Background. lncRNAs have been indicated to involve in cell invasion, proliferation, and metastasis. However, function of DARS-AS1 in osteosarcoma remains poorly explored. Methods. DARS-AS1 and miR-532-3p level were measured using qRT-PCR. CCK-8 assay and cell invasion assay were done to study cell functions. Luciferase reporter assay was performed to study the mechanism about DARS-AS1 and miR-532-3p. Results. We firstly showed that DARS-AS1 expression is upregulated in 73.5% (25/34) of cases with osteosarcoma. Moreover, DARS-AS1 expression is overexpressed in osteosarcoma specimens than in nontumor samples. The DARS-AS1 is overexpressed in the osteosarcoma cell lines (Saos-2, SOSP-9607, U2OS, and MG-63) compared to hFOB. Overexpression of DARS-AS1 promotes cell growth and invasion in MG-63 osteosarcoma cell. DARS-AS1 plays as one sponge for miR-532-3p in osteosarcoma cell, and miR-532-3p overexpression inhibits luciferase activity of DARS-AS1-WT, not DARS-AS1-MUT in MG-63 cell. Ectopic expression of DARS-AS1 inhibits miR-532-3p expression in MG-63 cell. Furthermore, miR-532-3p expression is downregulated in osteosarcoma specimens compared to in paired nontumor samples. MiR-532-3p expression is downregulated in osteosarcoma cell lines compared to hFOB. MiR-532-3p expression is negatively associated with DARS-AS1 expression in osteosarcoma specimens. miR-532-3p directly regulates CCR7 expression in osteosarcoma cell. Elevated DARS-AS1 expression enhances cell growth and invasion via regulating CCR7. Conclusions. These data firstly suggested that DARS-AS1 exerted as one oncogene in osteosarcoma partly via regulating miR-532-3p/CCR7.

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Multiple myeloma (MM) is one of the most frequent malignant hematopoietic diseases, the pathogenesis of which remains unclear. It is well known that miRNAs are aberrantly expressed in many tumors, thus, investigating the target genes of miRNAs contributes to understanding the functional effect of miRNAs on MM. In this study, plasma samples of 147 patients with MM and 15 normal donors were collected. Using high-throughout microarray and limma package to screen the differentially expressed genes. Furthermore, to accurately predict the optimal target genes of MM, the logFC, targetScanCS and targetScanPCT values of known genes in four miRNAs (i.e. has-miR-21, has-miR-20a, has-miR-148a and has-miR-99b) were used to compute the targetScore values. As a result, 171 genes with larger difference were screened out using t-test, F-test and eBayes statistics analysis. Furthermore, 34 potential target genes associated with MM were selected by integrating the differentially expressed genes (DEGs) and the genes obtained by targetScore algorithm. Additionally, combining with the mutated genes in MM and the obtained DEGs, 41 consistently expressed genes were obtained. Finally, 5 optimal target genes, including SYK, LCP1, HIF1A, ALDH1A1 and MAFB, were screened out by the intersection of 34 DEGs and 41 mutated genes. In a word, this novel target gene prediction algorithm may contribute to improve our understanding on the pathogenesis of miRNAs in MM, which open up a new approach for future study.