Improving molecular subtypes and prognosis of pancreatic cancer through multi group analysis and machine learning
Abstract Background Pancreatic cancer (PAC) has a complex tumor immune microenvironment, and currently, there is a lack of accurate personalized treatment. Establishing a novel consensus machine learning driven signature (CMLS) that offers a unique predictive model and possible treatment targets for...
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| Format: | Article |
| Language: | English |
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Springer
2025-01-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-01841-8 |
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| author | Xue-Jian Zhang Fang-Fang Lin Ya-Qing Wen Kun-Ping Guan |
| author_facet | Xue-Jian Zhang Fang-Fang Lin Ya-Qing Wen Kun-Ping Guan |
| author_sort | Xue-Jian Zhang |
| collection | DOAJ |
| description | Abstract Background Pancreatic cancer (PAC) has a complex tumor immune microenvironment, and currently, there is a lack of accurate personalized treatment. Establishing a novel consensus machine learning driven signature (CMLS) that offers a unique predictive model and possible treatment targets for this condition was the goal of this study. Methods This study integrated multiple omics data of PAC patients, applied ten clustering techniques and ten machine learning approaches to construct molecular subtypes for PAC, and created a new CMLS. Results Using multi-omics clustering, we discovered two cancer subtypes (CSs) associated with prognosis, among which CS1 exhibited poor prognostic outcomes. Subsequently, 13 central genes were identified through screening, constituting CMLS with a significant prognostic ability. The low CMLS group had a better prognosis and was more likely to possess a “hot” tumor phenotype. The prognosis for the high CMLS group was dismal. Still, the tumor mutation burden (TMB) and tumor neoantigen burden (TNB) levels in this group of patients were higher than in the low CMLS group, which were more favorable for immune therapy response. Conclusion This study emphasizes that CMLS provides a beneficial instrument for early prediction of patient prognosis and screening of probable patients appropriate for immunotherapy and has broad implications for clinical practice. |
| format | Article |
| id | doaj-art-500d37845f634f759d0ff6b2693d25b5 |
| institution | Kabale University |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-500d37845f634f759d0ff6b2693d25b52025-02-02T12:30:37ZengSpringerDiscover Oncology2730-60112025-01-0116111610.1007/s12672-025-01841-8Improving molecular subtypes and prognosis of pancreatic cancer through multi group analysis and machine learningXue-Jian Zhang0Fang-Fang Lin1Ya-Qing Wen2Kun-Ping Guan3Department of Laboratory, the Second Hospital of Shanxi Medical UniversityDepartment of Laboratory, the Second Hospital of Shanxi Medical UniversityDepartment of Laboratory, the Second Hospital of Shanxi Medical UniversityDepartment of Laboratory, the Second Hospital of Shanxi Medical UniversityAbstract Background Pancreatic cancer (PAC) has a complex tumor immune microenvironment, and currently, there is a lack of accurate personalized treatment. Establishing a novel consensus machine learning driven signature (CMLS) that offers a unique predictive model and possible treatment targets for this condition was the goal of this study. Methods This study integrated multiple omics data of PAC patients, applied ten clustering techniques and ten machine learning approaches to construct molecular subtypes for PAC, and created a new CMLS. Results Using multi-omics clustering, we discovered two cancer subtypes (CSs) associated with prognosis, among which CS1 exhibited poor prognostic outcomes. Subsequently, 13 central genes were identified through screening, constituting CMLS with a significant prognostic ability. The low CMLS group had a better prognosis and was more likely to possess a “hot” tumor phenotype. The prognosis for the high CMLS group was dismal. Still, the tumor mutation burden (TMB) and tumor neoantigen burden (TNB) levels in this group of patients were higher than in the low CMLS group, which were more favorable for immune therapy response. Conclusion This study emphasizes that CMLS provides a beneficial instrument for early prediction of patient prognosis and screening of probable patients appropriate for immunotherapy and has broad implications for clinical practice.https://doi.org/10.1007/s12672-025-01841-8Pancreatic cancerImmune microenvironmentMolecular subtypesMachine learningMulti-omics |
| spellingShingle | Xue-Jian Zhang Fang-Fang Lin Ya-Qing Wen Kun-Ping Guan Improving molecular subtypes and prognosis of pancreatic cancer through multi group analysis and machine learning Discover Oncology Pancreatic cancer Immune microenvironment Molecular subtypes Machine learning Multi-omics |
| title | Improving molecular subtypes and prognosis of pancreatic cancer through multi group analysis and machine learning |
| title_full | Improving molecular subtypes and prognosis of pancreatic cancer through multi group analysis and machine learning |
| title_fullStr | Improving molecular subtypes and prognosis of pancreatic cancer through multi group analysis and machine learning |
| title_full_unstemmed | Improving molecular subtypes and prognosis of pancreatic cancer through multi group analysis and machine learning |
| title_short | Improving molecular subtypes and prognosis of pancreatic cancer through multi group analysis and machine learning |
| title_sort | improving molecular subtypes and prognosis of pancreatic cancer through multi group analysis and machine learning |
| topic | Pancreatic cancer Immune microenvironment Molecular subtypes Machine learning Multi-omics |
| url | https://doi.org/10.1007/s12672-025-01841-8 |
| work_keys_str_mv | AT xuejianzhang improvingmolecularsubtypesandprognosisofpancreaticcancerthroughmultigroupanalysisandmachinelearning AT fangfanglin improvingmolecularsubtypesandprognosisofpancreaticcancerthroughmultigroupanalysisandmachinelearning AT yaqingwen improvingmolecularsubtypesandprognosisofpancreaticcancerthroughmultigroupanalysisandmachinelearning AT kunpingguan improvingmolecularsubtypesandprognosisofpancreaticcancerthroughmultigroupanalysisandmachinelearning |