Identification of Vitamin D-related gene signature to predict colorectal cancer prognosis.

Colorectal cancer (CRC) is one of the most common malignant carcinomas worldwide with poor prognosis, imposing an increasingly heavy burden on patients. Previous experiments and epidemiological studies have shown that vitamin D and vitamin D-related genes play a vital role in CRC. Therefore, we aimed to construct a vitamin D-related gene signature to predict prognosis in CRC. The CRC data from The Cancer Genome Atlas (TCGA) was performed as the training set. A total of 173 vitamin D-related genes in the TCGA CRC dataset were screened, and 17 genes associated with CRC prognosis were identified from them. Then, a vitamin D-related gene signature consisting of those 17 genes was established by univariate and multivariate Cox analyses. Moreover, four external datasets (GSE17536, GSE103479, GSE39582, and GSE17537) were used as testing set to validate the stability of this signature. The high-risk group presented a significantly poorer overall survival than low-risk group in both of training set and testing sets. Besides, the areas under the curve (AUCs) for signature on OS in training set at 1, 3, and 5 years were 0.710, 0.708, 0.710 respectively. The AUCs of the ROC curve in GSE17536 for 1, 3, and 5 years were 0.649, 0.654, and 0.694. These results indicated the vitamin D-related gene signature model could effectively predict the survival status of CRC patients. This vitamin D-related gene signature was also correlated with TNM stage in CRC clinical parameters, and the higher risk score from this model was companied with higher clinical stage. Furthermore, the high accuracy of this prognostic signature was validated and confirmed by nomogram model. In conclusion, we have proposed a novel vitamin D-related gene model to predict the prognosis of CRC, which will help provide new therapeutic targets and act as potential prognostic biomarkers for CRC.

Curcumin enhances anti­cancer efficacy of either gemcitabine or docetaxel on pancreatic cancer cells.

Curcumin is a natural compound extracted from turmeric (Curcuma longa), which has been reported to be a promising anti­cancer drug in various human cancers. However, the effects of combination treatment of curcumin with gemcitabine or docetaxel on pancreatic cancer remains elusive. In the present study, the combinatory effects of curcumin with either gemcitabine or docetaxel on the proliferation, apoptosis, migration as well as invasion of PC cells were investigated. Calcusyn software was used to determine whether curcumin has is synergistic with gemcitabine or docetaxel. Combination index values from combinational use were all lower than 1, indicating the synergism of curcumin with gemcitabine or docetaxel on PC cells in vitro. EdU assay showed that curcumin could enhance the ability of gemcitabine or docetaxel to inhibit the proliferation of PC cells. Furthermore, the results from transmission electron microscope, DAPI staining experiments and western blot analysis revealed that curcumin may trigger apoptosis of PC cells via PARP/caspase­3 signaling pathway and reinforced pro­apoptotic ability of either gemcitabine or docetaxel. In addition, curcumin exhibited marked suppressive ability on metastasis of PC cells by wound healing and matrigel­transwell assay. Mechanistically, upregulation of TIMP1/TIMP2 with concomitant downregulation of MMP2/MMP9/N­cadherin proteins may be involved in this process. In conclusion, curcumin showed synergistic anti­cancer effects with either gemcitabine or docetaxel on PC cells.

Scaling analysis of stock markets.

In this paper, we apply the detrended fluctuation analysis (DFA), local scaling detrended fluctuation analysis (LSDFA), and detrended cross-correlation analysis (DCCA) to investigate correlations of several stock markets. DFA method is for the detection of long-range correlations used in time series. LSDFA method is to show more local properties by using local scale exponents. DCCA method is a developed method to quantify the cross-correlation of two non-stationary time series. We report the results of auto-correlation and cross-correlation behaviors in three western countries and three Chinese stock markets in periods 2004-2006 (before the global financial crisis), 2007-2009 (during the global financial crisis), and 2010-2012 (after the global financial crisis) by using DFA, LSDFA, and DCCA method. The findings are that correlations of stocks are influenced by the economic systems of different countries and the financial crisis. The results indicate that there are stronger auto-correlations in Chinese stocks than western stocks in any period and stronger auto-correlations after the global financial crisis for every stock except Shen Cheng; The LSDFA shows more comprehensive and detailed features than traditional DFA method and the integration of China and the world in economy after the global financial crisis; When it turns to cross-correlations, it shows different properties for six stock markets, while for three Chinese stocks, it reaches the weakest cross-correlations during the global financial crisis.