RESUMEN
BACKGROUND: Currently, there are many therapeutic methods for lung adenocarcinoma (LUAD), but the 5-year survival rate is still only 15% at later stages. Epithelial- mesenchymal transition (EMT) has been shown to be closely associated with local dissemination and subsequent metastasis of solid tumors. However, the role of EMT in the occurrence and development of LUAD remains unclear. AIM: To further elucidate the value of EMT-related genes in LUAD prognosis. METHODS: Univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were applied to establish and validate a new EMT-related gene signature for predicting LUAD prognosis. The risk model was evaluated by Kaplan-Meier survival analysis, principal component analysis, and functional enrichment analysis and was used for nomogram construction. The potential structures of drugs to which LUAD is sensitive were discussed with respect to EMT-related genes in this model. RESULTS: Thirty-three differentially expressed genes related to EMT were found to be highly associated with overall survival (OS) by using univariate Cox regression analysis (log2FC ≥ 1, false discovery rate < 0.001). A prognostic signature of 7 EMT-associated genes was developed to divide patients into two risk groups by high or low risk scores. Kaplan-Meier survival analysis showed that the OS of patients in the high-risk group was significantly poorer than that of patients in the low-risk group (P < 0.05). Multivariate Cox regression analysis showed that the risk score was an independent risk factor for OS (HR > 1, P < 0.05). The results of receiver operator characteristic curve analysis suggested that the 7-gene signature had a perfect ability to predict prognosis (all area under the curves > 0.5). CONCLUSION: The EMT-associated gene signature classifier could be used as a feasible indicator for predicting OS.
RESUMEN
Taking the winter wheat planting areas in Rugao City and Haian County of Jiangsu Province as test objects, the clustering defining of wheat growth management zones was made, based on the spatial variability analysis and principal component extraction of the normalized difference vegetation index (NDVI) data calculated from the HJ-1A/B CCD images (30 m resolution) at different growth stages of winter wheat, and of the soil nutrient indices (total nitrogen, organic matter, available phosphorus, and available potassium). The results showed that the integration of the NDVI at heading stage with above-mentioned soil nutrient indices produced the best results of wheat growth management zone defining, with the variation coefficients of NDVI and soil nutrient indices in each defined zone ranged in 4.5% -6.1% and 3.3% -87.9%, respectively. However, the variation coefficients were much larger when the wheat growth management zones were defined individually by NDVI or by soil nutrient indices, suggesting that the newly developed defining method could reduce the variability within the defined management zones and improve the crop management precision, and thereby, contribute to the winter wheat growth management and process simulation at regional scale.
