A novel ferroptosis-related lncRNA signature for prognosis prediction in gastric cancer

被引:39
|
作者
Wei, Jianming [1 ]
Zeng, Ye [2 ]
Gao, Xibo [3 ]
Liu, Tong [1 ]
机构
[1] Tianjin Med Univ Gen Hosp, Dept Gen Surg, Tianjin, Peoples R China
[2] Huazhong Univ Sci & Technol, Wuhan Childrens Hosp, Tongji Med Coll, Dept Lab Med, Wuhan, Hubei, Peoples R China
[3] Tianjin Childrens Hosp, Dept Dermatol, Tianjin, Peoples R China
基金
中国国家自然科学基金;
关键词
Ferroptosis; Gastric cancer; Long non-coding RNA; Prognosis; Bioinformatics; LONG NONCODING RNA; INHIBITION; RESISTANCE; CELLS;
D O I
10.1186/s12885-021-08975-2
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background Gastric cancer (GC) is a common malignant cancer with a poor prognosis. Ferroptosis has been shown to play crucial roles in GC development. Long non-coding RNAs (lncRNAs) is also associated with tumor progression in GC. This study aimed to screen the prognostic ferroptosis-related lncRNAs and to construct a prognostic risk model for GC. Methods Ferroptosis-related lncRNAs from The Cancer Genome Atlas (TCGA) GC expression data was downloaded. First, single factor Cox proportional hazard regression analysis was used to select seven prognostic ferroptosis-related lncRNAs from TCGA database. And then, the selected lncRNAs were further included in the multivariate Cox proportional hazard regression analysis to establish the prognostic model. A nomogram was constructed to predict individual survival probability. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the risk model. Results We constructed a prognostic ferroptosis-related lncRNA signature in this study. Kaplan-Meier curve analysis revealed a significantly better prognosis for the low-risk group than for the high-risk group (P = 2.036e-05). Multivariate Cox proportional risk regression analysis demonstrated that risk score was an independent prognostic factor [hazard ratio (HR) = 1.798, 95% confidence interval (CI) =1.410-2.291, P < 0.001]. A nomogram, receiver operating characteristic curve, and principal component analysis were used to predict individual prognosis. Finally, the expression levels of AP003392.1, AC245041.2, AP001271.1, and BOLA3-AS1 in GC cell lines and normal cell lines were tested by qRT-PCR. Conclusions This risk model was shown to be a novel method for predicting prognosis for GC patients.
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页数:12
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