A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma
Objective. This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC). Methods. For this study, we first found the PPAR pathway-related genes on the gene set enrichment a...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | PPAR Research |
| Online Access: | http://dx.doi.org/10.1155/2020/6937475 |
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| author | Yingkun Xu Xiunan Li Yuqing Han Zilong Wang Chenglin Han Ningke Ruan Jianyi Li Xiao Yu Qinghua Xia Guangzhen Wu |
| author_facet | Yingkun Xu Xiunan Li Yuqing Han Zilong Wang Chenglin Han Ningke Ruan Jianyi Li Xiao Yu Qinghua Xia Guangzhen Wu |
| author_sort | Yingkun Xu |
| collection | DOAJ |
| description | Objective. This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC). Methods. For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis. Results. We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients’ prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat. Conclusions. To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians. |
| format | Article |
| id | doaj-art-8122e8725e204333851344f53eecdca2 |
| institution | Kabale University |
| issn | 1687-4757 1687-4765 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | PPAR Research |
| spelling | doaj-art-8122e8725e204333851344f53eecdca22025-02-03T06:46:29ZengWileyPPAR Research1687-47571687-47652020-01-01202010.1155/2020/69374756937475A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell CarcinomaYingkun Xu0Xiunan Li1Yuqing Han2Zilong Wang3Chenglin Han4Ningke Ruan5Jianyi Li6Xiao Yu7Qinghua Xia8Guangzhen Wu9Department of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, ChinaDepartment of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, ChinaDepartment of Radiology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, ChinaDepartment of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, ChinaDepartment of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, ChinaThe Nursing College of Zhengzhou University, Zhengzhou, Henan 450001, ChinaDepartment of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, ChinaDepartment of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, ChinaDepartment of Urology, Shandong Provincial Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250021, ChinaDepartment of Urology, The First Affiliated Hospital of Dalian Medical University, Dalian, Liaoning 116011, ChinaObjective. This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC). Methods. For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis. Results. We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients’ prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat. Conclusions. To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.http://dx.doi.org/10.1155/2020/6937475 |
| spellingShingle | Yingkun Xu Xiunan Li Yuqing Han Zilong Wang Chenglin Han Ningke Ruan Jianyi Li Xiao Yu Qinghua Xia Guangzhen Wu A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma PPAR Research |
| title | A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma |
| title_full | A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma |
| title_fullStr | A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma |
| title_full_unstemmed | A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma |
| title_short | A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma |
| title_sort | new prognostic risk model based on ppar pathway related genes in kidney renal clear cell carcinoma |
| url | http://dx.doi.org/10.1155/2020/6937475 |
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