Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: UMLS:C0546837 (esophageal cancer)
 8,907 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

BACKGROUND Accumulating evidence suggests the involvement of long non-coding RNAs (lncRNAs) as oncogenic or tumor suppressive regulators in the development of various cancers. In the present study, we aimed to identify a lncRNA signature based on RNA sequencing (RNA-seq) data to predict survival in esophageal cancer. MATERIAL AND METHODS The RNA-seq lncRNA expression data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed lncRNAs were screened out between esophageal cancer and normal tissues. Univariate and multivariate Cox regression analysis were performed to establish a lncRNA-related prognostic model. Receiver operating characteristic (ROC) analysis was conducted to test the sensitivity and specificity of the model. GO (gene ontology) functional and KEGG pathway enrichment analyses were performed for mRNAs co-expressed with the lncRNAs to explore the potential functions of the prognostic lncRNAs. RESULTS A total of 265 differentially expressed lncRNAs were identified between esophageal cancer and normal tissues. After univariate and multivariate Cox regression analysis, eight lncRNAs (GS1-600G8.5, LINC00365, CTD-2357A8.3, RP11-705O24.1, LINC01554, RP1-90J4.1, RP11-327J17.1, and LINC00176) were finally screened out to establish a predictive model by which patients could be classified into high-risk and low-risk groups with significantly different overall survival. Further analysis indicated independent prognostic capability of the 8-lncRNA signature from other clinicopathological factors. ROC curve analysis demonstrated good performance of the 8-lncRNA signature. Functional enrichment analysis showed that the prognostic lncRNAs were mainly associated with esophageal cancer related biological processes such as regulation of glucose metabolic process and amino acid and lipids metabolism. CONCLUSIONS Our study developed a novel candidate model providing additional and more powerful prognostic information beyond conventional clinicopathological factors for survival prediction of esophageal cancer patients. Moreover, it also brings us new insights into the molecular mechanisms underlying esophageal cancer.
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PMID:Identification of a RNA-Seq Based 8-Long Non-Coding RNA Signature Predicting Survival in Esophageal Cancer. 2802 7

Background: The objective of this study was to identify key molecules that included long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs involved in esophagus cancer with KH-type splicing regulatory protein (KHSRP) knockdown. Materials and Methods: GSE99422 and GSE99423 from Gene Expression Omnibus database were extracted. After differentially expressed analysis of miRNAs, lncRNAs, and mRNAs, the lncRNAs-mRNAs interaction was obtained. Then the protein-protein interaction (PPI) network and module analyses were performed for differentially expressed mRNAs (DEmRNAs). Combined with miRWalk tool and DIANA-LncBase tool, regulating relationship between differentially expressed miRNAs (DEmiRNAs) and DEmRNAs/DElncRNAs were predicted. Finally, mRNA-miRNA-lncRNA regulatory network construction was established by Cytoscape software. Finally, the key genes were validated based on the Gene Expression Profiling Interactive Analysis (GEPIA) database. Results: Totally, 2,027 DEmiRNAs, 3,480 DElncRNAs, and 18,293 DEmRNAs were screened. The PPI network included 399 nodes and 1671 interaction pairs, and two function models (Cluster 1 and Cluster 2) were separately identified. The PLK1 was a hub in the Cluster 1, which mainly enriched in the function of nuclear division. Then the competing endogenous RNA (ceRNA) network was constructed with 20 miRNAs, 66 lncRNA, and 202 mRNA. Here, lncRNA RP11-159D12.2 might function as a ceRNA in regulating BIRC5 expression of esophagus cancer through competitively binding to hsa-miR-4430. BIRC5 was mainly enriched in the function of mitotic nuclear division. Besides, the expression of BIRC5 and PLK1 genes was upregulated in tumor tissues compared with normal controls. Also, the correlation analysis showed that the key genes (BIRC5 and PLK1) were validated to be positively correlated with KHSRP. Conclusions: PLK1, BIRC5, hsa-miR-4430, and lncRNA RP11-159D12.2 were likely to be associated with esophagus cancer development.
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PMID:Identification of KHSRP-Regulated RNAs in Esophagus Cancer by Integrated Bioinformatics Analysis. 3267 63