Research On The Construction Method Of Gene Regulatory Network For Tumor Immune Escape Analysis

For a long time,tumors have been regarded as a genetic disease characterized by a large number of mutations that lead to the development of cancer.Increasing evidence suggests that immune escape of tumor cells is not only a hallmark of tumors,but may also be a potential cause of tumor occurrence and development.Most tumor patients are diagnosed with locally advanced or metastatic tumors and complex tumor immune escape mechanisms,resulting in poor prognosis with traditional treatment options.Treatment decisions in the fight against cancer increasingly rely on tumor molecular characteristics.Understanding the mechanisms of tumor immune escape,identifying biomarkers and molecular targets for tumor progression,and personalized immunotherapy for tumors remain a challenge.The immune system recognizes tumor cells and kills cancer cells through complex mechanisms,which are described in their most basic form by gene regulatory network(GRN).Gene expression regulation has a direct impact on the complex regulatory mechanisms of tumor immune escape.Due to the computational cost resulting from tumor heterogeneity and the high-dimensional features of multi-omics data,existing methods for constructing GRN and network analysis methods are unable to accurately locate the escape mechanisms of individual tumor patients or immune subtypes,and these methods cannot effectively utilize multi-omics data to reconstruct GRN for analyzing tumor escape mechanisms.In order to overcome these challenges,this paper proposes a GRN construction method for tumor immune escape mechanisms combined with network analysis technology,dynamically understanding the interactions between tumor cells and immune cells,and revealing the molecular mechanisms and signaling pathways related to the survival of tumor patients.The main work and innovation of this paper are as follows:(1)This article provides an overview of domestic and foreign research on tumor immune escape,introduces omics data and GRN construction methods,and discusses their application in the study of tumor immune escape.(2)This article proposes a GRN construction and immune escape analysis method based on multi-source omics data,which mainly includes two modules: ImmuCycReg and L0 Reg.ImmuCycReg and L0 Reg are respectively applicable to single tumor samples and tumor immune subtypes.They integrate multi-source and multi-omics data to construct GRN for single tumor samples or tumor immune subtypes,and identify and analyze potential tumor immune escape mechanisms through network analysis.By combining the results of the two modules,the interpretability and biological significance of the method can be improved.The experimental results show that this method can effectively integrate and utilize multi-omics data,achieve unbiased GRN reconstruction,and identify and analyze tumor immune escape pathways.In addition,a Lasso-Cox-based risk factor prognosis scoring model was established based on genes and transcription factors(TFs)obtained from immune escape analysis to validate whether key TFs and genes affect patient prognosis,and new biomarkers were identified that may be used to guide tumor immune therapy.(3)This article proposes a method called inferCSN for inferring cell type-specific gene regulatory network(CSN)from single-cell RNA-sequencing(scRNA-seq)data.The method first infers pseudotime information from scRNA-seq data,and cells are reordered according to the pseudotime information.Considering that the distribution of cells in quasi-time information is not uniform,the regulatory relationship will be biased towards the high-density area of cells,using the Gaussian kernel density estimation method,combined with the cell state,to dynamically divide cells into windows of different sizes to eliminate pseudotime information differences caused by cell density.For each window,a generalized additive model was used to calculate the variance of the gene in the quasi-time and the P-value of this variance,and the Bonferroni method was used to test and correct the P-value.Significantly changed genes were selected andCSNs were constructed for different windows using sparse regression model combined with reference network information.The experimental results using simulated and real scRNA-seq datasets show that the method performs better than other methods in multiple performance metrics.The method is also shown to be highly robust when applied to different types of datasets,and datasets with varying numbers of cells and genes.Network centrality analysis indicates that the method can accurately infer key regulatory factors in the process of tumor immune evasion.