Dataset Information

Prognostic Inflammasome-Related Signature Construction in Kidney Renal Clear Cell Carcinoma Based on a Pan-Cancer Landscape.

ABSTRACT: Objective:To investigate the expression patterns and prognostic characteristics of inflammasome-related genes (IRGs) across cancer types and develop a robust biomarker for the prognosis of KIRC. Methods:The differentially expressed IRGs and prognostic genes among 10 cancers were analyzed based on The Cancer Genome Atlas (TCGA) dataset. Subsequently, an IRGs risk signature was developed in KIRC. Its prognostic accuracy was evaluated by receiver operating characteristic (ROC) analysis. The independent predictive capacity was identified by stratification survival and multivariate Cox analyses. The gene ontology (GO) analysis and principal component analysis (PCA) were performed to explore biological functions of the IRGs signature in KIRC. Results:The expression patterns and prognostic association of IRGs varied from different cancers, while KIRC showed the most abundant survival-related dysregulated IRGs. The IRG signature for KIRC was able to independently predict survival, and the signature genes were mainly involved inimmune-related processes. Conclusions:The pan-cancer analysis provided a comprehensive landscape of IRGs across cancer types and identified a strong association between IRGs and the prognosis of KIRC. Further IRGs signature represented a reliable prognostic predictor for KIRC and verified the prognostic value of inflammasomes in KIRC, contributing to our understanding of therapies targeting inflammasomes for human cancers.

SUBMITTER: Zheng T

PROVIDER: S-EPMC7157792 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Prognostic Inflammasome-Related Signature Construction in Kidney Renal Clear Cell Carcinoma Based on a Pan-Cancer Landscape.

Zheng Tianyu T Wang Xindong X Yue Peipei P Han Tongtong T Hu Yue Y Wang Biyao B Zhao Baohong B Zhang Xinwen X Yan Xu X

Evidence-based complementary and alternative medicine : eCAM 20200403

<h4>Objective</h4>To investigate the expression patterns and prognostic characteristics of inflammasome-related genes (IRGs) across cancer types and develop a robust biomarker for the prognosis of KIRC.<h4>Methods</h4>The differentially expressed IRGs and prognostic genes among 10 cancers were analyzed based on The Cancer Genome Atlas (TCGA) dataset. Subsequently, an IRGs risk signature was developed in KIRC. Its prognostic accuracy was evaluated by receiver operating characteristic (ROC) analys ...[more]

PMID: 32328125

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Project description:BackgroundMETTL14, as one of N6-methyladenosine (m6A) related genes, has been found to be associated with promoting tumorigenesis in different types of cancers. This study was aimed to investigate the prognostic value of METTL14 in clear cell renal cell carcinoma (ccRCC).MethodsWe collected ccRCC patients' clinicopathological parameters information and 13 m6A related genes expression from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses were conducted to investigate whether METTL14 could serve as an independent factor correlated with overall survival (OS). Gene Set Enrichment Analysis (GSEA) was carried out to identify METTL14-related signaling pathways. Moreover, a risk score (RS) was calculated to predict the prognosis of ccRCC. Quantitative real-time PCR (qRT-PCR) was also utilized to verify the expression of METTL14 in clinical specimens.ResultsDifferently expressed m6A related genes were identified between ccRCC tissues and normal tissues. Therein, METTL14 was lowly expressed in ccRCC tissues and verified by qRT-PCR (all p?<?0.01). Survival analysis indicated that high expression of METTL14 was associated with better OS (p?=?1e-05). GSEA results revealed that high METTL14 expression was enriched in ERBB pathway, MAPK pathway, mTOR pathway, TGF-? pathway and Wnt pathway. Moreover, METTL14 was proved to be an independent prognostic factor by means of univariate and multivariate Cox regression analyses. Nomogram integrating both the METTL14 expression and clinicopathologic variables was also established to provide clinicians with a quantitative approach for predicting survival probabilities of ccRCC. Furthermore, a METTL14-based riskscore (RS) was developed with significant OS (p?=?6.661e-16) and increased AUC of 0.856. Besides, significant correlated genes with METTL14 were also provided.ConclusionsOur results indicated that METTL14 could serve as a favorable prognostic factor for ccRCC. Moreover, this study also provided a prognostic signature to predict prognosis of ccRCC and identified METTL14-related signaling pathways.

Project description: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.

Project description:Abnormal autophagic levels have been implicated in the pathogenesis of multiple cancers, however, its role in tumors is complex and has not yet been explored clearly. Hence, we aimed to explore the prognostic values of autophagy-related genes (ARGs) for kidney renal clear cell carcinoma (KIRC). Differentially expressed ARGs and transcription factors (TFs) were identified in KIRC patients obtaining from the The Cancer Genome Atlas (TCGA) database. Then, networks between TFs and ARGs, gene ontology functional annotations and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were conducted. Next, we performed consensus clustering, COX regression analysis and Lasso regression analysis to identify the prognostic ARGs. Finally, an individual prognostic index (PI, riskScore) was established. Based on TCGA cohort and ArrayExpress cohort, Survival analysis, ROC curve, independent prognostic analysis, and clinical correlation analysis were also performed to evaluate this PI. Based on differentially expressed ARGs, KIRC patients were successfully divided into two clusters (P = 5.916e-04). AS for PI, it was constructed based on 11 ARGs and significantly classified KIRC patients into high-risk group and low-risk group in terms of OS (P = 4.885e-15 for TCGA cohort, P = 6.366e-03 for ArrayExpress cohort). AUC of its ROC curve reached 0.747 for TCGA cohort and 0.779 for ArrayExpress cohort. What's more, this PI was proven to be a valuable independent prognostic factor in both univariate and multivariate COX regression analysis (P < .001). Prognostic nomograms were also performed to visualize the relationship between individual predictors and survival rates in patients with KIRC. By means of connectivity map database, emetine, cephaeline and co-dergocrine mesilate related to ARGs were found to be negatively correlated with KIRC. This study provided an effective PI for KIRC and also displayed networks between TFs and ARGs. KIRC patients were successfully divided into two clusters based on differentially expressed ARGs. Besides, small molecule drugs related to ARGs were also identified for KIRC.

Dataset Information

Prognostic Inflammasome-Related Signature Construction in Kidney Renal Clear Cell Carcinoma Based on a Pan-Cancer Landscape.

Publications

Prognostic Inflammasome-Related Signature Construction in Kidney Renal Clear Cell Carcinoma Based on a Pan-Cancer Landscape.

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