Construction And Evaluation Of A Prognostic Signature Of Endometrial Cancer Based On Cuproptosis-associated LncRNA

Background: Endometrial cancer(EC)is one of the most common gynecological cancers and poses a major threat to women’s health.More and more studies have shown that the abnormal expression of long non-coding RNA(long non-coding RNA,lnc RNA)is closely related to the occurrence and development of endometrial cancer.Copper death is a new type of programmed cell death,which is involved in the occurrence and development of malignant tumors.Different from apoptosis,ferroptosis,and necroptosis,copper death depends on mitochondrial respiration.Therefore,targeted therapy against copper death may be a unique cancer treatment technique.However,studies on copper death-associated long noncoding RNAs(Cuproptosis-associated lnc RNAs,CALs)in endometrial cancer are lacking.Moreover,the relationship between CALs and the tumor immune microenvironment in endometrial cancer is still unclear,and the prognostic value of these lnc RNAs needs further study.Objective: The purpose of this study is to develop a CALs-based model for predicting the prognosis and risk of endometrial cancer,and to explore the correlation between the model and the immune microenvironment and the application value of predicting chemotherapeutic drug sensitivity.To provide a scientific basis for optimizing the individualized management and treatment of patients with endometrial cancer.Methods:1.The transcriptome and clinical data of endometrial cancer were obtained from The Cancer Genome Atlas(TCGA),using Co-expression Network Analysis(CENA),Cox regression analysis and absolute shrinkage and The Least absolute shrinkage and selection operator(LASSO)method was used to identify CALs and establish a risk prognosis model.2.The predictive performance of the model was verified and identified through comprehensive methods,and a nomogram was constructed in combination with clinicopathological parameters to predict the 1-year,3-year,and 5-year survival probabilities of endometrial cancer patients.3.Apply functional enrichment analysis(GO,KEGG,GSVA,GSEA)and immunological methods(ss GSEA)to study the differences in biological and immunological functions of differentially expressed genes(DEGs)between high and low risk groups.Tumor mutation burden(TMB)and tumor immune dysfunction and rejection(TIDE)scores were used to evaluate the response to immunotherapy in high-and low-risk groups.Combining with the CALs prognostic model,screening chemotherapy drugs sensitive to endometrial cancer in different risk groups.4.Use external datasets to validate the prognostic value of CALs.Results:1.We constructed a prognostic model of endometrial cancer patients containing 7 genes(AC090617.5,AC009271.1,AP001189.1,AC019080.5,AC113349.1,LINC01224 and RAB11B-AS1),which can effectively predict patient survival.2.We used Principal component analysis(PCA)to show that the model can accurately distinguish between high-risk and low-risk groups;Kaplan-Meier(K-M)analysis showed that the prognosis of patients in the high-risk group was poor(P < 0.001);The Receiver operating characteristic(ROC)curve showed that the areas under the ROC curve(AUC)of the model predicting the 1-year,3-year,and 5-year survival rates of endometrial cancer patients were0.735,0.854,and 0.845,respectively.Moreover,the analysis results of the verification cohort and the total cohort maintain a high consistency with the results of the training cohort.The diagnostic accuracy of the CALs prognostic model is superior to the prognostic models developed by other researchers.The C-index of our model is 0.708,which is the highest among all other 4 prognostic models.In addition,the results of clinical subgroup survival analysis show that the model is suitable for Endometrial cancer patients with different clinical features(p < 0.05).3.The prognostic model was significantly correlated with the immune status of patients with endometrial cancer.Through K-M analysis,it was found that the superimposed effect of patients in the high-risk group and low-TMB group led to shortened survival(P < 0.001).In addition,the TIDE score was higher in the low-risk group(P < 0.001),suggesting that these patients had a poor response to immune checkpoint blockade.The prognostic model can clearly distinguish patients with microsatellite instability high mutation(MSI-H)and low mutation(MIS-L)in endometrial cancer(p < 0.05),and patients with microsatellite stable(MSS)and MSI-H(p < 0.001).The risk score of the prognostic model was positively correlated with the RNA stemness index(m RNAsi)(P < 0.001).4.Combined with clinicopathological factors,a nomogram was constructed to predict the survival probability of patients.Both the calibration curve and the ROC curve showed good predictive ability.The areas under the ROC curve(Area Under Curve,AUC)of the 1-year,3-year,and 5-year survival rates were 0.799,0.814,and 0.827,respectively.Based on the CALs score,we analyzed the sensitivity of different chemotherapy drugs in patients with high and low risk groups,and the results showed that patients in different risk subgroups also had significant differences in response to different chemotherapy drugs(P < 0.001).And our CALs are well validated in other databases.Conclusion: We identified CALs as independent prognostic factors in endometrial cancer with good predictive power.At the same time,CALs may also provide accurate decision-making basis for the selection of immunotherapy and chemotherapy drugs in patients with endometrial cancer,help to formulate effective chemotherapy regimens and provide potential therapeutic targets for clinical endometrial cancer patients.