Exploration and validation of the prognostic value of RNA-binding proteins in hepatocellular carcinoma

OBJECTIVE: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Increasing evidence suggests that the dysregulation of RNA-binding proteins (RBPs) is involved in the development of various cancers. However, there is a paucity of studies investigating the roles of RBPs in HCC. MATERIALS AND METHODS: Data on HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (available at: www.ncbi.nlm.nih.gov/geo), and data regarding human RBPs were integrated from SONAR, XRNAX, and CARIC results. We identified modules associated with prognosis using weighted gene co-expression network analysis (WGCNA) and performed functional enrichment analysis. Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify prognostic RBPs and establish a prediction model. According to the median risk score, we separated patients into high- and low-risk groups and investigated the differences in immune cell infiltration, somatic mutations, and gene set enrichment. Univariate and multivariate regression analyses were used to identify prognostic factors for HCC. A nomogram was constructed, and its performance was evaluated with calibration curves. RESULTS: Sixteen RBPs (MEX3A, TTK, MRPL53, IQGAP3, PFN2, IMPDH1, TCOF1, DYNC1LI1, EIF2B4, NOL10, GNL2, EIF1B, PSMD1, AHSA1, SEC61A1, and YBX1) were identified as prognostic genes, and a prognostic model was established. Survival analysis indicated that the model had good predictive performance and that a high-risk score was significantly related to a poor prognosis. Additionally, there were significant differences in immune cell infiltration, somatic mutations, and gene set enrichment between the high- and low-risk groups. Univariate and multivariate regression analyses indicated that the RBP-based signature was an independent prognostic factor for HCC. The nomogram based on 16 RBPs performed well in predicting the overall survival of HCC patients. CONCLUSIONS: The RBP-based signature is an independent prognostic factor for HCC, and this study could provide an innovative method for analyzing prognostic biomarkers and therapeutic targets for HCC. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	J. Wang [verfasserIn] K. Han [verfasserIn] Y. Li [verfasserIn] C. Zhang [verfasserIn] W.-H. Cui [verfasserIn] L.-H. Zhu [verfasserIn] T. Luo [verfasserIn] C.-J. Bian [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Übergeordnetes Werk:	In: European Review for Medical and Pharmacological Sciences - Verduci Editore, 2021, 26(2022)
Übergeordnetes Werk:	volume:26 ; year:2022

Links:	Link aufrufen Link aufrufen Journal toc

Katalog-ID:	DOAJ00108447X

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520			\|a OBJECTIVE: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Increasing evidence suggests that the dysregulation of RNA-binding proteins (RBPs) is involved in the development of various cancers. However, there is a paucity of studies investigating the roles of RBPs in HCC. MATERIALS AND METHODS: Data on HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (available at: www.ncbi.nlm.nih.gov/geo), and data regarding human RBPs were integrated from SONAR, XRNAX, and CARIC results. We identified modules associated with prognosis using weighted gene co-expression network analysis (WGCNA) and performed functional enrichment analysis. Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify prognostic RBPs and establish a prediction model. According to the median risk score, we separated patients into high- and low-risk groups and investigated the differences in immune cell infiltration, somatic mutations, and gene set enrichment. Univariate and multivariate regression analyses were used to identify prognostic factors for HCC. A nomogram was constructed, and its performance was evaluated with calibration curves. RESULTS: Sixteen RBPs (MEX3A, TTK, MRPL53, IQGAP3, PFN2, IMPDH1, TCOF1, DYNC1LI1, EIF2B4, NOL10, GNL2, EIF1B, PSMD1, AHSA1, SEC61A1, and YBX1) were identified as prognostic genes, and a prognostic model was established. Survival analysis indicated that the model had good predictive performance and that a high-risk score was significantly related to a poor prognosis. Additionally, there were significant differences in immune cell infiltration, somatic mutations, and gene set enrichment between the high- and low-risk groups. Univariate and multivariate regression analyses indicated that the RBP-based signature was an independent prognostic factor for HCC. The nomogram based on 16 RBPs performed well in predicting the overall survival of HCC patients. CONCLUSIONS: The RBP-based signature is an independent prognostic factor for HCC, and this study could provide an innovative method for analyzing prognostic biomarkers and therapeutic targets for HCC.
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allfields	(DE-627)DOAJ00108447X (DE-599)DOAJ16866a27a24642ffa027b07abdf346c4 DE-627 ger DE-627 rakwb eng RM1-950 J. Wang verfasserin aut Exploration and validation of the prognostic value of RNA-binding proteins in hepatocellular carcinoma 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier OBJECTIVE: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Increasing evidence suggests that the dysregulation of RNA-binding proteins (RBPs) is involved in the development of various cancers. However, there is a paucity of studies investigating the roles of RBPs in HCC. MATERIALS AND METHODS: Data on HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (available at: www.ncbi.nlm.nih.gov/geo), and data regarding human RBPs were integrated from SONAR, XRNAX, and CARIC results. We identified modules associated with prognosis using weighted gene co-expression network analysis (WGCNA) and performed functional enrichment analysis. Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify prognostic RBPs and establish a prediction model. According to the median risk score, we separated patients into high- and low-risk groups and investigated the differences in immune cell infiltration, somatic mutations, and gene set enrichment. Univariate and multivariate regression analyses were used to identify prognostic factors for HCC. A nomogram was constructed, and its performance was evaluated with calibration curves. RESULTS: Sixteen RBPs (MEX3A, TTK, MRPL53, IQGAP3, PFN2, IMPDH1, TCOF1, DYNC1LI1, EIF2B4, NOL10, GNL2, EIF1B, PSMD1, AHSA1, SEC61A1, and YBX1) were identified as prognostic genes, and a prognostic model was established. Survival analysis indicated that the model had good predictive performance and that a high-risk score was significantly related to a poor prognosis. Additionally, there were significant differences in immune cell infiltration, somatic mutations, and gene set enrichment between the high- and low-risk groups. Univariate and multivariate regression analyses indicated that the RBP-based signature was an independent prognostic factor for HCC. The nomogram based on 16 RBPs performed well in predicting the overall survival of HCC patients. CONCLUSIONS: The RBP-based signature is an independent prognostic factor for HCC, and this study could provide an innovative method for analyzing prognostic biomarkers and therapeutic targets for HCC. Therapeutics. Pharmacology K. Han verfasserin aut Y. Li verfasserin aut C. Zhang verfasserin aut W.-H. Cui verfasserin aut L.-H. Zhu verfasserin aut T. Luo verfasserin aut C.-J. Bian verfasserin aut In European Review for Medical and Pharmacological Sciences Verduci Editore, 2021 26(2022) (DE-627)65427049X (DE-600)2598628-4 22840729 nnns volume:26 year:2022 https://doaj.org/article/16866a27a24642ffa027b07abdf346c4 kostenfrei https://www.europeanreview.org/wp/wp-content/uploads/8945-8958.pdf kostenfrei https://doaj.org/toc/1128-3602 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2153 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 26 2022
spelling	(DE-627)DOAJ00108447X (DE-599)DOAJ16866a27a24642ffa027b07abdf346c4 DE-627 ger DE-627 rakwb eng RM1-950 J. Wang verfasserin aut Exploration and validation of the prognostic value of RNA-binding proteins in hepatocellular carcinoma 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier OBJECTIVE: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Increasing evidence suggests that the dysregulation of RNA-binding proteins (RBPs) is involved in the development of various cancers. However, there is a paucity of studies investigating the roles of RBPs in HCC. MATERIALS AND METHODS: Data on HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (available at: www.ncbi.nlm.nih.gov/geo), and data regarding human RBPs were integrated from SONAR, XRNAX, and CARIC results. We identified modules associated with prognosis using weighted gene co-expression network analysis (WGCNA) and performed functional enrichment analysis. Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify prognostic RBPs and establish a prediction model. According to the median risk score, we separated patients into high- and low-risk groups and investigated the differences in immune cell infiltration, somatic mutations, and gene set enrichment. Univariate and multivariate regression analyses were used to identify prognostic factors for HCC. A nomogram was constructed, and its performance was evaluated with calibration curves. RESULTS: Sixteen RBPs (MEX3A, TTK, MRPL53, IQGAP3, PFN2, IMPDH1, TCOF1, DYNC1LI1, EIF2B4, NOL10, GNL2, EIF1B, PSMD1, AHSA1, SEC61A1, and YBX1) were identified as prognostic genes, and a prognostic model was established. Survival analysis indicated that the model had good predictive performance and that a high-risk score was significantly related to a poor prognosis. Additionally, there were significant differences in immune cell infiltration, somatic mutations, and gene set enrichment between the high- and low-risk groups. Univariate and multivariate regression analyses indicated that the RBP-based signature was an independent prognostic factor for HCC. The nomogram based on 16 RBPs performed well in predicting the overall survival of HCC patients. CONCLUSIONS: The RBP-based signature is an independent prognostic factor for HCC, and this study could provide an innovative method for analyzing prognostic biomarkers and therapeutic targets for HCC. Therapeutics. Pharmacology K. Han verfasserin aut Y. Li verfasserin aut C. Zhang verfasserin aut W.-H. Cui verfasserin aut L.-H. Zhu verfasserin aut T. Luo verfasserin aut C.-J. Bian verfasserin aut In European Review for Medical and Pharmacological Sciences Verduci Editore, 2021 26(2022) (DE-627)65427049X (DE-600)2598628-4 22840729 nnns volume:26 year:2022 https://doaj.org/article/16866a27a24642ffa027b07abdf346c4 kostenfrei https://www.europeanreview.org/wp/wp-content/uploads/8945-8958.pdf kostenfrei https://doaj.org/toc/1128-3602 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2153 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 26 2022
allfields_unstemmed	(DE-627)DOAJ00108447X (DE-599)DOAJ16866a27a24642ffa027b07abdf346c4 DE-627 ger DE-627 rakwb eng RM1-950 J. Wang verfasserin aut Exploration and validation of the prognostic value of RNA-binding proteins in hepatocellular carcinoma 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier OBJECTIVE: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Increasing evidence suggests that the dysregulation of RNA-binding proteins (RBPs) is involved in the development of various cancers. However, there is a paucity of studies investigating the roles of RBPs in HCC. MATERIALS AND METHODS: Data on HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (available at: www.ncbi.nlm.nih.gov/geo), and data regarding human RBPs were integrated from SONAR, XRNAX, and CARIC results. We identified modules associated with prognosis using weighted gene co-expression network analysis (WGCNA) and performed functional enrichment analysis. Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify prognostic RBPs and establish a prediction model. According to the median risk score, we separated patients into high- and low-risk groups and investigated the differences in immune cell infiltration, somatic mutations, and gene set enrichment. Univariate and multivariate regression analyses were used to identify prognostic factors for HCC. A nomogram was constructed, and its performance was evaluated with calibration curves. RESULTS: Sixteen RBPs (MEX3A, TTK, MRPL53, IQGAP3, PFN2, IMPDH1, TCOF1, DYNC1LI1, EIF2B4, NOL10, GNL2, EIF1B, PSMD1, AHSA1, SEC61A1, and YBX1) were identified as prognostic genes, and a prognostic model was established. Survival analysis indicated that the model had good predictive performance and that a high-risk score was significantly related to a poor prognosis. Additionally, there were significant differences in immune cell infiltration, somatic mutations, and gene set enrichment between the high- and low-risk groups. Univariate and multivariate regression analyses indicated that the RBP-based signature was an independent prognostic factor for HCC. The nomogram based on 16 RBPs performed well in predicting the overall survival of HCC patients. CONCLUSIONS: The RBP-based signature is an independent prognostic factor for HCC, and this study could provide an innovative method for analyzing prognostic biomarkers and therapeutic targets for HCC. Therapeutics. Pharmacology K. Han verfasserin aut Y. Li verfasserin aut C. Zhang verfasserin aut W.-H. Cui verfasserin aut L.-H. Zhu verfasserin aut T. Luo verfasserin aut C.-J. Bian verfasserin aut In European Review for Medical and Pharmacological Sciences Verduci Editore, 2021 26(2022) (DE-627)65427049X (DE-600)2598628-4 22840729 nnns volume:26 year:2022 https://doaj.org/article/16866a27a24642ffa027b07abdf346c4 kostenfrei https://www.europeanreview.org/wp/wp-content/uploads/8945-8958.pdf kostenfrei https://doaj.org/toc/1128-3602 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2153 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 26 2022
allfieldsGer	(DE-627)DOAJ00108447X (DE-599)DOAJ16866a27a24642ffa027b07abdf346c4 DE-627 ger DE-627 rakwb eng RM1-950 J. Wang verfasserin aut Exploration and validation of the prognostic value of RNA-binding proteins in hepatocellular carcinoma 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier OBJECTIVE: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Increasing evidence suggests that the dysregulation of RNA-binding proteins (RBPs) is involved in the development of various cancers. However, there is a paucity of studies investigating the roles of RBPs in HCC. MATERIALS AND METHODS: Data on HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (available at: www.ncbi.nlm.nih.gov/geo), and data regarding human RBPs were integrated from SONAR, XRNAX, and CARIC results. We identified modules associated with prognosis using weighted gene co-expression network analysis (WGCNA) and performed functional enrichment analysis. Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify prognostic RBPs and establish a prediction model. According to the median risk score, we separated patients into high- and low-risk groups and investigated the differences in immune cell infiltration, somatic mutations, and gene set enrichment. Univariate and multivariate regression analyses were used to identify prognostic factors for HCC. A nomogram was constructed, and its performance was evaluated with calibration curves. RESULTS: Sixteen RBPs (MEX3A, TTK, MRPL53, IQGAP3, PFN2, IMPDH1, TCOF1, DYNC1LI1, EIF2B4, NOL10, GNL2, EIF1B, PSMD1, AHSA1, SEC61A1, and YBX1) were identified as prognostic genes, and a prognostic model was established. Survival analysis indicated that the model had good predictive performance and that a high-risk score was significantly related to a poor prognosis. Additionally, there were significant differences in immune cell infiltration, somatic mutations, and gene set enrichment between the high- and low-risk groups. Univariate and multivariate regression analyses indicated that the RBP-based signature was an independent prognostic factor for HCC. The nomogram based on 16 RBPs performed well in predicting the overall survival of HCC patients. CONCLUSIONS: The RBP-based signature is an independent prognostic factor for HCC, and this study could provide an innovative method for analyzing prognostic biomarkers and therapeutic targets for HCC. Therapeutics. Pharmacology K. Han verfasserin aut Y. Li verfasserin aut C. Zhang verfasserin aut W.-H. Cui verfasserin aut L.-H. Zhu verfasserin aut T. Luo verfasserin aut C.-J. Bian verfasserin aut In European Review for Medical and Pharmacological Sciences Verduci Editore, 2021 26(2022) (DE-627)65427049X (DE-600)2598628-4 22840729 nnns volume:26 year:2022 https://doaj.org/article/16866a27a24642ffa027b07abdf346c4 kostenfrei https://www.europeanreview.org/wp/wp-content/uploads/8945-8958.pdf kostenfrei https://doaj.org/toc/1128-3602 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2153 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 26 2022
allfieldsSound	(DE-627)DOAJ00108447X (DE-599)DOAJ16866a27a24642ffa027b07abdf346c4 DE-627 ger DE-627 rakwb eng RM1-950 J. Wang verfasserin aut Exploration and validation of the prognostic value of RNA-binding proteins in hepatocellular carcinoma 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier OBJECTIVE: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Increasing evidence suggests that the dysregulation of RNA-binding proteins (RBPs) is involved in the development of various cancers. However, there is a paucity of studies investigating the roles of RBPs in HCC. MATERIALS AND METHODS: Data on HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (available at: www.ncbi.nlm.nih.gov/geo), and data regarding human RBPs were integrated from SONAR, XRNAX, and CARIC results. We identified modules associated with prognosis using weighted gene co-expression network analysis (WGCNA) and performed functional enrichment analysis. Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify prognostic RBPs and establish a prediction model. According to the median risk score, we separated patients into high- and low-risk groups and investigated the differences in immune cell infiltration, somatic mutations, and gene set enrichment. Univariate and multivariate regression analyses were used to identify prognostic factors for HCC. A nomogram was constructed, and its performance was evaluated with calibration curves. RESULTS: Sixteen RBPs (MEX3A, TTK, MRPL53, IQGAP3, PFN2, IMPDH1, TCOF1, DYNC1LI1, EIF2B4, NOL10, GNL2, EIF1B, PSMD1, AHSA1, SEC61A1, and YBX1) were identified as prognostic genes, and a prognostic model was established. Survival analysis indicated that the model had good predictive performance and that a high-risk score was significantly related to a poor prognosis. Additionally, there were significant differences in immune cell infiltration, somatic mutations, and gene set enrichment between the high- and low-risk groups. Univariate and multivariate regression analyses indicated that the RBP-based signature was an independent prognostic factor for HCC. The nomogram based on 16 RBPs performed well in predicting the overall survival of HCC patients. CONCLUSIONS: The RBP-based signature is an independent prognostic factor for HCC, and this study could provide an innovative method for analyzing prognostic biomarkers and therapeutic targets for HCC. Therapeutics. Pharmacology K. Han verfasserin aut Y. Li verfasserin aut C. Zhang verfasserin aut W.-H. Cui verfasserin aut L.-H. Zhu verfasserin aut T. Luo verfasserin aut C.-J. Bian verfasserin aut In European Review for Medical and Pharmacological Sciences Verduci Editore, 2021 26(2022) (DE-627)65427049X (DE-600)2598628-4 22840729 nnns volume:26 year:2022 https://doaj.org/article/16866a27a24642ffa027b07abdf346c4 kostenfrei https://www.europeanreview.org/wp/wp-content/uploads/8945-8958.pdf kostenfrei https://doaj.org/toc/1128-3602 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 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_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_2153 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 26 2022
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authorswithroles_txt_mv	J. Wang @@aut@@ K. Han @@aut@@ Y. Li @@aut@@ C. Zhang @@aut@@ W.-H. Cui @@aut@@ L.-H. Zhu @@aut@@ T. Luo @@aut@@ C.-J. Bian @@aut@@
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Wang</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Exploration and validation of the prognostic value of RNA-binding proteins in hepatocellular carcinoma</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2022</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">OBJECTIVE: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. 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title	Exploration and validation of the prognostic value of RNA-binding proteins in hepatocellular carcinoma
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title_sort	exploration and validation of the prognostic value of rna-binding proteins in hepatocellular carcinoma
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title_auth	Exploration and validation of the prognostic value of RNA-binding proteins in hepatocellular carcinoma
abstract	OBJECTIVE: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Increasing evidence suggests that the dysregulation of RNA-binding proteins (RBPs) is involved in the development of various cancers. However, there is a paucity of studies investigating the roles of RBPs in HCC. MATERIALS AND METHODS: Data on HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (available at: www.ncbi.nlm.nih.gov/geo), and data regarding human RBPs were integrated from SONAR, XRNAX, and CARIC results. We identified modules associated with prognosis using weighted gene co-expression network analysis (WGCNA) and performed functional enrichment analysis. Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify prognostic RBPs and establish a prediction model. According to the median risk score, we separated patients into high- and low-risk groups and investigated the differences in immune cell infiltration, somatic mutations, and gene set enrichment. Univariate and multivariate regression analyses were used to identify prognostic factors for HCC. A nomogram was constructed, and its performance was evaluated with calibration curves. RESULTS: Sixteen RBPs (MEX3A, TTK, MRPL53, IQGAP3, PFN2, IMPDH1, TCOF1, DYNC1LI1, EIF2B4, NOL10, GNL2, EIF1B, PSMD1, AHSA1, SEC61A1, and YBX1) were identified as prognostic genes, and a prognostic model was established. Survival analysis indicated that the model had good predictive performance and that a high-risk score was significantly related to a poor prognosis. Additionally, there were significant differences in immune cell infiltration, somatic mutations, and gene set enrichment between the high- and low-risk groups. Univariate and multivariate regression analyses indicated that the RBP-based signature was an independent prognostic factor for HCC. The nomogram based on 16 RBPs performed well in predicting the overall survival of HCC patients. CONCLUSIONS: The RBP-based signature is an independent prognostic factor for HCC, and this study could provide an innovative method for analyzing prognostic biomarkers and therapeutic targets for HCC.
abstractGer	OBJECTIVE: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Increasing evidence suggests that the dysregulation of RNA-binding proteins (RBPs) is involved in the development of various cancers. However, there is a paucity of studies investigating the roles of RBPs in HCC. MATERIALS AND METHODS: Data on HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (available at: www.ncbi.nlm.nih.gov/geo), and data regarding human RBPs were integrated from SONAR, XRNAX, and CARIC results. We identified modules associated with prognosis using weighted gene co-expression network analysis (WGCNA) and performed functional enrichment analysis. Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify prognostic RBPs and establish a prediction model. According to the median risk score, we separated patients into high- and low-risk groups and investigated the differences in immune cell infiltration, somatic mutations, and gene set enrichment. Univariate and multivariate regression analyses were used to identify prognostic factors for HCC. A nomogram was constructed, and its performance was evaluated with calibration curves. RESULTS: Sixteen RBPs (MEX3A, TTK, MRPL53, IQGAP3, PFN2, IMPDH1, TCOF1, DYNC1LI1, EIF2B4, NOL10, GNL2, EIF1B, PSMD1, AHSA1, SEC61A1, and YBX1) were identified as prognostic genes, and a prognostic model was established. Survival analysis indicated that the model had good predictive performance and that a high-risk score was significantly related to a poor prognosis. Additionally, there were significant differences in immune cell infiltration, somatic mutations, and gene set enrichment between the high- and low-risk groups. Univariate and multivariate regression analyses indicated that the RBP-based signature was an independent prognostic factor for HCC. The nomogram based on 16 RBPs performed well in predicting the overall survival of HCC patients. CONCLUSIONS: The RBP-based signature is an independent prognostic factor for HCC, and this study could provide an innovative method for analyzing prognostic biomarkers and therapeutic targets for HCC.
abstract_unstemmed	OBJECTIVE: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors worldwide. Increasing evidence suggests that the dysregulation of RNA-binding proteins (RBPs) is involved in the development of various cancers. However, there is a paucity of studies investigating the roles of RBPs in HCC. MATERIALS AND METHODS: Data on HCC samples were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases (available at: www.ncbi.nlm.nih.gov/geo), and data regarding human RBPs were integrated from SONAR, XRNAX, and CARIC results. We identified modules associated with prognosis using weighted gene co-expression network analysis (WGCNA) and performed functional enrichment analysis. Univariate and least absolute shrinkage and selection operator (LASSO) regression analyses were used to identify prognostic RBPs and establish a prediction model. According to the median risk score, we separated patients into high- and low-risk groups and investigated the differences in immune cell infiltration, somatic mutations, and gene set enrichment. Univariate and multivariate regression analyses were used to identify prognostic factors for HCC. A nomogram was constructed, and its performance was evaluated with calibration curves. RESULTS: Sixteen RBPs (MEX3A, TTK, MRPL53, IQGAP3, PFN2, IMPDH1, TCOF1, DYNC1LI1, EIF2B4, NOL10, GNL2, EIF1B, PSMD1, AHSA1, SEC61A1, and YBX1) were identified as prognostic genes, and a prognostic model was established. Survival analysis indicated that the model had good predictive performance and that a high-risk score was significantly related to a poor prognosis. Additionally, there were significant differences in immune cell infiltration, somatic mutations, and gene set enrichment between the high- and low-risk groups. Univariate and multivariate regression analyses indicated that the RBP-based signature was an independent prognostic factor for HCC. The nomogram based on 16 RBPs performed well in predicting the overall survival of HCC patients. CONCLUSIONS: The RBP-based signature is an independent prognostic factor for HCC, and this study could provide an innovative method for analyzing prognostic biomarkers and therapeutic targets for HCC.
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title_short	Exploration and validation of the prognostic value of RNA-binding proteins in hepatocellular carcinoma
url	https://doaj.org/article/16866a27a24642ffa027b07abdf346c4 https://www.europeanreview.org/wp/wp-content/uploads/8945-8958.pdf https://doaj.org/toc/1128-3602
remote_bool	true
author2	K. Han Y. Li C. Zhang W.-H. Cui L.-H. Zhu T. Luo C.-J. Bian
author2Str	K. Han Y. Li C. Zhang W.-H. Cui L.-H. Zhu T. Luo C.-J. Bian
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up_date	2024-07-03T18:19:35.951Z
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