scholarly journals Novel prognostic prediction model constructed through machine learning on the basis of methylation-driven genes in kidney renal clear cell carcinoma

2020 ◽  
Vol 40 (7) ◽  
Author(s):  
Weihao Tang ◽  
Yiling Cao ◽  
Xiaoke Ma
Keyword(s):  
Prediction Model ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Epigenetic Modification ◽  
Clinical Information ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Lasso Regression ◽  
Prognosis Model

Abstract Kidney renal clear cell carcinoma (KIRC) is a common tumor with poor prognosis and is closely related to many aberrant gene expressions. DNA methylation is an important epigenetic modification mechanism and a novel research target. Thus, exploring the relationship between methylation-driven genes and KIRC prognosis is important. The methylation profile, methylation-driven genes, and methylation characteristics in KIRC was revealed through the integration of KIRC methylation, RNA-seq, and clinical information data from The Cancer Genome Atlas. The Lasso regression was used to establish a prognosis model on the basis of methylation-driven genes. Then, a trans-omics prognostic nomogram was constructed and evaluated by combining clinical information and methylated prognosis model. A total of 242 methylation-driven genes were identified. The Gene Ontology terms of these methylation-driven genes mainly clustered in the activation, adhesion, and proliferation of immune cells. The methylation prognosis prediction model that was established using the Lasso regression included four genes in the methylation data, namely, FOXI2, USP44, EVI2A, and TRIP13. The areas under the receiver operating characteristic curve of 1-, 3-, and 5-year survival rates were 0.810, 0.824, and 0.799, respectively, in the training group and 0.794, 0.752, and 0.731, respectively, in the testing group. An easy trans-omics nomogram was successfully established. The C-indices of the nomogram in the training and the testing groups were 0.8015 and 0.8389, respectively. The present study revealed the overall perspective of methylation-driven genes in KIRC and can help in the evaluation of the prognosis of KIRC patients and provide new clues for further study.

scholarly journals AC010973.2 Promotes Cell Proliferation and Is One of Six Stemness-Related Genes That Predict Overall Survival of Renal Clear Cell Carcinoma

2021 ◽  
Author(s):  
Yingqing Liu ◽  
Yuang Wei ◽  
Xu Zhang ◽  
Xiaohan Ren ◽  
Jiawei Wang ◽  
...  
Keyword(s):  
Cell Proliferation ◽  
Overall Survival ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Learning Algorithm ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Prognosis Model

Abstract Background Extensive research has revealed that tumor stemness plays a central role in promoting tumor progression. However, the underlying involvement of stemness-related genes in renal clear cell carcinoma (ccRCC) remains controversial. Methods The data used for bioinformatics analysis were downloaded from The Cancer Genome Atlas database. The R software, SPSS and GraphPad Prism 8 were used for mapping and statistical analysis. Results We first quantified the stemness index of each patient through a machine learning algorithm. Then, we identified the differentially expressed genes between high and low stemness index as stemness-related genes. Based on these genes, we finally established a stable and effective prognosis model to predict patients' overall survival using a random forest algorithm (Training cohort; 1-year AUC: 0.67; 3-year AUC: 0.79; 5-year AUC: 0.73; Validation cohort; 1-year AUC: 0.66; 3-year AUC: 0.71; 5-year AUC: 0.7). The model genes include AC010973.2, RNU6-125P, AP001209.2, Z98885.1, KDM5C-IT1 and AL021368.3. The gene AC010973.2 was selected for further research for its highest importance. In vitro experiments demonstrated that AC010973.2 is highly expressed in ccRCC tissue and cell lines. Meanwhile, knockdown of AC010973.2 could significantly hamper the proliferation of ccRCC cells according to the colony formation and CCK8 assays. Conclusion In summary, our finding indicated that the stemness-related gene AC01097.3 is closely associated with patients' survival and could remarkably facilitate cell proliferation in ccRCC, making it potential to be a novel therapeutic target.

scholarly journals A Five-Gene Signature Predicts Prognosis in Patients with Kidney Renal Clear Cell Carcinoma

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Yueping Zhan ◽  
Wenna Guo ◽  
Ying Zhang ◽  
Qiang Wang ◽  
Xin-jian Xu ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Clinical Information ◽  
Gene Signature ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Cox Regression Analysis ◽  
Tcga Dataset

Kidney renal clear cell carcinoma (KIRC) is one of the most common cancers with high mortality all over the world. Many studies have proposed that genes could be used to predict prognosis in KIRC. In this study, RNA expression data from next-generation sequencing and clinical information of 523 patients downloaded from The Cancer Genome Atlas (TCGA) dataset were analyzed in order to identify the relationship between gene expression level and the prognosis of KIRC patients. A set of five genes that significantly associated with overall survival time was identified and a model containing these five genes was constructed by Cox regression analysis. By Kaplan-Meier and Receiver Operating Characteristic (ROC) analysis, we confirmed that the model had good sensitivity and specificity. In summary, expression of the five-gene model is associated with the prognosis outcomes of KIRC patients, and it may have an important clinical significance.

GALNT14 expression in renal clear cell carcinoma and its effect on prognosis

2021 ◽  
Author(s):  
Yuqin Wei ◽  
Fan Wu ◽  
Shengfeng Zhang ◽  
Yanlin Tan ◽  
Qunying Wu ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Regulatory Pathways ◽  
Cox Analysis ◽  
The Relationship

Abstract Background The expression of GALNT14 in kidney renal clear cell carcinoma (KIRC) and its clinical significance remains unknown. Methods The KIRC data expressed by GALNT14 was downloaded from The Cancer Genome Atlas (TCGA) database. The expression of GALNT14 was analyzed by R software, Perl software and online analysis database. The relationship between GALNT14 expression and clinicopathological features in KIRC was analyzed by univariate, multivariate Cox regression and some databases. Gene Expression Profling Interactive Analysis (GEPIA), Starbase v3.0, UALCAN, and Kaplan-Meier were used to analyze the relationship between GALNT14 expression and overall survival (OS) in KIRC. UALCAN detects the expression of GALNT14 methylation in KIRC. Linkedomics and Genemania were used to analyze the gene co-expression of GALNT14. Gene Set Enrichment Analysis (GSEA) was performed to search for potential regulatory pathways. Results We found that GALNT14 was overexpressed in KIRC (p=1.433e-25). Patients with high GALNT14 expression in KIRC had a better prognosis than patients with low GALNT14 expression (p=0.008). In addition, high GALNT14 expression in KIRC was significantly associated with low T stage and positive OS (p<0.05). Univariate Cox analysis showed that GALNT14 was positively correlated with OS (p<0.001). Multivariate Cox analysis showed that GALNT14 was associated with OS (p<0.001), age (p=0.01) and histological grade (p=0.02). GALNT14 methylation is low expressed in KIRC (p<0.001). GSEA analysis showed that GALNT14 was enriched in histidine metabolism, peroxisome, and renin-angiotensin system pathways. Conclusion GALNT14 can be used as an independent prognostic factor for renal clear cell carcinoma and a potential target for clinical diagnosis and treatment of KIRC.

scholarly journals Comprehensive Analysis of the Immune Infiltrates of Pyroptosis in Kidney Renal Clear Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Zhuolun Sun ◽  
Changying Jing ◽  
Xudong Guo ◽  
Mingxiao Zhang ◽  
Feng Kong ◽  
...  
Keyword(s):  
High Risk ◽  
Cell Carcinoma ◽  
Poor Prognosis ◽  
Immune Cell ◽  
Risk Model ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Risk Scores

Kidney renal clear cell carcinoma (KIRC) has long been identified as a highly immune-infiltrated tumor. However, the underlying role of pyroptosis in the tumor microenvironment (TME) of KIRC remains poorly described. Herein, we systematically analyzed the prognostic value, role in the TME, response to ICIs, and drug sensitivity of pyroptosis-related genes (PRGs) in KIRC patients based on The Cancer Genome Atlas (TCGA) database. Cluster 2, by consensus clustering for 24 PRGs, presented a poor prognosis, likely because malignancy-related hallmarks were remarkably enriched. Additionally, we constructed a prognostic prediction model that discriminated well between high- and low-risk patients and was further confirmed in external E-MTAB-1980 cohort and HSP cohort. By further analyzing the TME based on the risk model, higher immune cell infiltration and lower tumor purity were found in the high-risk group, which presented a poor prognosis. Patients with high risk scores also exhibited higher ICI expression, indicating that these patients may be more prone to profit from ICIs. The sensitivity to anticancer drugs that correlated with model-related genes was also identified. Collectively, the pyroptosis-related prognosis risk model may improve prognostic information and provide directions for current research investigations on immunotherapeutic strategies for KIRC patients.

scholarly journals LY96 Upregulation Promotes Kidney Renal Clear Cell Carcinoma (KIRC)

2021 ◽  
Author(s):  
Ji-li Xu ◽  
Yong Guo
Keyword(s):  
Transcription Factors ◽  
Cell Carcinoma ◽  
Dna Methyltransferase ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Related Factors ◽  
Beneficial Effects ◽  
Highly Correlated

Abstract Background: LY96 has been reported to be relevant with kidney inflammatory injury but the function of this gene in kidney renal clear cell carcinoma (KIRC) remains unknown.Methods: Various online tools were applied to analyze the roles of LY96 in KIRC using data from the Cancer Genome Atlas. Differential LY96 expression and overall survival (OS) based on different expression levels were analyzed through Oncomine and GEPIA tools. The alterations, related genes, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes pathways of LY96 were explored via cBioPortal and STRING database. LinkedOmics and Cistrome DB Toolkit were utilized to identify targets of kinase, miRNAs, and transcription factors. The relationship between LY96 and some associated genes or regulatory factors was displayed via GeneMANIA and TIMER tool. TISIDB revealed correlations between LY96 expression and immune-associated factors in the tumor microenvironment. Results: High LY96 expression level was observed in KIRC and associated with poor prognosis and diverse clinical characteristics. LY96 often amplified in KIRC and was mostly linked to the inflammatory response. Several highly correlated genes, kinase targets, transcription factors, and DNA methyltransferase that may interact with LY96 were all identified. Our study also demonstrated that various immune-related factors were relevant to LY96 in KIRC. Conclusions: Our study has shown the complex relationships between LY96 and KIRC from diverse angles. High LY96 expression had an adverse effect on the prognosis of KIRC. To find effective demethylation agents and transcription factors inhibitors targeting LY96 may have beneficial effects on the survival of KIRC patients.

scholarly journals Identification of Tumor Microenvironment-Related Genes in Kidney Renal Clear Cell Carcinoma Patients via Bioinformatic Analysis

2021 ◽  
Author(s):  
Rongjiong Zheng ◽  
Yaosen SHao ◽  
Mingming Wang ◽  
Yeli Tang ◽  
Meiling Hu
Keyword(s):  
Tumor Microenvironment ◽  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Expression Profiles ◽  
Prognostic Significance ◽  
Enrichment Analysis ◽  
Renal Clear Cell Carcinoma ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment

Abstract BackgroundTumor microenvironment has been implicated in the development and progression of cancers. However, the prognostic significance of tumor microenvironment-related genes in kidney renal clear cell carcinoma (KIRC) remains unclear. MethodsIn this study, we obtained and analyzed gene expression profiles from The Cancer Genome Atlas database. Stromal and immune scores were calculated based on the ESTIMATE algorithm. ResultsIn the discovery series of 537 patients, we identified a list of differentially expressed genes which was significantly associated with prognosis in KIRC patients. Protein-protein interaction networks and functional enrichment analysis were both performed, indicating that these identified genes were related to the immune response. ConclusionsThe tumor microenvironment-related genes could serve as the potential biomarkers for KIRC.

OSkirc: a web tool for identifying prognostic biomarkers in kidney renal clear cell carcinoma

2019 ◽  
Vol 15 (27) ◽  
pp. 3103-3110 ◽  
Author(s):  
Longxiang Xie ◽  
Qiang Wang ◽  
Yifang Dang ◽  
Linna Ge ◽  
Xiaoxiao Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Carcinoma ◽  
Web Application ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
P Value ◽  
Survival Plot

Aim: To develop a free and quick analysis online tool that allows users to easily investigate the prognostic potencies of interesting genes in kidney renal clear cell carcinoma (KIRC). Patients & methods: A total of 629 KIRC cases with gene expression profiling data and clinical follow-up information are collected from public Gene Expression Omnibus and The Cancer Genome Atlas databases. Results: One web application called Online consensus Survival analysis for KIRC (OSkirc) that can be used for exploring the prognostic implications of interesting genes in KIRC was constructed. By OSkirc, users could simply input the gene symbol to receive the Kaplan–Meier survival plot with hazard ratio and log-rank p-value. Conclusion: OSkirc is extremely valuable for basic and translational researchers to screen and validate the prognostic potencies of genes for KIRC, publicly accessible at http://bioinfo.henu.edu.cn/KIRC/KIRCList.jsp

scholarly journals Identification of a Prognostic Risk Signature of Kidney Renal Clear Cell Carcinoma Based on Regulating the Immune Response Pathway Exploration

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Guangzhen Wu ◽  
Yingkun Xu ◽  
Chenglin Han ◽  
Zilong Wang ◽  
Jiayi Li ◽  
...  
Keyword(s):  
Immune Response ◽  
Cell Carcinoma ◽  
Sensitivity And Specificity ◽  
Cox Regression ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Lasso Regression ◽  
Regression Curve ◽  
Cox Regression Analysis

Purpose. To construct a survival model for predicting the prognosis of patients with kidney renal clear cell carcinoma (KIRC) based on gene expression related to immune response regulation. Materials and Methods. KIRC mRNA sequencing data and patient clinical data were downloaded from the TCGA database. The pathways and genes involved in the regulation of the immune response were identified from the GSEA database. A single factor Cox analysis was used to determine the association of mRNA in relation to patient prognosis P < 0.05 . The prognostic risk model was further established using the LASSO regression curve. The survival prognosis model was constructed, and the sensitivity and specificity of the model were evaluated using the ROC curve. Results. Compared with normal kidney tissues, there were 28 dysregulated mRNA expressions in KIRC tissues P < 0.05 . Univariate Cox regression analysis revealed that 12 mRNAs were related to the prognosis of patients with renal cell carcinoma. The LASSO regression curve drew a risk signature consisting of six genes: TRAF6, FYN, IKBKG, LAT2, C2, IL4, EREG, TRAF2, and IL12A. The five-year ROC area analysis (AUC) showed that the model has good sensitivity and specificity (AUC >0.712). Conclusion. We constructed a risk prediction model based on the regulated immune response-related genes, which can effectively predict the survival of patients with KIRC.

scholarly journals NFIB promotes the migration and progression of kidney renal clear cell carcinoma by regulating PINK1 transcription

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10848
Author(s):  
Ninghua Wang ◽  
Jing Yuan ◽  
Fei Liu ◽  
Jun Wei ◽  
Yu Liu ◽  
...  
Keyword(s):  
Cell Carcinoma ◽  
Clear Cell ◽  
Clear Cell Carcinoma ◽  
Renal Clear Cell Carcinoma ◽  
Gene Expression Omnibus ◽  
The Cancer Genome Atlas ◽  
Transcriptional Level ◽  
Nuclear Factor I ◽  
Cancer Genome Atlas ◽  
Metastasis Rate

Kidney renal clear cell carcinoma (KIRC) is the most common and aggressive type of renal cell carcinoma. Due to high mortality rate, high metastasis rate and chemical resistance, the prognosis of KIRC patients is poor. Therefore, it is necessary to study the mechanisms of KIRC development and to develop more effective prognostic molecular biomarkers to help clinical patients. In our study, we used The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to investigate that the expression of nuclear factor I B (NFIB) is significantly higher in KIRC than in adjacent tissues. Moreover, NFIB expression levels are associated with multiple clinical pathological parameters of KIRC, and KIRC patients with high NFIB expression have poor prognosis, suggesting that NFIB may play vital roles in the malignant development of KIRC. Further studies demonstrated that NFIB could promote the progression and metastasis of KIRC and participate in the regulation of PTEN induced kinase 1 (PINK1). Furthermore, we used chromatin immunoprecipitation (ChIP) experiments to confirm that NFIB binds to the PINK1 promoter and regulates its expression at the transcriptional level. Further experiments also confirmed the important roles of PINK1 in promoting the development of tumors by NFIB. Hence, our data provide a new NFIB-mediated regulatory mechanism for the tumor progression of KIRC and suggest that NFIB can be applied as a new predictor and therapeutic target for KIRC.

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