Mechanical Stimulus-Related Risk Signature Plays a Key Role in the Prognostic Nomogram For Endometrial Cancer

BackgroundTumor biomechanics correlates with the progression and prognosis of endometrial carcinoma (EC). The objective of this study is to construct a risk model using the mechanical stimulus-related genes in EC.MethodsWe retrieved the transcriptome profiling and clinical data of EC from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB). Differentially expressed mechanical stimulus-related genes were extracted from the databases, and then the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a risk model. A nomogram integrating the genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival and receiver operating characteristic (ROC) curves to estimate the overall survival (OS) of EC patients. Protein profiling technology and immunofluorescence technique were performed to verify the connection between biomechanics and EC.ResultsIn total, 79 mechanical stimulus-related genes were identified by analyzing the two databases. Based on the LASSO regression analysis, 7 genes were selected for the establishment of the risk model. This model showed a good performance in terms of the prognostic accuracy in high- and low-risk groups. The area under the ROC curves (AUC) of this model was 0.697, 0.712 and 0.723 for 3-, 5- and 7-year OS, respectively. Then, a nomogram integrating the genes of the risk model and clinical features was constructed. The nomogram could accurately predict the OS (AUC = 0.779, 0.812 and 0.806 for 3-, 5- and 7-year OS, respectively). The results of the protein profiling technology and immunofluorescence revealed the expression of cytoskeleton proteins to be correlated with the Matrigel stiffness degree.ConclusionsIn summary, a risk model of 7 mechanical stimulus-related genes was identified in EC. A nomogram based on this risk model and combining the clinicopathological features to assess the overall survival of EC showed high practical value. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Xin Xu [verfasserIn] Xingchen Li [verfasserIn] Jingyi Zhou [verfasserIn] Jianliu Wang [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	endometrial cancer mechanical stimulus nomogram risk model overall survival

Übergeordnetes Werk:	In: Frontiers in Oncology - Frontiers Media S.A., 2012, 11(2021)
Übergeordnetes Werk:	volume:11 ; year:2021

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fonc.2021.753910

Katalog-ID:	DOAJ054821967

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520			\|a BackgroundTumor biomechanics correlates with the progression and prognosis of endometrial carcinoma (EC). The objective of this study is to construct a risk model using the mechanical stimulus-related genes in EC.MethodsWe retrieved the transcriptome profiling and clinical data of EC from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB). Differentially expressed mechanical stimulus-related genes were extracted from the databases, and then the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a risk model. A nomogram integrating the genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival and receiver operating characteristic (ROC) curves to estimate the overall survival (OS) of EC patients. Protein profiling technology and immunofluorescence technique were performed to verify the connection between biomechanics and EC.ResultsIn total, 79 mechanical stimulus-related genes were identified by analyzing the two databases. Based on the LASSO regression analysis, 7 genes were selected for the establishment of the risk model. This model showed a good performance in terms of the prognostic accuracy in high- and low-risk groups. The area under the ROC curves (AUC) of this model was 0.697, 0.712 and 0.723 for 3-, 5- and 7-year OS, respectively. Then, a nomogram integrating the genes of the risk model and clinical features was constructed. The nomogram could accurately predict the OS (AUC = 0.779, 0.812 and 0.806 for 3-, 5- and 7-year OS, respectively). The results of the protein profiling technology and immunofluorescence revealed the expression of cytoskeleton proteins to be correlated with the Matrigel stiffness degree.ConclusionsIn summary, a risk model of 7 mechanical stimulus-related genes was identified in EC. A nomogram based on this risk model and combining the clinicopathological features to assess the overall survival of EC showed high practical value.
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allfields	10.3389/fonc.2021.753910 doi (DE-627)DOAJ054821967 (DE-599)DOAJ4c734999525a462ba5a3ad31c24a5521 DE-627 ger DE-627 rakwb eng RC254-282 Xin Xu verfasserin aut Mechanical Stimulus-Related Risk Signature Plays a Key Role in the Prognostic Nomogram For Endometrial Cancer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundTumor biomechanics correlates with the progression and prognosis of endometrial carcinoma (EC). The objective of this study is to construct a risk model using the mechanical stimulus-related genes in EC.MethodsWe retrieved the transcriptome profiling and clinical data of EC from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB). Differentially expressed mechanical stimulus-related genes were extracted from the databases, and then the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a risk model. A nomogram integrating the genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival and receiver operating characteristic (ROC) curves to estimate the overall survival (OS) of EC patients. Protein profiling technology and immunofluorescence technique were performed to verify the connection between biomechanics and EC.ResultsIn total, 79 mechanical stimulus-related genes were identified by analyzing the two databases. Based on the LASSO regression analysis, 7 genes were selected for the establishment of the risk model. This model showed a good performance in terms of the prognostic accuracy in high- and low-risk groups. The area under the ROC curves (AUC) of this model was 0.697, 0.712 and 0.723 for 3-, 5- and 7-year OS, respectively. Then, a nomogram integrating the genes of the risk model and clinical features was constructed. The nomogram could accurately predict the OS (AUC = 0.779, 0.812 and 0.806 for 3-, 5- and 7-year OS, respectively). The results of the protein profiling technology and immunofluorescence revealed the expression of cytoskeleton proteins to be correlated with the Matrigel stiffness degree.ConclusionsIn summary, a risk model of 7 mechanical stimulus-related genes was identified in EC. A nomogram based on this risk model and combining the clinicopathological features to assess the overall survival of EC showed high practical value. endometrial cancer mechanical stimulus nomogram risk model overall survival Neoplasms. Tumors. Oncology. Including cancer and carcinogens Xingchen Li verfasserin aut Jingyi Zhou verfasserin aut Jianliu Wang verfasserin aut Jianliu Wang verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 11(2021) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:11 year:2021 https://doi.org/10.3389/fonc.2021.753910 kostenfrei https://doaj.org/article/4c734999525a462ba5a3ad31c24a5521 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2021.753910/full kostenfrei https://doaj.org/toc/2234-943X 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_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_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/fonc.2021.753910 doi (DE-627)DOAJ054821967 (DE-599)DOAJ4c734999525a462ba5a3ad31c24a5521 DE-627 ger DE-627 rakwb eng RC254-282 Xin Xu verfasserin aut Mechanical Stimulus-Related Risk Signature Plays a Key Role in the Prognostic Nomogram For Endometrial Cancer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundTumor biomechanics correlates with the progression and prognosis of endometrial carcinoma (EC). The objective of this study is to construct a risk model using the mechanical stimulus-related genes in EC.MethodsWe retrieved the transcriptome profiling and clinical data of EC from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB). Differentially expressed mechanical stimulus-related genes were extracted from the databases, and then the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a risk model. A nomogram integrating the genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival and receiver operating characteristic (ROC) curves to estimate the overall survival (OS) of EC patients. Protein profiling technology and immunofluorescence technique were performed to verify the connection between biomechanics and EC.ResultsIn total, 79 mechanical stimulus-related genes were identified by analyzing the two databases. Based on the LASSO regression analysis, 7 genes were selected for the establishment of the risk model. This model showed a good performance in terms of the prognostic accuracy in high- and low-risk groups. The area under the ROC curves (AUC) of this model was 0.697, 0.712 and 0.723 for 3-, 5- and 7-year OS, respectively. Then, a nomogram integrating the genes of the risk model and clinical features was constructed. The nomogram could accurately predict the OS (AUC = 0.779, 0.812 and 0.806 for 3-, 5- and 7-year OS, respectively). The results of the protein profiling technology and immunofluorescence revealed the expression of cytoskeleton proteins to be correlated with the Matrigel stiffness degree.ConclusionsIn summary, a risk model of 7 mechanical stimulus-related genes was identified in EC. A nomogram based on this risk model and combining the clinicopathological features to assess the overall survival of EC showed high practical value. endometrial cancer mechanical stimulus nomogram risk model overall survival Neoplasms. Tumors. Oncology. Including cancer and carcinogens Xingchen Li verfasserin aut Jingyi Zhou verfasserin aut Jianliu Wang verfasserin aut Jianliu Wang verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 11(2021) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:11 year:2021 https://doi.org/10.3389/fonc.2021.753910 kostenfrei https://doaj.org/article/4c734999525a462ba5a3ad31c24a5521 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2021.753910/full kostenfrei https://doaj.org/toc/2234-943X 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_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_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/fonc.2021.753910 doi (DE-627)DOAJ054821967 (DE-599)DOAJ4c734999525a462ba5a3ad31c24a5521 DE-627 ger DE-627 rakwb eng RC254-282 Xin Xu verfasserin aut Mechanical Stimulus-Related Risk Signature Plays a Key Role in the Prognostic Nomogram For Endometrial Cancer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundTumor biomechanics correlates with the progression and prognosis of endometrial carcinoma (EC). The objective of this study is to construct a risk model using the mechanical stimulus-related genes in EC.MethodsWe retrieved the transcriptome profiling and clinical data of EC from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB). Differentially expressed mechanical stimulus-related genes were extracted from the databases, and then the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a risk model. A nomogram integrating the genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival and receiver operating characteristic (ROC) curves to estimate the overall survival (OS) of EC patients. Protein profiling technology and immunofluorescence technique were performed to verify the connection between biomechanics and EC.ResultsIn total, 79 mechanical stimulus-related genes were identified by analyzing the two databases. Based on the LASSO regression analysis, 7 genes were selected for the establishment of the risk model. This model showed a good performance in terms of the prognostic accuracy in high- and low-risk groups. The area under the ROC curves (AUC) of this model was 0.697, 0.712 and 0.723 for 3-, 5- and 7-year OS, respectively. Then, a nomogram integrating the genes of the risk model and clinical features was constructed. The nomogram could accurately predict the OS (AUC = 0.779, 0.812 and 0.806 for 3-, 5- and 7-year OS, respectively). The results of the protein profiling technology and immunofluorescence revealed the expression of cytoskeleton proteins to be correlated with the Matrigel stiffness degree.ConclusionsIn summary, a risk model of 7 mechanical stimulus-related genes was identified in EC. A nomogram based on this risk model and combining the clinicopathological features to assess the overall survival of EC showed high practical value. endometrial cancer mechanical stimulus nomogram risk model overall survival Neoplasms. Tumors. Oncology. Including cancer and carcinogens Xingchen Li verfasserin aut Jingyi Zhou verfasserin aut Jianliu Wang verfasserin aut Jianliu Wang verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 11(2021) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:11 year:2021 https://doi.org/10.3389/fonc.2021.753910 kostenfrei https://doaj.org/article/4c734999525a462ba5a3ad31c24a5521 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2021.753910/full kostenfrei https://doaj.org/toc/2234-943X 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_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_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/fonc.2021.753910 doi (DE-627)DOAJ054821967 (DE-599)DOAJ4c734999525a462ba5a3ad31c24a5521 DE-627 ger DE-627 rakwb eng RC254-282 Xin Xu verfasserin aut Mechanical Stimulus-Related Risk Signature Plays a Key Role in the Prognostic Nomogram For Endometrial Cancer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundTumor biomechanics correlates with the progression and prognosis of endometrial carcinoma (EC). The objective of this study is to construct a risk model using the mechanical stimulus-related genes in EC.MethodsWe retrieved the transcriptome profiling and clinical data of EC from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB). Differentially expressed mechanical stimulus-related genes were extracted from the databases, and then the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a risk model. A nomogram integrating the genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival and receiver operating characteristic (ROC) curves to estimate the overall survival (OS) of EC patients. Protein profiling technology and immunofluorescence technique were performed to verify the connection between biomechanics and EC.ResultsIn total, 79 mechanical stimulus-related genes were identified by analyzing the two databases. Based on the LASSO regression analysis, 7 genes were selected for the establishment of the risk model. This model showed a good performance in terms of the prognostic accuracy in high- and low-risk groups. The area under the ROC curves (AUC) of this model was 0.697, 0.712 and 0.723 for 3-, 5- and 7-year OS, respectively. Then, a nomogram integrating the genes of the risk model and clinical features was constructed. The nomogram could accurately predict the OS (AUC = 0.779, 0.812 and 0.806 for 3-, 5- and 7-year OS, respectively). The results of the protein profiling technology and immunofluorescence revealed the expression of cytoskeleton proteins to be correlated with the Matrigel stiffness degree.ConclusionsIn summary, a risk model of 7 mechanical stimulus-related genes was identified in EC. A nomogram based on this risk model and combining the clinicopathological features to assess the overall survival of EC showed high practical value. endometrial cancer mechanical stimulus nomogram risk model overall survival Neoplasms. Tumors. Oncology. Including cancer and carcinogens Xingchen Li verfasserin aut Jingyi Zhou verfasserin aut Jianliu Wang verfasserin aut Jianliu Wang verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 11(2021) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:11 year:2021 https://doi.org/10.3389/fonc.2021.753910 kostenfrei https://doaj.org/article/4c734999525a462ba5a3ad31c24a5521 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2021.753910/full kostenfrei https://doaj.org/toc/2234-943X 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_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_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/fonc.2021.753910 doi (DE-627)DOAJ054821967 (DE-599)DOAJ4c734999525a462ba5a3ad31c24a5521 DE-627 ger DE-627 rakwb eng RC254-282 Xin Xu verfasserin aut Mechanical Stimulus-Related Risk Signature Plays a Key Role in the Prognostic Nomogram For Endometrial Cancer 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundTumor biomechanics correlates with the progression and prognosis of endometrial carcinoma (EC). The objective of this study is to construct a risk model using the mechanical stimulus-related genes in EC.MethodsWe retrieved the transcriptome profiling and clinical data of EC from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB). Differentially expressed mechanical stimulus-related genes were extracted from the databases, and then the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a risk model. A nomogram integrating the genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival and receiver operating characteristic (ROC) curves to estimate the overall survival (OS) of EC patients. Protein profiling technology and immunofluorescence technique were performed to verify the connection between biomechanics and EC.ResultsIn total, 79 mechanical stimulus-related genes were identified by analyzing the two databases. Based on the LASSO regression analysis, 7 genes were selected for the establishment of the risk model. This model showed a good performance in terms of the prognostic accuracy in high- and low-risk groups. The area under the ROC curves (AUC) of this model was 0.697, 0.712 and 0.723 for 3-, 5- and 7-year OS, respectively. Then, a nomogram integrating the genes of the risk model and clinical features was constructed. The nomogram could accurately predict the OS (AUC = 0.779, 0.812 and 0.806 for 3-, 5- and 7-year OS, respectively). The results of the protein profiling technology and immunofluorescence revealed the expression of cytoskeleton proteins to be correlated with the Matrigel stiffness degree.ConclusionsIn summary, a risk model of 7 mechanical stimulus-related genes was identified in EC. A nomogram based on this risk model and combining the clinicopathological features to assess the overall survival of EC showed high practical value. endometrial cancer mechanical stimulus nomogram risk model overall survival Neoplasms. Tumors. Oncology. Including cancer and carcinogens Xingchen Li verfasserin aut Jingyi Zhou verfasserin aut Jianliu Wang verfasserin aut Jianliu Wang verfasserin aut In Frontiers in Oncology Frontiers Media S.A., 2012 11(2021) (DE-627)684965518 (DE-600)2649216-7 2234943X nnns volume:11 year:2021 https://doi.org/10.3389/fonc.2021.753910 kostenfrei https://doaj.org/article/4c734999525a462ba5a3ad31c24a5521 kostenfrei https://www.frontiersin.org/articles/10.3389/fonc.2021.753910/full kostenfrei https://doaj.org/toc/2234-943X 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_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_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
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The objective of this study is to construct a risk model using the mechanical stimulus-related genes in EC.MethodsWe retrieved the transcriptome profiling and clinical data of EC from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB). Differentially expressed mechanical stimulus-related genes were extracted from the databases, and then the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a risk model. A nomogram integrating the genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival and receiver operating characteristic (ROC) curves to estimate the overall survival (OS) of EC patients. Protein profiling technology and immunofluorescence technique were performed to verify the connection between biomechanics and EC.ResultsIn total, 79 mechanical stimulus-related genes were identified by analyzing the two databases. Based on the LASSO regression analysis, 7 genes were selected for the establishment of the risk model. This model showed a good performance in terms of the prognostic accuracy in high- and low-risk groups. The area under the ROC curves (AUC) of this model was 0.697, 0.712 and 0.723 for 3-, 5- and 7-year OS, respectively. Then, a nomogram integrating the genes of the risk model and clinical features was constructed. The nomogram could accurately predict the OS (AUC = 0.779, 0.812 and 0.806 for 3-, 5- and 7-year OS, respectively). The results of the protein profiling technology and immunofluorescence revealed the expression of cytoskeleton proteins to be correlated with the Matrigel stiffness degree.ConclusionsIn summary, a risk model of 7 mechanical stimulus-related genes was identified in EC. 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callnumber-first	R - Medicine
author	Xin Xu
spellingShingle	Xin Xu misc RC254-282 misc endometrial cancer misc mechanical stimulus misc nomogram misc risk model misc overall survival misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens Mechanical Stimulus-Related Risk Signature Plays a Key Role in the Prognostic Nomogram For Endometrial Cancer
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topic_title	RC254-282 Mechanical Stimulus-Related Risk Signature Plays a Key Role in the Prognostic Nomogram For Endometrial Cancer endometrial cancer mechanical stimulus nomogram risk model overall survival
topic	misc RC254-282 misc endometrial cancer misc mechanical stimulus misc nomogram misc risk model misc overall survival misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_unstemmed	misc RC254-282 misc endometrial cancer misc mechanical stimulus misc nomogram misc risk model misc overall survival misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
topic_browse	misc RC254-282 misc endometrial cancer misc mechanical stimulus misc nomogram misc risk model misc overall survival misc Neoplasms. Tumors. Oncology. Including cancer and carcinogens
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title	Mechanical Stimulus-Related Risk Signature Plays a Key Role in the Prognostic Nomogram For Endometrial Cancer
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title_full	Mechanical Stimulus-Related Risk Signature Plays a Key Role in the Prognostic Nomogram For Endometrial Cancer
author_sort	Xin Xu
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author_browse	Xin Xu Xingchen Li Jingyi Zhou Jianliu Wang
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title_sort	mechanical stimulus-related risk signature plays a key role in the prognostic nomogram for endometrial cancer
callnumber	RC254-282
title_auth	Mechanical Stimulus-Related Risk Signature Plays a Key Role in the Prognostic Nomogram For Endometrial Cancer
abstract	BackgroundTumor biomechanics correlates with the progression and prognosis of endometrial carcinoma (EC). The objective of this study is to construct a risk model using the mechanical stimulus-related genes in EC.MethodsWe retrieved the transcriptome profiling and clinical data of EC from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB). Differentially expressed mechanical stimulus-related genes were extracted from the databases, and then the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a risk model. A nomogram integrating the genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival and receiver operating characteristic (ROC) curves to estimate the overall survival (OS) of EC patients. Protein profiling technology and immunofluorescence technique were performed to verify the connection between biomechanics and EC.ResultsIn total, 79 mechanical stimulus-related genes were identified by analyzing the two databases. Based on the LASSO regression analysis, 7 genes were selected for the establishment of the risk model. This model showed a good performance in terms of the prognostic accuracy in high- and low-risk groups. The area under the ROC curves (AUC) of this model was 0.697, 0.712 and 0.723 for 3-, 5- and 7-year OS, respectively. Then, a nomogram integrating the genes of the risk model and clinical features was constructed. The nomogram could accurately predict the OS (AUC = 0.779, 0.812 and 0.806 for 3-, 5- and 7-year OS, respectively). The results of the protein profiling technology and immunofluorescence revealed the expression of cytoskeleton proteins to be correlated with the Matrigel stiffness degree.ConclusionsIn summary, a risk model of 7 mechanical stimulus-related genes was identified in EC. A nomogram based on this risk model and combining the clinicopathological features to assess the overall survival of EC showed high practical value.
abstractGer	BackgroundTumor biomechanics correlates with the progression and prognosis of endometrial carcinoma (EC). The objective of this study is to construct a risk model using the mechanical stimulus-related genes in EC.MethodsWe retrieved the transcriptome profiling and clinical data of EC from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB). Differentially expressed mechanical stimulus-related genes were extracted from the databases, and then the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a risk model. A nomogram integrating the genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival and receiver operating characteristic (ROC) curves to estimate the overall survival (OS) of EC patients. Protein profiling technology and immunofluorescence technique were performed to verify the connection between biomechanics and EC.ResultsIn total, 79 mechanical stimulus-related genes were identified by analyzing the two databases. Based on the LASSO regression analysis, 7 genes were selected for the establishment of the risk model. This model showed a good performance in terms of the prognostic accuracy in high- and low-risk groups. The area under the ROC curves (AUC) of this model was 0.697, 0.712 and 0.723 for 3-, 5- and 7-year OS, respectively. Then, a nomogram integrating the genes of the risk model and clinical features was constructed. The nomogram could accurately predict the OS (AUC = 0.779, 0.812 and 0.806 for 3-, 5- and 7-year OS, respectively). The results of the protein profiling technology and immunofluorescence revealed the expression of cytoskeleton proteins to be correlated with the Matrigel stiffness degree.ConclusionsIn summary, a risk model of 7 mechanical stimulus-related genes was identified in EC. A nomogram based on this risk model and combining the clinicopathological features to assess the overall survival of EC showed high practical value.
abstract_unstemmed	BackgroundTumor biomechanics correlates with the progression and prognosis of endometrial carcinoma (EC). The objective of this study is to construct a risk model using the mechanical stimulus-related genes in EC.MethodsWe retrieved the transcriptome profiling and clinical data of EC from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB). Differentially expressed mechanical stimulus-related genes were extracted from the databases, and then the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a risk model. A nomogram integrating the genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival and receiver operating characteristic (ROC) curves to estimate the overall survival (OS) of EC patients. Protein profiling technology and immunofluorescence technique were performed to verify the connection between biomechanics and EC.ResultsIn total, 79 mechanical stimulus-related genes were identified by analyzing the two databases. Based on the LASSO regression analysis, 7 genes were selected for the establishment of the risk model. This model showed a good performance in terms of the prognostic accuracy in high- and low-risk groups. The area under the ROC curves (AUC) of this model was 0.697, 0.712 and 0.723 for 3-, 5- and 7-year OS, respectively. Then, a nomogram integrating the genes of the risk model and clinical features was constructed. The nomogram could accurately predict the OS (AUC = 0.779, 0.812 and 0.806 for 3-, 5- and 7-year OS, respectively). The results of the protein profiling technology and immunofluorescence revealed the expression of cytoskeleton proteins to be correlated with the Matrigel stiffness degree.ConclusionsIn summary, a risk model of 7 mechanical stimulus-related genes was identified in EC. A nomogram based on this risk model and combining the clinicopathological features to assess the overall survival of EC showed high practical value.
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title_short	Mechanical Stimulus-Related Risk Signature Plays a Key Role in the Prognostic Nomogram For Endometrial Cancer
url	https://doi.org/10.3389/fonc.2021.753910 https://doaj.org/article/4c734999525a462ba5a3ad31c24a5521 https://www.frontiersin.org/articles/10.3389/fonc.2021.753910/full https://doaj.org/toc/2234-943X
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The objective of this study is to construct a risk model using the mechanical stimulus-related genes in EC.MethodsWe retrieved the transcriptome profiling and clinical data of EC from The Cancer Genome Atlas (TCGA) and Molecular Signatures Database (MSigDB). Differentially expressed mechanical stimulus-related genes were extracted from the databases, and then the least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a risk model. A nomogram integrating the genes and the clinicopathological characteristics was established and validated using the Kaplan-Meier survival and receiver operating characteristic (ROC) curves to estimate the overall survival (OS) of EC patients. Protein profiling technology and immunofluorescence technique were performed to verify the connection between biomechanics and EC.ResultsIn total, 79 mechanical stimulus-related genes were identified by analyzing the two databases. Based on the LASSO regression analysis, 7 genes were selected for the establishment of the risk model. This model showed a good performance in terms of the prognostic accuracy in high- and low-risk groups. The area under the ROC curves (AUC) of this model was 0.697, 0.712 and 0.723 for 3-, 5- and 7-year OS, respectively. Then, a nomogram integrating the genes of the risk model and clinical features was constructed. The nomogram could accurately predict the OS (AUC = 0.779, 0.812 and 0.806 for 3-, 5- and 7-year OS, respectively). The results of the protein profiling technology and immunofluorescence revealed the expression of cytoskeleton proteins to be correlated with the Matrigel stiffness degree.ConclusionsIn summary, a risk model of 7 mechanical stimulus-related genes was identified in EC. A nomogram based on this risk model and combining the clinicopathological features to assess the overall survival of EC showed high practical value.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">endometrial cancer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">mechanical stimulus</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">nomogram</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">risk model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">overall survival</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Neoplasms. Tumors. Oncology. 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Mechanical Stimulus-Related Risk Signature Plays a Key Role in the Prognostic Nomogram For Endometrial Cancer

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