A 16-gene signature predicting prognosis of patients with oral tongue squamous cell carcinoma

Qiu, Zeting; Sun, Wei; S, Gao; Zhou, Huaqiang; Tan, Wulin; Cao, Minghui; Huang, Wenqi

doi:10.7717/peerj.4062

Cited by 22 publications

(18 citation statements)

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“…To further compare the performance among diverse predictive models established from the publicly available TCGA data set, we reviewed the relevant literature and identified three models based on mRNA or lncRNA abundance in OSCC samples (Qiu et al, ; C. Zhao, Sun, Zeng, & Cui, ; X. Zhao et al, ). As listed in Table S5, our AS predictive models seemed superior to these three models as evidenced by the AUC values.…”

Section: Resultsmentioning

confidence: 99%

“…-h). The model with the integrative 7 types of AS events had a superior predictive ability as compared to models with individual AS event, as evidenced by the AUC value of 0.7(Figure 6i).To further compare the performance among diverse predictive models established from the publicly available TCGA data set, we reviewed the relevant literature and identified three models based on mRNA or lncRNA abundance in OSCC samples(Qiu et al, 2017; …”

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confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

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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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“…The gene EVI2A, as the human homolog of mouse genes, may be associated with other proteins in the membrane as a part of a cell surface receptor complex [ 37 ]; EVI2A has been shown to be an oncogene [ 38 ]; EVI2A is highly expressed in oral tongue squamous cell carcinoma [ 39 ] and osteosarcoma [ 40 ] and is a risk factor for cancer prognosis. In this study, we found that EVI2A is one of the MDG of ccRCC, and its hypomethylation leads to poor prognosis ( Figure 9(d) ).…”

Section: Discussionmentioning

confidence: 99%

Prognostic Value of DNA Methylation-Driven Genes in Clear Cell Renal Cell Carcinoma: A Study Based on Methylation and Transcriptome Analyses

Xie

Hou

et al. 2020

Disease Markers

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Background. Few previous studies have comprehensively explored the level of DNA methylation and gene expression in ccRCC. The purpose of this study was to identify the key clear cell renal cell carcinoma- (ccRCC-) related DNA methylation-driven genes (MDG) and to build a prognostic model based on the level of DNA methylation. Methods. RNA-seq transcriptome data and DNA methylation data were obtained from The Cancer Genome Atlas. Based on the MethylMix algorithm, we obtain ccRCC-related MDG. The univariate and multivariate Cox regression analyses were employed to investigate the correlation between patient overall survival and the methylation level of each MDG. Finally, a prognosis risk score was established based on a linear combination of the regression coefficient derived from the multivariate Cox regression model (β) multiplied with the methylation level of the gene. Results. 19 ccRCC-related MDG were identified. Three MDG (NCKAP1L, EVI2A, and BATF) were further screened and integrated into a prognostic risk score model, risk score=3.710∗methylation level of NCKAP1L+−3.892∗methylation level of EVI2A+−3.907∗methylation level of BATF. The risk model was independent from conventional clinical characteristics as a prognostic factor for ccRCC (HR=1.221, 95% confidence interval: 1.063–1.402, and P=0.005). The joint survival analysis showed that the gene expression and methylation levels of the prognostic genes EVI2A and BATF were significantly related with prognosis. Conclusion. This study provided an important bioinformatics foundation for in-depth studies of ccRCC DNA methylation.

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“…OSCC is an aggressive malignancy of the head and neck, with a 5-year survival rate of 40-50% worldwide (20,21). Although great efforts have been made over the past few decades to develop signatures for prognostic predictions, the lack of specificity and sensitivity to predict survival remains due to the complex molecular and cellular heterogeneity of OSCC.…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of seven prognostic long non‑coding RNAs in oral squamous cell carcinoma

Miao

Wei

et al. 2020

Oncol Lett

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Oral squamous cell carcinoma (OSCC) is one of the most common malignancies worldwide, due to poor diagnosis and treatment. There is increasing evidence that demonstrates the involvement of long non-coding RNAs (lncRNAs) in carcinogenesis and cancer progression. Therefore, the aim of the present study was to explore potential lncRNA-associated features of patients with OSCC as a valuable and independent prognostic biomarker. A total of 268 lncRNA expression profiles and clinical patient information on OSCC were downloaded from The Cancer Genome Atlas database. The clinical information was exploited for prescreening, using Cox regression analysis, and differentially expressed lncRNAs (DElncRNAs) were identified using edgeR software. Using the 'caret' package, the datasets were categorized into test datasets and training datasets, respectively. Through bioinformatics, seven prognostic DElncRNAs were selected. Using the regression coefficients, a risk score based on the seven-DElncRNA signature was developed to assess the prognostic function of key DElncRNAs. According to the median risk score, patients were classified into high-risk and low-risk groups in the training and test datasets. Additionally, receiver operating characteristic (ROC) curve analysis was conducted to evaluate the sensitivity and specificity of the prognostic DElncRNAs, and the optimal cutoff point was obtained from ROC analysis. Based on the optimal cutoff point, the patients were also categorized into high-risk and low-risk groups. Notably, the optimal cutoff point was more sensitive than the median risk score, particularly in the test dataset. The Kaplan-Meier survival and log rank test analysis results indicated that the P-value, based on the optimal cutoff , was less than the median risk cutoff. Additionally, stratified analysis results revealed that the seven-DElncRNAs signature was also independent of OSCC age. Furthermore, the findings of the present study suggested that the seven-DElncRNA signature can be used as a potential prognostic indicator and may have important clinical significance in OSCC.

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A 16-gene signature predicting prognosis of patients with oral tongue squamous cell carcinoma

Cited by 22 publications

References 50 publications

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

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

Prognostic Value of DNA Methylation-Driven Genes in Clear Cell Renal Cell Carcinoma: A Study Based on Methylation and Transcriptome Analyses

Identification and validation of seven prognostic long non‑coding RNAs in oral squamous cell carcinoma

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