The effect the aqueous and ethanol extracts of Rosmarinus officinalis L. aerial parts on morphine withdrawal syndrome was investigated in mice. The aqueous and ethanol extracts induced a significant antinociceptive activity in the writhing test. This activity was inhibited by naloxone pretreatment. Dependence was induced using subcutaneous injections of morphine daily for 3 days. On day 4, morphine was injected 2 h prior to the intraperitoneal injection of naloxone. The number of jumps during the 30 min period after naloxone injection was considered as a measure of the withdrawal syndrome. The results indicated that the aqueous (1.68 g/kg and 2.4 g/kg, i.p.) and ethanol (0.96 g/kg, i.p.) extracts reduced the number of jumps. Phytochemical study indicated that only the aqueous extract of R. officinalis has an alkaloid component. It is concluded that the aqueous and ethanol extracts of R. officinalis aerial parts could diminish morphine withdrawal syndrome.

Breast cancer is one of the most common tumors in women. Current data indicate that the overexpression of some microRNAs (miRNAs) is associated with breast cancer, in relation to stage, tumor size and potential for metastasis. Some studies have reported that miRNAs have critical roles in cellular processes implicated in breast cancer cell growth, migration and metastasis by targeting the PI3K/AKT oncogenic signaling pathway. Therefore, identifying novel regulatory miRNAs for this oncogenic pathway and discovery of their related target genes may represent a promising therapeutic approach for breast cancer therapy. This review highlights the recent findings about the potential role of PI3K/AKT signaling regulatory miRNAs in breast cancer tumorigenesis.

Prediction of driver genes (tumor suppressors and oncogenes) is an essential step in understanding cancer development and discovering potential novel treatments. We recently proposed Moonlight as a bioinformatics framework to predict driver genes and analyze them in a system-biology-oriented manner based on -omics integration. Moonlight uses gene expression as a primary data source and combines it with patterns related to cancer hallmarks and regulatory networks to identify oncogenic mediators. Once the oncogenic mediators are identified, it is important to include extra levels of evidence, called mechanistic indicators, to identify driver genes and to link the observed gene expression changes to the underlying alteration that promotes them. Such a mechanistic indicator could be for example a mutation in the regulatory regions for the candidate gene. Here, we developed new functionalities and released Moonlight2 to provide the user with a mutation-based mechanistic indicator as a second layer of evidence. These functionalities analyze mutations in a cancer cohort to classify them into driver and passenger mutations. Those oncogenic mediators with at least one driver mutation are retained as the final set of driver genes. We applied Moonlight2 to the basal-like breast cancer subtype, lung adenocarcinoma and thyroid carcinoma using data from The Cancer Genome Atlas. For example, in basal-like breast cancer, we found four oncogenes (COPZ2, SF3B4, KRTCAP2 and POLR2J) and nine tumor suppressor genes (KIR2DL4, KIF26B, ARL15, ARHGAP25, EMCN, GMFG, TPK1, NR5A2 and TEK) containing a driver mutation in their promoter region, possibly explaining their deregulation. Moonlight2R is available at https://github.com/ELELAB/Moonlight2R.

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