Objective: To explore the regulatory mechanism of competitive endogenous RNAs(ce RNA) in gastric cancer(GC) and to predict the prognosis of GC. Materials and Methods: Expression profiles of long noncoding RNAs(lnc RNA...Objective: To explore the regulatory mechanism of competitive endogenous RNAs(ce RNA) in gastric cancer(GC) and to predict the prognosis of GC. Materials and Methods: Expression profiles of long noncoding RNAs(lnc RNAs), micro RNAs(mi RNAs), and m RNAs were obtained from The Cancer Genome Atlas platform. Differentially expressed RNAs(DERNAs) were screened to construct a lnc RNA-mi RNA-m RNA ce RNA network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed on the ce RNA network-related differentially expressed m RNAs(DEm RNAs). Next, the DERNAs were subjected to Cox regression and survival analyses to identify crucial prognostic factors for patients with GC. Results: We detected 1029 differentially expressed lnc RNAs, 104 differentially expressed mi RNAs, and 1659 DEm RNAs in patients with GC. Next, we performed bioinformatic analysis to construct the lnc RNA-mi RNA-m RNA ce RNA network, which included 10 mi RNAs, 65 lnc RNAs, and 10 m RNAs. Subsequently, Kaplan Meier(K-M) analysis showed that the survival rate of the high-risk group was significantly lower than that of the low-risk group, and the area under the curve value of the receiver operating characteristic curve revealed that the polygenic model had good predictive ability. The results indicated that ADAMTS9-AS1, ATAD2, and CADM2 might be potential therapeutic targets and prognostic biomarkers for GC. Conclusions: Our study has implications for predicting prognosis and monitoring surveillance of GC and provides a new theoretical and experimental basis for the clinical prognosis of GC.展开更多
基金supported by the Shanghai Natural Science Foundation of China(Grant No.16ZR1447300)。
文摘Objective: To explore the regulatory mechanism of competitive endogenous RNAs(ce RNA) in gastric cancer(GC) and to predict the prognosis of GC. Materials and Methods: Expression profiles of long noncoding RNAs(lnc RNAs), micro RNAs(mi RNAs), and m RNAs were obtained from The Cancer Genome Atlas platform. Differentially expressed RNAs(DERNAs) were screened to construct a lnc RNA-mi RNA-m RNA ce RNA network. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses were performed on the ce RNA network-related differentially expressed m RNAs(DEm RNAs). Next, the DERNAs were subjected to Cox regression and survival analyses to identify crucial prognostic factors for patients with GC. Results: We detected 1029 differentially expressed lnc RNAs, 104 differentially expressed mi RNAs, and 1659 DEm RNAs in patients with GC. Next, we performed bioinformatic analysis to construct the lnc RNA-mi RNA-m RNA ce RNA network, which included 10 mi RNAs, 65 lnc RNAs, and 10 m RNAs. Subsequently, Kaplan Meier(K-M) analysis showed that the survival rate of the high-risk group was significantly lower than that of the low-risk group, and the area under the curve value of the receiver operating characteristic curve revealed that the polygenic model had good predictive ability. The results indicated that ADAMTS9-AS1, ATAD2, and CADM2 might be potential therapeutic targets and prognostic biomarkers for GC. Conclusions: Our study has implications for predicting prognosis and monitoring surveillance of GC and provides a new theoretical and experimental basis for the clinical prognosis of GC.
