Bioinformatics Analysis Of M6A-related LncRNA In Gastric Cancer

Objective: The aim of this study was to investigate prognostic value of m6A-related lnc RNA in GC and construct a prognostic risk model by bioinformatics analysis.Methods: RNA sequencing profiles with corresponding clinicopathological information associated with GC were extracted from The Cancer Genome Atlas.Pearson correlation analysis was used to identify m6 Arelated lnc RNA.GC patients were divided into experimental group and verification group,and the combination of patients in the two groups were defined as the combination group.In the experimental group,univariate Cox regression analysis,LASSO regression analysis and multivariate Cox regression analysis were gradually performed to screen out lnc RNA with independent prognostic value for the construction of prognostic risk model.According to the risk coefficient of the m6A-related lnc RNA in the prognostic risk model and expression level of the lnc RNA in GC patient,the risk score was calculated for each GC patient.Then,the GC patients were divided into high-and low-risk groups based on the median risk score.The predictive efficacy of the prognostic risk model was assessed by Kaplan-Meier survival analysis and receiver operating curve.At the same time,according to the median risk score value of GC patients in the experimental group,the GC patients in the validation group and the combination group were also divided into high and low risk group,and KM survival analysis was conducted to verify the accuracy of the prognostic risk model.The independent prognostic value of risk score and clinicopathological features was assessed by univariate and multivariate Cox regression analysis.Nomogram were constructed using risk score and clinicopathological features.Then,the standard receiver operating characteristic curve of the risk score and each clinicopathological feature were plotted.By comparing the area under the curve to reflect the advantage of the risk score in predicting the prognosis of GC patients.The utility of prognostic risk model in predicting the prognosis of GC patients under different clinicopathological features,the tumor mutation burden,and the tumor microenvironment was assessed by KM survival analysis.The immune escape potential of GC patients in the high-and low-risk group was analyzed by the Tumor Immune Dysfunction and Exclusion database.Meanwhile,the expression level of common immune checkpoints including PD-1,PD-L1,Tim3 and TIGIT between high-and low-risk groups were analyzed.Finally,the sensitivity of GC patients in the high-and low-risk groups to common chemotherapy drugs was analyzed through the Cancer Genome Project database.Results: 1.A prognostic risk model containing 11 m6A-related lnc RNAs(ZBED5-AS1,PINK1-AS,AC016737.1,AL355574.1,AC245041.1,LINC01315,PVT1,AP000695.1,AP001318.2,AC078883.2,and MIR100HG)was constructed in this study.2.KM Survival analysis showed significant differences in overall survival between high-and low-risk groups in both three groups(P < 0.05).The GC patients in high-risk group had a worse prognosis than that in the low-risk group.3.Multivariate Cox regression showed that risk score was an independent prognostic risk factor for GC patients(P < 0.001).4.The nomogram showed that the risk score could accurately predict the prognosis of GC patients,and the area under the standard ROC curve was 0.831,which was significantly higher than that of age(0.587),gender(0.535),tumor grade(0.561),TNM stage(0.631)and other factors.5.The prognostic risk model based on 11 m6 A associated lnc RNAs has prominent prognostic value for GC,and its predictive efficacy was not affected by clinicopathological characteristics,the tumor mutation burden and the tumor microenvironment.6.Immune evasion analysis showed that the TIDE score and Dysfunction score of the low-risk group were lower than those in the high-risk group,indicating a lower immune escape potential and a higher sensitivity to immunotherapy in the low-risk group.The expression level of PD-L1 and Tim3 of GC patients in the high-risk group were higher than that in the low-risk group,but there was no significant difference in the expression level of PD-1and TIGIT between the two groups.Sensitivity analysis of chemotherapy drugs showed that for 5-fluorouracil,the half inhibitory concentration of the low-risk group was lower,indicating that the low-risk group was more sensitive for the treatment of 5-fluorouracil.However,for the treatment of paclitaxel,there was no significant difference between the two groups.Conclusion: 1.In this study,a prognostic risk model consisting of 11m6A-related lnc RNAs was constructed through bioinformatics analysis,which has a strong predictive effect on the prognosis of GC patients.2.The prognostic risk model has strong practicability even in different clinicopathological features,tumor mutation burden and tumor microenvironment.3.This study can provide a certain basis for the individualized diagnosis and treatment options for GC patients.It is also conducive to further analyzing the mechanism of GC occurrence and progression.