Exosomal hsa-miR-151a-3p and hsa-miR-877-5p are potential novel biomarkers for predicting bone metastasis in lung cancer.

Exosomal miRNAs (exo-miRNAs) have arisen as novel diagnostic biomarkers for various cancers. However, few reports on exo-miRNAs related to bone metastasis (BM) in lung cancer exist. This study aims to screen out key exo-miRNAs and estimate their prognostic values for predicting BM in lung cancer. The differentially expressed exo-miRNAs between the highly-metastatic (95D) and lowly-metastatic (A549) human lung cancer cell lines were comprehensively analyzed using high-throughput sequencing followed by bioinformatic analyses. 29 candidate exo-miRNAs were identified, and 101 BM-related target genes were predicted. Enrichment analysis revealed that these target genes were mainly involved in regulating transcription and pathways in cancer. An exosomal miRNA-mRNA regulatory network consisting of 7 key miRNAs and 10 hub genes was constructed. Further function analysis indicated that these 10 hub genes were mainly enriched in regulating cancer's apoptosis and central carbon metabolism. The survival analysis indicated that 7 of 10 hub genes were closely related to prognosis. Mutation analysis showed that lung cancer patients presented certain genetic alterations in the 7 real hub genes. GSEA for a single hub gene suggested that 6 of 7 real hub genes had close associations with lung cancer development. Finally, ROC analysis revealed that hsa-miR-151a-3p and hsa-miR-877-5p provided high diagnostic accuracy in discriminating patients with bone metastasis (BM+) from patients without bone metastasis (BM-). These findings provided a comprehensive analysis of exo-miRNAs and target genes in the regulatory network of BM in lung cancer. In particular, hsa-miR-151a-3p and hsa-miR-877-5p may be novel biomarkers for predicting BM in lung cancer.

Identification of biomarkers and key pathways in synovial sarcoma cells exposed to anlotinib by integrating bioinformatics analysis and experimental validation.

OBJECTIVE: To identify potential biomarkers, key pathways and modules following the exposure of synovial sarcoma (SS) cells to anlotinib. METHODS: In the current study, we integrated multiple bioinformatics methods to identify the hub genes and key pathways associated with the effects of anlotinib treatment in SS cells. In addition, we used reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) to validate the expression levels of the identified hub genes in SS cells treated with anlotinib. RESULTS: In total, 183 differentially expressed genes (DEGs) were identified, of which 47 were upregulated and 136 were downregulated. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses showed that the DEGs were predominantly involved in cell division and cell cycle progression. A total of two modules were identified from the protein-protein interaction network using the MCODE plugin in Cytoscape, where module 1 was the most significant. By combining the results of CytoHubba analysis based on the module 1 and The Cancer Genome Atlas database, six real hub genes, cyclin (CCN) A2, kinesin family member 2C, cell division cycle 20, CCNB2, aurora kinase B and CCNB1, were identified. Subsequent GO and KEGG pathway analysis revealed that these six real hub genes were significantly associated with the cell cycle and mitosis. Finally, RT-qPCR verified that the mRNA expression levels of these six real hub genes were significantly decreased in SS cells treated with anlotinib compared with those in the control group. Altogether, our study identified biomarkers and key pathways associated with the effects of anlotinib treatment in SS cells, which may provide novel insights into the underlying mechanism of anlotinib treatment in SS.