Validation of a Proteomic-Based Prognostic Model for Breast Cancer and Immunological Analysis
Breast cancer (BC) has emerged as an extremely destructive malignancy, causing significant harm to female patients and society at large. Proteomic research holds great promise for early diagnosis and treatment of diseases, and the integration of proteomics with genomics can offer valuable assistance...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-01-01
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| Series: | International Journal of Genomics |
| Online Access: | http://dx.doi.org/10.1155/2023/1738750 |
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| _version_ | 1849695759680667648 |
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| author | Yunlin Yu Linhuan Dong Changjun Dong Xianlin Zhang |
| author_facet | Yunlin Yu Linhuan Dong Changjun Dong Xianlin Zhang |
| author_sort | Yunlin Yu |
| collection | DOAJ |
| description | Breast cancer (BC) has emerged as an extremely destructive malignancy, causing significant harm to female patients and society at large. Proteomic research holds great promise for early diagnosis and treatment of diseases, and the integration of proteomics with genomics can offer valuable assistance in the early diagnosis, treatment, and improved prognosis of BC patients. In this study, we downloaded breast cancer protein expression data from The Cancer Genome Atlas (TCGA) and combined proteomics with genomics to construct a proteomic-based prognostic model for BC. This model consists of nine proteins (HEREGULIN, IDO, PEA15, MERIT40_pS29, CIITA, AKT2, CD171 DVL3, and CABL9). The accuracy of the model in predicting the survival prognosis of BC patients was further validated through risk curve analysis, survival curve analysis, and independent prognostic analysis. We further confirmed the impact of differential expression of these nine key proteins on overall survival in BC patients, and the differential expression of the key proteins and their encoding genes was validated using immunohistochemical staining. Enrichment analysis revealed functional associations primarily related to PPAR signaling pathway, steroid hormone metabolism, chemokine signaling pathway, DNA conformation changes, immunoglobulin production, and immunoglobulin complex in the high- and low-risk groups. Immune infiltration analysis revealed differential expression of immune cells between the high- and low-risk groups, providing a theoretical basis for subsequent immunotherapy. The model constructed in this study can predict the survival of BC patients, and the identified key proteins may serve as biomarkers to aid in the early diagnosis of BC. Enrichment analysis and immune infiltration analysis provide a necessary theoretical basis for further exploration of the molecular mechanisms and subsequent immunotherapy. |
| format | Article |
| id | doaj-art-99de7673f09040da93d6fc27f6533bd3 |
| institution | DOAJ |
| issn | 2314-4378 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Genomics |
| spelling | doaj-art-99de7673f09040da93d6fc27f6533bd32025-08-20T03:19:39ZengWileyInternational Journal of Genomics2314-43782023-01-01202310.1155/2023/1738750Validation of a Proteomic-Based Prognostic Model for Breast Cancer and Immunological AnalysisYunlin Yu0Linhuan Dong1Changjun Dong2Xianlin Zhang3Department of General SurgeryDepartment of General SurgeryDepartment of General SurgeryDepartment of General SurgeryBreast cancer (BC) has emerged as an extremely destructive malignancy, causing significant harm to female patients and society at large. Proteomic research holds great promise for early diagnosis and treatment of diseases, and the integration of proteomics with genomics can offer valuable assistance in the early diagnosis, treatment, and improved prognosis of BC patients. In this study, we downloaded breast cancer protein expression data from The Cancer Genome Atlas (TCGA) and combined proteomics with genomics to construct a proteomic-based prognostic model for BC. This model consists of nine proteins (HEREGULIN, IDO, PEA15, MERIT40_pS29, CIITA, AKT2, CD171 DVL3, and CABL9). The accuracy of the model in predicting the survival prognosis of BC patients was further validated through risk curve analysis, survival curve analysis, and independent prognostic analysis. We further confirmed the impact of differential expression of these nine key proteins on overall survival in BC patients, and the differential expression of the key proteins and their encoding genes was validated using immunohistochemical staining. Enrichment analysis revealed functional associations primarily related to PPAR signaling pathway, steroid hormone metabolism, chemokine signaling pathway, DNA conformation changes, immunoglobulin production, and immunoglobulin complex in the high- and low-risk groups. Immune infiltration analysis revealed differential expression of immune cells between the high- and low-risk groups, providing a theoretical basis for subsequent immunotherapy. The model constructed in this study can predict the survival of BC patients, and the identified key proteins may serve as biomarkers to aid in the early diagnosis of BC. Enrichment analysis and immune infiltration analysis provide a necessary theoretical basis for further exploration of the molecular mechanisms and subsequent immunotherapy.http://dx.doi.org/10.1155/2023/1738750 |
| spellingShingle | Yunlin Yu Linhuan Dong Changjun Dong Xianlin Zhang Validation of a Proteomic-Based Prognostic Model for Breast Cancer and Immunological Analysis International Journal of Genomics |
| title | Validation of a Proteomic-Based Prognostic Model for Breast Cancer and Immunological Analysis |
| title_full | Validation of a Proteomic-Based Prognostic Model for Breast Cancer and Immunological Analysis |
| title_fullStr | Validation of a Proteomic-Based Prognostic Model for Breast Cancer and Immunological Analysis |
| title_full_unstemmed | Validation of a Proteomic-Based Prognostic Model for Breast Cancer and Immunological Analysis |
| title_short | Validation of a Proteomic-Based Prognostic Model for Breast Cancer and Immunological Analysis |
| title_sort | validation of a proteomic based prognostic model for breast cancer and immunological analysis |
| url | http://dx.doi.org/10.1155/2023/1738750 |
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