Kidney renal cell carcinoma (KIRC), which is the most common subtype of kidney cancer, has a poor prognosis and a high mortality rate. In this study, a multi-omics analysis is performed to build a multi-gene prognosis signature for KIRC. A combination of a DNA methylation analysis and a gene expression data analysis revealed 863 methylated differentially expressed genes (MDEGs). Seven MDEGs (BID, CCNF, DLX4, FAM72D, PYCR1, RUNX1, and TRIP13) were further screened using LASSO Cox regression and integrated into a prognostic risk score model. Then, KIRC patients were divided into high- and low-risk groups. A univariate cox regression analysis revealed a significant association between the high-risk group and a poor prognosis. The time-dependent receiver operating characteristic (ROC) curve shows that the risk group performs well in predicting overall survival. Furthermore, the risk group is contained in the best multivariate model that was obtained by a multivariate stepwise analysis, which further confirms that the risk group can be used as a potential prognostic biomarker. In addition, a nomogram was established for the best multivariate model and shown to perform well in predicting the survival of KIRC patients. In summary, a seven-MDEG signature is a powerful prognosis factor for KIRC patients and may provide useful suggestions for their personalized therapy.
Satellite‐based rainfall products have great potential for estimating rainfall erosivity, as they can provide continuous spatiotemporal distribution of precipitation estimates over large areas, especially for monitoring in areas with complex terrain and extreme climates. This paper uses the daily rainfall derived from the satellite‐based CHIRPS product on the GEE platform and Zhang's daily rainfall erosivity model to calculate the rainfall erosivity on the Loess Plateau during the period of 1981–2020. The accuracy of rainfall erosivity for the CHIRPS product is evaluated by comparing the results to estimates from national meteorological stations, and then the calculation formula of rainfall erosivity is optimized to improve the accuracy of rainfall erosivity based on CHIRPS products. The results show that the annual average rainfall of the CHIRPS product at the locations of national meteorological stations in 1981–2020 is 473.7 mm, which is 6.8% higher than that observed by the meteorological stations. The multi‐year average value of daily rainfall and the yearly average rainfall both for days with rainfall larger than or equal to 12 mm are 15.9% and 18.2% greater than those of the meteorological stations, respectively, and eventually resulting in an overestimation of rainfall erosivity on the Loess Plateau by 44.0%. The annual mean rainfall erosivity interpolated based on meteorological stations and calculated from the gridded CHIRPS product in 1981–2020 is 1344.2 MJ·mm/(hm2·h) and 2013.7 MJ·mm/(hm2·h), respectively. This result suggests that rainfall erosivity estimated by gridded CHIRPS product is overestimated by 49.8%, of which 46.1% is due to the overestimation of erosive rainfall in gridded CHIRPS and 3.7% is caused by site density and interpolation. Satellite‐based CHIRPS product is similar to the observations of meteorological stations in total rainfall and trends, but differs in rainfall frequency and intensity, which is an important reason for the difference in rainfall erosivity between satellite‐based rainfall products and meteorological stations. The CHIRPS‐based rainfall erosivity calculated using the optimized parameters reduces the overestimation from 49.8% to 3.2%, greatly reducing the satellite‐based rainfall erosivity estimation error.
This study explores the response characteristics of runoff to the variability of meteorological factors. A modified vector autoregressive (VAR) model is proposed by combining time-varying parameters (TVP) and stochastic volatility (SV). Markov chain Monte Carlo (MCMC) is used to estimate parameters. The TVP-SV-VAR model of daily runoff response to the variability of meteorological factors is established and applied to the daily runoff series from the Linjiacun hydrological station, Shaanxi Province, China. It is found that the posterior estimates of the stochastic volatility of the four variables fluctuate significantly with time, and the variance fluctuations of runoff and precipitation have strong synchronicity. The simultaneous impact of precipitation and evaporation on the pulse of runoff is close to 0. Runoff has a positive impulse response to precipitation, which decreases as the lag time increases, and a negative impulse response to temperature and evaporation with fluctuation. The response speed is precipitation > evaporation > temperature. The TVP-SV-VAR model avoids the hypothesis of homoscedasticity of variance and allows the variance to be randomly variable, which significantly improves the analysis performance. It provides theoretical support for the study of runoff response and water resource management under the conditions of climate change.
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