Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: UMLS:C0017638 (glioma)
 30,880 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Long non-coding RNAs have recently become a key regulatory factor for cancers, whereas FER1L4, a newly discovered long non-coding RNA, has been mostly studied in gastric carcinoma and colon cancer cases. The functions and molecular mechanism of FER1L4 have been rarely reported in glioma malignant phenotypes. In this study, it was found that the expression of LncRNA FER1L4 is upregulated in high-grade gliomas than in low-grade cases and that a high expression of LncRNA FER1L4 predicts poor prognosis of gliomas. Meanwhile, in vitro study suggests that expression of FER1L4 with SiRNA knockdown obviously suppresses cell cycle and proliferation. It is further demonstrated by experiments that the FER1L4 knockdown suppresses growth of in vivo glioma. Besides, it is found in our study that LncRNA FER1L4 expression is positively correlated with E2F1 mRNA expression. After knockdown of FER1L4 expression, E2F1 expression is significantly down-regulated, whereas the expression of miR-372 is significantly up-regulated; the up-regulation of miR-372 leads to significant down-regulation of FER1L4 and E2F1 expression. In addition, it is also found that FER1L4 can be used as competitive endogenous RNA to interact or bind with miR-371 and thereby up-regulate E2F1, thus promoting the cycle and proliferation of glioma cells. It may be one of the molecular mechanisms in which FER1L4 plays its oncogene-like role in gliomas.
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PMID:FER1L4/miR-372/E2F1 works as a ceRNA system to regulate the proliferation and cell cycle of glioma cells. 3088 57

Background: Glioma is the most common primary brain tumor with a dismal prognosis. It is urgent to develop novel molecular biomarkers and conform to individualized schemes. Methods: Differentially expressed pseudogenes between low grade glioma (LGG) and glioblastoma multiforme (GBM) were identified in the training cohort. Least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox proportional hazards regression analyses were used to select pseudogenes associated with prognosis of glioma. A risk signature was constructed based on the selected pseudogenes for predicting the survival of glioma patients. A pseudogene-miRNA-mRNA regulatory network was established and visualized using Cytoscape 3.5.1. Gene Oncology (GO) and signaling pathway analyses were performed on the targeted genes to investigate functional roles of the risk signature. Results: Five pseudogenes (ANXA2P2, EEF1A1P9, FER1L4, HILS1, and RAET1K) correlating with glioma survival were selected and used to establish a risk signature. Time-dependent receiver operating characteristic (ROC) curves revealed that the risk signature could accurately predict the 1, 3, and 5-year survival of glioma patients. GO and signaling pathway analyses showed that the risk signature was involved in regulation of proliferation, migration, angiogenesis, and apoptosis in glioma. Conclusions: In this study, a risk signature with five pseudogenes was constructed and shown to accurately predict 1-, 3-, and 5-year survival for glioma patient. The risk signature may serve as a potential target against glioma.
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PMID:Identification of a Five-Pseudogene Signature for Predicting Survival and Its ceRNA Network in Glioma. 3168 95