RESUMEN
The present study involved building a model of immune-related genes (IRGs) that can predict the survival outcomes of tongue squamous cell carcinoma (TSCC). Using the TCGA database, we collected the gene expression profiles of patients with TSCC and analyzed the differences in IRGs obtained from the ImmPort database. Subsequently, we constructed a predictive model. Transcription factors and differentially expressed IRGs can be used to construct TSCC regulatory network. CIBERSORT tool was used to analyze the relative proportion of 22 tumor-infiltrating immune cells in TSCC samples. Finally, a prognostic model is constructed. We established an IRG model formed by seven genes. The receiver operating characteristic value of the prognostic model based on IRGs is 0.739. After the analysis of the correlation between IRGs and clinical and pathological conditions, we found that Gast was related to grade, IRF9, LTB, and T stage. Among the 22 tumor-infiltrating immune cells, the resting natural killer (NK) cells were found to be related to the 5-year survival rate. This study constructed a prognostic model formed by seven IRGs and discussed the tumor-infiltrating immune cells, which are related to the survival outcome, reflecting the potential regulatory role of TSCC tumor immune microenvironment that could potentially promote individualized treatment.
RESUMEN
Although remote sensing has enabled rapid monitoring of grassland aboveground biomass (AGB) at a regional scale, it is still a difficult challenge to construct an accurate estimation model of grassland AGB in a vast region to support the AGB dynamics analysis over a long time series. In this study, extensive grassland AGB measurements (collected in North China during the grassland growing season of 2000-2019), MODIS data, and environmental factors (climate, topography and soil) were employed to construct the grassland AGB models using four machine learning algorithms (random forest, support vector machine, artificial neural network and extreme learning machine) combined with four variable selections. The spatial distributions of annual grassland AGB from 2000 to 2019 were simulated based on the optimal AGB model. The temporal change and future trend of AGB series from 2000 to 2019 were comprehensively analyzed by the slope model and Hurst exponent. The influences of natural and anthropogenic factors on grassland AGB dynamics were explored quantitatively using the Geodetector model. The results showed that (1) the random forest model constructed from the variables selected by the successive projections algorithm is the optimal grassland AGB model. (2) The 20-year average grassland AGB in North China showed an overall spatial distribution of being low in the central and western parts and high in the southeastern part. (3) The annual maximum grassland AGB in most regions (82.71%) showed an increasing trend during 2000-2019; and most of the grasslands with a decreasing trend of AGB were located in regions with low AGB values and arid climates. (4) The future trend of grassland AGB after the study period may be optimistic, as reflected by more grassland AGB was predicted to increase rather than decrease (70.38% vs. 29.62%). (5) The main driving factors of spatiotemporal dynamics of grassland AGB were precipitation, soil type, and livestock density; the interactive influence of two drivers on AGB showed mutual enhancement.
