Clinical laboratory · 2018
Identification of Long Non-Coding RNAs for Predicting Prognosis Among Patients with Thymoma.
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Gong Jian, Jin Shengxin, Pan Xujing, Wang Guiye, Ye Lifang, Tao Hongqun, Wen Huaikai, Liu Yu, Xie Qipeng
Аннотация
BACKGROUND: Thymoma is the most common primary anterior mediastinal neoplasm with a high recurrence rate. Long noncoding RNAs (lncRNAs) have recently been indicated to be used as diagnostic and prognostic indicators for different cancers. The aim of this study was to identify new tumor-specific prognostic lncRNA markers that can improve the treatment and follow-up of patients with thymomas.
METHODS: One hundred seventeen thymoma patients with clinical information and level 3 RNAseqv2 data were downloaded from The Cancer Genome Atlas. Prognostic lncRNAs were identified using Kaplan-Meier survival analyses and univariate Cox proportional hazards regression analyses. A predictive risk scoring model was subsequently created using independently significant lncRNAs from a multivariate Cox regression analysis.
RESULTS: Masaoka stage and 13 lncRNAs were significantly associated with RFS among 117 thymoma patients, while 59 lncRNAs were significantly associated with OS (all p < 0.05). Multivariate analyses revealed that OS was only independently associated with one lncRNA (JPX) and that RFS was only independently associated with three lncRNAs (AFAP1-AS1, LINC00324, and VLDLR-AS1). A risk score model constructed by the three lncRNA expressions showed that the high-risk group was more likely to experience recurrence.
CONCLUSIONS: The expression profile for three lncRNAs (AFAP1-AS1, LINC00324, and VLDLR-AS1) could be used to independently predict RFS among thymoma patients, which may be as prognostic biomarkers for thymoma.
METHODS: One hundred seventeen thymoma patients with clinical information and level 3 RNAseqv2 data were downloaded from The Cancer Genome Atlas. Prognostic lncRNAs were identified using Kaplan-Meier survival analyses and univariate Cox proportional hazards regression analyses. A predictive risk scoring model was subsequently created using independently significant lncRNAs from a multivariate Cox regression analysis.
RESULTS: Masaoka stage and 13 lncRNAs were significantly associated with RFS among 117 thymoma patients, while 59 lncRNAs were significantly associated with OS (all p < 0.05). Multivariate analyses revealed that OS was only independently associated with one lncRNA (JPX) and that RFS was only independently associated with three lncRNAs (AFAP1-AS1, LINC00324, and VLDLR-AS1). A risk score model constructed by the three lncRNA expressions showed that the high-risk group was more likely to experience recurrence.
CONCLUSIONS: The expression profile for three lncRNAs (AFAP1-AS1, LINC00324, and VLDLR-AS1) could be used to independently predict RFS among thymoma patients, which may be as prognostic biomarkers for thymoma.