Han Yan1, Dan Tan1, Pan Xie1, Zhaoqian Liu1, Xi Li1. 1. Department of Clinical Pharmacology, Xiangya Hospital, Central South University, Changsha 410008; Institute of Clinical Pharmacology, Central South University; Hunan Key Laboratory of Pharmacogenetics, Changsha 410078, China.
Abstract
OBJECTIVE: To find the relationship between long non-coding RNA (lncRNA) based on data mining methods. Methods: We use a whole genome mRNA expression data set (GSE15605) from the gene expression ominibus database to find the lncRNAs which relate to melanoma. Results: Four lncRNAs (LINC01213, PGM5-AS1, LINC01133 and LOC284578) were significantly associated with the incidence and development of melanoma. Meanwhile, LINC01213, LINC01133 and LOC284578 were correlated with BRAF mutation, and PGM5-AS1 was related to NRAS mutation. Data mining study found that the model based on the expression values of these 4 lncRNAs can easily preferable classify the study samples. Conclusion: LncRNAs may play important roles in the incidence and development of melanoma.
OBJECTIVE: To find the relationship between long non-coding RNA (lncRNA) based on data mining methods. Methods: We use a whole genome mRNA expression data set (GSE15605) from the gene expression ominibus database to find the lncRNAs which relate to melanoma. Results: Four lncRNAs (LINC01213, PGM5-AS1, LINC01133 and LOC284578) were significantly associated with the incidence and development of melanoma. Meanwhile, LINC01213, LINC01133 and LOC284578 were correlated with BRAF mutation, and PGM5-AS1 was related to NRAS mutation. Data mining study found that the model based on the expression values of these 4 lncRNAs can easily preferable classify the study samples. Conclusion: LncRNAs may play important roles in the incidence and development of melanoma.