Background: The tumor microenvironment (TME) has emerged as a crucial factor in cancer development and progression. Recent findings have indicated that tumor-infiltrating immune cells (TICs) in the TME may predict cancer prognosis and response to treatment. Herein, we sought to identify critical modulators of the kidney renal clear cell carcinoma (KIRC) TME.Methods: KIRC datasets from The Cancer Genome Atlas (TCGA) were analyzed using the ESTIMATE algorithm to determine the ImmuneScore and StromalScore. By profiling the differentially expressed genes (DEGs) in the ImmuneScore and StromalScore, we finally identified the immune-and stromal-related DEGs of the cases, through which we then performed intersection analysis to determine the immunerelated genes (IRGs). Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis were used to identify critical IRGs and construct a prognostic model. The CIBERSORT algorithm was used to calculate the relative content of 22 immune cell types. Finally, the datasets from the Gene Expression Omnibus (GEO) database were analyzed to validate results from the above analyses.Experimental validation was used on KIRC tissues by quantitative polymerase chain reaction (qPCR) and western blot.Results: We found that the ImmuneScore was negatively correlated with patients' prognosis. Intersection analysis of the ImmuneScore and StromalScore identified 118 IRGs that were enriched in immune-related functions. Following IRGs screening by Cox and LASSO regression analyses, six genes were identified and used to construct a KIRC prognostic model. Intersection analysis of these six genes and protein-protein interaction (PPI) were performed and obtained the most critical gene: Potassium Calcium-Activated Channel Subfamily N Member 4 (KCNN4). Further analysis showed that KCNN4 expression was higher in tumor samples relative to normal controls, and was negatively correlated with prognosis. CIBERSORT analysis revealed significant correlation between KCNN4 expression and multiple types of TICs, demonstrating that KCNN4 may affect KIRC prognosis by influencing the TME immune status. Ultimately, the GEO datasets and validation experiments confirmed that KCNN4 was highly expressed in tumor tissues compared to the corresponding normal tissues.
The potential to use a three-dimensional (3D) computational fluid dynamics (CFD) model to produce the complexity of the flows in water-pump intakes and the prospects to use it as an effective assistant in the design or fixing of the related problems are reported. A scaled model of a real water-pump intake with flow conditions corresponding to the prototype was selected and studied. The Reynolds number of the model flow is 120 000, based on the diameter and bulk velocity in the pump column. The 3D CFD model solves the Reynolds averaged Navier–Stokes (RANS) equations with the k–ɛ turbulence model with wall function. A multi-block structured mesh was used. Numerical simulations are processed to reveal the important flow features in the entire flow field, compare the streamwise velocity distribution in the approaching channel, at and above the pump throat, as well as the swirl of flow at the pump throat. Numerical results provide insights into the complexity of flow around and inside the pump column under different incoming flows. This study makes significant strides from a simple intake to a real one and shows good prospects of further use of this 3D model to simulate flows in practical water-pump intakes.
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