A Gene Signature of Survival Prediction for Kidney Renal Cell Carcinoma by Multi-Omic Data Analysis

Hu, Fuyan; Zeng, Wenying; Li, Xiaoping

doi:10.3390/ijms20225720

Cited by 53 publications

(51 citation statements)

References 75 publications

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“…After testing for collinearity, stepwise multivariate Cox regression analysis was performed to establish the IRG-derived risk signature in primary LGG. The following formula based on a combination of Cox coefficient and gene expression was used to calculate the risk score (Lossos et al, 2004;Chen et al, 2007;Hu et al, 2019):…”

Section: Establishment Of the Immune-related Risk Signaturementioning

confidence: 99%

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma

et al. 2020

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Objective: Despite several clinicopathological factors being integrated as prognostic biomarkers, the individual variants and risk stratification have not been fully elucidated in lower grade glioma (LGG). With the prevalence of gene expression profiling in LGG, and based on the critical role of the immune microenvironment, the aim of our study was to develop an immune-related signature for risk stratification and prognosis prediction in LGG. Methods: RNA-sequencing data from The Cancer Genome Atlas (TCGA), Genome Tissue Expression (GTEx), and Chinese Glioma Genome Atlas (CGGA) were used. Immune-related genes were obtained from the Immunology Database and Analysis Portal (ImmPort). Univariate, multivariate cox regression, and Lasso regression were employed to identify differentially expressed immune-related genes (DEGs) and establish the signature. A nomogram was constructed, and its performance was evaluated by Harrell's concordance index (C-index), receiver operating characteristic (ROC), and calibration curves. Relationships between the risk score and tumor-infiltrating immune cell abundances were evaluated using CIBERSORTx and TIMER. Results: Noted, 277 immune-related DEGs were identified. Consecutively, 6 immune genes (CANX, HSPA1B, KLRC2, PSMC6, RFXAP, and TAP1) were identified as risk signature and Kaplan-Meier curve, ROC curve, and risk plot verified its performance in TCGA and CGGA datasets. Univariate and multivariate Cox regression indicated that the risk group was an independent predictor in primary LGG. The prognostic signature showed fair accuracy for 3-and 5-year overall survival in both internal (TCGA) and external (CGGA) validation cohorts. However, predictive performance was poor in the recurrent LGG cohort. The CIBERSORTx algorithm revealed that naïve CD4 + T cells were significant higher in low-risk group. Conversely, the infiltration levels of

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Section: Establishment Of the Immune-related Risk Signaturementioning

confidence: 99%

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma

et al. 2020

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“…BID promotes the occurrence and metastasis of ccRCC by altering TNF signals [ 20 ]. Other studies have shown that BID is closely related to the survival prognosis of ccRCC [ 21 – 23 ]. SPHK1 can be used as a target gene of HIF-1 to influence the prognosis of ccRCC [ 24 ].…”

Section: Discussionmentioning

confidence: 99%

Establishment and Validation of a Prognostic Risk Model for Autophagy-Related Genes in Clear Cell Renal Cell Carcinoma

Han

Che

et al. 2020

Disease Markers

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Background. Autophagy plays an essential role in tumorigenesis. At present, due to the unclear role of autophagy in renal clear cell carcinoma, we studied the potential value of autophagy-related genes (ARGs) in renal clear cell carcinoma (ccRCC). Methods. We obtained all ccRCC data from The Cancer Genome Atlas (TCGA). We extracted the expression data of ARGs for difference analysis and carried out biological function analysis on the different results. The autophagy risk model was constructed. The 5-year survival rate was assessed using the model, and the predictive power of the model was evaluated from multiple perspectives. Cox regression analysis was use to assess whether the model could be an independent prognostic factor. Finally, the correlation between the model and clinical indicators is analyzed. Results. The patients were divided into the high-risk group and low-risk group according to the median of autophagy risk score, and the results showed that the prognosis of the low-risk group was better than that of a high-risk group. The validation results of external data sets show that our model has good predictive value for ccRCC patients. The model can be an independent prognostic factor. Finally, the results show that our model has a stable predictive ability. Conclusion. The autophagy gene model we constructed can be used as an excellent prognostic indicator for ccRCC. Our study provides the possibility of individualized and precise treatment for ccRCC patients.

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“…In addition, silencing of TRIP13 can suppress cell growth and metastasis of hepatocellular carcinoma by activating TGF-β1/SMAd3 signaling (47). TRIP13 is 1 of the 7 hypomethylated genes in kidney renal cell carcinoma that have been suggested as a prognostic factor (48). downregulation of ZNF878 has been suggested to be correlated with a poor prognosis of patients with LUAd (49).…”

Section: Multivariate -----------------------------------------------mentioning

confidence: 99%

A risk score system based on DNA methylation levels and a nomogram survival model for lung squamous cell carcinoma

Zhang

Sun

et al. 2020

Int J Mol Med

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Lung squamous cell carcinoma (LScc) is one of the primary types of non-small cell lung carcinoma, and patients with recurrent LScc usually have a poor prognosis. The present study was conducted to build a risk score (RS) system for LScc. Methylation data on LScc (training set) and on head and neck squamous cell carcinoma (validation set 2) were obtained from The cancer Genome Atlas database, and GSE39279 (validation set 1) was retrieved from the Gene Expression Omnibus database. differentially methylated protein-coding genes (dMGs)/long non-coding RNAs (dM-lncRNAs) between recurrence-associated samples and nonrecurrence samples were screened out using the limma package, and their correlation analysis was conducted using the cor.test() function. Following identification of the optimal combinations of dMGs or dM-lncRNAs using the penalized package in R, RS systems were built, and the system with optimal performance was selected. Using the rms package, a nomogram survival model was then constructed. For the differentially expressed genes (dEGs) between the high-and low-risk groups, pathway enrichment analysis was performed by Gene Set Enrichment Analysis. There were 335 dMGs and dM-lncRNAs in total. Following screening out of the top 10 genes (aldehyde dehydrogenase 7 family member A1, chromosome 8 open reading frame 48, cytokine-like 1, heat shock protein 90 alpha family class A member 1, isovaleryl-coA dehydrogenase, phosphodiesterase 3A, PNMA family member 2, SAM domain, SH3 domain and nuclear localization signals 1, thyroid hormone receptor interactor 13 and zinc finger protein 878) and 6 top lncRNAs, RS systems were constructed. According to Kaplan-Meier analysis, the dNA methylation level-based RS system exhibited the best performance. In combination with independent clinical prognostic factors, a nomogram survival model was built and successfully predicted patient survival. Furthermore, 820 dEGs between the high-and low-risk groups were identified, and 3 pathways were identified to be enriched in this gene set. The 10-DMG methylation level-based RS system and the nomogram survival model may be applied for predicting the outcomes of patients with LScc.

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A Gene Signature of Survival Prediction for Kidney Renal Cell Carcinoma by Multi-Omic Data Analysis

Cited by 53 publications

References 75 publications

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in Lower-Grade Glioma

Establishment and Validation of a Prognostic Risk Model for Autophagy-Related Genes in Clear Cell Renal Cell Carcinoma

A risk score system based on DNA methylation levels and a nomogram survival model for lung squamous cell carcinoma

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