A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis

BackgroundBreast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have...
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Autor*in:	Wenlong Chen [verfasserIn] Yakun Kang [verfasserIn] Wenyi Sheng [verfasserIn] Qiyan Huang [verfasserIn] Jiale Cheng [verfasserIn] Shengbin Pei [verfasserIn] You Meng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	breast cancer POLQ prognostic signature immune landscape drug screening

Übergeordnetes Werk:	In: Frontiers in Immunology - Frontiers Media S.A., 2011, 15(2024)
Übergeordnetes Werk:	volume:15 ; year:2024

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fimmu.2024.1331841

Katalog-ID:	DOAJ09597587X

Internformat


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520			\|a BackgroundBreast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients.MethodsBRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the ‘limma’ package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function.ResultsOur study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells.ConclusionOur 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics.
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allfields	10.3389/fimmu.2024.1331841 doi (DE-627)DOAJ09597587X (DE-599)DOAJ976b55787d75475cbbea2d2818c2a9f9 DE-627 ger DE-627 rakwb eng RC581-607 Wenlong Chen verfasserin aut A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundBreast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients.MethodsBRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the ‘limma’ package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function.ResultsOur study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells.ConclusionOur 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics. breast cancer POLQ prognostic signature immune landscape drug screening Immunologic diseases. Allergy Yakun Kang verfasserin aut Wenyi Sheng verfasserin aut Qiyan Huang verfasserin aut Jiale Cheng verfasserin aut Shengbin Pei verfasserin aut Shengbin Pei verfasserin aut You Meng verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 15(2024) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:15 year:2024 https://doi.org/10.3389/fimmu.2024.1331841 kostenfrei https://doaj.org/article/976b55787d75475cbbea2d2818c2a9f9 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2024.1331841/full kostenfrei https://doaj.org/toc/1664-3224 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 15 2024
spelling	10.3389/fimmu.2024.1331841 doi (DE-627)DOAJ09597587X (DE-599)DOAJ976b55787d75475cbbea2d2818c2a9f9 DE-627 ger DE-627 rakwb eng RC581-607 Wenlong Chen verfasserin aut A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundBreast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients.MethodsBRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the ‘limma’ package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function.ResultsOur study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells.ConclusionOur 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics. breast cancer POLQ prognostic signature immune landscape drug screening Immunologic diseases. Allergy Yakun Kang verfasserin aut Wenyi Sheng verfasserin aut Qiyan Huang verfasserin aut Jiale Cheng verfasserin aut Shengbin Pei verfasserin aut Shengbin Pei verfasserin aut You Meng verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 15(2024) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:15 year:2024 https://doi.org/10.3389/fimmu.2024.1331841 kostenfrei https://doaj.org/article/976b55787d75475cbbea2d2818c2a9f9 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2024.1331841/full kostenfrei https://doaj.org/toc/1664-3224 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 15 2024
allfields_unstemmed	10.3389/fimmu.2024.1331841 doi (DE-627)DOAJ09597587X (DE-599)DOAJ976b55787d75475cbbea2d2818c2a9f9 DE-627 ger DE-627 rakwb eng RC581-607 Wenlong Chen verfasserin aut A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundBreast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients.MethodsBRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the ‘limma’ package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function.ResultsOur study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells.ConclusionOur 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics. breast cancer POLQ prognostic signature immune landscape drug screening Immunologic diseases. Allergy Yakun Kang verfasserin aut Wenyi Sheng verfasserin aut Qiyan Huang verfasserin aut Jiale Cheng verfasserin aut Shengbin Pei verfasserin aut Shengbin Pei verfasserin aut You Meng verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 15(2024) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:15 year:2024 https://doi.org/10.3389/fimmu.2024.1331841 kostenfrei https://doaj.org/article/976b55787d75475cbbea2d2818c2a9f9 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2024.1331841/full kostenfrei https://doaj.org/toc/1664-3224 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 15 2024
allfieldsGer	10.3389/fimmu.2024.1331841 doi (DE-627)DOAJ09597587X (DE-599)DOAJ976b55787d75475cbbea2d2818c2a9f9 DE-627 ger DE-627 rakwb eng RC581-607 Wenlong Chen verfasserin aut A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundBreast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients.MethodsBRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the ‘limma’ package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function.ResultsOur study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells.ConclusionOur 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics. breast cancer POLQ prognostic signature immune landscape drug screening Immunologic diseases. Allergy Yakun Kang verfasserin aut Wenyi Sheng verfasserin aut Qiyan Huang verfasserin aut Jiale Cheng verfasserin aut Shengbin Pei verfasserin aut Shengbin Pei verfasserin aut You Meng verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 15(2024) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:15 year:2024 https://doi.org/10.3389/fimmu.2024.1331841 kostenfrei https://doaj.org/article/976b55787d75475cbbea2d2818c2a9f9 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2024.1331841/full kostenfrei https://doaj.org/toc/1664-3224 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 15 2024
allfieldsSound	10.3389/fimmu.2024.1331841 doi (DE-627)DOAJ09597587X (DE-599)DOAJ976b55787d75475cbbea2d2818c2a9f9 DE-627 ger DE-627 rakwb eng RC581-607 Wenlong Chen verfasserin aut A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier BackgroundBreast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients.MethodsBRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the ‘limma’ package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function.ResultsOur study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells.ConclusionOur 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics. breast cancer POLQ prognostic signature immune landscape drug screening Immunologic diseases. Allergy Yakun Kang verfasserin aut Wenyi Sheng verfasserin aut Qiyan Huang verfasserin aut Jiale Cheng verfasserin aut Shengbin Pei verfasserin aut Shengbin Pei verfasserin aut You Meng verfasserin aut In Frontiers in Immunology Frontiers Media S.A., 2011 15(2024) (DE-627)657998354 (DE-600)2606827-8 16643224 nnns volume:15 year:2024 https://doi.org/10.3389/fimmu.2024.1331841 kostenfrei https://doaj.org/article/976b55787d75475cbbea2d2818c2a9f9 kostenfrei https://www.frontiersin.org/articles/10.3389/fimmu.2024.1331841/full kostenfrei https://doaj.org/toc/1664-3224 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 15 2024
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Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients.MethodsBRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the ‘limma’ package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function.ResultsOur study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells.ConclusionOur 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">breast cancer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">POLQ</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">prognostic signature</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">immune landscape</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">drug screening</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Immunologic diseases. 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callnumber-first	R - Medicine
author	Wenlong Chen
spellingShingle	Wenlong Chen misc RC581-607 misc breast cancer misc POLQ misc prognostic signature misc immune landscape misc drug screening misc Immunologic diseases. Allergy A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis
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topic_title	RC581-607 A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis breast cancer POLQ prognostic signature immune landscape drug screening
topic	misc RC581-607 misc breast cancer misc POLQ misc prognostic signature misc immune landscape misc drug screening misc Immunologic diseases. Allergy
topic_unstemmed	misc RC581-607 misc breast cancer misc POLQ misc prognostic signature misc immune landscape misc drug screening misc Immunologic diseases. Allergy
topic_browse	misc RC581-607 misc breast cancer misc POLQ misc prognostic signature misc immune landscape misc drug screening misc Immunologic diseases. Allergy
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title	A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis
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title_full	A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis
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author_browse	Wenlong Chen Yakun Kang Wenyi Sheng Qiyan Huang Jiale Cheng Shengbin Pei You Meng
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title_sort	new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating wgcna and bioinformatics analysis
callnumber	RC581-607
title_auth	A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis
abstract	BackgroundBreast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients.MethodsBRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the ‘limma’ package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function.ResultsOur study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells.ConclusionOur 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics.
abstractGer	BackgroundBreast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients.MethodsBRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the ‘limma’ package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function.ResultsOur study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells.ConclusionOur 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics.
abstract_unstemmed	BackgroundBreast cancer (BRCA) is a common malignancy in women, and its resistance to immunotherapy is a major challenge. Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients.MethodsBRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the ‘limma’ package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function.ResultsOur study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells.ConclusionOur 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics.
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title_short	A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis
url	https://doi.org/10.3389/fimmu.2024.1331841 https://doaj.org/article/976b55787d75475cbbea2d2818c2a9f9 https://www.frontiersin.org/articles/10.3389/fimmu.2024.1331841/full https://doaj.org/toc/1664-3224
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up_date	2024-07-03T17:40:21.830Z
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Abnormal expression of genes is important in the occurrence and development of BRCA and may also affect the prognosis of patients. Although many BRCA prognosis model scores have been developed, they are only applicable to a limited number of disease subtypes. Our goal is to develop a new prognostic score that is more accurate and applicable to a wider range of BRCA patients.MethodsBRCA patient data from The Cancer Genome Atlas database was used to identify breast cancer-related genes (BRGs). Differential expression analysis of BRGs was performed using the ‘limma’ package in R. Prognostic BRGs were identified using co-expression and univariate Cox analysis. A predictive model of four BRGs was established using Cox regression and the LASSO algorithm. Model performance was evaluated using K-M survival and receiver operating characteristic curve analysis. The predictive ability of the signature in immune microenvironment and immunotherapy was investigated. In vitro experiments validated POLQ function.ResultsOur study identified a four-BRG prognostic signature that outperformed conventional clinicopathological characteristics in predicting survival outcomes in BRCA patients. The signature effectively stratified BRCA patients into high- and low-risk groups and showed potential in predicting the response to immunotherapy. Notably, significant differences were observed in immune cell abundance between the two groups. In vitro experiments demonstrated that POLQ knockdown significantly reduced the viability, proliferation, and invasion capacity of MDA-MB-231 or HCC1806 cells.ConclusionOur 4-BRG signature has the potential as an independent biomarker for predicting prognosis and treatment response in BRCA patients, complementing existing clinicopathological characteristics.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">breast cancer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">POLQ</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">prognostic signature</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">immune landscape</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">drug screening</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Immunologic diseases. 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A new 4-gene-based prognostic model accurately predicts breast cancer prognosis and immunotherapy response by integrating WGCNA and bioinformatics analysis

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