Dataset Information

Development and Validation of a Clinical Prognostic Model Based on Immune-Related Genes Expressed in Clear Cell Renal Cell Carcinoma.

ABSTRACT: Background: Clear cell renal cell carcinoma (ccRCC) is the most frequent and terminal subtype of RCC. Reliable markers associated with the immune response are not available to predict the prognosis of patients with ccRCC. We exploited the extensive number of ccRCC samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repository to perform a comprehensive analysis of immune-related genes (IRGs). Methods: Based on TCGA data, we incorporated IRGs and their expression profiles of 72 normal and 539 ccRCC samples. Univariate Cox analysis was used to evaluate the relationship between overall survival (OS) and IRGs expression. The Lasso Cox regression model identified prognostic genes used to establish a clinical immune prognostic model. The TF-IRG network was used to study the potential molecular mechanisms of action and properties of ccRCC-specific IRGs. Multivariate Cox analysis established a clinical prognostic model of IRGs. Results: We found a significant correlation among 15 differentially expressed IRGs with the OS of patients with ccRCC. Gene function enrichment analysis showed that these IRGs are significantly associated with response to receptor ligand activity. Lasso Cox regression analysis identified 10 genes with the greatest prognostic value. A clinical prognostic model based on six IRGs, which performed well for predicting prognosis, revealed significant associations of patients' survival with age, sex, stage, tumor, node, and metastasis. Moreover, these findings reflect the infiltration of tumors by various immune cells. Conclusion: We identified six clinically significant IRGs and incorporated them into a clinical prognostic model with great significance for monitoring and predicting prognosis of ccRCC.

SUBMITTER: Ren S

PROVIDER: S-EPMC7485294 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Development and Validation of a Clinical Prognostic Model Based on Immune-Related Genes Expressed in Clear Cell Renal Cell Carcinoma.

Ren Shiqi S Wang Wei W Shen Hanyu H Zhang Chenlin C Hao Haiyan H Sun Mengjing M Wang Yingjing Y Zhang Xiaojing X Lu Bing B Chen Chen C Wang Ziheng Z

Frontiers in oncology 20200828

<b>Background:</b> Clear cell renal cell carcinoma (ccRCC) is the most frequent and terminal subtype of RCC. Reliable markers associated with the immune response are not available to predict the prognosis of patients with ccRCC. We exploited the extensive number of ccRCC samples from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repository to perform a comprehensive analysis of immune-related genes (IRGs). <b>Methods:</b> Based on TCGA data, we incorporated IRGs and their expr ...[more]

PMID: 32983989

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Project description:Objective: To construct a prognostic evaluation model for clear cell renal cell carcinoma (ccRCC) patients using bioinformatics method and to screen potential drugs for ccRCC. Methods: ccRCC RNA sequencing data, clinical data, and protein expression data were downloaded from the TCGA database. Univariate Cox and Lasso regression analyses were performed on the combined data to screen out the proteins related to the prognosis, and they were included in a multivariate Cox proportional hazard model. The patients were divided into high and low-risk groups for a survival difference analysis. The predictive power of the model was evaluated on the basis of overall survival, progression-free survival, independent prognostic, clinically relevant receiver operating characteristic (ROC) curve, C-index, principal component, and clinical data statistics analyses. GSEA enrichment and immune function correlation analyses were performed. The samples were divided into different subtypes based on the expression of the risk proteins, and survival analysis of the subtypes was performed. The risk-related protein and RNA sequencing data were analyzed to screen out sensitive drugs with significant differences between the high and low-risk groups. Results: A total of 469 ccRCC-related proteins were screened, of which 13 proteins with independent prognostic significance were screened by univariate Cox, Lasso, and multivariate Cox regression analyses to construct the prognostic model. The sensitivity and accuracy of the model in predicting the survival of patients with ccRCC were high (1 year: 0.811, 3 years: 0.783, 5 years: 0.777). The 13 proteins were closely related to immunity, and the model proteins were different between kidney and tumor tissues according to the HPA database. The samples were divided into three subtypes, and there were obvious clinical characteristics of the three subtypes in the grade and T, N and M stages. According to the IC50 values, CGP-60474, vinorelbine, doxorubicin, etoposide, FTI-277, JQ12, OSU-03012, pyrimethamine, and other drugs were more sensitive in the high-risk group. Conclusions: A prognostic model of protein expression in ccRCC was successfully constructed, which had good predictive ability for the prognosis of ccRCC patients. The ccRCC-related proteins in the model can be used as targets for studying the pathogenesis and targeted therapy.

Dataset Information

Development and Validation of a Clinical Prognostic Model Based on Immune-Related Genes Expressed in Clear Cell Renal Cell Carcinoma.

Publications

Development and Validation of a Clinical Prognostic Model Based on Immune-Related Genes Expressed in Clear Cell Renal Cell Carcinoma.

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