Study On Prognosis Model Of Gastric Cancer And Analysis Of Related Mechanisms Of Lymphatic Metastasis Based On Transcriptome Data

Objective:Based on transcriptome data from the Cancer Genome Atlas(TCGA)database and clinical information of patients with gastric cancer(GC),a genetic risk prediction model for the prognosis of GC patients was constructed by searching for differential genes and combining the total survival data of patients.Intersecting with single cell transcriptome sequencing data of GC lymph node metastasis,conducting comprehensive bioinformatics analysis of differential gene screening,GSVA signal pathway annotation,pseudochronology,and intercellular communication,and improving the understanding of intercellular interaction networks,providing theoretical basis for studying the mechanism of GC lymph node metastasis and searching for new treatment methods for patients with GC lymph node metastasis.Methods:1.Using TCGAGC transcriptome data and clinical data as the research object,the normal group was used as the control group,and the GC group was used as the experimental group.2.Taking GSE163558 single cell transcriptome data as the object,we conducted quality control filtering on Sc RNA-seq data,and conducted dimensionality reduction,clustering,and visualization on the data results.We selected the top 10 significantly high expression genes for cell annotation,screened out differentially expressed genes, and conducted quasi temporal analysis and gene set variation analysis to analyze inter cell communication.Results:1.The study found 3668 differential genes in GC patients,including 2373 up-regulated genes and 1295 down-regulated genes in GC patients.A risk prediction model for the prognosis of eight gene GC patients was constructed(MATN3,RP11-174O3.3,SOX14,H2 BFWT,CYMP,RP11-105C19.2,RNASE1,and LINC01614).The risk prediction model for prognosis of eight gene GC patients has a 1-year AUC value of0.667,a 3-year AUC value of 0.726,and a 5-year AUC value of 0.717.Patients were divided into high-risk and low-risk groups based on the median value of risk prediction assessment.The K-M curve showed that the prognosis of the high-risk group was worse than that of the low-risk group.2.A single cell dataset of GC data was reanalyzed,and nine types of cells were isolated and identified,including T cells,NK cells,B cells,neutrophils,epithelial cells,macrophages,fibroblasts,stromal cells,and mast cells.The study intersected the single cell dataset with the TCGA single factor Cox ratio model genes,and found that the intersection genes PPP1R1 B,PSCA,ADAM12,CHRDL1,FNDC1,RCAN2,FAP,SLC22A17,SERPINE1,GPX3,COL10A1,COL11A1,DNASE1L3,RNASE1,INHBA,MCEMP1,GADD45 B were associated with epithelial cells,fibroblasts,macrophages,and B cells.Finally,a series of signal pathways related to GC lymph node metastasis were found,such as CLEC,FLT3,IL1,GRN,SEMA7,ANGPT,TWEAK,NT,IL6,ESAM,PROS,and NEGR.Conclusion:1.Based on TCGA GC transcriptome data and bioinformatics methods,this study successfully screened differential genes related to the prognosis of GC.A risk prediction model for the prognosis of eight gene GC patients was constructed.After further evaluation of the model’s differentiation and calibration,it was determined that the model had good predictive value.2.The study reanalyzed the single cell dataset of GC data,isolating and identifying 9types of cells;Subsequently,the study intersected the single cell dataset with the TCGA single factor Cox ratio model gene,and found intersection genes related to epithelial cells,fibroblasts,macrophages,and B cells;Finally,a series of signal pathways related to GC lymph node metastasis were found,providing theoretical basis for studying the mechanism of GC lymph node metastasis and searching for new treatment methods for patients with GC lymph node metastasis.