No abstract

Background: Morphine and its substitutes are frequently used in the clinical treatment of acute severe pain and advanced cancer patients. Long-term irregular use of morphine will lead to severe dependence.However, the genes behind the analgesic and addictive effects of morphine still need to be revealed. Methods:We retrieved and downloaded RNA expression data sets related to morphine pain and addiction effects from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes.Functional enrichment analysis was performed to analyze relevant pathways. Gene expression trends was used to screen key genes associated with addiction effects. miRNAs and PPIs were used to explore the functional mechanisms of genes.Results: A total of 163 up-regulated and 277 down-regulated genes were obtained in the dataset for analgesic effects. A total of 1,482 up-regulated and 1,754 down-regulated differentially expressed genes (DEGs) were obtained in the dataset for addictive effects. By taking the intersection, 8 up-regulated and 22 down-regulated mRNAs which showed high correlations with both analgesic and addictive effects were identified. Based on the DEGs, a comprehensive network combining the mRNA-miRNA network and protein-protein interaction (PPI) network was established. Among the networks, 1 up-regulated miRNA (miR-129) and 3 down-regulated miRNAs (miR-714, miR-2135, and miR-2145) were identified. Gene expression trends and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms indicated that Fos may be a biomarker for morphine addiction.Conclusions: Our findings will provide a valuable foundation for future genetic mechanism studies of the analgesic and addictive effects of morphine and provide inspiration for finding analgesic substitutes and relieving the addiction of analgesic drugs.

PCOS is a widespread disease that primarily caused in-pregnancy in pregnant-age women. Normoandrogen (NA) and Hyperandrogen (HA) PCOS are distinct subtypes of PCOS, while bio-markers and expression patterns for NA PCOS and HA PCOS have not been disclosed. We performed microarray analysis on granusola cells from NA PCOS, HA PCOS and normal tissue from 12 individuals. Afterwards, microarray data were processed and specific genes for NA PCOS and HA PCOS were identified. Further functional analysis selected IL6R and CD274 as new NA PCOS functional markers, and meanwhile selected CASR as new HA PCOS functional marker. IL6R, CD274 and CASR were afterwards experimentally validated on mRNA and protein level. Subsequent causal relationship analysis based on Apriori Rules Algorithm and co-occurrence methods identified classification markers for NA PCOS and HA PCOS. According to classification markers, downloaded transcriptome datasets were merged with our microarray data. Based on merged data, causal knowledge graph was constructed for NA PCOS or HA PCOS and female infertility on NA PCOS and HA PCOS. Gene-drug interaction analysis was then performed and drugs for HA PCOS and NA PCOS were predicted. Our work was among the first to indicate the NA PCOS and HA PCOS functional and classification markers and using markers to construct knowledge graphs and afterwards predict drugs for NA PCOS and HA PCOS based on transcriptome data. Thus, our study possessed biological and clinical value on further understanding the inner mechanism on the difference between NA PCOS and HA PCOS.