mTOR (Phospho-Ser2481) ELISA Kit

Targeted methods that dominated toxicological research until recently did not allow for screening of all molecular changes involved in toxic response. Therefore, it is difficult to infer if all major mechanisms of toxicity have already been discovered, or if some of them are still overlooked. We used data on 591,084 unique chemical-gene interactions to identify genes and molecular pathways most sensitive to chemical exposures. The list of identified pathways did not change significantly when analyses were done on different subsets of data with non-overlapping lists of chemical compounds indicative that our dataset is saturated enough to provide unbiased results. One of the most important findings of this study is that almost every known molecular mechanism may be affected by chemical exposures. Predictably, xeno-biotic metabolism pathways, and mechanisms of cellular response to stress and damage were among the most sensitive. Additionally, we identified highly sensitive molecular pathways, which are not widely recognized as major targets of toxicants, including lipid metabolism pathways, longevity regulation cascade, and cytokine-mediated signaling. These mechanisms are relevant to significant public health problems, such as aging, cancer, metabolic and autoimmune disease. Thus, public health field will benefit from future focus of toxicological research on identified sensitive mechanisms. (C) 2020 Elsevier Ltd. All rights reserved.

Colorectal cancer (CRC) is a commonly occurring tumour with poor prognosis. Autophagy-related long non-coding RNAs (lncRNAs) have received much attention as biomarkers for cancer prognosis and diagnosis. However, few studies have focused on their prognostic predictive value specifically in CRC. This research aimed to construct a robust autophagy-related lncRNA prognostic signature for CRC. Autophagy-related lncRNAs from The Cancer Genome Atlas database were screened using univariate Cox, LASSO, and multivariate Cox regression analyses, and the resulting key lncRNAs were used to establish a prognostic risk score model. Furthermore, quantitative real-time polymerase chain reaction (qRT-PCR) analysis was performed to detect the expression of several lncRNAs in cancer tissues from CRC patients and in normal tissues adjacent to the cancer tissues. A prognostic signature comprising lncRNAs AC125603.2, LINC00909, AC016876.1, MIR210HG, AC009237.14, and LINC01063 was identified in patients with CRC. A graphical nomogram based on the autophagy-related lncRNA signature was developed to predict CRC patients' 1-, 3-, and 5-year survival. Overall survival in patients with low risk scores was significantly better than in those with high risk scores (P < 0.0001); a similar result was obtained in an internal validation sample. The nomogram was shown to be suitable for clinical use and gave correct predictions. The 1- and 3-year values of the area under the receiver operating characteristic curve were 0.797 and 0.771 in the model sample, and 0.656 and 0.642 in the internal validation sample, respectively. The C-index values for the verification samples and training samples were 0.756 (95% CI = 0.668-0.762) and 0.715 (95% CI = 0.683-0.829), respectively. Gene set enrichment analysis showed that the six autophagy-related lncRNAs were greatly enriched in CRC-related signalling pathways, including p53 and VEGF signalling. The qRT-PCR results showed that the expression of lncRNAs in CRC was higher than that in adjacent tissues, consistent with the expression trends of lncRNAs in the CRC data set. In summary, we established a signature of six autophagy-related lncRNAs that could effectively guide clinical prediction of prognosis in patients with CRC. This lncRNA signature has significant clinical implications for improving the prediction of outcomes and, with further prospective validation, could be used to guide tailored therapy for CRC patients.