scholarly journals Identification of a glycolysis-related lncRNA prognostic signature for clear cell renal cell carcinoma

2021 ◽  
Vol 41 (8) ◽  
Author(s):  
Wei Ma ◽  
Manli Zhong ◽  
Xiaowu Liu
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Roc Curve ◽  
Renal Cell ◽  
Prognostic Value ◽  
Risk Model ◽  
Cox Regression ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Kaplan Meier

Abstract Background: The present study investigated the independent prognostic value of glycolysis-related long noncoding (lnc)RNAs in clear cell renal cell carcinoma (ccRCC). Methods: A coexpression analysis of glycolysis-related mRNAs–long noncoding RNAs (lncRNAs) in ccRCC from The Cancer Genome Atlas (TCGA) was carried out. Clinical samples were randomly divided into training and validation sets. Univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to establish a glycolysis risk model with prognostic value for ccRCC, which was validated in the training and validation sets and in the whole cohort by Kaplan–Meier, univariate and multivariate Cox regression, and receiver operating characteristic (ROC) curve analyses. Principal component analysis (PCA) and functional annotation by gene set enrichment analysis (GSEA) were performed to evaluate the risk model. Results: We identified 297 glycolysis-associated lncRNAs in ccRCC; of these, 7 were found to have prognostic value in ccRCC patients by Kaplan–Meier, univariate and multivariate Cox regression, and ROC curve analyses. The results of the GSEA suggested a close association between the 7-lncRNA signature and glycolysis-related biological processes and pathways. Conclusion: The seven identified glycolysis-related lncRNAs constitute an lncRNA signature with prognostic value for ccRCC and provide potential therapeutic targets for the treatment of ccRCC patients.

scholarly journals Up Regulation of GSDMB Is Correlated With Poor Prognosis and Immune Infiltrates in Clear Cell Renal Cell Carcinoma

2021 ◽  
Author(s):  
Yuanshan Cui ◽  
Zhongbao Zhou ◽  
Yumeng Chai ◽  
Yong Zhang
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Mrna Expression ◽  
Roc Curve ◽  
Renal Cell ◽  
Poor Prognosis ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Normal Tissues ◽  
Kaplan Meier

Abstract Background: Gasdermin B (GSDMB) is part of the gasdermin (GSDM) family and they use varying means of domain interactions in molecules to adjust their pore-forming and lipid-binding actions. The GSDM family has roles in the regulation of cell differentiation and proliferation, particularly in the process of pyroptosis. Nonetheless, the correlation of GSDMB with immune infiltrates and its prognostic values in Clear Cell Renal Cell Carcinoma (ccRCC) are still undefined. Therefore, we assessed the correlation of GSDMB with immune infiltrates and its prognostic role in ccRCC. Methods: The transcriptional expression profiles of GSDMB in ccRCC tissues in addition to normal tissues were retrieved from the Cancer Genome Atlas (TCGA), and additionally verified in a different independent cohort, which was obtained from the gene expression omnibus (GEO) database. The Human Protein Atlas and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) were accessed to assess the protein expression of GSDMB. To differentiate between ccRCC and surrounding normal tissues, the receiver operating characteristic (ROC) curve was applied. Relationships between GSDMB expression, clinicopathologicical variables, and overall survival (OS) were evaluated with multivariate methods as well as Kaplan-Meier survival curves. Protein-protein interaction (PPI) networks were created with String. Functional enrichment analyses were conducted by utilizing the “ClusterProfiler” package. The tumor immune estimation resource (TIMER) and tumor-immune system interaction database (TISIDB) were utilized to determine the association between the mRNA expression of GSDMB and immune infiltrates. Results: GSDMB expression was significantly more up regulated in ccRCC tissues compared to surrounding normal tissues. An increase in the mRNA expression of GSDMB was related to high pathologic stage and advanced TNM stage. The analysis of the ROC curve indicated that GSDMB had an AUC value of 0.820 to distinguish between ccRCC tissues and adjacent normal controls. Kaplan-Meier survival analysis indicated that ccRCC patients with high-GSDMB had a poorer prognosis compared to those with low-GSDMB (P < 0.001). Correlation analysis showed that the mRNA expression of GSDMB was associated with immune infiltrates and the purity of the tumor. Upregulation of GSDMB is significantly related to immune infiltrates and poor survival in ccRCC. Conclusions: The results of this study indicates that GSDMB could be regarded as a biomarker for the detection of poor prognosis and potential target of immune treatment in ccRCC.

scholarly journals A Risk Signature with Autophagy-Related Long Noncoding RNAs for Predicting the Prognosis of Clear Cell Renal Cell Carcinoma: Based on the TCGA Database and Bioinformatics

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Yundong Xuan ◽  
Weihao Chen ◽  
Kan Liu ◽  
Yu Gao ◽  
Shidong Zuo ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Noncoding Rna ◽  
Prognostic Value ◽  
Cox Regression ◽  
Clear Cell ◽  
Gene List ◽  
Tumor Biology ◽  
Cell Renal Cell Carcinoma

Background. Disorders of autophagic processes have been reported to affect the survival outcome of clear cell renal cell carcinoma (ccRCC) patients. The purpose of our study was to identify and validate the candidate prognostic long noncoding RNA signature of autophagy. Methods. Transcriptome profiles were obtained from The Cancer Genome Atlas. The autophagy gene list was obtained from the Human Autophagy Database. Based on coexpression analysis, we obtained a list of autophagy-related lncRNAs (ARlncRNAs). GO enrichment analysis and KEGG pathway analysis were conducted to explore the functional annotation of these ARlncRNAs. Univariate and multivariate Cox regression analyses were conducted to elucidate the correlation between overall survival and the expression level of each ARlncRNAs. We then established a prognostic signature that was a linear combination of the regression coefficients from the multivariate Cox regression model ( β ) multiplied by the expression levels of the respective ARlncRNAs in the training cohort. The predictive performance was tested in the validation cohort. Additionally, the independence of the risk signature was assessed, and the relationship between the risk signature and conventional clinicopathological features was explored. Results. Seven autophagy-related lncRNAs with prognostic value (SNHG3, SNHG17, MELTF-AS1, HOTAIRM1, EPB41L4A-DT, AP003352.1, and AC145423.2) were identified and integrated into a risk signature, dividing patients into low-risk and high-risk groups. The risk signature was independent of conventional clinical characteristics as a prognostic indicator of ccRCC (HR, 1.074, 95% confidence interval: 1.036-1.113, p < 0.001 ) and was valuable in the prediction of ccRCC progression. Conclusion. Our risk signature has potential prognostic value in ccRCC, and these ARlncRNAs may play a significant role in ccRCC tumor biology.

scholarly journals Upregulated GSDMB in Clear Cell Renal Cell Carcinoma Is Associated with Immune Infiltrates and Poor Prognosis

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Yuanshan Cui ◽  
Zhongbao Zhou ◽  
Yumeng Chai ◽  
Yong Zhang
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Mrna Expression ◽  
Roc Curve ◽  
Renal Cell ◽  
Poor Prognosis ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Normal Tissues ◽  
Kaplan Meier

Gasdermin B (GSDMB) is part of the gasdermin (GSDM) family, and they use varying means of domain interactions in molecules to adjust their pore-forming and lipid-binding actions. The GSDM family has roles in the regulation of cell differentiation and proliferation, particularly in the process of pyroptosis. Nonetheless, the correlation of GSDMB with immune infiltrates and its prognostic values in clear cell renal cell carcinoma (ccRCC) are still undefined. Therefore, we assessed the correlation of GSDMB with immune infiltrates and its prognostic role in ccRCC. The transcriptional expression profiles of GSDMB in ccRCC tissues in addition to normal tissues were retrieved from The Cancer Genome Atlas (TCGA) and additionally verified in a different independent cohort, which was obtained from the Gene Expression Omnibus (GEO) database. The Human Protein Atlas and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) were used to assess the protein expression of GSDMB. To assess the effectiveness of GSDMB in distinguishing ccRCC from normal samples, the receiver operating characteristic (ROC) curve analysis was performed. Relationships between GSDMB expression, clinicopathological variables, and overall survival (OS) were evaluated with multivariate methods as well as Kaplan-Meier survival curves. Protein-protein interaction (PPI) networks were created with STRING. Functional enrichment analyses were conducted by utilizing the “ClusterProfiler” package. The Tumor Immune Estimation Resource (TIMER) and tumor-immune system interaction database (TISIDB) were utilized to determine the association between the mRNA expression of GSDMB and immune infiltrates. GSDMB expression was significantly more upregulated in ccRCC tissues compared to surrounding normal tissues. An increase in the mRNA expression of GSDMB was related to the high pathologic stage and advanced TNM stage. The analysis of the ROC curve indicated that GSDMB had an AUC value of 0.820 to distinguish between ccRCC tissues and adjacent normal controls. Kaplan-Meier survival analysis indicated that ccRCC patients with high GSDMB had a poorer prognosis compared to those with low GSDMB ( P < 0.001 ). Correlation analysis showed that the mRNA expression of GSDMB was associated with immune infiltrates and the purity of the tumor. Upregulation of GSDMB is significantly related to immune infiltrates and poor survival in ccRCC. The results of this study indicate that GSDMB could be regarded as a biomarker for the detection of poor prognosis and potential target of immune treatment in ccRCC.

scholarly journals Integrated Analysis of the Roles of RNA Binding Proteins and Their Prognostic Value in Clear Cell Renal Cell Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Bowen Wang ◽  
Haoran Zhao ◽  
Shaobin Ni ◽  
Beichen Ding
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Roc Curve ◽  
Renal Cell ◽  
Prognostic Value ◽  
Prognostic Model ◽  
Rna Binding ◽  
Clear Cell ◽  
Rna Binding Proteins ◽  
Cell Renal Cell Carcinoma

Background and Purpose. The renal cell carcinoma is one of the main malignant tumors in the genitourinary system, which seriously affects human health. Unregulated expression of RNA binding proteins (RBPs) is thought to be involved in the progression of many cancers. However, the role of RBPs in the clear cell renal cell carcinoma (ccRCC) is not yet clear. Methods. We downloaded the RNA sequencing data of ccRCC from the Cancer Genome Atlas (TCGA) database and identified differently expressed RBPs in different tissues. In this study, we used bioinformatics to analyze the expression and prognostic value of RBPs; then, we performed functional analysis and constructed a protein interaction network for them. We also screened out some RBPs related to the prognosis of ccRCC. Finally, based on the identified RBPs, we constructed a prognostic model that can predict patients’ risk of illness and survival time. Also, the data in the HPA database were used for verification. Results. In our experiment, we obtained 539 ccRCC samples and 72 normal controls. In the subsequent analysis, 87 upregulated RBPs and 38 downregulated RBPs were obtained. In addition, 9 genes related to the prognosis of patients were selected, namely, RPL36A, THOC6, RNASE2, NOVA2, TLR3, PPARGC1A, DARS, LARS2, and U2AF1L4. We further constructed a prognostic model based on these genes and plotted the ROC curve. This ROC curve performed well in judgement and evaluation. A nomogram that can judge the patient’s life span is also made. Conclusion. In conclusion, we have identified differentially expressed RBPs in ccRCC and carried out a series of in-depth research studies, the results of which may provide ideas for the diagnosis of ccRCC and the research of new targeted drugs.

scholarly journals A novel ferroptosis-related 12-gene signature predicts clinical prognosis and reveals immune relevancy in clear cell renal cell carcinoma

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yingkai Hong ◽  
Mingen Lin ◽  
Dehua Ou ◽  
Zhuangkai Huang ◽  
Peilin Shen
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Risk Score ◽  
Renal Cell ◽  
Prognostic Value ◽  
Cox Regression ◽  
Clear Cell ◽  
Gene Signature ◽  
Cell Renal Cell Carcinoma ◽  
Prognostic Predictor

Abstract Background Clear cell renal cell carcinoma (ccRCC) is still highly aggressive and lethal even with various therapeutic approaches. As the kidney is an iron metabolism-related organ, exploring and assessing the clinical value of ferroptosis, an iron-dependent regulated cell death, is practical and important. Methods Prognostic ferroptosis-related differentially expressed genes (DEGs) were identified from the KIRC cohort in the cancer genome atlas (TCGA) database, from which a prognostic signature was established using Lasso-penalized Cox regression analysis. Each patient in the KIRC cohort and the E-MTAB-1980 cohort (from the ArrayExpress database) was assigned a calculated signature-correlated risk score and categorized to be either in the high- or low-risk group divided by the median risk score in the KIRC cohort. Then, the independent prognostic value of the signature was further assessed by Kaplan-Meier (K-M) survival, time-dependent receiver operating characteristic (ROC) and Cox regression analyses based on overall survival (OS) in both cohorts. Finally, risk-related DEGs were identified in both cohorts and subjected to enrichment analyses for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and immune infiltration. Results Among 60 ferroptosis-related genes, 32 prognostic DEGs were identified, from which we constructed a prognostic 12-gene signature with CARS1, HMGCR, CHAC1, GOT1, CD44, STEAP3, AKR1C1, CBS, DPP4, FANCD2, SLC1A5 and NCOA4. Patients in both cohorts were divided into high- and low-risk groups, which were visually distributed in two sets and had positive-risk-related mortality. The K-M survival and the ROC curves validated that the signature has prognostic value with P < 0.05 and area under the curve > 0.7 in both cohorts, respectively. Multivariate Cox regression further confirmed the risk score as an independent prognostic predictor for OS. Commonly enriched terms in GO and KEGG not only showed a high iron correlation but also, interestingly, immune relevance of 3 immune cells (macrophages, mast cells and regulatory T cells) and 1 immune-related function (antigen processing cell co-stimulation). Conclusion We established a novel 12 ferroptosis-related-gene signature that was proven to be an independent prognostic predictor for OS and inferred to be related to tumour immunity in ccRCC; however, the underlying mechanism is still poorly characterized and needs further exploration.

scholarly journals The Prognostic Value of Radiomics Features Extracted From Computed Tomography in Patients With Localized Clear Cell Renal Cell Carcinoma After Nephrectomy

2021 ◽  
Vol 11 ◽  
Author(s):  
Xin Tang ◽  
Tong Pang ◽  
Wei-feng Yan ◽  
Wen-lei Qian ◽  
You-ling Gong ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Hierarchical Clustering ◽  
Renal Cell ◽  
Prognostic Value ◽  
Cox Regression ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Calibration Curves

Background and purposeRadiomics is an emerging field of quantitative imaging. The prognostic value of radiomics analysis in patients with localized clear cell renal cell carcinoma (ccRCC) after nephrectomy remains unknown.MethodsComputed tomography images of 167 eligible cases were obtained from the Cancer Imaging Archive database. Radiomics features were extracted from the region of interest contoured manually for each patient. Hierarchical clustering was performed to divide patients into distinct groups. Prognostic assessments were performed by Kaplan–Meier curves, COX regression, and least absolute shrinkage and selection operator COX regression. Besides, transcriptome mRNA data were also included in the prognostic analyses. Endpoints were overall survival (OS) and disease-free survival (DFS). Concordance index (C-index), decision curve analysis and calibration curves with 1,000 bootstrapping replications were used for model’s validation.ResultsHierarchical clustering groups from nephrographic features and mRNA can divide patients into different prognostic groups while clustering groups from corticomedullary or unenhanced phase couldn’t distinguish patients’ prognosis. In multivariate analyses, 11 OS-predicting and eight DFS-predicting features were identified in nephrographic phase. Similarly, seven OS-predictors and seven DFS-predictors were confirmed in mRNA data. In contrast, limited prognostic features were found in corticomedullary (two OS-predictor and two DFS-predictors) and unenhanced phase (one OS-predictors and two DFS-predictors). Prognostic models combining both nephrographic features and mRNA showed improved C-index than any model alone (C-index: 0.927 and 0.879 for OS- and DFS-predicting, respectively). In addition, decision curves and calibration curves also revealed the great performance of the novel models.ConclusionWe firstly investigated the prognostic significance of preoperative radiomics signatures in ccRCC patients. Radiomics features obtained from nephrographic phase had stronger predictive ability than features from corticomedullary or unenhanced phase. Multi-omics models combining radiomics and transcriptome data could further increase the predictive accuracy.

scholarly journals Prognostic value of immune-related genes in clear cell renal cell carcinoma

Aging ◽  
2019 ◽  
Vol 11 (23) ◽  
pp. 11474-11489 ◽  
Author(s):  
Bangbei Wan ◽  
Bo Liu ◽  
Yuan Huang ◽  
Gang Yu ◽  
Cai Lv
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Prognostic Value ◽  
Clear Cell ◽  
Cell Renal Cell Carcinoma ◽  
Immune Related Genes

scholarly journals NOVEL PLASMA GLYCOPROTEIN BIOMARKERS PREDICT PROGRESSION-FREE SURVIVAL IN SURGICALLY RESECTED CLEAR CELL RENAL CELL CARCINOMA

2022 ◽  
Author(s):  
Daniel Serie ◽  
Amanda A Myers ◽  
Daniela A Haehn ◽  
Alexander Parker ◽  
Essa Bajalia ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Differential Expression ◽  
Renal Cell ◽  
Clear Cell ◽  
Progression Free Survival ◽  
P Value ◽  
Cell Renal Cell Carcinoma ◽  
Free Survival ◽  
Kaplan Meier

Introduction: Limited data exists on utilization of protein post-translational modifications as biomarkers for clear cell renal cell carcinoma (ccRCC). We employed high-throughput glycoproteomics to evaluate differential expression of glycoprotein-isoforms as novel markers for ccRCC progression-free survival (PFS). Methods: Plasma samples were obtained from 77 patients treated surgically for ccRCC. Glycoproteomic analyses were carried out after liquid chromatography tandem mass spectrometry. Age-adjusted Cox proportional hazard models were constructed to evaluate PFS. Optimized Harrells c-index was employed to dichotomize the collective for the construction of Kaplan-Meier curves. Results: The average length of follow-up was 3.4 (range: 0.04-9.83) years. Glycoproteomic analysis identified 39 glycopeptides and 14 non-glycosylated peptides that showed statistically significant (false discovery rate p ≤0.05) differential expression associated with PFS. Five of the glycosylated peptides conferred continuous hazard ratio of > 6 (range 6.3-11.6). These included prothrombin A2G2S glycan motif (HR=6.47, P=9.53E-05), immunoglobulin J chain FA2G2S2 motif (HR=10.69, P=0.001), clusterin A2G2 motif (HR=7.38, P=0.002), complement component C8A A2G2S2 motif (HR=11.59, P=0.002), and apolipoprotein M glycopeptide with non-fucosylated and non-sialylated hybrid-type glycan (HR=6.30, P=0.003). Kaplan-Meier curves based on dichotomous expression of these five glycopeptides resulted in hazard ratios of 3.9-10.7, all with p-value < 0.03. Kaplan-Meyer plot using the multivariable model comprising 3 of the markers yielded HR of 11.96 (p <0.0001). Conclusion: Differential glyco-isoform abundance of plasma proteins may be a useful source of biomarkers for the clinical course and prognosis of ccRCC.

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