RESUMO
Glioma is the most common primary intracranial tumor and is related to poor clinical outcomes. The developments of sensitive markers can be applied to reveal the mechanisms involved in the progression of glioma. This study examined CDCA2 expression in glioma samples and its significance in predicting glioma patient outcome. GEPIA and GEO datasets were used to explore the expression of CDCA2 in glioma. Kaplan-Meier and multivariate assays were applied to delve into the prognostic values of CDCA2 expression in glioma patients using CGGA datasets. Our group also determined the associations between CDCA2 and clinical characteristics. Coexpression analysis was performed. In this research, we observed that CDCA2 expression was distinctly upregulated in glioma specimens compared with nontumor specimens. The prognosis of glioma with high CDCA2 expression was distinctly worse compared with that of glioma with low CDCA2 expression. Additionally, multivariate Cox regression analysis revealed that high CDCA2 expression was an independent poor prognostic indicator for glioma patients. High expression of CDCA2 was positively associated with advanced clinical progression. Coexpression analysis revealed that CDCA2 could be positively related to ASPM, SKA1, DLGAP5, NCAPG, and CDCA8 and was negatively associated with ETNPPL, LDHD, MRVI1, CBX7, and CENPJ. Overall, our findings revealed that CDCA2 might serve as an independent prognosis indicator for glioma.
Assuntos
Neoplasias Encefálicas , Glioma , Neoplasias Encefálicas/genética , Proteínas de Transporte/metabolismo , Proteínas de Ciclo Celular/genética , Proteínas de Ciclo Celular/metabolismo , Proteínas Cromossômicas não Histona/genética , Proteínas Cromossômicas não Histona/metabolismo , Glioma/genética , Glioma/patologia , Humanos , Proteínas Nucleares/metabolismo , Complexo Repressor Polycomb 1/metabolismo , PrognósticoRESUMO
BACKGROUND: Large cell neuroendocrine carcinoma (LCNEC) of the lung is a rare neuroendocrine neoplasm. Previous studies have shown that microRNAs (miRNAs) are widely involved in tumor regulation through targeting critical genes. However, it is unclear which miRNAs play vital roles in the pathogenesis of LCNEC, and how they interact with transcription factors (TFs) to regulate cancer-related genes. METHODS: To determine the novel TF-miRNA-target gene feed-forward loop (FFL) model of LCNEC, we integrated multi-omics data from Gene Expression Omnibus (GEO), Transcriptional Regulatory Relationships Unraveled by Sentence-Based Text Mining (TRRUST), Transcriptional Regulatory Element Database (TRED), and The experimentally validated microRNA-target interactions database (miRTarBase database). First, expression profile datasets for mRNAs (GSE1037) and miRNAs (GSE19945) were downloaded from the GEO database. Overlapping differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were identified through integrative analysis. The target genes of the FFL were obtained from the miRTarBase database, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed on the target genes. Then, we screened for key miRNAs in the FFL and performed gene regulatory network analysis based on key miRNAs. Finally, the TF-miRNA-target gene FFLs were constructed by the hypergeometric test. RESULTS: A total of 343 DEGs and 60 DEMs were identified in LCNEC tissues compared to normal tissues, including 210 down-regulated and 133 up-regulated genes, and 29 down-regulated and 31 up-regulated miRNAs. Finally, the regulatory network of TF-miRNA-target gene was established. The key regulatory network modules included ETS1-miR195-CD36, TAOK1-miR7-1-3P-GRIA1, E2F3-miR195-CD36, and TEAD1-miR30A-CTHRC1. CONCLUSIONS: We constructed the TF-miRNA-target gene regulatory network, which is helpful for understanding the complex LCNEC regulatory mechanisms.
