lncRNA ARAP1-AS1 enhances proliferation and impairs apoptosis of lymphoma cells by sponging miR-6867-5p.

BACKGROUND: Numerous evidence have suggested the vital role of lncRNAs in human tumorigenesis. And lncRNA APAP1-AS1 has been proved to act as an oncogene. OBJECTIVE: Nevertheless, the molecular process underlying ARAP1-AS1 for the lymphoma progression has not been well studied. METHODS: RT-qPCR was used to ascertain the miR-6867-5p and ARAP1-AS1 in lymphoma cells and tissues. The localization of ARAP1-AS1 was determined via subcellular fractionation analysis. A xenograft model was used to investigate the influence of ARAP1-AS1 in formation of tumor in vivo. In addition, interactions between ARAP-AS1 and miR-6867-5p were tested by bioinformatics analysis, RIP assay, luciferase reporter and Pearson's correlation analysis. Combined with loss-of-function experiments, MTT assays and flow cytometry were performed to evaluate the function of miR-6867-5p and also ARAP-AS1 in proliferation and apoptosis of lymphoma cells, respectively. RESULTS: ARAP1-AS1 was remarkably upregulated in lymphoma cells and tissues, while miR-6867-5p expression was downregulated. Furthermore, high ARAP1-AS1 expression suppressed miR-6867-5p expression in lymphoma cell lines (Raji and CA46), and Pearson's analysis showed negative correlation between ARAP1-AS1 expression and also miR-6867-5p expression. In addition, knockdown of ARAP1-AS1 resulted in weakened cell viability and uplifted apoptosis rate of lymphoma cells (Raji and CA46) as well as a delay in the tumor growth in vivo. Further investigations illustrated that miR-6867-5p inhibitor reversed all above biological activities. CONCLUSIONS: LncRNA ARAP1-AS1 served as a tumor-promoter in lymphoma cells by sponging with miR-6867-5p, which may help to provide potential therapeutic target gene for lymphoma patients.

Study of TCM clinical records based on LSA and LDA SHTDT model.

Description of syndromes and symptoms in traditional Chinese medicine are extremely complicated. The method utilized to diagnose a patient's syndrome more efficiently is the primary aim of clinical health care workers. In the present study, two models were presented concerning this issue. The first is the latent semantic analysis (LSA)-based semantic classification model, which is employed when the classification and words used to depict these classfications have been confirmed. The second is the symptom-herb-therapies-diagnosis topic (SHTDT), which is employed when the classification has not been confirmed or described. The experimental results showed that this method was successful, and symptoms can be diagnosed to a certain extent. The experimental results indicated that the topic feature reflected patient characteristics and the topic structure was obtained, which was clinically significant. The experimental results showed that when provided with a patient's symptoms, the model can be used to predict the theme and diagnose the disease, and administer appropriate drugs and treatments. Additionally, the SHTDT model prediction results did not yield completely accurate results because this prediction is equivalent to multi-label prediction, whereby the drugs, treatment and diagnosis are considered as labels. In conclusion, diagnosis, and the drug and treatment administered are based on human factors.