A Prognostic Model For Colon Cancer Based On CeRNA Network-related LncRNA,along With Analysis Of Immune And Drug Response

Objective: This study aimed to investigate the potential roles of the ceRNA network in colon cancer by constructing a ceRNA regulatory network using bioinformatics analysis.The lncRNAs within the ceRNA network were utilized to develop a prognostic model for predicting the prognosis of colon cancer patients.The samples were stratified into high-risk and low-risk groups based on the median risk score,and the differences in tumor mutation burden(TMB),immune checkpoint(IC)expression,and drug sensitivity between the two groups were analyzed.The results of this study provide novel insights into the early diagnosis,early treatment,individualized therapy,and prognosis assessment of colon cancer.Methods:(1)Obtained gene expression and clinical data related to colon cancer from the TCGA database(included lncRNAs,miRNAs and mRNAs data).(2)Conducted differential analysis of gene expression data between cancer and normal adjacent tissues,and identified differentially expressed lncRNAs,miRNAs,and mRNAs using R language.(3)Predicted corresponding target genes based on the ceRNA hypothesis and constructed a ceRNA regulatory network for colon cancer.(4)Conducted GO and KEGG enrichment analysis of the target genes in the ceRNA network using the Metascape online tool.(5)Employed Kaplan-Meier method and single-factor Cox regression analysis to screen for survival-related genes in the ceRNA network,and constructed a sub-network related to colon cancer prognosis.(6)Extracted lncRNA expression data from the ceRNA network,and screened for lncRNAs with independent prognostic significance using single-factor Cox regression,Lasso regression,and multivariable Cox regression analysis,and constructed a prognostic model.Evaluated the scientificity of risk grouping using principal component analysis and survival state evaluation,and assessed the accuracy of the model using survival analysis,ROC curves,and concordance index.The calibration curve was used to evaluate the predictive efficacy of the nomogram.(7)Obtained 13 immune function gene sets from the Imm Port database,and evaluated the impact of the risk model on immune function using ss GSEA.(8)Analyzed the differences in tumor mutation burden and IC between high-risk and low-risk groups using gene mutation data related to colon cancer obtained from the TCGA database.(9)Analyzing differential drug sensitivity between high and low-risk groups using the "p RRophetic" package in R language.Results:(1)Through analysis of gene differences between cancer and adjacent tissues in colon cancer patients,we successfully constructed a ceRNA network comprising 232 lncRNAs,21 miRNAs,and 704 mRNAs,and predicted target genes.(2)GO and KEGG enrichment analysis revealed that the target genes in the ceRNA network were primarily associated with the Wnt signaling pathway,mineral absorption,and myocardial adrenergic signaling pathway.(3)Survival analysis identified two colon cancer prognostic-related ceRNA subnetworks,specifically lncRNA ALMS1-IT1/hsa-mir-129-5p/ASB4 and lncRNA HOTAIR/hsa-mir-129-5p/ASB4.(4)Nine lncRNAs were identified with independent prognostic significance using univariate Cox regression,LASSO regression,and multivariate Cox regression,namely AC020907.1,AL139002.1,AL445438.1,ALMS1-IT1,CYP1B1-AS1,FAM138 B,LINC00343,MYO16-AS1,and SACS-AS1.(5)Patients were divided into high-risk and low-risk groups according to the median value of their risk score.The results of the survival analysis revealed that the OS and PFS of the high-risk group were shorter than those of the low-risk group.Furthermore,immune function analysis indicated that only chemokine receptor(CCR)exhibited differences between the two groups,suggesting that there may be a certain relationship between colon cancer and CCR.(6)The results of the analysis on gene mutations and tumor mutational burden reveal a significant increase in mutation frequency and TMB in the high-risk group,compared to the low-risk group.Furthermore,patients with both high-risk and high TMB exhibit the worst prognosis.Notably,the expression levels of numerous IC markers,including CD274,HAVCR2,LAIR1,NRP1,PDCD1,TNFSF4,and VTCN1,were significantly elevated in the high-risk group,as compared to the low-risk group.(7)Analysis using the "p RRophetic" package revealed significant differences in IC50 values for drugs such as trametinib,tivozanib,sunitinib,paclitaxel,masitinib,dasatinib,cytarabine and axitinib between the high-risk and low-risk groups.Conclusions:(1)Through bioinformatics analysis,it was found that the lncRNA ALMS1-IT1/hsa-mir-129-5p/ASB4 axis and the lncRNA HOTAIR/hsa-mir-129-5p/ASB4 axis may be potential regulatory pathways for colon cancer.(2)Nine lncRNAs with independent prognostic significance were screened out,and the risk prognostic model composed of the above 9 lncRNAs can better distinguish between high-risk and low-risk populations and predict the prognosis of colon cancer patients.(3)The tumor mutation burden of the high-risk population is higher than that of the low-risk group,which means that the effect of immunotherapy is better.The IC expression level in this population is generally higher,and it is expected to obtain help from corresponding ICI treatment.(4)Drugs such as trametinib,tivozanib,sunitinib,paclitaxel,masitinib,dasatinib,cytarabine and axitinib may have potential therapeutic effects on colon cancer patients.