Bioinformatics-Based Analysis Of Cuproptosis And The Roles Of Related LncRNA In Prognosis And Immune Microenvironment Of Clear Cell Renal Cell Carcinoma And The Construction Of Prognostic Models

Objective:The occurrenceand development of clear cell renal cell carcinoma(ccRCC)are closely related to the metabolic reprogramming of tumors,and so far,satisfactory clinical results have not been achieved in its treatment and prognosis.Although both cuproptosis and long non-coding ribonucleic acid(LncRNA)are closely related to cell metabolism and tumor occurrenceand development,research on the association between cuproptosis-related lncRNA and ccRCC is scarce.Therefore,our goal is to comprehensively explore the potential role of cuproptosis and cuproptosis-related lncRNA(CR-lncRNA)in ccRCC,and to identify a new prognostic model based on cuproptosis-related LncRNA to improve the prognosis management of ccRCC patients.Methods:Gene expression and clinical data of ccRCC patients were downloaded from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus database(GEO).A list of cuproptosis related genes(CRGS)was retrieved and downloaded from recent research reports.The Human Protein Atlas(HPA)database was used to download the protein immunohistochemical results of CRGS.The prognostic value of CRGS was studied by univariate Cox regression analysis,identifying different cuproptosis subtypes using consensus clustering analysis and exploring potential molecular pathways between different subtypes through gene set variation analysis(GSVA).Weighted gene co-expression network analysis(WGCNA)was used to identify cuproptosis-related lncRNA.The TCGA queue was randomly divided into a training set and a validation set at a ratio of 7:3 using "caret" R package.The model was trained on the training set and validated on the validation set.Various algorithms were used to construct the prognosis model for cuproptosis-related lncRNA based on univariate Cox regression analysis,Least Absolute Selection and Shrinkage Operator(LASSO),Random Forest(RF),and multivariate stepwise Cox regression analysis.Risk scores of samples were calculated,and ccRCC patients were divided into high-and low-risk groups based on the median risk score.Time-dependent receiver operating characteristic curve(time-dependent ROC),Concordanceindex(C-index),and Kaplan-Meier(KM)survival analysis were used to evaluate the model.Single-sample gene set enrichment analysis(ssGSEA)was then used to explore the relationship between prognosis risk scores and tumor immune microenvironment(TIME),and the Tumor Immune Dysfunction and Exclusion(TIDE)website was used to further explore the predictive role of the prognosis model in immunotherapy response.The "pRRophetic" R package was used to screen for sensitive drugs in different risk groups.Finally,an endogenous competitiveRNA(ceRNA)network of lncRNAmiRNA-mRNA was constructed based on four online prediction websites: mircode,miRDB,miRTar Base,and Target Scan.Results:TCGA,GEO,and HPA databases showed significant differential expression of CRGS in ccRCC.Univariate COX regression analysis demonstrated that except for CDKN2 A,all other CRGS were protective genes in ccRCC.Consensus clustering analysis identified two cuproptosis subtypes,including the cuproptosis activation subtype and the cuproptosis inhibition subtype.The cuproptosis inhibition subtype was significantly associated with poorer prognosis and higher grade and stage.GSVA analysis showed that the cuproptosis inhibition subtype was significantly associated with multiple tumor development and tumor immune-related pathways,such as DNA REPAIR,MYC TARGETS,REACTIVE OXYGEN SPECIES PATHWAY,and KRAS SIGNALING PATHWAY.TIDE analysis indicated that the high-risk group was significantly associated with poorer response to immunotherapy.A prognosis feature consisting of 13 CRlncRNA was constructed based on WGCNA,univariate COX regression analysis,LASSO,RF,and multivariate COX regression analysis.ROC curve and C-index results showed that the prognostic model had good prognostic efficacy.Patients in the high-risk group were closely associated with poorer prognosis and higher grade and stage.ssGSEA results showed that patients in the high-risk group had a higher proportion of Activated CD4 T cell,Activated CD8 T cell,and MDSC infiltration,while Neutrophil,Immature dendritic cell,and Mast cell had higher infiltration in the low-risk group and were significantly associated with poorer response to immunotherapy in the high-risk group.Drug sensitivity analysis showed that patients in the highrisk group were more sensitive to Acadesine(AICAR),all-trans retinoic acid(ATRA),Palbociclib(PD-0332991),and Cisplatin,while GSK1904529 A and KIN001-102 were more sensitive to patients in the low-risk group.Conclusion:Cuproptosis and its related lncRNA are closely related to the prognosis,tumor immune microenvironment,and immunotherapy response of ccRCC patients.A new prognostic risk model has been successfully developed based on 13 CR-lncRNA,and a ceRNA network of lncRNA-miRNA-mRNA has been constructed based on CR-lncRNA relevant to prognosis.