Xuzhou Medical College

About: Xuzhou Medical College is a education organization based out in Xuzhou, China. It is known for research contribution in the topics: Cancer & Cell growth. The organization has 12721 authors who have published 7802 publications receiving 102970 citations.

MiR-613 induces cell cycle arrest by targeting CDK4 in non-small cell lung cancer

Abstract: Deregulation of microRNAs (miRNAs) has been associated with a variety of cancers, including non-small cell lung cancer (NSCLC). Here, we investigated anomalous miR-613 expression and its possible functional consequences in primary NSCLC samples and NSCLC-derived cell lines. The expression of miR-613 was measured by quantitative RT-PCR in 56 primary NSCLC tissues and adjacent non-tumor tissues. The effect of miR-613 up- or down-regulation in NSCLC-derived cells was evaluated in vitro by cell viability and colony formation assays and in vivo by growth assays in xenografted nude mice. Using quantitative RT-PCR, we found that miR-613 was down-regulated in 76.8 % (43/56) of the primary NSCLC tissues tested when compared to the adjacent non-tumor tissues. We also found that the miR-613 mimic used reduced in vitro cell viability and colony formation by inducing cell cycle arrest in NSCLC-derived cells, and inhibited in vivo tumor cell growth in xenografted nude mice. Inversely, we found that the miR-613 inhibitor used increased the viability and colony forming capacity of NSCLC-derived cells and tumor cell growth in xenografted nude mice. In addition, we identified CDK4 as a potential target of miR-613 using in silico Miranda predictions. Subsequent dual-luciferase reporter assays revealed that CDK4 acts as a direct target of miR-613. Concordantly, we found that both miR-613 mimics and inhibitors could decrease and increase CDK4 protein levels in NSCLC-derived cells, respectively. From our results we conclude that miR-613 may act as a tumor suppressor in NSCLC and may serve as a tool for miRNA-based NSCLC therapy.

Crosstalk between TGF-β signaling and miRNAs in breast cancer metastasis.

Abstract: Transforming growth factor-β (TGF-β) signaling pathway is a key regulator of various cancer biologies, including cancer cell migration, invasion, angiogenesis, proliferation, as well as apoptosis, and it is one of indispensable signaling pathways during cancer metastasis. TGF-β signaling pathway can regulate and be regulated by a series of molecular and signaling pathways where microRNAs (miRNAs) seem to play important roles. miRNAs are small non-coding RNAs that can regulate expressions of their target genes. Emerging evidence suggest that miRNAs participate in various biological and pathologic processes such as cancer cells apoptosis, proliferation, invasion, migration, and metastasis by influencing multiple signaling pathways. In this article, we focus on the interaction between miRNAs and TGF-β in breast cancer (BC) metastasis through modulating invasion-metastasis-related factors, including epithelial-to-mesenchymal transition (EMT), cancer stem cells (CSCs), matrix metalloproteinase (MMP), tissue inhibitors of MMPs (TIMPs), cell adhesion molecules (CAMs), and tumor microenvironment (TME). Through a clear understanding of the complicated links between TGF-β pathway and miRNAs, it may provide a novel and safer therapeutic target to prevent BC metastasis.

An ab initio simulation of the UV/Visible spectra of substituted chalcones

Abstract: The electronic absorption spectra of 29 phenyl-ring substituted chalcones have been investigated with the time-dependent density functional theory (TD-DFT) and polarizable continuum TD-DFT (PCM-TD-DFT). It turns out that the hybrid PBE1PBE functional with the 6-31G basis set provide reliable λmax when the solvent effects are included in the model. Comparisons with experimental values lead to a mean absolute error of 12 nm (0.136 eV). Moreover, the observed substituent effects are reproduced by calculation qualitatively. The λmax of substituted chalcone in phenyl ring A is less sensitive to substitution than that in ring B. The linear correlation of Hammett’s substituent constants (σP) with LUMO energies is better with respect to HOMO energies. The calculation reveals that the maximum absorption band mainly results from the π→π* transition from HOMO to LUMO. The analysis of the electron density plots of frontier molecular orbitals show that most transitions should be of valence excitation nature.

A Computational Study of Potential miRNA-Disease Association Inference Based on Ensemble Learning and Kernel Ridge Regression.

Abstract: As increasing experimental studies have shown that microRNAs (miRNAs) are closely related to multiple biological processes and the prevention, diagnosis and treatment of human diseases, a growing number of researchers are focusing on the identification of associations between miRNAs and diseases. Identifying such associations purely via experiments is costly and demanding, which prompts researchers to develop computational methods to complement the experiments. In this paper, a novel prediction model named Ensemble of Kernel Ridge Regression based MiRNA-Disease Association prediction (EKRRMDA) was developed. EKRRMDA obtained features of miRNAs and diseases by integrating the disease semantic similarity, the miRNA functional similarity and the Gaussian interaction profile kernel similarity for diseases and miRNAs. Under the computational framework that utilized ensemble learning and feature dimensionality reduction, multiple base classifiers that combined two Kernel Ridge Regression classifiers from the miRNA side and disease side, respectively, were obtained based on random selection of features. Then average strategy for these base classifiers was adopted to obtain final association scores of miRNA-disease pairs. In the global and local leave-one-out cross validation, EKRRMDA attained the AUCs of 0.9314 and 0.8618, respectively. Moreover, the model's average AUC with standard deviation in 5-fold cross validation was 0.9275 ± 0.0008. In addition, we implemented three different types of case studies on predicting miRNAs associated with five important diseases. As a result, there were 90% (Esophageal Neoplasms), 86% (Kidney Neoplasms), 86% (Lymphoma), 98% (Lung Neoplasms), and 96% (Breast Neoplasms) of the top 50 predicted miRNAs verified to have associations with these diseases.