IntroductionLung cancer is one of the most common cancers and a significant cause of cancer-related deaths. Non-small cell lung cancer (NSCLC) accounts for about 85% of all lung cancer cases. Therefore, it is crucial to identify effective diagnostic and therapeutic methods. In addition, transcription factors are essential for eukaryotic cells to regulate their gene expression, and aberrant expression transcription factors are an important step in the process of oncogenesis in NSCLC.MethodsDifferentially expressed transcription factors between NSCLC and normal tissues by analyzing mRNA profiling from The Cancer Genome Atlas (TCGA) database program were identified. Weighted correlation network analysis (WGCNA) and line plot of least absolute shrinkage and selection operator (LASSO) were performed to find prognosis-related transcription factors. The cellular functions of transcription factors were performed by 5-ethynyl-2'-deoxyuridine (EdU) assay, wound healing assay, cell invasion assay in lung cancer cells.ResultsWe identified 725 differentially expressed transcription factors between NSCLC and normal tissues. Three highly related modules for survival were discovered, and transcription factors highly associated with survival were obtained by using WGCNA. Then line plot of LASSO was applied to screen transcription factors related to prognosis and build a prognostic model. Consequently, SETDB2, SNAI3, SCML4, and ZNF540 were identified as prognosis-related transcription factors and validated in multiple databases. The low expression of these hub genes in NSCLC was associated with poor prognosis. The deletions of both SETDB2 and SNAI3 were found to promote proliferation, invasion, and stemness in lung cancer cells. Furthermore, there were significant differences in the proportions of 22 immune cells between the high- and low-score groups.DiscussionTherefore, our study identified the transcription factors involved in regulating NSCLC, and we constructed a panel for the prediction of prognosis and immune infiltration to inform the clinical application of transcription factor analysis in the prevention and treatment of NSCLC.

An integrated energy system can improve the reliability and flexibility of the energy supply, and has good economic and environmental benefits. Integrated energy system evaluation is of great significance to the implementation of an integrated energy system project. The integrated energy system evaluation method is presented based on the minimum cross-entropy model and the prospect theory. Firstly, the minimum cross-entropy model is used to obtain the comprehensive column vector of each evaluation index. Secondly, the index weight is determined according to the different weight methods. Finally, through the establishment of positive and negative prospect value matrices, according to the comprehensive prospect value, the advantages and disadvantages of integrated energy system schemes are sorted. The effectiveness of the proposed method is verified by hotel-integrated energy system data.

GDP is one of the best known and most commonly used indicators to measure the health of a country's economy, but the rapid growth of GDP does not adequately take into account resource and environmental factors. In order to choose an appropriate method to construct the GGDP, the article considers various indicators affecting the environment from multiple perspectives, and uses hierarchical analysis and global entropy weighting to calculate the importance of each indicator, assigning weights to each indicator, and through weighted summation can calculate the GGDP of each country, and compares the GDP and GGDP of five countries, followed by using regression models to calculate the two level The regression model was then used to calculate the contribution of the two first-order indicators to GGDP and GDP, and it was concluded that the use of GGDP accounting can reduce the cost of environmental downgrading and increase the benefits of environmental improvement. Finally, the benefits and drawbacks of using GGDP are summarized.