Machine learning based identification of an amino acid metabolism related signature for predicting prognosis and immune microenvironment in pancreatic cancer
Abstract Background Pancreatic cancer is a highly aggressive neoplasm characterized by poor diagnosis. Amino acids play a prominent role in the occurrence and progression of pancreatic cancer as essential building blocks for protein synthesis and key regulators of cellular metabolism. Understanding...
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BMC
2025-01-01
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| Series: | BMC Cancer |
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| Online Access: | https://doi.org/10.1186/s12885-024-13374-4 |
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| author | Xiaohong Liu Xing Wang Jie Ren Yuan Fang Minzhi Gu Feihan Zhou Ruiling Xiao Xiyuan Luo Jialu Bai Decheng Jiang Yuemeng Tang Bo Ren Lei You Yupei Zhao |
| author_facet | Xiaohong Liu Xing Wang Jie Ren Yuan Fang Minzhi Gu Feihan Zhou Ruiling Xiao Xiyuan Luo Jialu Bai Decheng Jiang Yuemeng Tang Bo Ren Lei You Yupei Zhao |
| author_sort | Xiaohong Liu |
| collection | DOAJ |
| description | Abstract Background Pancreatic cancer is a highly aggressive neoplasm characterized by poor diagnosis. Amino acids play a prominent role in the occurrence and progression of pancreatic cancer as essential building blocks for protein synthesis and key regulators of cellular metabolism. Understanding the interplay between pancreatic cancer and amino acid metabolism offers potential avenues for improving patient clinical outcomes. Methods A comprehensive analysis integrating 10 machine learning algorithms was executed to pinpoint amino acid metabolic signature. The signature was validated across both internal and external cohorts. Subsequent GSEA was employed to unveil the enriched gene sets and signaling pathways within high- and low-risk subgroups. TMB and drug sensitivity analyses were carried out via Maftools and oncoPredict R packages. CIBERSORT and ssGSEA were harnessed to delve into the immune landscape disparities. Single-cell transcriptomics, qPCR, and Immunohistochemistry were performed to corroborate the expression levels and prognostic significance of this signature. Results A four gene based amino acid metabolic signature with superior prognostic capabilities was identified by the combination of 10 machine learning methods. It showed that the novel prognostic model could effectively distinguish patients into high- and low-risk groups in both internal and external cohorts. Notably, the risk score from this novel signature showed significant correlations with TMB, drug resistance, as well as a heightened likelihood of immune evasion and suboptimal responses to immunotherapeutic interventions. Conclusion Our findings suggested that amino acid metabolism-related signature was closely related to the development, prognosis and immune microenvironment of pancreatic cancer. |
| format | Article |
| id | doaj-art-cc66edf746b84ca589552abd0cfdf3d0 |
| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-cc66edf746b84ca589552abd0cfdf3d02025-01-05T12:33:06ZengBMCBMC Cancer1471-24072025-01-0125112110.1186/s12885-024-13374-4Machine learning based identification of an amino acid metabolism related signature for predicting prognosis and immune microenvironment in pancreatic cancerXiaohong Liu0Xing Wang1Jie Ren2Yuan Fang3Minzhi Gu4Feihan Zhou5Ruiling Xiao6Xiyuan Luo7Jialu Bai8Decheng Jiang9Yuemeng Tang10Bo Ren11Lei You12Yupei Zhao13Department of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesDepartment of General Surgery, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical SciencesAbstract Background Pancreatic cancer is a highly aggressive neoplasm characterized by poor diagnosis. Amino acids play a prominent role in the occurrence and progression of pancreatic cancer as essential building blocks for protein synthesis and key regulators of cellular metabolism. Understanding the interplay between pancreatic cancer and amino acid metabolism offers potential avenues for improving patient clinical outcomes. Methods A comprehensive analysis integrating 10 machine learning algorithms was executed to pinpoint amino acid metabolic signature. The signature was validated across both internal and external cohorts. Subsequent GSEA was employed to unveil the enriched gene sets and signaling pathways within high- and low-risk subgroups. TMB and drug sensitivity analyses were carried out via Maftools and oncoPredict R packages. CIBERSORT and ssGSEA were harnessed to delve into the immune landscape disparities. Single-cell transcriptomics, qPCR, and Immunohistochemistry were performed to corroborate the expression levels and prognostic significance of this signature. Results A four gene based amino acid metabolic signature with superior prognostic capabilities was identified by the combination of 10 machine learning methods. It showed that the novel prognostic model could effectively distinguish patients into high- and low-risk groups in both internal and external cohorts. Notably, the risk score from this novel signature showed significant correlations with TMB, drug resistance, as well as a heightened likelihood of immune evasion and suboptimal responses to immunotherapeutic interventions. Conclusion Our findings suggested that amino acid metabolism-related signature was closely related to the development, prognosis and immune microenvironment of pancreatic cancer.https://doi.org/10.1186/s12885-024-13374-4Amino acid metabolismPrognosisImmune microenvironmentPancreatic cancerMachine-learning |
| spellingShingle | Xiaohong Liu Xing Wang Jie Ren Yuan Fang Minzhi Gu Feihan Zhou Ruiling Xiao Xiyuan Luo Jialu Bai Decheng Jiang Yuemeng Tang Bo Ren Lei You Yupei Zhao Machine learning based identification of an amino acid metabolism related signature for predicting prognosis and immune microenvironment in pancreatic cancer BMC Cancer Amino acid metabolism Prognosis Immune microenvironment Pancreatic cancer Machine-learning |
| title | Machine learning based identification of an amino acid metabolism related signature for predicting prognosis and immune microenvironment in pancreatic cancer |
| title_full | Machine learning based identification of an amino acid metabolism related signature for predicting prognosis and immune microenvironment in pancreatic cancer |
| title_fullStr | Machine learning based identification of an amino acid metabolism related signature for predicting prognosis and immune microenvironment in pancreatic cancer |
| title_full_unstemmed | Machine learning based identification of an amino acid metabolism related signature for predicting prognosis and immune microenvironment in pancreatic cancer |
| title_short | Machine learning based identification of an amino acid metabolism related signature for predicting prognosis and immune microenvironment in pancreatic cancer |
| title_sort | machine learning based identification of an amino acid metabolism related signature for predicting prognosis and immune microenvironment in pancreatic cancer |
| topic | Amino acid metabolism Prognosis Immune microenvironment Pancreatic cancer Machine-learning |
| url | https://doi.org/10.1186/s12885-024-13374-4 |
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