Systematic analysis of survival‐associated alternative splicing signatures uncovers prognostic predictors for head and neck cancer

Liang, Ying; Song, Jukun; Dan, He; Xia, Yu; Wu, Yadong; Yin, Xinhai; Liu, Jianguo

doi:10.1002/jcp.28241

Cited by 23 publications

(21 citation statements)

References 30 publications

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“…However, the area under the curve (AUC) for the model was only 0.723. Further, four candidate genes (TPM1, CLASRP, PRRC1, and DNASE1L1) were screened out among the 42,849 alternating splicing events identified in 10,121 genes in another study, which built a prognostic prediction model with an AUC of 0.704 [ 31 ]. These results were much lower than the AUC of 0.963 in our study.…”

Section: Discussionmentioning

confidence: 99%

Novel Prognostic Model Based on Immune Signature for Head and Neck Squamous Cell Carcinoma

Wang

Hua

et al. 2020

BioMed Research International

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Background. Deciphering the immune characteristics within tumors and identifying the immune signals related to the prognostic factor are helpful for the treatment and management of tumor patients. However, systematic analysis of immune signatures in head and neck squamous cell carcinoma (HNSCC) remains largely unstudied. Methods. A total of 718 immune-related genes were extracted from RNA sequencing data from 519 HNSCC patients in the TCGA database, and survival analysis with integrated bioinformatics analyses was performed to build the final predictive prognosis model. Results. The 178 survival-associated genes ( P < 0.05 ) participated in important immune functions, including immune cell activation and migration. Multivariate regression analysis using 93 genes ( P < 0.01 ), together with survival-associated clinicopathological parameters, identified 35 independent prognostic factors. The most significant 8 independent factors were CD3E, CD40LG, TNFRSF4, CD3G, CD5, ITGA2B, ABCB1, and TNFRSF13b. The final prognostic model achieved outstanding predictive efficiency with the highest AUC of 0.963. Conclusion. Our prognostic model based on the immune signature could effectively predict the prognosis of HNSCC patients, providing novel predictive biomarkers and potential therapeutic targets for HNSCC patients.

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Section: Discussionmentioning

confidence: 99%

Novel Prognostic Model Based on Immune Signature for Head and Neck Squamous Cell Carcinoma

Wang

Hua

et al. 2020

BioMed Research International

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“…Subsequently, systematic profiling of prognostic AS signatures has been reported in esophageal carcinoma, ovarian cancer, papillary thyroid cancer, gastrointestinal adenocarcinomas as well as head neck cancer (Lin et al, ; Mao et al, ; Zhu, Chen, & Yong, ). Noticeably, comprehensive analyses of AS events in head neck squamous cell carcinoma (HNSCC) have been reported and predictive models based on AS have been established as prognostic predictors with high performance (Liang et al, ; Xing, Zhang, & Tong, ). However, HNSCC is a heterogeneous group of epithelial malignancies with diverse etiologic factors, tumorigenic processes and treatment modalities, as exemplified by HPV infection as well as its roles between OSCC and oropharyngeal SCC (Chi et al, ; D'Souza et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…Noticeably, comprehensive analyses of AS events in head neck squamous cell carcinoma (HNSCC) have been reported and predictive models based on AS have been established as prognostic predictors with high performance (Liang et al, 2019;Xing, Zhang, & Tong, 2019). However, HNSCC is a heterogeneous group of epithelial malignancies with diverse etiologic factors, tumorigenic processes and treatment modalities, as exemplified by HPV infection as well as its roles between OSCC and oropharyngeal SCC (Chi et al, 2015;D'Souza et al, 2017).…”

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confidence: 99%

Identification of a prognostic alternative splicing signature in oral squamous cell carcinoma

Zhang

Diao

et al. 2019

Journal Cellular Physiology

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Alternative splicing (AS) is critically associated with tumorigenesis and patient's prognosis.Here, we systematically analyzed survival-associated AS signatures in oral squamous cell carcinoma (OSCC) and evaluated their prognostic predictive values. Survival-related AS events were identified by univariate and multivariate Cox regression analyses using OSCC data from the TCGA head neck squamous cell carcinoma data set. The Percent Spliced In calculated by SpliceSeq from 0 to 1 was used to quantify seven types of AS events. A predictive model based on AS events was constructed by least absolute shrinkage and selection operator Cox regression assay and further validated using a training-testing cohort design. Patient survival was estimated using the Kaplan-Meier method and compared with Log-rank test. The receiver operating characteristics curve area under the curves was used to evaluate the predictive abilities of these predictive models.Furthermore, gene-gene interaction networks and the splicing factors (SFs)-AS regulatory network was generated by Cytoscape. A total of 825 survival-related AS events within 719 genes were identified in OSCC samples. The integrative predictive model was better at predicting outcomes of patients as compared to those models built with the individual AS event. The predictive model based on three AS-related genes also effectively predicted patients' survival. Moreover, seven survival-related SFs were detected in OSCC including RBM4, HNRNPD, and HNRNPC, which have been linked to tumorigenesis. The SF-AS network revealed a significant correlation between survival-related AS genes and these SFs. Our findings revealed a systemic portrait of survival-associated AS events and the splicing network in OSCC, suggesting that AS events might serve as novel prognostic biomarkers and therapeutic targets for OSCC. K E Y W O R D Salternative splicing, oral squamous cell carcinoma, prognosis, splicing factor

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“…In nonmuscle cells, TPM1 acts as a cell transformation inhibitor. Many of the splicing isoforms of TPM1, including the AT‐splicing variant of TPM1 were found in our analysis to be involved in various types of tumors, such as head‐and‐neck cancer, 39 esophageal cancer, 40 breast cancer, 41 and lung cancer 42 . At the same time, for the first time, we found that high expression of the steroid‐assisted regulatory gene CALCOCO1 AS RI was significantly associated with poor prostate cancer prognosis.…”

Section: Discussionmentioning

confidence: 50%

Systematic profiling of alternative splicing signature reveals prognostic predictor for prostate cancer

Zhao

Chang

et al. 2020

Cancer Science

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Alternative splicing (AS) provides the primary mechanism for producing protein diversity. There is growing evidence that AS is involved in the development and progression of cancers. The rapid accumulation of high‐throughput sequencing technologies and clinical data sets offers an opportunity to systematically profile the relationship between mRNA variants and clinical outcomes. However, there is a lack of systematic analysis of AS in prostate cancer: Download RNA‐seq data and clinical information from The Cancer Genome Atlas (TCGA) data portal. Evaluate RNA splicing patterns by SpliceSeq and calculate splicing percentage (PSI) values. Different expressions were identified as differently expressed AS events (DEAs) based on PSI values. Bioinformatics methods were used for further analysis of DEAs and their splicing networks. Kaplan‐Meier, Cox proportional regression, and unsupervised cluster analysis were used to assess the correlation between DEAs and clinical characteristics. In total, 43 834 AS events were identified, of which 1628 AS events were differentially expressed. The parental genes of these DEAs played a significant role in the regulation of prostate cancer‐related processes. In total, 226 DEAs events were found to be associated with disease‐free survival. Four clusters of molecules with different survival modes were revealed by unsupervised cluster analysis of DEAs. AS events may be important determinants of prognosis and bio‐modulation in prostate cancer. In this study, we established strong prognostic predictors, discovered a splicing network that may be a potential mechanism, and provided further validated therapeutic targets.

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Systematic analysis of survival‐associated alternative splicing signatures uncovers prognostic predictors for head and neck cancer

Cited by 23 publications

References 30 publications

Novel Prognostic Model Based on Immune Signature for Head and Neck Squamous Cell Carcinoma

Novel Prognostic Model Based on Immune Signature for Head and Neck Squamous Cell Carcinoma

Identification of a prognostic alternative splicing signature in oral squamous cell carcinoma

Systematic profiling of alternative splicing signature reveals prognostic predictor for prostate cancer

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