Transcriptome Analyses Identify an RNA Binding Protein Related Prognostic Model for Clear Cell Renal Cell Carcinoma

RNA binding proteins (RBPs) play a key role in post-transcriptional gene regulation. They have been shown to be dysfunctional in a variety of cancers and are closely related to the occurrence and progression of cancers. However, the biological function and clinical significance of RBPs in clear cell...
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Autor*in:	Yue Wu [verfasserIn] Xian Wei [verfasserIn] Huan Feng [verfasserIn] Bintao Hu [verfasserIn] Bo Liu [verfasserIn] Yang Luan [verfasserIn] Yajun Ruan [verfasserIn] Xiaming Liu [verfasserIn] Zhuo Liu [verfasserIn] Shaogang Wang [verfasserIn] Jihong Liu [verfasserIn] Tao Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	clear cell renal cell carcinoma RNA binding proteins prognostic model survival analysis bioinformatics

Übergeordnetes Werk:	In: Frontiers in Genetics - Frontiers Media S.A., 2011, 11(2021)
Übergeordnetes Werk:	volume:11 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fgene.2020.617872

Katalog-ID:	DOAJ058991190

Internformat


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520			\|a RNA binding proteins (RBPs) play a key role in post-transcriptional gene regulation. They have been shown to be dysfunctional in a variety of cancers and are closely related to the occurrence and progression of cancers. However, the biological function and clinical significance of RBPs in clear cell renal carcinoma (ccRCC) are unclear. In our current study, we downloaded the transcriptome data of ccRCC patients from The Cancer Genome Atlas (TCGA) database and identified differential expression of RBPs between tumor tissue and normal kidney tissue. Then the biological function and clinical value of these RBPs were explored by using a variety of bioinformatics techniques. We identified a total of 40 differentially expressed RBPs, including 10 down-regulated RBPs and 30 up-regulated RBPs. Eight RBPs (APOBEC3G, AUH, DAZL, EIF4A1, IGF2BP3, NR0B1, RPL36A, and TRMT1) and nine RBPs (APOBEC3G, AUH, DDX47, IGF2BP3, MOV10L1, NANOS1, PIH1D3, TDRD9, and TRMT1) were identified as prognostic related to overall survival (OS) and disease-free survival (DFS), respectively, and prognostic models for OS and DFS were constructed based on these RBPs. Further analysis showed that OS and DFS were worse in high-risk group than in the low-risk group. The area under the receiver operator characteristic curve of the model for OS was 0.702 at 3 years and 0.726 at 5 years in TCGA cohort and 0.783 at 3 years and 0.795 at 5 years in E-MTAB-1980 cohort, showing good predictive performance. Both models have been shown to independently predict the prognosis of ccRCC patients. We also established a nomogram based on these prognostic RBPs for OS and performed internal validation in the TCGA cohort, showing an accurate prediction of ccRCC prognosis. Stratified analysis showed a significant correlation between the prognostic model for OS and ccRCC progression.
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allfields	10.3389/fgene.2020.617872 doi (DE-627)DOAJ058991190 (DE-599)DOAJa63bee2aba8e46d0aeda8f7f74ee9f15 DE-627 ger DE-627 rakwb eng QH426-470 Yue Wu verfasserin aut Transcriptome Analyses Identify an RNA Binding Protein Related Prognostic Model for Clear Cell Renal Cell Carcinoma 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier RNA binding proteins (RBPs) play a key role in post-transcriptional gene regulation. They have been shown to be dysfunctional in a variety of cancers and are closely related to the occurrence and progression of cancers. However, the biological function and clinical significance of RBPs in clear cell renal carcinoma (ccRCC) are unclear. In our current study, we downloaded the transcriptome data of ccRCC patients from The Cancer Genome Atlas (TCGA) database and identified differential expression of RBPs between tumor tissue and normal kidney tissue. Then the biological function and clinical value of these RBPs were explored by using a variety of bioinformatics techniques. We identified a total of 40 differentially expressed RBPs, including 10 down-regulated RBPs and 30 up-regulated RBPs. Eight RBPs (APOBEC3G, AUH, DAZL, EIF4A1, IGF2BP3, NR0B1, RPL36A, and TRMT1) and nine RBPs (APOBEC3G, AUH, DDX47, IGF2BP3, MOV10L1, NANOS1, PIH1D3, TDRD9, and TRMT1) were identified as prognostic related to overall survival (OS) and disease-free survival (DFS), respectively, and prognostic models for OS and DFS were constructed based on these RBPs. Further analysis showed that OS and DFS were worse in high-risk group than in the low-risk group. The area under the receiver operator characteristic curve of the model for OS was 0.702 at 3 years and 0.726 at 5 years in TCGA cohort and 0.783 at 3 years and 0.795 at 5 years in E-MTAB-1980 cohort, showing good predictive performance. Both models have been shown to independently predict the prognosis of ccRCC patients. We also established a nomogram based on these prognostic RBPs for OS and performed internal validation in the TCGA cohort, showing an accurate prediction of ccRCC prognosis. Stratified analysis showed a significant correlation between the prognostic model for OS and ccRCC progression. clear cell renal cell carcinoma RNA binding proteins prognostic model survival analysis bioinformatics Genetics Yue Wu verfasserin aut Xian Wei verfasserin aut Xian Wei verfasserin aut Huan Feng verfasserin aut Huan Feng verfasserin aut Bintao Hu verfasserin aut Bintao Hu verfasserin aut Bo Liu verfasserin aut Yang Luan verfasserin aut Yang Luan verfasserin aut Yajun Ruan verfasserin aut Yajun Ruan verfasserin aut Xiaming Liu verfasserin aut Xiaming Liu verfasserin aut Zhuo Liu verfasserin aut Zhuo Liu verfasserin aut Shaogang Wang verfasserin aut Shaogang Wang verfasserin aut Jihong Liu verfasserin aut Jihong Liu verfasserin aut Tao Wang verfasserin aut Tao Wang verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2021) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2021 https://doi.org/10.3389/fgene.2020.617872 kostenfrei https://doaj.org/article/a63bee2aba8e46d0aeda8f7f74ee9f15 kostenfrei https://www.frontiersin.org/articles/10.3389/fgene.2020.617872/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021
spelling	10.3389/fgene.2020.617872 doi (DE-627)DOAJ058991190 (DE-599)DOAJa63bee2aba8e46d0aeda8f7f74ee9f15 DE-627 ger DE-627 rakwb eng QH426-470 Yue Wu verfasserin aut Transcriptome Analyses Identify an RNA Binding Protein Related Prognostic Model for Clear Cell Renal Cell Carcinoma 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier RNA binding proteins (RBPs) play a key role in post-transcriptional gene regulation. They have been shown to be dysfunctional in a variety of cancers and are closely related to the occurrence and progression of cancers. However, the biological function and clinical significance of RBPs in clear cell renal carcinoma (ccRCC) are unclear. In our current study, we downloaded the transcriptome data of ccRCC patients from The Cancer Genome Atlas (TCGA) database and identified differential expression of RBPs between tumor tissue and normal kidney tissue. Then the biological function and clinical value of these RBPs were explored by using a variety of bioinformatics techniques. We identified a total of 40 differentially expressed RBPs, including 10 down-regulated RBPs and 30 up-regulated RBPs. Eight RBPs (APOBEC3G, AUH, DAZL, EIF4A1, IGF2BP3, NR0B1, RPL36A, and TRMT1) and nine RBPs (APOBEC3G, AUH, DDX47, IGF2BP3, MOV10L1, NANOS1, PIH1D3, TDRD9, and TRMT1) were identified as prognostic related to overall survival (OS) and disease-free survival (DFS), respectively, and prognostic models for OS and DFS were constructed based on these RBPs. Further analysis showed that OS and DFS were worse in high-risk group than in the low-risk group. The area under the receiver operator characteristic curve of the model for OS was 0.702 at 3 years and 0.726 at 5 years in TCGA cohort and 0.783 at 3 years and 0.795 at 5 years in E-MTAB-1980 cohort, showing good predictive performance. Both models have been shown to independently predict the prognosis of ccRCC patients. We also established a nomogram based on these prognostic RBPs for OS and performed internal validation in the TCGA cohort, showing an accurate prediction of ccRCC prognosis. Stratified analysis showed a significant correlation between the prognostic model for OS and ccRCC progression. clear cell renal cell carcinoma RNA binding proteins prognostic model survival analysis bioinformatics Genetics Yue Wu verfasserin aut Xian Wei verfasserin aut Xian Wei verfasserin aut Huan Feng verfasserin aut Huan Feng verfasserin aut Bintao Hu verfasserin aut Bintao Hu verfasserin aut Bo Liu verfasserin aut Yang Luan verfasserin aut Yang Luan verfasserin aut Yajun Ruan verfasserin aut Yajun Ruan verfasserin aut Xiaming Liu verfasserin aut Xiaming Liu verfasserin aut Zhuo Liu verfasserin aut Zhuo Liu verfasserin aut Shaogang Wang verfasserin aut Shaogang Wang verfasserin aut Jihong Liu verfasserin aut Jihong Liu verfasserin aut Tao Wang verfasserin aut Tao Wang verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2021) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2021 https://doi.org/10.3389/fgene.2020.617872 kostenfrei https://doaj.org/article/a63bee2aba8e46d0aeda8f7f74ee9f15 kostenfrei https://www.frontiersin.org/articles/10.3389/fgene.2020.617872/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021
allfields_unstemmed	10.3389/fgene.2020.617872 doi (DE-627)DOAJ058991190 (DE-599)DOAJa63bee2aba8e46d0aeda8f7f74ee9f15 DE-627 ger DE-627 rakwb eng QH426-470 Yue Wu verfasserin aut Transcriptome Analyses Identify an RNA Binding Protein Related Prognostic Model for Clear Cell Renal Cell Carcinoma 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier RNA binding proteins (RBPs) play a key role in post-transcriptional gene regulation. They have been shown to be dysfunctional in a variety of cancers and are closely related to the occurrence and progression of cancers. However, the biological function and clinical significance of RBPs in clear cell renal carcinoma (ccRCC) are unclear. In our current study, we downloaded the transcriptome data of ccRCC patients from The Cancer Genome Atlas (TCGA) database and identified differential expression of RBPs between tumor tissue and normal kidney tissue. Then the biological function and clinical value of these RBPs were explored by using a variety of bioinformatics techniques. We identified a total of 40 differentially expressed RBPs, including 10 down-regulated RBPs and 30 up-regulated RBPs. Eight RBPs (APOBEC3G, AUH, DAZL, EIF4A1, IGF2BP3, NR0B1, RPL36A, and TRMT1) and nine RBPs (APOBEC3G, AUH, DDX47, IGF2BP3, MOV10L1, NANOS1, PIH1D3, TDRD9, and TRMT1) were identified as prognostic related to overall survival (OS) and disease-free survival (DFS), respectively, and prognostic models for OS and DFS were constructed based on these RBPs. Further analysis showed that OS and DFS were worse in high-risk group than in the low-risk group. The area under the receiver operator characteristic curve of the model for OS was 0.702 at 3 years and 0.726 at 5 years in TCGA cohort and 0.783 at 3 years and 0.795 at 5 years in E-MTAB-1980 cohort, showing good predictive performance. Both models have been shown to independently predict the prognosis of ccRCC patients. We also established a nomogram based on these prognostic RBPs for OS and performed internal validation in the TCGA cohort, showing an accurate prediction of ccRCC prognosis. Stratified analysis showed a significant correlation between the prognostic model for OS and ccRCC progression. clear cell renal cell carcinoma RNA binding proteins prognostic model survival analysis bioinformatics Genetics Yue Wu verfasserin aut Xian Wei verfasserin aut Xian Wei verfasserin aut Huan Feng verfasserin aut Huan Feng verfasserin aut Bintao Hu verfasserin aut Bintao Hu verfasserin aut Bo Liu verfasserin aut Yang Luan verfasserin aut Yang Luan verfasserin aut Yajun Ruan verfasserin aut Yajun Ruan verfasserin aut Xiaming Liu verfasserin aut Xiaming Liu verfasserin aut Zhuo Liu verfasserin aut Zhuo Liu verfasserin aut Shaogang Wang verfasserin aut Shaogang Wang verfasserin aut Jihong Liu verfasserin aut Jihong Liu verfasserin aut Tao Wang verfasserin aut Tao Wang verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2021) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2021 https://doi.org/10.3389/fgene.2020.617872 kostenfrei https://doaj.org/article/a63bee2aba8e46d0aeda8f7f74ee9f15 kostenfrei https://www.frontiersin.org/articles/10.3389/fgene.2020.617872/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021
allfieldsGer	10.3389/fgene.2020.617872 doi (DE-627)DOAJ058991190 (DE-599)DOAJa63bee2aba8e46d0aeda8f7f74ee9f15 DE-627 ger DE-627 rakwb eng QH426-470 Yue Wu verfasserin aut Transcriptome Analyses Identify an RNA Binding Protein Related Prognostic Model for Clear Cell Renal Cell Carcinoma 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier RNA binding proteins (RBPs) play a key role in post-transcriptional gene regulation. They have been shown to be dysfunctional in a variety of cancers and are closely related to the occurrence and progression of cancers. However, the biological function and clinical significance of RBPs in clear cell renal carcinoma (ccRCC) are unclear. In our current study, we downloaded the transcriptome data of ccRCC patients from The Cancer Genome Atlas (TCGA) database and identified differential expression of RBPs between tumor tissue and normal kidney tissue. Then the biological function and clinical value of these RBPs were explored by using a variety of bioinformatics techniques. We identified a total of 40 differentially expressed RBPs, including 10 down-regulated RBPs and 30 up-regulated RBPs. Eight RBPs (APOBEC3G, AUH, DAZL, EIF4A1, IGF2BP3, NR0B1, RPL36A, and TRMT1) and nine RBPs (APOBEC3G, AUH, DDX47, IGF2BP3, MOV10L1, NANOS1, PIH1D3, TDRD9, and TRMT1) were identified as prognostic related to overall survival (OS) and disease-free survival (DFS), respectively, and prognostic models for OS and DFS were constructed based on these RBPs. Further analysis showed that OS and DFS were worse in high-risk group than in the low-risk group. The area under the receiver operator characteristic curve of the model for OS was 0.702 at 3 years and 0.726 at 5 years in TCGA cohort and 0.783 at 3 years and 0.795 at 5 years in E-MTAB-1980 cohort, showing good predictive performance. Both models have been shown to independently predict the prognosis of ccRCC patients. We also established a nomogram based on these prognostic RBPs for OS and performed internal validation in the TCGA cohort, showing an accurate prediction of ccRCC prognosis. Stratified analysis showed a significant correlation between the prognostic model for OS and ccRCC progression. clear cell renal cell carcinoma RNA binding proteins prognostic model survival analysis bioinformatics Genetics Yue Wu verfasserin aut Xian Wei verfasserin aut Xian Wei verfasserin aut Huan Feng verfasserin aut Huan Feng verfasserin aut Bintao Hu verfasserin aut Bintao Hu verfasserin aut Bo Liu verfasserin aut Yang Luan verfasserin aut Yang Luan verfasserin aut Yajun Ruan verfasserin aut Yajun Ruan verfasserin aut Xiaming Liu verfasserin aut Xiaming Liu verfasserin aut Zhuo Liu verfasserin aut Zhuo Liu verfasserin aut Shaogang Wang verfasserin aut Shaogang Wang verfasserin aut Jihong Liu verfasserin aut Jihong Liu verfasserin aut Tao Wang verfasserin aut Tao Wang verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2021) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2021 https://doi.org/10.3389/fgene.2020.617872 kostenfrei https://doaj.org/article/a63bee2aba8e46d0aeda8f7f74ee9f15 kostenfrei https://www.frontiersin.org/articles/10.3389/fgene.2020.617872/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021
allfieldsSound	10.3389/fgene.2020.617872 doi (DE-627)DOAJ058991190 (DE-599)DOAJa63bee2aba8e46d0aeda8f7f74ee9f15 DE-627 ger DE-627 rakwb eng QH426-470 Yue Wu verfasserin aut Transcriptome Analyses Identify an RNA Binding Protein Related Prognostic Model for Clear Cell Renal Cell Carcinoma 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier RNA binding proteins (RBPs) play a key role in post-transcriptional gene regulation. They have been shown to be dysfunctional in a variety of cancers and are closely related to the occurrence and progression of cancers. However, the biological function and clinical significance of RBPs in clear cell renal carcinoma (ccRCC) are unclear. In our current study, we downloaded the transcriptome data of ccRCC patients from The Cancer Genome Atlas (TCGA) database and identified differential expression of RBPs between tumor tissue and normal kidney tissue. Then the biological function and clinical value of these RBPs were explored by using a variety of bioinformatics techniques. We identified a total of 40 differentially expressed RBPs, including 10 down-regulated RBPs and 30 up-regulated RBPs. Eight RBPs (APOBEC3G, AUH, DAZL, EIF4A1, IGF2BP3, NR0B1, RPL36A, and TRMT1) and nine RBPs (APOBEC3G, AUH, DDX47, IGF2BP3, MOV10L1, NANOS1, PIH1D3, TDRD9, and TRMT1) were identified as prognostic related to overall survival (OS) and disease-free survival (DFS), respectively, and prognostic models for OS and DFS were constructed based on these RBPs. Further analysis showed that OS and DFS were worse in high-risk group than in the low-risk group. The area under the receiver operator characteristic curve of the model for OS was 0.702 at 3 years and 0.726 at 5 years in TCGA cohort and 0.783 at 3 years and 0.795 at 5 years in E-MTAB-1980 cohort, showing good predictive performance. Both models have been shown to independently predict the prognosis of ccRCC patients. We also established a nomogram based on these prognostic RBPs for OS and performed internal validation in the TCGA cohort, showing an accurate prediction of ccRCC prognosis. Stratified analysis showed a significant correlation between the prognostic model for OS and ccRCC progression. clear cell renal cell carcinoma RNA binding proteins prognostic model survival analysis bioinformatics Genetics Yue Wu verfasserin aut Xian Wei verfasserin aut Xian Wei verfasserin aut Huan Feng verfasserin aut Huan Feng verfasserin aut Bintao Hu verfasserin aut Bintao Hu verfasserin aut Bo Liu verfasserin aut Yang Luan verfasserin aut Yang Luan verfasserin aut Yajun Ruan verfasserin aut Yajun Ruan verfasserin aut Xiaming Liu verfasserin aut Xiaming Liu verfasserin aut Zhuo Liu verfasserin aut Zhuo Liu verfasserin aut Shaogang Wang verfasserin aut Shaogang Wang verfasserin aut Jihong Liu verfasserin aut Jihong Liu verfasserin aut Tao Wang verfasserin aut Tao Wang verfasserin aut In Frontiers in Genetics Frontiers Media S.A., 2011 11(2021) (DE-627)65799829X (DE-600)2606823-0 16648021 nnns volume:11 year:2021 https://doi.org/10.3389/fgene.2020.617872 kostenfrei https://doaj.org/article/a63bee2aba8e46d0aeda8f7f74ee9f15 kostenfrei https://www.frontiersin.org/articles/10.3389/fgene.2020.617872/full kostenfrei https://doaj.org/toc/1664-8021 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2003 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 11 2021
language	English
source	In Frontiers in Genetics 11(2021) volume:11 year:2021
sourceStr	In Frontiers in Genetics 11(2021) volume:11 year:2021
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	clear cell renal cell carcinoma RNA binding proteins prognostic model survival analysis bioinformatics Genetics
isfreeaccess_bool	true
container_title	Frontiers in Genetics
authorswithroles_txt_mv	Yue Wu @@aut@@ Xian Wei @@aut@@ Huan Feng @@aut@@ Bintao Hu @@aut@@ Bo Liu @@aut@@ Yang Luan @@aut@@ Yajun Ruan @@aut@@ Xiaming Liu @@aut@@ Zhuo Liu @@aut@@ Shaogang Wang @@aut@@ Jihong Liu @@aut@@ Tao Wang @@aut@@
publishDateDaySort_date	2021-01-01T00:00:00Z
hierarchy_top_id	65799829X
id	DOAJ058991190
language_de	englisch
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callnumber-first	Q - Science
author	Yue Wu
spellingShingle	Yue Wu misc QH426-470 misc clear cell renal cell carcinoma misc RNA binding proteins misc prognostic model misc survival analysis misc bioinformatics misc Genetics Transcriptome Analyses Identify an RNA Binding Protein Related Prognostic Model for Clear Cell Renal Cell Carcinoma
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topic_title	QH426-470 Transcriptome Analyses Identify an RNA Binding Protein Related Prognostic Model for Clear Cell Renal Cell Carcinoma clear cell renal cell carcinoma RNA binding proteins prognostic model survival analysis bioinformatics
topic	misc QH426-470 misc clear cell renal cell carcinoma misc RNA binding proteins misc prognostic model misc survival analysis misc bioinformatics misc Genetics
topic_unstemmed	misc QH426-470 misc clear cell renal cell carcinoma misc RNA binding proteins misc prognostic model misc survival analysis misc bioinformatics misc Genetics
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title	Transcriptome Analyses Identify an RNA Binding Protein Related Prognostic Model for Clear Cell Renal Cell Carcinoma
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title_full	Transcriptome Analyses Identify an RNA Binding Protein Related Prognostic Model for Clear Cell Renal Cell Carcinoma
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author_browse	Yue Wu Xian Wei Huan Feng Bintao Hu Bo Liu Yang Luan Yajun Ruan Xiaming Liu Zhuo Liu Shaogang Wang Jihong Liu Tao Wang
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title_sort	transcriptome analyses identify an rna binding protein related prognostic model for clear cell renal cell carcinoma
callnumber	QH426-470
title_auth	Transcriptome Analyses Identify an RNA Binding Protein Related Prognostic Model for Clear Cell Renal Cell Carcinoma
abstract	RNA binding proteins (RBPs) play a key role in post-transcriptional gene regulation. They have been shown to be dysfunctional in a variety of cancers and are closely related to the occurrence and progression of cancers. However, the biological function and clinical significance of RBPs in clear cell renal carcinoma (ccRCC) are unclear. In our current study, we downloaded the transcriptome data of ccRCC patients from The Cancer Genome Atlas (TCGA) database and identified differential expression of RBPs between tumor tissue and normal kidney tissue. Then the biological function and clinical value of these RBPs were explored by using a variety of bioinformatics techniques. We identified a total of 40 differentially expressed RBPs, including 10 down-regulated RBPs and 30 up-regulated RBPs. Eight RBPs (APOBEC3G, AUH, DAZL, EIF4A1, IGF2BP3, NR0B1, RPL36A, and TRMT1) and nine RBPs (APOBEC3G, AUH, DDX47, IGF2BP3, MOV10L1, NANOS1, PIH1D3, TDRD9, and TRMT1) were identified as prognostic related to overall survival (OS) and disease-free survival (DFS), respectively, and prognostic models for OS and DFS were constructed based on these RBPs. Further analysis showed that OS and DFS were worse in high-risk group than in the low-risk group. The area under the receiver operator characteristic curve of the model for OS was 0.702 at 3 years and 0.726 at 5 years in TCGA cohort and 0.783 at 3 years and 0.795 at 5 years in E-MTAB-1980 cohort, showing good predictive performance. Both models have been shown to independently predict the prognosis of ccRCC patients. We also established a nomogram based on these prognostic RBPs for OS and performed internal validation in the TCGA cohort, showing an accurate prediction of ccRCC prognosis. Stratified analysis showed a significant correlation between the prognostic model for OS and ccRCC progression.
abstractGer	RNA binding proteins (RBPs) play a key role in post-transcriptional gene regulation. They have been shown to be dysfunctional in a variety of cancers and are closely related to the occurrence and progression of cancers. However, the biological function and clinical significance of RBPs in clear cell renal carcinoma (ccRCC) are unclear. In our current study, we downloaded the transcriptome data of ccRCC patients from The Cancer Genome Atlas (TCGA) database and identified differential expression of RBPs between tumor tissue and normal kidney tissue. Then the biological function and clinical value of these RBPs were explored by using a variety of bioinformatics techniques. We identified a total of 40 differentially expressed RBPs, including 10 down-regulated RBPs and 30 up-regulated RBPs. Eight RBPs (APOBEC3G, AUH, DAZL, EIF4A1, IGF2BP3, NR0B1, RPL36A, and TRMT1) and nine RBPs (APOBEC3G, AUH, DDX47, IGF2BP3, MOV10L1, NANOS1, PIH1D3, TDRD9, and TRMT1) were identified as prognostic related to overall survival (OS) and disease-free survival (DFS), respectively, and prognostic models for OS and DFS were constructed based on these RBPs. Further analysis showed that OS and DFS were worse in high-risk group than in the low-risk group. The area under the receiver operator characteristic curve of the model for OS was 0.702 at 3 years and 0.726 at 5 years in TCGA cohort and 0.783 at 3 years and 0.795 at 5 years in E-MTAB-1980 cohort, showing good predictive performance. Both models have been shown to independently predict the prognosis of ccRCC patients. We also established a nomogram based on these prognostic RBPs for OS and performed internal validation in the TCGA cohort, showing an accurate prediction of ccRCC prognosis. Stratified analysis showed a significant correlation between the prognostic model for OS and ccRCC progression.
abstract_unstemmed	RNA binding proteins (RBPs) play a key role in post-transcriptional gene regulation. They have been shown to be dysfunctional in a variety of cancers and are closely related to the occurrence and progression of cancers. However, the biological function and clinical significance of RBPs in clear cell renal carcinoma (ccRCC) are unclear. In our current study, we downloaded the transcriptome data of ccRCC patients from The Cancer Genome Atlas (TCGA) database and identified differential expression of RBPs between tumor tissue and normal kidney tissue. Then the biological function and clinical value of these RBPs were explored by using a variety of bioinformatics techniques. We identified a total of 40 differentially expressed RBPs, including 10 down-regulated RBPs and 30 up-regulated RBPs. Eight RBPs (APOBEC3G, AUH, DAZL, EIF4A1, IGF2BP3, NR0B1, RPL36A, and TRMT1) and nine RBPs (APOBEC3G, AUH, DDX47, IGF2BP3, MOV10L1, NANOS1, PIH1D3, TDRD9, and TRMT1) were identified as prognostic related to overall survival (OS) and disease-free survival (DFS), respectively, and prognostic models for OS and DFS were constructed based on these RBPs. Further analysis showed that OS and DFS were worse in high-risk group than in the low-risk group. The area under the receiver operator characteristic curve of the model for OS was 0.702 at 3 years and 0.726 at 5 years in TCGA cohort and 0.783 at 3 years and 0.795 at 5 years in E-MTAB-1980 cohort, showing good predictive performance. Both models have been shown to independently predict the prognosis of ccRCC patients. We also established a nomogram based on these prognostic RBPs for OS and performed internal validation in the TCGA cohort, showing an accurate prediction of ccRCC prognosis. Stratified analysis showed a significant correlation between the prognostic model for OS and ccRCC progression.
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title_short	Transcriptome Analyses Identify an RNA Binding Protein Related Prognostic Model for Clear Cell Renal Cell Carcinoma
url	https://doi.org/10.3389/fgene.2020.617872 https://doaj.org/article/a63bee2aba8e46d0aeda8f7f74ee9f15 https://www.frontiersin.org/articles/10.3389/fgene.2020.617872/full https://doaj.org/toc/1664-8021
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up_date	2024-07-03T21:11:55.745Z
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