Construction of a circadian rhythm-related gene signature for predicting the prognosis and immune infiltration of breast cancer
ObjectivesIn this study, we constructed a model based on circadian rhythm associated genes (CRRGs) to predict prognosis and immune infiltration in patients with breast cancer (BC).Materials and methodsBy using TCGA and CGDB databases, we conducted a comprehensive analysis of circadian rhythm gene ex...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Molecular Biosciences |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmolb.2025.1540672/full |
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| author | Lin Ni Lin Ni He Li Yanqi Cui Wanqiu Xiong Shuming Chen Hancong Huang Zhiwei Wang Hu Zhao Hu Zhao Hu Zhao Bing Wang Bing Wang Bing Wang |
| author_facet | Lin Ni Lin Ni He Li Yanqi Cui Wanqiu Xiong Shuming Chen Hancong Huang Zhiwei Wang Hu Zhao Hu Zhao Hu Zhao Bing Wang Bing Wang Bing Wang |
| author_sort | Lin Ni |
| collection | DOAJ |
| description | ObjectivesIn this study, we constructed a model based on circadian rhythm associated genes (CRRGs) to predict prognosis and immune infiltration in patients with breast cancer (BC).Materials and methodsBy using TCGA and CGDB databases, we conducted a comprehensive analysis of circadian rhythm gene expression and clinicopathological data. Three different machine learning algorithms were used to screen out the characteristic circadian genes associated with BC prognosis. On this basis, a circadian gene prediction model about BC prognosis was constructed and validated. We also evaluated the association of the model’s risk score with immune cells and immune checkpoint genes, and analyzed prognostic genes and drug sensitivity in this model.ResultsWe screened 62 DEGs, including 30 upregulated genes and 32 downregulated genes, and performed GO and KEGG analysis on them. The above 62 DEGs were included in Cox analysis, LASSO regression, Random Forest and SVMV-RFE, respectively, and then the intersection was used to obtain 5 prognostic related characteristic genes (SUV39H2, OPN4, RORB, FBXL6 and SIAH2). The Risk Score of each sample was calculated according to the expression level and risk coefficient of 5 genes, Risk Score= (SUV39H2 expression level ×0.0436) + (OPN4 expression level ×1.4270) + (RORB expression level ×0.1917) + (FBXL6 expression level ×0.3190) + (SIAH2 expression level × -0.1984).ConclusionSUV39H2, OPN4, RORB and FBXL6 were positively correlated with Risk Score, while SIAH2 was negatively correlated with Risk Score. The above five circadian rhythm genes can construct a risk model for predicting the prognosis and immune invasion of BC. |
| format | Article |
| id | doaj-art-5edc73fe4bb54165abf258993d980276 |
| institution | Kabale University |
| issn | 2296-889X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Molecular Biosciences |
| spelling | doaj-art-5edc73fe4bb54165abf258993d9802762025-02-06T05:21:55ZengFrontiers Media S.A.Frontiers in Molecular Biosciences2296-889X2025-02-011210.3389/fmolb.2025.15406721540672Construction of a circadian rhythm-related gene signature for predicting the prognosis and immune infiltration of breast cancerLin Ni0Lin Ni1He Li2Yanqi Cui3Wanqiu Xiong4Shuming Chen5Hancong Huang6Zhiwei Wang7Hu Zhao8Hu Zhao9Hu Zhao10Bing Wang11Bing Wang12Bing Wang13Department of General Surgery, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, PLA, Fuzhou, ChinaDepartment of General Surgery, Fuzhou General Teaching Hospital, Fujian University of Traditional Chinese Medicine, 900TH Hospital of Joint Logistics Support Force, Fuzhou, ChinaDepartment of General Surgery, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, PLA, Fuzhou, ChinaDepartment of Cardiothoracic surgery, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, PLA, Fuzhou, ChinaDepartment of General Surgery, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, PLA, Fuzhou, ChinaDepartment of General Surgery, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, PLA, Fuzhou, ChinaDepartment of General Surgery, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, PLA, Fuzhou, ChinaDepartment of General Surgery, Fuzhou General Teaching Hospital, Fujian University of Traditional Chinese Medicine, 900TH Hospital of Joint Logistics Support Force, Fuzhou, ChinaDepartment of General Surgery, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, PLA, Fuzhou, ChinaDepartment of General Surgery, Fuzhou General Teaching Hospital, Fujian University of Traditional Chinese Medicine, 900TH Hospital of Joint Logistics Support Force, Fuzhou, ChinaDepartment of General Surgery, Dongfang Hospital of Xiamen University, School of Medicine, Xiamen University, 900TH Hospital of Joint Logistics Support Force, Fuzhou, ChinaDepartment of General Surgery, Fuzong Clinical Medical College of Fujian Medical University, 900TH Hospital of Joint Logistics Support Force, PLA, Fuzhou, ChinaDepartment of General Surgery, Fuzhou General Teaching Hospital, Fujian University of Traditional Chinese Medicine, 900TH Hospital of Joint Logistics Support Force, Fuzhou, ChinaDepartment of General Surgery, Dongfang Hospital of Xiamen University, School of Medicine, Xiamen University, 900TH Hospital of Joint Logistics Support Force, Fuzhou, ChinaObjectivesIn this study, we constructed a model based on circadian rhythm associated genes (CRRGs) to predict prognosis and immune infiltration in patients with breast cancer (BC).Materials and methodsBy using TCGA and CGDB databases, we conducted a comprehensive analysis of circadian rhythm gene expression and clinicopathological data. Three different machine learning algorithms were used to screen out the characteristic circadian genes associated with BC prognosis. On this basis, a circadian gene prediction model about BC prognosis was constructed and validated. We also evaluated the association of the model’s risk score with immune cells and immune checkpoint genes, and analyzed prognostic genes and drug sensitivity in this model.ResultsWe screened 62 DEGs, including 30 upregulated genes and 32 downregulated genes, and performed GO and KEGG analysis on them. The above 62 DEGs were included in Cox analysis, LASSO regression, Random Forest and SVMV-RFE, respectively, and then the intersection was used to obtain 5 prognostic related characteristic genes (SUV39H2, OPN4, RORB, FBXL6 and SIAH2). The Risk Score of each sample was calculated according to the expression level and risk coefficient of 5 genes, Risk Score= (SUV39H2 expression level ×0.0436) + (OPN4 expression level ×1.4270) + (RORB expression level ×0.1917) + (FBXL6 expression level ×0.3190) + (SIAH2 expression level × -0.1984).ConclusionSUV39H2, OPN4, RORB and FBXL6 were positively correlated with Risk Score, while SIAH2 was negatively correlated with Risk Score. The above five circadian rhythm genes can construct a risk model for predicting the prognosis and immune invasion of BC.https://www.frontiersin.org/articles/10.3389/fmolb.2025.1540672/fullbreast cancercircadian rhythmmachine learninga risk modelpredict prognosis |
| spellingShingle | Lin Ni Lin Ni He Li Yanqi Cui Wanqiu Xiong Shuming Chen Hancong Huang Zhiwei Wang Hu Zhao Hu Zhao Hu Zhao Bing Wang Bing Wang Bing Wang Construction of a circadian rhythm-related gene signature for predicting the prognosis and immune infiltration of breast cancer Frontiers in Molecular Biosciences breast cancer circadian rhythm machine learning a risk model predict prognosis |
| title | Construction of a circadian rhythm-related gene signature for predicting the prognosis and immune infiltration of breast cancer |
| title_full | Construction of a circadian rhythm-related gene signature for predicting the prognosis and immune infiltration of breast cancer |
| title_fullStr | Construction of a circadian rhythm-related gene signature for predicting the prognosis and immune infiltration of breast cancer |
| title_full_unstemmed | Construction of a circadian rhythm-related gene signature for predicting the prognosis and immune infiltration of breast cancer |
| title_short | Construction of a circadian rhythm-related gene signature for predicting the prognosis and immune infiltration of breast cancer |
| title_sort | construction of a circadian rhythm related gene signature for predicting the prognosis and immune infiltration of breast cancer |
| topic | breast cancer circadian rhythm machine learning a risk model predict prognosis |
| url | https://www.frontiersin.org/articles/10.3389/fmolb.2025.1540672/full |
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