A great mass of long noncoding RNAs (lncRNAs) have been identified in mouse genome and increasing evidences in the last decades have revealed their crucial roles in diverse biological processes. Nevertheless, the biological roles of lncRNAs in the mouse retina remains largely unknown due to the lack of a comprehensive annotation of lncRNAs expressed in the retina. In this study, we applied the long-reads sequencing strategy to unravel the transcriptomes of developing mouse retinas and identified a total of 940 intergenic lncRNAs (lincRNAs) in embryonic and neonatal retinas, including about 13% of them were transcribed from unannotated gene loci. Subsequent analysis revealed that function of lincRNAs expressed in mouse retinas were closely related to the physiological roles of this tissue, including 90 lincRNAs that were differentially expressed after the functional loss of key regulators of retinal ganglion cell (RGC) differentiation. In situ hybridization results demonstrated the enrichment of three class IV POU-homeobox genes adjacent lincRNAs (linc-3a, linc-3b and linc-3c) in ganglion cell layer and indicated they were potentially RGC-specific. In summary, this study systematically annotated the lincRNAs expressed in embryonic and neonatal mouse retinas and implied their crucial regulatory roles in retinal development such as RGC differentiation.

BACKGROUND: Pulmonary metastasis is the most frequent cause of death in osteosarcoma (OS) patients. Recently, several bioinformatics studies specific to pulmonary metastatic osteosarcoma (PMOS) have been applied to identify genetic alterations. However, the interpretation and reliability of the results obtained were limited for the independent database analysis. OBJECTIVE: The expression profiles and key pathways specific to PMOS remain to be comprehensively explored. Therefore, in our study, three original datasets of GEO database were selected. METHODS: Initially, three microarray datasets (GSE14359, GSE14827, and GSE85537) were downloaded from the GEO database. Differentially expressed genes (DEGs) between PMOS and nonmetastatic osteosarcoma (NMOS) were identified and mined using DAVID. Subsequently, GO and KEGG pathway analyses were carried out for DEGs. Corresponding PPI network of DEGs was constructed based on the data collected from STRING datasets. The network was visualized with Cytoscape software, and ten hub genes were selected from the network. Finally, survival analysis of these hub genes also used the TARGET database. RESULTS: In total, 569 upregulated and 1238 downregulated genes were filtered as DEGs between PMOS and NMOS. Based on the GO analysis result, these DEGs were significantly enriched in the anatomical structure development, extracellular matrix, biological adhesion, and cell adhesion terms. Based on the KEGG pathway analysis result, these DEGs were mainly enriched in the pathways in cancer, PI3K-Akt signaling, MAPK signaling, focal adhesion, cytokine-cytokine receptor interaction, and IL-17 signaling. Hub genes (ANXA1 and CXCL12) were significantly associated with overall survival time in OS patient. CONCLUSION: Our results may provide new insight into pulmonary metastasis of OS. However, experimental studies remain necessary to elucidate the biological function and mechanism underlying PMOS.