer treated with platinum. The model has robust prognostic ability and wide applicability. More prospective studies will be needed to assess the practical application of this model for precision therapy.

scholarly journals Role of survival-associated alternative splicing events in the prognosis of ovarian cancer

2021 ◽  
Author(s):  
Congbo Yue ◽  
Tianyi Zhao ◽  
Shoucai Zhang ◽  
Yingjie Liu ◽  
GUIXI ZHENG ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Alternative Splicing ◽  
Cox Regression ◽  
Risk Group ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
High Risk Group ◽  
Functional Enrichment ◽  
Prognostic Models

Abstract Objective Alternative splicing (AS) events play a crucial role in the tumorigenesis and progression of various cancers. In the present study, we aimed to identify specific AS events, which might be prognostic markers and therapeutic targets for ovarian cancer (OV). Methods Transcriptome data, clinical information, and Percent Spliced In (PSI) values were downloaded from TCGA database and TCGA SpliceSeq to explore the role of AS events in the prognosis of OV patients. Univariate and multivariate Cox regression analyses were performed to identify survival-associated AS events and develop multi-AS-based prognostic models. The K-M curves and ROC curves were conducted based on prognostic AS event models. Moreover, a splicing regulatory network was established according to the correlation between AS events and splicing factors (SFs). Finally, we performed functional enrichment analysis by GO terms and KEGG pathways. Results We identified 1,472 AS events that were associated with the survival of OV patients, and exon skipping (ES) was the most important type. We also found that prognostic models based on AS events were good predictors of OV prognosis, which could discriminate the high-risk group from the low-risk group (P < 0.05). Notably, the AUC value of AD, AP, AT, ES, ME, and the whole cohort was more than 0.70, indicating that these six models had valuable prediction strength. The risk score of prognostic models was identified as an independent prognostic factor. Furthermore, the AS-SF correlation network revealed several hub SF genes, including DDX39B, PNN, LUC7L3, ZC3H4 and SRSF11, and so on. Conclusions In the present study, we constructed powerful prognostic predictors for OV patients and uncovered interesting splicing networks. Collectively, our findings provided valuable insights into the underlying mechanisms of OV.

scholarly journals Genome-wide profiling of alternative splicing in glioblastoma and their clinical value

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Youwei Li ◽  
Dongsheng Guo
Keyword(s):  
Alternative Splicing ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Functional Enrichment ◽  
Correlation Network ◽  
Splicing Factors ◽  
Lasso Regression ◽  
Clinical Value ◽  
Cox Analysis

Abstract Background Alternative splicing (AS), one of the main post-transcriptional biological regulation mechanisms, plays a key role in the progression of glioblastoma (GBM). Systematic AS profiling in GBM is limited and urgently needed. Methods TCGA SpliceSeq data and the corresponding clinical data were downloaded from the TCGA data portal. Survival-related AS events were identified through Kaplan–Meier survival analysis and univariate Cox analysis. Then, splicing correlation network was constructed based on these AS events and associated splicing factors. LASSO regression followed by multivariate Cox analysis was performed to validate independent AS biomarkers and to construct a risk prediction model. Enrichment analysis was subsequently conducted to explore potential signaling pathways of these AS events. Results A total of 132 TCGA GBM samples and 45,610 AS events were included in our study, among which 416 survival-related AS events were identified. An AS correlation network, including 54 AS events and 94 splicing factors, was constructed, and further functional enrichment was performed. Moreover, the novel risk prediction model we constructed displayed moderate performance (the area under the curves were > 0.7) at both one, two and three years. Conclusions Survival-related AS events may be vital factors of both biological function and prognosis. Our findings in this study can deepen the understanding of the complicated mechanisms of AS in GBM and provide novel insights for further study. Moreover, our risk prediction model is ready for preliminary clinical applications. Further verification is required.

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