Yong Liao1, Wen Cao2, Kunpeng Zhang1, Yang Zhou1, Xin Xu1, Xiaoling Zhao1, Xu Yang1, Jitao Wang1, Shouwen Zhao1, Shiyu Zhang1, Longfei Yang1, Dengxiang Liu1, Yanpeng Tian3, Weizhong Wu4. 1. Department of Hepatobiliary Surgery, Xingtai People's Hospital of Hebei Medical University, Xingtai, 054001, Hebei, People's Republic of China. 2. Department of Neurology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei, People's Republic of China. 3. Department of Obstetrics and Gynecology, The Second Hospital of Hebei Medical University, No. 215 West Heping Road, Shijiazhuang, 050000, Hebei, People's Republic of China. tianyanp2020@163.com. 4. Department of General Surgery, The First Hospital of Hebei Medical University, No. 89 Donggang Road, Shijiazhuang, 050000, Hebei, People's Republic of China. wwzdhr@sohu.com.
Abstract
BACKGROUND: lncRNAs-miRNAs-mRNAs networks play an important role in Gastric adenocarcinoma (GA). Identification of these networks provide new insight into the role of these RNAs in gastric cancer. OBJECTIVES: Biological information databases were screened to characterize and examine the regulatory networks and to further investigate the potential prognostic relationship this regulation has in GA. METHODS: By mining The Cancer Genome Atlas (TCGA) database, we gathered information on GA-related lncRNAs, miRNAs, and mRNAs. We identified differentially expressed (DE) lncRNAs, miRNAs, and mRNAs using R software. The lncRNA-miRNA-mRNA interaction network was constructed and subsequent survival examination was performed. Representative genes were selected out using The Biological Networks Gene Ontology plug-in tool on Cytoscape. Additional analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms were used to screen representative genes for functional enrichment. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) were used to identify the expression of five candidate differential expressed RNAs. RESULTS: Information of samples from 375 cases of gastric cancer and 32 healthy cases (normal tissues) were downloaded from the TCGA database. A total of 1632 DE-mRNAs, 1008 DE-lncRNAs and 104 DE-miRNAs were identified and screened. Among them, 65 DE-lncRNAs, 10 DE-miRNAs, and 10 DE-mRNAs form lncRNAs-miRNAs-mRNAs regulatory network. Additionally, 10 lncRNAs and 2 mRNAs were associated with the prognosis of GA. Multivariable COX analysis revealed that AC018781.1 and VCAN-AS1 were independent risk factors for GA. GO functional enrichment analysis found DE-mRNA was significantly enriched TERM (P < 0.05). The KEGG signal regulatory network analysis found 11 significantly enrichment networks, the most prevailing was for the AGE-RAGE signaling pathway associated with Diabetic complications. Results of RT-qPCR was consistent with the in silico results. CONCLUSIONS: The results of the present study represent a view of GA from a analysis of lncRNA, miRNA and mRNA. The network of lncRNA-miRNA-mRNA interactions revealed here may potentially further experimental studies and may help biomarker development for GA.
BACKGROUND: lncRNAs-miRNAs-mRNAs networks play an important role in Gastric adenocarcinoma (GA). Identification of these networks provide new insight into the role of these RNAs in gastric cancer. OBJECTIVES: Biological information databases were screened to characterize and examine the regulatory networks and to further investigate the potential prognostic relationship this regulation has in GA. METHODS: By mining The Cancer Genome Atlas (TCGA) database, we gathered information on GA-related lncRNAs, miRNAs, and mRNAs. We identified differentially expressed (DE) lncRNAs, miRNAs, and mRNAs using R software. The lncRNA-miRNA-mRNA interaction network was constructed and subsequent survival examination was performed. Representative genes were selected out using The Biological Networks Gene Ontology plug-in tool on Cytoscape. Additional analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms were used to screen representative genes for functional enrichment. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) were used to identify the expression of five candidate differential expressed RNAs. RESULTS: Information of samples from 375 cases of gastric cancer and 32 healthy cases (normal tissues) were downloaded from the TCGA database. A total of 1632 DE-mRNAs, 1008 DE-lncRNAs and 104 DE-miRNAs were identified and screened. Among them, 65 DE-lncRNAs, 10 DE-miRNAs, and 10 DE-mRNAs form lncRNAs-miRNAs-mRNAs regulatory network. Additionally, 10 lncRNAs and 2 mRNAs were associated with the prognosis of GA. Multivariable COX analysis revealed that AC018781.1 and VCAN-AS1 were independent risk factors for GA. GO functional enrichment analysis found DE-mRNA was significantly enriched TERM (P < 0.05). The KEGG signal regulatory network analysis found 11 significantly enrichment networks, the most prevailing was for the AGE-RAGE signaling pathway associated with Diabetic complications. Results of RT-qPCR was consistent with the in silico results. CONCLUSIONS: The results of the present study represent a view of GA from a analysis of lncRNA, miRNA and mRNA. The network of lncRNA-miRNA-mRNA interactions revealed here may potentially further experimental studies and may help biomarker development for GA.
Authors: Matthew Dixon; Alyson L Mahar; Lucy K Helyer; Jovanka Vasilevska-Ristovska; Calvin Law; Natalie G Coburn Journal: Gastric Cancer Date: 2014-11-25 Impact factor: 7.370
Authors: Ahmedin Jemal; Freddie Bray; Melissa M Center; Jacques Ferlay; Elizabeth Ward; David Forman Journal: CA Cancer J Clin Date: 2011-02-04 Impact factor: 508.702
Authors: J Ferlay; E Steliarova-Foucher; J Lortet-Tieulent; S Rosso; J W W Coebergh; H Comber; D Forman; F Bray Journal: Eur J Cancer Date: 2013-02-26 Impact factor: 9.162