| Objective:To date,cancer remains a challenge for all mankind to overcome.Among the most prevalent cancers in women,cervical cancer remains one of the top four cancers in the world,with a high number of new cases each year.The pathogenesis of cervical cancer is still not fully elucidated,and although treatment options for early-stage cervical cancer have been standardized,the prognosis is still far from expected,and salvage treatment options for recurrent and metastatic cases are still being explored.Therefore,the search for alternative treatments for cervical cancer is urgent.A paper published in Science in March 2022 proposed a novel form of cell death different from other metal-regulated cell death(For example Ferroptosis)— — Cuprotosis,which refers to the regulation of cells by copper affecting the tricarboxylic acid(TCA)cycle causing proteotoxic stress and eventually inducing cell death,and the cell death process is closely related to changes in copper metabolism.Current evidence confirms that Cuprotosis is involved in the development of malignancies,but the relevant genes,pathways and mechanisms affecting the regulation of copper metabolism are not fully understood.Similarly,the prognostic significance and function of copper-induced cell death-related genes in cervical cancer are unclear.To investigate the role and possible mechanisms of Cuprotosis in cervical cancer,this study intends to explore Cuprotosis-related genes(CRG)in cervical cancer using a bioinformatic multi-omics approach based on a joint public database to find potential tumor markers for the treatment and prognosis of cervical cancer.Methods:The open database TCGA(TCGA-CESC)and GEO database(GSE30759,GSE44001 datasets)were used to obtain transcriptome profiles,corresponding clinical information and tumor mutation data for joint bioinformatics multi-omics analysis,and the corresponding packages in Strawberry Perl script and R language were used for statistical and analysis of the data.First: Cuprotosis-related gene clusters(CRGcluster)associated with tumors were screened in the known research literature,and bioinformatics analysis was performed in combination with the corresponding cervical cancer data obtained from TCGA data,and differential analysis of genes,TMB,CNV,and chromosome circle maps were performed.Second: The data obtained from TCGA and GEO databases were combined with CRGcluster to screen the CRGs associated with cervical cancer,and the CRGs were analyzed for survival differences and prognostic correlation,followed by survival and expression differences,PCA,GSVA,and ss GSEA after their typing treatment.Third: Screening of differential genes for typing,differential genes for GO and KEGG pathway enrichment and genotyping-related analysis,where differential genes are typed for survival,differential expression analysis and differential expression of CRG.Finally:risk-prognosis models were constructed by LASSO-COX regression analysis based on differential genes and acquired clinical information,and relevant bioinformatic multi-omics analysis and model validation were performed on the risk-prognosis models.Results:1.19 CRGclusters associated with tumors were screened by searching the known literature,and genetic differential analysis of CRGcluster with the transcriptome data obtained from TCGA database(T=306,N=3)in cervical cancer showed that among them,SLC31A1 gene and CDKN2 A gene were upregulated in tumor tissues;TMB analysis showed 2% mutation frequency of CDKN2 A gene,and increased copies of SLC31A1 gene and decreased copies of CDKN2 A gene in CNV analysis.2.TCGA and GEO data were combined to analyze CRGcluster and screen 17 CRGs associated with prognosis of cervical cancer.17 CRGs were analyzed by Kaplan Meier survival curve difference analysis and network prognostic correlation analysis which showed that 12 CRGs had survival difference,among which SLC31A1 gene and CDKN2 A gene had significant prognostic correlations.Subsequently,the samples of CRGs were obtained two subtypes(K=2)according to the combined clinical information,as subtype A and subtype B(PCA analysis showed the distribution of subtypes),and the results of survival difference analysis done for the two subtypes showed that patients with subtype B had a good survival prognosis;the SLC31A1 gene showed a more significant high expression in subtype A in the CRGs typing;the results of survival difference analysis by GSVA,The results of ss GSEA analysis showed a significant correlation between the two subtypes in terms of pathway enrichment and associated immune cell infiltration.3.differential gene analysis was done for both subtypes,153 differential genes were analyzed by GO and found to be enriched mainly in the endoplasmic reticulum lumen and secretory granule membrane,further analyzed by KEGG pathway and found to be enriched mainly in signaling pathways such as antigen processing and presentation and mineral uptake;the differential genes were typed into 5 subgroups(K=5),and survival analysis and heat map expression showed that group E patients had the best prognosis,and among them,15 CRGs were differentially expressed among subgroups;then risk prognosis models(Train and Test groups)were constructed by LASSO-COX regression analysis and model formula,and high and low risk groups were classified according to the median risk score(HR),and the results of statistical analysis by software showed that the risk scores were different between subtypes(P < 0.05),and and among them,SLC31A1 gene and CDKN2 A gene were significantly different between the risk groups.Finally,the results of survival analysis,ROC curve analysis and risk curve analysis of the prognostic model showed poor prognosis in the high-risk group and also validated the accuracy of the predictive ability of the risk prognostic model.In this study,immune cell correlation analysis revealed that γδ-T cells were significantly associated with the prognosis of patients.TMB was found to be different between high and low risk groups and typing,and stem cells were also correlated with patient prognostic risk.Conclusion:In this study,a combined multi-database bioinformatic multi-omics analysis was performed to screen for SLC31 A and CDKN2 A,genes associated with Cuprotosis prognosis in cervical cancer,and a risk-prognosis model was constructed to analyze and validate these two genes,which are expected to be potential biological therapeutic targets for cervical cancer. |
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