Dataset Information

Construction and Validation of an Autophagy-Related Prognostic Risk Signature for Survival Predicting in Clear Cell Renal Cell Carcinoma Patients.

ABSTRACT: Background: Clear cell renal cell carcinoma (ccRCC) is a common type of malignant tumors in urinary system. Evaluating the prognostic outcome at the time of initial diagnosis is essential for patients. Autophagy is known to play a significant role in tumors. Here, we attempted to construct an autophagy-related prognostic risk signature based on the expression profile of autophagy-related genes (ARGs) for predicting the long-term outcome and effect of precise treatments for ccRCC patients. Methods: We obtained the expression profile of ccRCC from the cancer genome atlas (TCGA) database and extract the portion of ARGs. We conducted differentially expressed analysis on ARGs and then performed enrichment analyses to confirm the anomalous autophagy-related biological functions. Then, we performed univariate Cox regression to screen out overall survival (OS)-related ARGs. With these genes, we established an autophagy-related risk signature by least absolute shrinkage and selection operator (LASSO) Cox regression. We validated the reliability of the risk signature with receiver operating characteristic (ROC) analysis, survival analysis, clinic correlation analysis, and Cox regression. Then we analyzed the function of each gene in the signature by single-gene gene set enrichment analysis (GSEA). Finally, we analyzed the correlation between our risk score and expression level of several targets of immunotherapy and targeted therapy. Results: We established a seven-gene prognostic risk signature, according to which we could divide patients into high or low risk groups and predict their outcomes. ROC analysis and survival analysis validated the reliability of the signature. Clinic correlation analysis found that the risk group is significantly correlated with severity of ccRCC. Multivariate Cox regression revealed that the risk score could act as an independent predictor for the prognosis of ccRCC patients. Correlation analysis between risk score and targets of precise treatments showed that our risk signature could predict the effects of precise treatment powerfully. Conclusion: Our study provided a brand new autophagy-related seven-gene prognostic risk signature, which could perform as a prognostic indicator for ccRCC. Meanwhile, our study provides a novel sight to understand the role of autophagy and suggest therapeutic strategies in the category of precise treatment in ccRCC.

SUBMITTER: Yang H

PROVIDER: S-EPMC7214632 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Construction and Validation of an Autophagy-Related Prognostic Risk Signature for Survival Predicting in Clear Cell Renal Cell Carcinoma Patients.

Yang Huiying H Han Mengjiao M Li Hua H

Frontiers in oncology 20200505

<b>Background:</b> Clear cell renal cell carcinoma (ccRCC) is a common type of malignant tumors in urinary system. Evaluating the prognostic outcome at the time of initial diagnosis is essential for patients. Autophagy is known to play a significant role in tumors. Here, we attempted to construct an autophagy-related prognostic risk signature based on the expression profile of autophagy-related genes (ARGs) for predicting the long-term outcome and effect of precise treatments for ccRCC patients. ...[more]

PMID: 32432045

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Project description:Cumulative studies have shown that RNA binding proteins (RBPs) play an important role in numerous malignant tumors and are related to the occurrence and progression of tumors. However, the role of RBPs in kidney renal clear cell carcinoma (KIRC) is not fully understood. In this study, we first downloaded gene expression data and corresponding clinical information of KIRC from the Cancer Genome Atlas (TCGA) database, International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) database, respectively. A total of 137 differentially expressed RBPs (DERBPs) were then identified between normal and tumor tissue, including 38 downregulated and 99 upregulated RBPs. Nine RBPs (EIF4A1, RPL36A, EXOSC5, RPL28, RPL13, RPS19, RPS2, EEF1A2, and OASL) were served as prognostic genes and exploited to construct a prognostic model through survival analysis. Kaplan-Meier curves analysis showed that the low-risk group had a better survival outcome when compared with the high-risk group. The area under the curve (AUC) value of the prognostic model was 0.713 in the TCGA data set (training data set), 0.706 in the ICGC data set, and 0.687 in the GSE29609 data set, respectively, confirming a good prognostic model. The prognostic model was also identified as an independent prognostic factor for KIRC survival by performing cox regression analysis. In addition, we also built a nomogram relying on age and the prognostic model and internal validation in the TCGA data set. The clinical benefit of the prognostic model was revealed by decision curve analysis (DCA). Gene set enrichment analysis revealed several crucial pathways (ERBB signaling pathway, pathways in cancer, MTOR signaling pathway, WNT signaling pathway, and TGF BETA signaling pathway) that may explain the underlying mechanisms of KIRC. Furthermore, potential drugs for KIRC treatment were predicted by the Connectivity Map (Cmap) database based on DERBPs, including several important drugs, such as depudecin and vorinostat, that could reverse KIRC gene expression, which may provide reference for the treatment of KIRC. In summary, we developed and validated a robust nine-RBP signature for KIRC prognosis prediction. A nomogram with risk score and age can be applied to promote the individualized prediction of overall survival in patients with KIRC. Moreover, the two drugs depudecin and vorinostat may contribute to KIRC treatment.

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.

Project description:BackgroundClear cell renal cell carcinoma (ccRCC) is characterized by the accumulation of lipid-reactive oxygen species. Ferroptosis, due to the lipid peroxidation, has been reported to be strongly correlated with tumorigenesis and progression. However, the functions of the ferroptosis process in ccRCC remain unclear.MethodsAfter sample cleaning, data integration, and batch effect removal, we used the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases to screen out the expression and prognostic value of ferroptosis-related lncRNAs and then performed the molecular subtyping using the K-means method. Then, the functional pathway enrichment and immune microenvironment infiltration between the different clusters were carried out. The results showed a significant difference in immune cell infiltration between the two clusters and the associated marker responded to individualized differences in treatment. Then, least absolute shrinkage and selection operator (LASSO) Cox regression was used to establish a prognostic signature based on 5 lncRNAs. This signature could accurately predicted patient prognosis and served as an independent clinical risk factor. We then combined significant clinical parameters in multivariate Cox regression and the prognostic signature to construct a clinical predictive nomogram, which provides appropriate guidance for predicting the overall survival of ccRCC patients.ResultsThe prognostic differentially expressed ferroptosis-related LncRNAs (DEFRlncRNAs) were found, and 5 lncRNAs were finally used to establish the prognostic signature in the TCGA cohort, with subsequently validation in the internal and external cohorts. Moreover, we conducted the molecular subtyping and divided the patients in the TCGA cohort into two clusters showing differences in Hallmark pathways, immune infiltration, immune target expression, and drug therapies. Differences between clusters contributed to individualizing treatment. Furthermore, a nomogram was established to better predict the clinical outcomes of the ccRCC patients.ConclusionsOur study conducted molecular subtyping and established a novel predictive signature based on the ferroptosis-related lncRNAs, which contributed to the prognostic prediction and individualizing treatment of ccRCC patients.

Dataset Information

Construction and Validation of an Autophagy-Related Prognostic Risk Signature for Survival Predicting in Clear Cell Renal Cell Carcinoma Patients.

Publications

Construction and Validation of an Autophagy-Related Prognostic Risk Signature for Survival Predicting in Clear Cell Renal Cell Carcinoma Patients.

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