Cuproptosis-Related 4-Gene Risk Model for Predicting Immunotherapy Drug Response and Prognosis of Kidney Renal Clear Cell Carcinoma / 中国医学科学杂志(英文版)

Background Kidney renal clear cell carcinoma (KIRC) is one of the most common renal malignancies with a high mortality rate. Cuproptosis, a novel form of cell death, is strongly linked to mitochondrial metabolism and is mediated by protein lipoylation, leading to a proteotoxic stress response and cell death. To date, few studies have ellucidated the holistic role of cuproptosis-related genes (CRGs) in the pathogenesis of KIRC.Methods We comprehensively and completely analyzed the RNA sequencing data and corresponding clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We screened for differentially expressed CRGs and constructed a prognostic risk model using univariate and multivariate Cox proportional regression analyses. Kaplan-Meier analysis was performed and receiver operating characteristic (ROC) curves were plotted to predict the prognosis of KIRC patients. Functional enrichment analysis was utilized to explore the internal mechanisms. Immune-related functions were analyzed using single-sample gene set enrichment analysis (ssGSEA), tumour immune dysfunction and exclusion (TIDE) scores, and drug sensitivity analysis.Results We established a concise prognostic risk model consisting of four CRGs (DBT, DLAT, LIAS and PDHB) to predict the overall survival (OS) in KIRC patients. The results of the survival analysis indicated a significantly lower OS in the high-risk group as compared to the patients in the low-risk group. The area under the time-dependent ROC curve (AUC) at 1, 3, and 5 year was 0.691, 0.618, and 0.614 in KIRC. Functional enrichment analysis demonstrated that CRGs were significantly enriched in tricarboxylic acid (TCA) cycle-related processes and metabolism-related pathways. Sorafenib, doxorubicin, embelin, and vinorelbine were more sensitive in the high-risk group.Conclusions We constructed a concise CRGs risk model to evaluate the prognosis of KIRC patients and this may be a new direction for the diagnosis and treatment of KIRC.

A novel defined pyroptosis-related genes prognostic risk model for predicting the prognosis of kidney renal clear cell carcinoma / 解剖学报

Objective To establish a novel defined pyroptosis-related genes risk model of kidney renal clear cell carcinoma. Methods Data of 522 patients with KIRC and 72 normal tissue samples were respectively downloaded from the Cancer Genome Atlas ( TCGA) database and Genotype-Tissue Expression ( GTEx) database. Differential analysis was performed between data of TCGA and GTEx. Univariate Cox regression analysis, multivariate Cox regression analyses and LASSO Cox regression analysis were used to establish a prognostic risk model. Data from the International Cancer Genome Consortium (ICGC) database was used as an external validation cohort. Gene ontology ( GO) enrichment analysis and Kyoto Encylopedia of Genes and Genomes (KEGG) pathway analysis were used to explore the differences of gene functions and pathways between high-risk and low-risk groups. The CIBERSORT database was used to explore the immune infiltration of high-risk and low-risk groups. Results Through differential analysis, we obtained 13 differentially expressed pyroptosis-related genes. Univariate Cox regression analysis, multivariable Cox regression analyses and LASSO Cox regression analysis were used to establish a 6-gene risk model. Kaplan-Meier analysis indicated that survival time in high-risk group was shorter than low-risk group in both cohorts. The area under the curve ( AUC) was 0. 710 for 1-year, 0. 683 for 2-year, and 0. 727 for 3-year survival in the TCGA_KIRC cohort. The AUC was 0. 592 for 1-year, 0. 531 for 2-year, and 0. 545 for 3-year survival in the ICGC_RECA cohort. Independent prognostic analysis indicated that risk score was an independent prognostic factor. GO enrichment analysis and KEGG pathway analysis showed that it was mainly associated with immune and inflammatory responses. The result of tumor immune infiltration showed that the high-risk group had low infiltration levels of regulatory T cells , natural killer cells, monocytes, M2 macrophages and eosinophils and high infiltration level of B cells, CD8+T cells and follicular helper T cells. Conclusion Pyrolysis-related genes may play an important role in KIRC tumor immunity, and the 6-gene risk model can provide a forecast basis for personalized treatment of patients with KIRC.