Integrated analysis of RNA-binding proteins in human colorectal cancer
| Autor: | Fanghan Guo, Xuehui Fan, Di Zhong, Guozhong Li, Yue Shi, Lili Liu, Xiuli Zhao, Haining Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
genetic structures
lcsh:Surgery RNA-binding protein Computational biology lcsh:RC254-282 Transcriptome 03 medical and health sciences Colorectal cancer (CRC) 0302 clinical medicine Databases Genetic Prognostic model construction Medicine Humans Gene Regulatory Networks Protein Interaction Maps KEGG Gene Survival analysis Proportional hazards model Mechanism (biology) business.industry Research Gene Expression Profiling Computational Biology RNA-Binding Proteins lcsh:RD1-811 lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens Prognosis RNA-binding protein (RBP) Gene Expression Regulation Neoplastic Oncology Ribonucleoproteins 030220 oncology & carcinogenesis 030211 gastroenterology & hepatology Surgery business Colorectal Neoplasms Function (biology) |
| Zdroj: | World Journal of Surgical Oncology World Journal of Surgical Oncology, Vol 18, Iss 1, Pp 1-14 (2020) |
| ISSN: | 1477-7819 |
| Popis: | Background Although RNA-binding proteins play an essential role in a variety of different tumours, there are still limited efforts made to systematically analyse the role of RNA-binding proteins (RBPs) in the survival of colorectal cancer (CRC) patients. Methods Analysis of CRC transcriptome data collected from the TCGA database was conducted, and RBPs were extracted from CRC. R software was applied to analyse the differentially expressed genes (DEGs) of RBPs. To identify related pathways and perform functional annotation of RBP DEGs, Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out using the database for annotation, visualization and integrated discovery. Protein-protein interactions (PPIs) of these DEGs were analysed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized by Cytoscape software. Based on the Cox regression analysis of the prognostic value of RBPs (from the PPI network) with survival time, the RBPs related to survival were identified, and a prognostic model was constructed. To verify the model, the data stored in the TCGA database were designated as the training set, while the chip data obtained from the GEO database were treated as the test set. Then, both survival analysis and ROC curve verification were conducted. Finally, the risk curves and nomograms of the two groups were generated to predict the survival period. Results Among RBP DEGs, 314 genes were upregulated while 155 were downregulated, of which twelve RBPs (NOP14, MRPS23, MAK16, TDRD6, POP1, TDRD5, TDRD7, PPARGC1A, LIN28B, CELF4, LRRFIP2, MSI2) with prognostic value were obtained. Conclusions The twelve identified genes may be promising predictors of CRC and play an essential role in the pathogenesis of CRC. However, further investigation of the underlying mechanism is needed. |
| Databáze: | OpenAIRE |
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