| Objective: To construct a prognostic model based on m7G-related lnc RNA using bioinformatics analysis tools and to validate the accuracy of the model in predicting survival of gastric cancer patients.Methods: The Cancer Genome Atlas(TCGA)database was used to obtain gene expression matrices and general clinical information for gastric cancer patients.Gene annotation files containing lnc RNAs were downloaded from the "GENCODE" database.The m7 G methylation regulatory genes and all lnc RNA expression data of gastric cancer patients were extracted from the obtained gene expression matrix,and Pearson correlation analysis was performed using the "corrplot" package in R software to obtain m7G-related lnc RNAs(R>0.3,P<0.05).Univariate Cox analysis was performed using the "survival" package in R software to screen for m7G-related lnc RNAs(prognostic m7G-related lnc RNAs)associated with survival in gastric cancer patients.Least absolute shrinkage and selection operator(LASSO)regression analysis was performed using the R package "glmnet" and a 10-fold cross-validation was set to determine the prognostic model and obtain the hazard score coefficients,and multifactor COX regression analysis was used to construct the Nomogram(Nomogram).Based on the median risk score coefficient,patients with gastric cancer were classified into high-risk and low-risk groups.In addition,Kaplan-Meier analysis,univariate and multivariate Cox regression analysis,calibration plots of the Nomogram,receiver operating characteristic(ROC)curves,and principal component analysis(PCA)were used to validate the reliability of the prognostic model.The expression of m7G-associated lnc RNAs in the prognostic model was further validated using q-PCR within in vitro cell lines.Data analysis was performed mainly using R software(version 4.0.3)and Perl software(version 5.3).In this study,univariate and multifactorial Cox regression,Lasso regression,Kaplan-Meier method,PCA and ROC analyses were used.kruskal-Wallis test was used to compare differences between groups.pearson coefficient was used to evaluate correlations.P<0.05 was considered statistically significant(*P<0.05,**P<0.01,and ***P<0.001).Results: 1.In this study,441 samples of gastric cancer patients were obtained from TCGA database,and samples with survival time less than 30 days and missing expression data and clinical data were deleted,and finally an expression matrix containing 337 samples was obtained.2.22 genes were identified as m7 G methylation regulatory genes from existing studies,and 442 m7G-related lnc RNAs were screened by correlation analysis,among which 25 lnc RNAs were correlated with survival of gastric cancer patients(P<0.05),and 7 lnc RNAs were finally identified by Lasso regression analysis(AL161785.1,LINC01094,CHROMR,AP001528.1,AC245041.1,AL355574.1,AC005586.1)were finally identified as prognostic model by Lasso regression analysis.3.multifactorial and univariate COX analyses indicated that the risk score of the prognostic model was an independent risk factor.The correction curve of Nomogram indicated that the model had some accuracy.Kaplan-Meier curve indicated that the survival time of the high-risk group was shorter than that of the low-risk group(P<0.05).PCA analysis indicated that the hazard score could effectively distinguish between high-and low-risk groups.ROC curve indicated that the hazard score of the prognostic model had some specificity and accuracy in predicting survival time.q-PCR results indicated that CHROMR,LNC01094,AC245041.1 and AL355574.1 were expressed at significantly higher levels in tumor cell lines,while AC005586.1,AL16178.5 and AP001528.1 were opposite.Conclusions: In this study,seven m7G-related lnc RNAs with high correlation with prognosis of GC patients were screened based on the TCGA database,and a prognostic model for gastric cancer patients was constructed based on this.After validation,the model has a certain accuracy and specificity in predicting the prognosis of gastric cancer patients.Objective: To explore the relationship between m7G-related lnc RNA-based prognostic model and clinicopathological features,immune cell infiltration,immune checkpoints and chemotherapeutic drug sensitivity in gastric cancer patients.To provide a basis for applying the prognostic model to assist in determining the pathological stage and sensitivity to treatment of gastric cancer patients.Methods: Patients with gastric cancer were divided into two groups based on median risk score.GO and KEGG pathway enrichment analysis was performed using GSEA software(version 3.0).Data on clinicopathological characteristics(TNM stage,AJCC stage,grade,gender,age,survival time)of gastric cancer patients in the first part were collected from the TCGA database,and a matrix of immune infiltrating cells was obtained using the Perl program for immune infiltration analysis using the R package "CIBERSORT".The "p RRophetic" package was used to compare the differences in IC50 values of chemotherapeutic agents used to treat gastric cancer.Correlation analysis was performed as in Part I.Pearson’s correlation test was used for correlation analysis.P<0.05 was considered statistically significant(*P<0.05,**P<0.01,and ***P<0.001).Results: There were significant differences between the two groups in terms of T stage,N stage,AJCC stage and age.For immune cells,the high-risk group had CD4 T cells resting,monocytes,M2 macrophages,dendritic cells(DCs),mast cells resting and Neutrophils infiltrated in higher abundance,and M0 macrophages and follicular helper T cells(FHCs)infiltrated in higher abundance in the low-risk group(P<0.05).Immune checkpoint gene expression levels were higher in the high-risk group compared with the low-risk group.Risk scores were negatively correlated with the drug semi-inhibitory concentrations(IC50)of docetaxel and cisplatin,and patients with high risk scores were less sensitive to chemotherapeutic agents.Conclusions: 1.This model can initially identify the pathological grading characteristics and tumor staging characteristics of the high and low risk groups,which has certain reference value for the early diagnosis of gastric cancer and can be used as a biomarker to predict the prognosis and early diagnosis of gastric cancer patients2.The 7 lnc RNAs screened in this study can initially identify the degree of immune cell infiltration and the expression level of immune checkpoint genes in GC patients.It also suggested that the 7 m7G-related lnc RNAs were associated with GC immune infiltration and immune escape and led to tumor progression and poor prognosis,providing a reference for the study of GC progression mechanism.3.The risk score constructed by this model can be used to evaluate the resistance of patients to docetaxel and cisplatin,which can help in future individualized treatment decision making. |
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