Human metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) is a newly identified metastasis-associated long non-coding RNA. In a previous study, it was identified that plasma levels of MALAT1 were significantly increased in gastric cancer patients with metastasis compared with gastric cancer patients without metastasis and healthy control individuals. However, it is unclear whether plasma levels of MALAT1 may act as a biomarker for evaluating the development of metastasis in epithelial ovarian cancer (EOC). In the present study, groups that consisted of 47 patients with EOC and metastasis (EOC/DM), 47 patients with EOC without metastasis (EOC/NDM), and 47 healthy control (HC) individuals were established. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to detect the level of plasma MALAT1 in these groups. The results showed that levels of plasma MALAT1 were significantly increased in the EOC/DM group compared with the EOC/NDM and HC groups (P<0.001). Receiver operating characteristic (ROC) analysis indicated that plasma MALAT1 yielded an area under the curve (AUC) of 0.820 [95% confidence interval (CI), 0.734–0.905; P<0.001], distinguishing between EOC/DM and EOC/NDM. ROC analysis also yielded an AUC of 0.884 (95% CI, 0.820–0.949; P<0.001), with 89.4% sensitivity and 72.3% specificity for distinguishing between EOC/DM and HC. Furthermore, multivariate analysis indicated that overexpression of MALAT1, differentiation (poor), tumor-node-metastasis stage (IV), lymph node metastasis (N3), peritoneal invasion (present) and higher serum carbohydrate antigen 125 levels were independent predictors of survival (hazard ratio, 3.322; P=0.028) in patients with EOC. Kaplan-Meier analysis revealed that patients with increased MALAT1 expression had a poorer disease-free survival time. In conclusion, the levels of plasma MALAT1 may act as a valuable biomarker for the diagnosis of metastasis.
Solar energy is considered one of the most hopeful alternative sources to avoiding dependence on fossil fuels, and it does not cause any air pollution. GIS-based solar energy potential evaluation is mainly focused on regional scale; further, more solar energy potential evaluation with building scale is calculated through observation data and mathematical model. Therefore, in this paper, a GIS-based joint solar energy potential evaluation is developed to evaluate the distributed photovoltaic potential and centralized photovoltaic potential. Shanxi province in China, which has abundant coal resources, is used as the study area. The raster grid scale is used as the minimum research scale, which could not only deal with the distributed photovoltaic potential but could also calculate the centralized photovoltaic potential. The obtained results indicate that the developed method could effectively deal with problems associated with the distributed photovoltaic potential and centralized photovoltaic potential in the raster grid scale.
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