| BackgroundColorectal cancer(CRC)is the second leading cause of cancer related deaths in the world.The occurrence,progression and metastasis of colorectal cancer is a multi-step process,which involves a series of histological,morphological and epigenetic changes accumulated over time.Studies have found that most of the metabolic end products and key enzymes regulated by sphingolipids metabolism-related genes(SMG)play an important role in the occurrence and development of tumors,and are widely involved in tumor proliferation,metastasis,angiogenesis and drug resistance.However,the mechanism of sphingolipids metabolism in human colorectal cancer has not been fully understood,so this study aims to analyze the expression of sphingolipids metabolism-related genes in colorectal cancer,explore the possibility of sphingolipids metabolism-related genes as potential prognostic biomarkers in colorectal cancer,explore its biological pathway and molecular mechanism,and establish a prognostic model of colorectal cancer based on sphingolipids metabolism-related genes,To explore the clinical potential prognostic value of colorectal cancer patients.MethodThis study integrates the open data of the COAD and READ queues in the TCGA database as a training set for data analysis.97 genes related to sphingolipids metabolism were obtained through InnateDB database.The expression level of sphingolipids metabolism-related test between cancer group and control group was analyzed,and GO and KEGG enrichment analysis was performed,and the PPI network was constructed.Using single-factor COX analysis,LASSO algorithm and multi-factor COX analysis,the prognostic genes related to colorectal cancer were screened out.According to the median risk score,all samples were divided into high-risk and low-risk groups to establish the ROC curve.The prognostic value of the model was evaluated by calculating the AUC under the curve.The relationship between colorectal cancer patients with different clinical characteristics and risk score was analyzed by Wilcoxon test;GSEA was used to perform functional enrichment analysis on genes in high and low risk groups,CIBERSORT was used to compare the infiltration difference of immune cells in high risk groups and low risk groups,Pearson correlation was used to analyze the relationship between colorectal cancer-related prognostic genes and differentially expressed immune cells,and finally,a ceRNA network was constructed to explore the potential interaction mechanism of sphingolipids metabolism in colorectal cancer.ResultsIn this study,a total of 52 differentially expressed genes related to sphingolipids metabolism in colorectal cancer were screened,and the prognostic risk score of colorectal cancer was constructed.After testing,the remaining 6 genes were SPHK1,GLB1,TRAF2,CERS1,HEXB,and MAP7.The risk score model was evaluated and validated in the training set and the validation set respectively using KM survival curve and ROC curve,and the results showed that the model performance was good.At the same time,combined with risk score and other clinical indicators,including age,pathological stage,tumor stage,and lymph node involvement,the prognosis model of colorectal cancer was constructed.The calibration curve and ROC curve were used to evaluate the model,and the results showed that the model had good performance.Finally,the relationship between risk score and clinical characteristics and the level of infiltration of immune infiltrating cells was further studied,and the ceRNA network of model genes was constructed.The genes that make up the risk score can be used as the basis and direction for the next study of gene markers related to sphingolipids metabolism in colorectal cancer. |
