| Objective(s):To explore the prognostic significance of immune-related Long noncoding RNA(Lnc RNA)in ovarian cancer,and to construct a prognostic risk model of ovarian cancer,to analyze changes in immune function,immune cells,and immune checkpoints in patients with ovarian cancer,and to screen potential drugs for ovarian cancer based on prognostic-related lnc RNA,to provide theoretical basis and evidence for identifying prognostic biomarkers of ovarian cancer,evaluating the prognosis of patients with ovarian cancer and predicting potential targeted drugs of ovarian cancer.Methods:1.Downloading and obtaining the transcriptome and clinical data of ovarian cancer and samples from TCGA database,and obtaining immune genes from Immport database,the expression matrix of normal ovarian lnc RNA was obtained from GTEX database,and the immunoreactive lncrnas of ovarian cancer and normal ovarian tissues were obtained after treatment.By univariate Cox analysis,the immune lncrnas associated with survival time and status were screened out.2.Construction of ovarian cancer prognostic model: ovarian cancer immune lnc RNAs with significant correlation with survival time and status were identified by one-way Cox analysis and named as prognosis-related immune lnc RNAs,and the ovarian cancer prognostic model was constructed by LASSO regression analysis,and the prognosis-related immune lncRNAs in the prognostic model were optimized by cross-validation.2.Optimization of immune lncrnas in the prognostic model by LASSO regression and cross-validation,using the construction of a prognostic risk model for ovarian cancer,based on the comparison of sample risk scores with median values,the patients with ovarian cancer were divided into high-risk group and low-risk group,and survival prognosis analysis,prognostic risk analysis,COX regression,univariate and multivariate independent prognostic analysis were performed,survival Curve,risk curve,Receiver Operating Characteristic(ROC)were plotted to evaluate the accuracy of the prognostic model and identify independent prognostic factors of ovarian cancer.3.Log into TIMER database to obtain immune infiltrating cells,immune-related functions and immune checkpoint sequencing data,edit R language script to read sample risk file and immune-related results file,to analyze the difference between high and low risk groups in the degree of activation of immune function,infiltration of immune cells and expression of immune checkpoint-related genes,to investigate the differences of immune function,immune infiltrating cells and immune checkpoint related genes among patients with different risk of prognosis.4.The 22 lnc RNA involved in the modeling were matched to recurrent cycles of more than 100 targeted agents in the drug sensitivity prediction package to screen for targeted agents targeting prognostic-related lnc RNA;To compare the sensitivity of each target drug in the high-and low-risk groups,obtain drugs with higher sensitivity to samples from the high-risk group for ovarian cancer as potential drugs targeting prognostic-related lnc RNA,and enter the drug chemical names in the Pubchem database,look at the chemical structure of a drug and visualize its three-dimensional structure.Results:1.Screening of immune-related lncrnas in ovarian cancer: there are 540 immune lnc RNA differentially expressed in ovarian cancer compared with normal ovarian tissue,the univariate Cox regression analysis showed that 49 lnc RNA were significantly associated with survival time and survival status(P<0.05),among which the lnc RNA AC008522.1 and the lnc RNA AC008992.1 were most significantly associated with the prognosis of ovarian cancer.2.Construction of prognostic risk model of ovarian cancer: Lasso regression analysis and cross-validation were optimized to obtain 22 immune lnc RNA related to the prognosis of ovarian cancer to participate in the construction of the prognostic risk model of ovarian cancer,the risk model formula is coef = multicoxsum $coefficients,riskscore = cofe gene(1)× Exp gene(1)+ cofe gene(2)× Exp gene(2)+...+cofe Gene(22)× Exp gene(22).The expression of 13 lnc RNA was positively correlated with the prognostic risk of ovarian cancer,the expressions of lnc RNA COLCA1,AC112491.1,MIRLET7 BHG,DLGAP1-AS1,AC134312.1,ALDH1L1-AS2,FAM157 C,PKP4-AS1,U62631.1,AL391807.1,AJ011932.1,SLX1A-SULT1A3,FRMD6-AS2 were positively correlated with the prognostic risk of ovarian cancer,the expression of 9 lnc RNA was negatively correlated with the prognostic risk of ovarian cancer and was a prognostic protective factor of ovarian cancer,the expression of lnc RNA AC008552.1,AL121845.4,CHRM3-AS2,AC027348.1,AC091806.1,DLGAP1-AS2,AC027020.2,DICER1-AS1,PSMB8-AS1 were negatively correlated with the prognostic risk of ovarian cancer and were the prognostic protective factors of ovarian cancer(p < 0.05).3.Prognostic Risk Model Assessment of ovarian cancer: Patients with ovarian cancer were divided into high-risk group and low-risk group according to the median value of the model.Kaplan-meier survival curve showed that the survival rate of the high-risk group was significantly lower than that of the low-risk group(p < 0.001),independent prognostic analysis showed that age and risk score were independent prognostic factors,and the area under ROC curve was 0.767,0.777,0.761 for 1-3-year survival,respectively,the Area Under Curve(AUC)calculated by risk model was0.767,0.710,0.548,0.500,0.500,respectively,all of them were in the range of 0.7-0.9,which indicated that the prediction accuracy of the model was high.4.Immune correlation analysis of ovarian cancer patients: Type II interferon-responsive function was active in the high-risk group of patients with ovarian cancer,in the low-risk group,the function of type I Histocompatibility complex and type I interferon response were active(P < 0.05).In the high-risk group,the infiltration of macrophages,neutrophil and tumor-associated fibroblasts was more frequent,there was more infiltration of effector B lymphocyte and Th1 subtype CD4+T cells in the low-risk group(P < 0.05),and the expression of Ido1 and BTLA in the high-risk group was significantly lower than that in the low-risk group(p <0.05).5.Potential drugs to target ovarian cancer immune-related lncRNA: 3 potential drugs were obtained using the drug sensitivity prediction package based on ovarian cancer immune-related lnc RNA:AP24534(Ponatinib),AUY922(Luminespib),and Axitinib,the IC50 values of the three targeted drugs in the high-risk group were all lower than those in the low-risk group,and the three targeted drugs were more sensitive to the patients in the high-risk group.Conclusion(s):1.The immune lncrnas associated with the prognosis of ovarian cancer were successfully mined by bioinformatics methods,and all of these lncrnas can accurately predict the prognostic risk of ovarian cancer patients and serve as molecular markers for the prognosis of ovarian cancer patients,this study provides a theoretical basis for studying the relationship between lncRNA and the prognosis of ovarian cancer.2.The age and the risk score calculated by the prognostic model are independent prognostic factors of ovarian cancer,which can accurately predict the prognosis of the patients and be used as an index to evaluate the prognosis of the patients with ovarian cancer.3.The prognostic risk of patients with ovarian cancer may be related to the degree of immune function activity,the type of immune infiltrating cells,and the expression of Ido1 and BTLA immune checkpoint,to provide a theoretical basis for the further study of immunotherapy of ovarian cancer.4.AP24534(Ponatinib),AUY-922(Luminespib)and Axitinib can inhibit the progression of ovarian cancer by targeting the immune lnc RNA of ovarian cancer,which may be a new treatment option for patients with ovarian cancer. |
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