Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer
BackgroundProgrammed cell death (PCD) is closely related to the occurrence, development, and treatment of breast cancer. The aim of this study was to investigate the association between various programmed cell death patterns and the prognosis of breast cancer (BRCA) patients.MethodsThe levels of 19...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1505934/full |
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| author | Longpeng Li Longpeng Li Jinfeng Zhao Yaxin Wang Zhibin Zhang Wanquan Chen Jirui Wang Yue Cai |
| author_facet | Longpeng Li Longpeng Li Jinfeng Zhao Yaxin Wang Zhibin Zhang Wanquan Chen Jirui Wang Yue Cai |
| author_sort | Longpeng Li |
| collection | DOAJ |
| description | BackgroundProgrammed cell death (PCD) is closely related to the occurrence, development, and treatment of breast cancer. The aim of this study was to investigate the association between various programmed cell death patterns and the prognosis of breast cancer (BRCA) patients.MethodsThe levels of 19 different programmed cell deaths in breast cancer were assessed by ssGSEA analysis, and these PCD scores were summed to obtain the PCDS for each sample. The relationship of PCDS with immune as well as metabolism-related pathways was explored. PCD-associated subtypes were obtained by unsupervised consensus clustering analysis, and differentially expressed genes between subtypes were analyzed. The prognostic signature (PCDRS) were constructed by the best combination of 101 machine learning algorithm combinations, and the C-index of PCDRS was compared with 30 published signatures. In addition, we analyzed PCDRS in relation to immune as well as therapeutic responses. The distribution of genes in different cells was explored by single-cell analysis and spatial transcriptome analysis. Potential drugs targeting key genes were analyzed by Cmap. Finally, the expression levels of key genes in clinical tissues were verified by RT-PCR.ResultsPCDS showed higher levels in cancer compared to normal. Different PCDS groups showed significant differences in immune and metabolism-related pathways. PCDRS, consisting of seven key genes, showed robust predictive ability over other signatures in different datasets. The high PCDRS group had a poorer prognosis and was strongly associated with a cancer-promoting tumor microenvironment. The low PCDRS group exhibited higher levels of anti-cancer immunity and responded better to immune checkpoint inhibitors as well as chemotherapy-related drugs. Clofibrate and imatinib could serve as potential small-molecule complexes targeting SLC7A5 and BCL2A1, respectively. The mRNA expression levels of seven genes were upregulated in clinical cancer tissues.ConclusionPCDRS can be used as a biomarker to assess the prognosis and treatment response of BRCA patients, which offers novel insights for prognostic monitoring and treatment personalization of BRCA patients. |
| format | Article |
| id | doaj-art-2fbb80a3551b42ae9b72813656438ced |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-2fbb80a3551b42ae9b72813656438ced2025-01-06T14:02:14ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011410.3389/fonc.2024.15059341505934Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancerLongpeng Li0Longpeng Li1Jinfeng Zhao2Yaxin Wang3Zhibin Zhang4Wanquan Chen5Jirui Wang6Yue Cai7Department of Anesthesiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, ChinaInstitute of Physical Education and Sport, Shanxi University, Taiyuan, ChinaInstitute of Physical Education and Sport, Shanxi University, Taiyuan, ChinaInstitute of Physical Education and Sport, Shanxi University, Taiyuan, ChinaInstitute of Physical Education and Sport, Shanxi University, Taiyuan, ChinaInstitute of Physical Education and Sport, Shanxi University, Taiyuan, ChinaInstitute of Physical Education and Sport, Shanxi University, Taiyuan, ChinaDepartment of Anesthesiology, Shanxi Province Cancer Hospital/Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences/Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, ChinaBackgroundProgrammed cell death (PCD) is closely related to the occurrence, development, and treatment of breast cancer. The aim of this study was to investigate the association between various programmed cell death patterns and the prognosis of breast cancer (BRCA) patients.MethodsThe levels of 19 different programmed cell deaths in breast cancer were assessed by ssGSEA analysis, and these PCD scores were summed to obtain the PCDS for each sample. The relationship of PCDS with immune as well as metabolism-related pathways was explored. PCD-associated subtypes were obtained by unsupervised consensus clustering analysis, and differentially expressed genes between subtypes were analyzed. The prognostic signature (PCDRS) were constructed by the best combination of 101 machine learning algorithm combinations, and the C-index of PCDRS was compared with 30 published signatures. In addition, we analyzed PCDRS in relation to immune as well as therapeutic responses. The distribution of genes in different cells was explored by single-cell analysis and spatial transcriptome analysis. Potential drugs targeting key genes were analyzed by Cmap. Finally, the expression levels of key genes in clinical tissues were verified by RT-PCR.ResultsPCDS showed higher levels in cancer compared to normal. Different PCDS groups showed significant differences in immune and metabolism-related pathways. PCDRS, consisting of seven key genes, showed robust predictive ability over other signatures in different datasets. The high PCDRS group had a poorer prognosis and was strongly associated with a cancer-promoting tumor microenvironment. The low PCDRS group exhibited higher levels of anti-cancer immunity and responded better to immune checkpoint inhibitors as well as chemotherapy-related drugs. Clofibrate and imatinib could serve as potential small-molecule complexes targeting SLC7A5 and BCL2A1, respectively. The mRNA expression levels of seven genes were upregulated in clinical cancer tissues.ConclusionPCDRS can be used as a biomarker to assess the prognosis and treatment response of BRCA patients, which offers novel insights for prognostic monitoring and treatment personalization of BRCA patients.https://www.frontiersin.org/articles/10.3389/fonc.2024.1505934/fullbreast cancermachine learningprogrammed cell deathprognostic signaturetumor microenvironment |
| spellingShingle | Longpeng Li Longpeng Li Jinfeng Zhao Yaxin Wang Zhibin Zhang Wanquan Chen Jirui Wang Yue Cai Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer Frontiers in Oncology breast cancer machine learning programmed cell death prognostic signature tumor microenvironment |
| title | Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer |
| title_full | Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer |
| title_fullStr | Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer |
| title_full_unstemmed | Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer |
| title_short | Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer |
| title_sort | integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer |
| topic | breast cancer machine learning programmed cell death prognostic signature tumor microenvironment |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1505934/full |
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