Long non-coding RNA

Prognostic significance of long non-coding RNA MALAT-1 in various human carcinomas: a meta-analysis

J. Wang, Xu, A. M., Zhang, J. Y., He, X. M., Pan, Y. S., Cheng, G., Qin, C., Hua, L. X., Wang, Z. J., Wang, J., Xu, A. M., Zhang, J. Y., He, X. M., Pan, Y. S., Cheng, G., Qin, C., Hua, L. X., Wang, Z. J., Wang, J., Xu, A. M., Zhang, J. Y., He, X. M., Pan, Y. S., Cheng, G., Qin, C., Hua, L. X., and Wang, Z. J., Prognostic significance of long non-coding RNA MALAT-1 in various human carcinomas: a meta-analysis, vol. 15, p. -, 2016.

The long non-coding RNA MALAT-1 plays an important role in cancer prognosis. The present research aimed to elucidate its precise predictive value in various human carcinomas. A quantitative meta-analysis was performed by searching PubMed, Embase, Web of Science, and Cochrane Library (most recently, January 2015) databases, and extracting data from studies that investigated the association between MALAT-1 expression and survival outcomes in patients of various cancers. Pooled hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated as a measure of generalized effect.

Identification of long non-coding RNA involved in osteogenic differentiation from mesenchymal stem cells using RNA-Seq data

W. Q. Song, Gu, W. Q., Qian, Y. B., Ma, X., Mao, Y. J., and Liu, W. J., Identification of long non-coding RNA involved in osteogenic differentiation from mesenchymal stem cells using RNA-Seq data, vol. 14, pp. 18268-18279, 2015.

The aim of this study was to identify long non-coding RNA (lncRNA) associated with osteogenic differentiation from mesenchymal stem cells (MSCs) using high-throughput RNA sequencing (RNA-Seq) data. RNA-Seq dataset was obtained from the European Bioinformatics Institute (accession No. PRJEB4496), including two replicates each for immortalized mesenchymal stem cells iMSC#3 cultured in growth medium (GM) and differentiation medium (DM) for 28 days. The clean reads were aligned to a hg19 reference genome by Tophat and assembled by Cufflinks to identify the known and novel transcripts.

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