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Background: Ovarian cancer (OC) is a highly heterogeneous disease, of which the mesenchymal subtype has the worst prognosis, is the most aggressive, and has the highest drug resistance. The cell cycle pathway plays a vital role in ovarian cancer development and progression. We aimed to screen the key cell cycle genes that regulated the mesenchymal subtype and construct a robust signature for ovarian cancer risk stratification. Methods: Network inference was conducted by integrating the differentially expressed cell cycle signature genes and target genes between the mesenchymal and non-mesenchymal subtypes of ovarian cancer and identifying the dominant cell cycle signature genes. Results: Network analysis revealed that two cell cycle signature genes (POLA2 and KIF20B) predominantly regulated the mesenchymal modalities of OC and used to construct a prognostic model, termed the Cell Cycle Prognostic Signature of Ovarian Cancer (CCPOC). The CCPOC-high patients showed an unfavorable prognosis in the GSE26712 cohort, consistent with the results in the seven public validation cohorts and one independent internal cohort (BL-OC cohort, qRT-PCR, n = 51). Functional analysis, drug-sensitive analysis, and survival analysis showed that CCPOC-low patients were related to strengthened tumor immunogenicity and sensitive to the anti-PD-1/PD-L1 response rate in pan-cancer (r = -0.47, OC excluded), which indicated that CCPOC-low patients may be more sensitive to anti-PD-1/PD-L1. Conclusion: We constructed and validated a subtype-specific, cell cycle-based prognostic signature for ovarian cancer, which has great potential for predicting the response of anti-PD-1/PD-L1.
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Based on the meteorological data of 143 meteorological site, we calculated aridity index (AI) with the potential evaporation formulated by FAO-56 Penman-Monteith and precipitation in Northwest China during 1989-2019. Mann-Kendall trend analysis, wavelet analysis and partial differential equation were used to examine the AI change trend, variation cycle, and contribution rate of main climate impact factors to AI. The results showed that there was a non-significant decreasing trend of AI in Northwest China on the whole, a significant decreasing trend of AI in Qinghai, and a non-significant increasing trend of AI in Xinjiang during 1989-2019. There was an abrupt change of AI in the study area in 2010. There was a primary 17-year periodicity in the change of AI in Northwest China. The spatial distribution of AI was shown as a larger AI in the middle of Northwest China and a smaller AI in the Southeast and Northwest in Northwest China. The tendency rates of AI were -1.27, -1.17·(10 a)-1, -0.41, -0.49, -1.77 and -2.73·(10 a)-1 in Northwest China, Gansu, Ningxia, Shanxi, Qinghai, and Xinjiang, respectively. The possibility of drought risk was higher in Xiaozaohuo, Korla, Aksu, and Turpan region. Precipitation and actual water vapor pressure were the dominant factors of AI changes in Gansu, Ningxia, Qinghai, and Shaanxi. But the potential evapotranspiration, solar radiation, and average temperature were the main climate factors for AI changes in Xinjiang.
Assuntos
Secas , Meteorologia , China , TemperaturaRESUMO
To explore the water consumption characteristics of trees, the thermal dissipation probe technology was used to monitor sap flow of Populus bolleana in east sandy land of Yellow River, from July to November in 2017. Microclimate variables were monitored. We analyzed the diurnal and seasonal variations of water consumption, and proposed the models for water consumption with back propagation neural network (BPNN) and Elman neural network (ENN) based on fuzzy rules. Results showed that the average sap flow rate of P. bolleana was 4.98 g·cm-2·h-1 in growing season (July to October), with solar radiation (Rs), temperature (T), vapor pressure deficit (VPD) and relative humidity (RH) as the main factors affecting sap flow. Due to the influence of meteorological factors, water consumption was characterized by obvious seasonal variation, with that in summer (July-August) being 1.4 times of that in autumn (September-October). BPNN and ENN models based on fuzzy rules were used to simulate water consumption of P. euphratica. The optimal parameter calibration of two models explained more than 80% of the total variation, which indicated that these two models could more accurately simulate water consumption. Compared with the BP neural network model, the simulated results of ENN model showed that the relative error was reduced by 27.0%, RMSE was reduced by 24.3%, Nash-Sutclife efficiency coefficient increased by 67.9%, R2 was higher than 0.80. The ENN model performed better than BPNN model with a higher efficiency and goodness of fitness. ENN model effectively improved the simulating accuracy of water consumption. Therefore, it could be used as an optimal model to estimate water consumption of P. bolleana in east sandy land of Yellow River.
