Systematic analysis of alternative splicing signature unveils prognostic predictor for kidney renal clear cell carcinoma

Song, Jukun; Liu, Yong Da; Su, Jiaming; Yuan, Dongbo; Sun, Fa; Zhu, Jianguo

doi:10.1002/jcp.28840

Cited by 52 publications

(45 citation statements)

References 24 publications

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“…Compared to previous research, our study had some differences [25,26]. First, our risk score (RS) strategy involved LASSO penalized regression which can analyse all independent variables as well as the most influential variables.…”

Section: Discussionmentioning

confidence: 99%

A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma

et al. 2020

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Background: Kidney renal clear cell carcinoma (KIRC) is a potentially fatal urogenital disease. It is a major cause of renal cell carcinoma and is often associated with late diagnosis and poor treatment outcomes. More evidence is emerging that genetic models can be used to predict the prognosis of KIRC. This study aimed to develop a model for predicting the overall survival of KIRC patients. Results: We identified 333 differentially expressed genes (DEGs) between KIRC and normal tissues from the Gene Expression Omnibus (GEO) database. We randomly divided 591 cases from The Cancer Genome Atlas (TCGA) into training and internal testing sets. In the training set, we used univariate Cox regression analysis to retrieve the survival-related DEGs and futher used multivariate Cox regression with the LASSO penalty to identify potential prognostic genes. A seven-gene signature was identified that included APOLD1, C9orf66, G6PC, PPP1R1A, CNN1G, TIMP1, and TUBB2B. The seven-gene signature was evaluated in the training set, internal testing set, and external validation using data from the ICGC database. The Kaplan-Meier analysis showed that the high risk group had a significantly shorter overall survival time than the low risk group in the training, testing, and ICGC datasets. ROC analysis showed that the model had a high performance with an AUC of 0.738 in the training set, 0.706 in the internal testing set, and 0.656 in the ICGC external validation set. Conclusion: Our findings show that a seven-gene signature can serve as an independent biomarker for predicting prognosis in KIRC patients.

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Section: Discussionmentioning

confidence: 99%

A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma

et al. 2020

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“…However, only two of them, the AD and ES signatures, were ultimately identified as independent prognostic factors for GBM compared with other clinicopathological parameters. Previous studies have investigated the novel prognostic value of various AS events and constructed the corresponding prognostic prediction models based on these AS events in multiple cancers, such as bladder urothelial carcinoma, renal clear cell carcinoma, and lung cancer (30)(31)(32). However, prognostic prediction models based on multiple AS events for GBM have not been reported before in the literature.…”

Section: Discussionmentioning

confidence: 99%

Development of a Nomogram With Alternative Splicing Signatures for Predicting the Prognosis of Glioblastoma: A Study Based on Large-Scale Sequencing Data

et al. 2020

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Purpose: Alternative splicing (AS) was reported to play a vital role in development and progression of glioblastoma (GBM), the most common and fatal brain tumor. Systematic analysis of survival-associated AS event profiles and prognostic prediction model based on multiple AS events in GBM was needed. Methods: Genome-wide AS and RNA sequencing profiles were generated in 152 patients with GBM in the cancer genome atlas (TCGA). Prognosis-associated AS events were screened by integrated Cox regression analysis to construct the prognostic risk score model in the training cohort (n = 101). The AS-based signature and clinicopathologic parameters were applied to construct a prognostic nomogram for 0.5-, 1-, and 3-year OS prediction. Finally, the regulatory networks between prognostic AS events and splicing factors (SFs) were constructed. Results: A total of 1,598 prognosis-related AS events from 1,183 source genes were determined. Eight prognostic risk score model based on integrated AS events and 7 AS types were established, respectively. Concordance index (C-index) and receiver operating characteristic (ROC) curve analysis demonstrated powerful ability in distinguishing patients' outcomes. Only Alternate Donor site (AD) and Exon Skip (ES) signature out of the eight types of AS signature were identified as independent prognostic factors for GBM, which was validated in the internal validation cohort. The nomogram with age, new event, pharmaceutical therapy, radiation therapy, AD signature and ES signature were constructed, with C-index of 0.892 (95% CI, 0.853-0.931; P = 5.13 × 10 −15). Calibration plots, ROC, and decision curve analysis suggested excellent predictive performance for the nomogram in both TCGA training cohort and validation cohort. Splicing network indicated distinguished correlations between prognostic AS events and SFs in GBM patients. Conclusions: AS-based prediction model could serve as a promising prognostic predictor and potential therapeutic target for GBM, facilitating better treatment strategies in clinical practice.

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“…Impressed by the profound influences exerted by dysregulated AS events in human cancers, we are propelled to explore the prognostic significance of AS events and the underlying mechanism in RCC. Despite Song J et al has built a preliminary prognostic model for KIRC based on profiles of AS events; they simply constructed the prognostic model and there was no validation of results or deep exploration of mechanism [21]. Moreover, the study of Song J et al ignored the prognostic role of splicing factors in KIRC and the regulatory relationship between AS and splicing factors in KIRC.…”

Section: Discussionmentioning

confidence: 99%

Genome-wide Analysis of the Alternative Splicing Profiles Revealed Novel Prognostic Index for Kidney Renal Cell Clear Cell Carcinoma

Huang

et al. 2020

J. Cancer

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Alternative splicing (AS) is a major mechanism that greatly enhanced the diversity of proteome. Mounting evidence demonstrated that aberration of AS are important steps for the initiation and progression of human cancers. Here, we comprehensively investigated the association between whole landscape of AS profiles and the survival outcome of renal cell carcinoma (RCC) patients using RNA-seq data from TCGA SpliceSeq. Because of the limited number size of deaths in kidney chromophobe renal cell carcinoma (KICH) and papillary renal cell carcinoma (KIRP) TCGA cohorts, we only conducted survival analysis in kidney clear renal cell carcinoma (KIRC). We further constructed prognostic index (PI) based on prognosis-related AS events and built correlation network for splicing factors and prognosis-related AS events. According to the results, a total of 5351 AS events in 3522 genes were significantly correlated with the overall survival (OS) of kidney clear cell renal cell carcinoma (KIRC) patients. Seven of the PI models exhibited preferable prognosis-predicting capacity for KIRC with PI-ALL reaching the highest area under curve value of 0.875. The splicing regulatory network between splicing factors and prognosis-related AS events depicted a tangled web of relationships between them. One of the splicing factors: KHDRBS3 was validated by immunohistochemistry to be down-regulated in KIRC tissues. In conclusion, the powerful efficiency of risk stratification of PI models indicated the potential of AS signature as promising prognostic markers for KIRC and the splicing regulation network provided possible genetic mechanism of KIRC.

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Systematic analysis of alternative splicing signature unveils prognostic predictor for kidney renal clear cell carcinoma

Cited by 52 publications

References 24 publications

A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma

A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma

Development of a Nomogram With Alternative Splicing Signatures for Predicting the Prognosis of Glioblastoma: A Study Based on Large-Scale Sequencing Data

Genome-wide Analysis of the Alternative Splicing Profiles Revealed Novel Prognostic Index for Kidney Renal Cell Clear Cell Carcinoma

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