| BackgroundOvarian Cancer(OC)is a gynecological malignancy with the highest mortality rate,which seriously affects the life and health of patients.Due to the late diagnosis time,chemotherapy resistance,poor treatment effect,and lack of effective clinical methods to evaluate prognosis,it is very important to find new diagnostic and treatment targets and explore effective prognostic indicators to improve the overall survival rate of ovarian cancer patients.Recent research has discovered a new oncogenic mechanism,Cellular Senescence(CS),defined as permanent cell cycle arrest that plays a key role in tumor development and progression.In recent years,with the rapid development of transcriptomic sequencing and biological technology,it has played a very important role in identifying the drivers of cancer progression and exploring the pathogenesis of tumors.Although many studies have confirmed that cell aging-related genes are closely related to the early diagnosis,chemotherapy drug sensitivity and prognosis of ovarian cancer,there are relatively few bioinformatics studies,so further exploration and confirmation are needed.ObjectiveThis study aims to use public databases to analyze cellular aging genes associated with ovarian cancer patients,construct a risk scoring model related to cellular senescence,and further explore the application value of the model in assessing immune cell infiltration,immune function and predicting chemotherapy drug sensitivity,so as to provide a new evaluation method for clinical practice.MethodFirst,CS-related genes were obtained from the Cell Senescence Database(CellAge)and transcriptomic sequencing datasets for ovarian cancer patients were downloaded from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO).Then,the CS gene was analyzed for differential expression to obtain the abnormally expressed cell aging-related gene(|log FC|>1,P<0.05)in ovarian cancer patients,and the CS gene was analyzed by univariate regression analysis to screen out the gene(P<0.05)significantly related to the prognosis of OC patients.The two parts of the gene are intersected,and these intersecting genes are thought to be closely related to the occurrence and development of OC.Next,in order to avoid redundancy caused by excessive number of genes,the minimum absolute contraction and selection operator(LASSO)regression analysis of these genes was carried out,the most critical genes were screened out and the risk coefficients of key genes were obtained,and the CS-related risk score model was developed by the risk score formula obtained by regression analysis,the risk score of each OC patient was calculated,and the high and low risk groups were divided according to the optimal cut-off value by ranking the risk score.The survival status of patients in the high-and low-risk groups and the expression levels of model genes were then characterized.In addition,prognosis assessment(Kaplan-Meier,K-M curve analysis),principal component analysis(Principal Component Analysis,PCA)and tumor microenvironment(Tumor Microenvironment,TME)analysis were also performed for patients in the high-and low-risk groups,in which TME analysis mainly included the analysis of immune cell infiltration,immune function,immune and matrix scores,and immune checkpoint expression of tumors.At the same time,the Gene Set Enrichment Analysis(GSEA)enrichment analysis method was used to analyze the differences between high and low risk groups.Finally,the relationship between model genes and tumor treatment drug susceptibility was explored using Pearsonian correlation analysis using the Cell Miner database(https://discover.nci.nih.gov/cellminer),which includes the efficacy of 263 drugs approved by the FDA and clinical studies.Result1.Among the 279 CS-related genes,a total of 47 genes abnormally expressed in ovarian cancer patients and 34 genes significantly related to the prognosis of ovarian cancer patients were obtained,and finally a total of 10 intersection genes were obtained,which were considered to be significantly related to the prognosis of OC patients(CAV1,CBX7,ETS2,ID4,IGFBP6,ITGB4,MORC3,RB1,TACC3 and TXN),compared with normal ovarian tissue,ITGB4,The expression of TACC3 and TXN was upregulated in ovarian cancer tissues,and CAV1,IGFBP6,CBX7,ETS2,ID4,MORC3 and RB1 were downregulated in ovarian cancer tissues.2.A total of 8 cell aging-related genes(CBX7,ETS2,ID4,IGFBP6,ITGB4,MORC3,RB1 and TXN)were closely related to the prognosis of ovarian cancer by LASSO regression analysis,and a risk score model was constructed by using these 8 genes.3.Through the score calculated by the model,the ranked OC patients were further divided into high-risk group and low-risk group.Compared with the low-risk group,patients in the high-risk group had significantly shorter survival.The analysis of the Receiver Operating Characteristic(ROC)curve showed that the area under the prognostic curve(AUC)of the risk score model was 0.703,0.710 and 0.743,respectively,indicating that the risk score model had strong prognostic prediction ability.In the validation dataset,this finding was confirmed,and the survival of patients in the high-risk group was significantly shortened,and the predicted AUC values for 1-year,3-year,and 5-year prognosis were 0.603,0.611,and0.628,respectively.4.Both univariate and multivariate analyses confirmed that risk score was an independent predictor of prognosis in patients with OC(P<0.05).In addition,patients with high-risk scores had significantly poorer prognosis among patients stratified by different clinicopathologic factors.5.In the tumor microenvironment analysis,M2 macrophages had increased infiltration in high-risk patients,and the immune score and matrix score of patients also increased with the increase of risk score.Memory B cells,activated NK cells,and activated dendritic cells were significantly increased in patients in the low-risk group.In the high-risk group,immune functions such as antigen-presenting cell(APC)co-inhibition,APC co-stimulation,CC motif chemokine receptor(CCR),immune checkpoint,cytolytic activity,parainflammation,T cell co-inhibition,T cell co-activation,and type II interferon response were more active.It was also found that the expression of programmed cell death-ligand 1(PD-L1)was significantly elevated in the high-risk group,indicating that the high-risk group showed some immunosuppression.6.The high-risk group was mainly enriched in calcium signaling pathway,c AMP signaling pathway,MAPK signaling pathway,Rap1 signaling pathway and Ras signaling pathway,while the low-risk group was enriched in myocardial contraction,oxidative phosphorylation,phenylalanine metabolism,proteasome and thiamine metabolism signaling pathways.7.A total of 16 model gene expression and therapeutic drug sensitivity pairs were obtained,which provided a reference for clinical treatment or basic research.Conclusion1.In this study,an ovarian cancer prognostic model based on eight cellular aging-related genes,namely CBX7,ETS2,ID4,IGFBP6,ITGB4,MORC3,RB1 and TXN,was constructed through CellAge database,TCGA database and GEO database,which can be used as an independent factor for predicting the prognosis of OC patients and has reliable predictive ability.2.This model is helpful in assessing immune cell infiltration,immune function,and immune checkpoint expression in ovarian cancer patients,which provides reference and recommendations for immunotherapy.3.The model has the potential value of predicting drug treatment sensitivity and provides guidance for patients to formulate treatment options. |
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