Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma: a SEER database analysis with external validation
BackgroundHigh-grade colorectal neuroendocrine carcinoma (HCNEC) is a rare but aggressive subset of neuroendocrine tumors. This study was designed to construct a risk model based on comprehensive clinical and mutational genomics data to facilitate clinical decision making.MethodsA retrospective anal...
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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.2025.1509170/full |
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| author | Ruixin Wu Ruixin Wu Sihao Chen Sihao Chen Yi He Yi He Ya Li Song Mu Aishun Jin Aishun Jin |
| author_facet | Ruixin Wu Ruixin Wu Sihao Chen Sihao Chen Yi He Yi He Ya Li Song Mu Aishun Jin Aishun Jin |
| author_sort | Ruixin Wu |
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| description | BackgroundHigh-grade colorectal neuroendocrine carcinoma (HCNEC) is a rare but aggressive subset of neuroendocrine tumors. This study was designed to construct a risk model based on comprehensive clinical and mutational genomics data to facilitate clinical decision making.MethodsA retrospective analysis was conducted using data from the Surveillance, Epidemiology, and End Results (SEER) database, spanning 2000 to 2019. The external validation cohort was sourced from two tertiary hospitals in Southwest China. Independent factors influencing both overall survival (OS) and cancer-specific survival (CSS) were identified using LASSO, Random Forest, and XGBoost regression techniques. Molecular data with the most common mutations in CNEC were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database.ResultsIn this prognostic analysis, the data from 714 participants with HCNEC were evaluated. The median OS for the cohort was 10 months, whereas CSS was 11 months. Six variables (M stage, LODDS, Nodes positive, Surgery, Radiotherapy, and Chemotherapy) were screened as key prognostic indicators. The machine learning model showed reliable performance across multiple evaluation dimensions. The most common mutations of CNEC identified in the COSMIC database were TP53, KRAS, and APC.ConclusionsIn this study, a refined machine learning predictive model was developed to assess the prognosis of HCNEC accurately and we briefly analyzed its genomic features, which might offer a valuable tool to address existing clinical challenges. |
| format | Article |
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| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Oncology |
| spelling | doaj-art-addd47d3bd21417db5a8d6f02498c0842025-01-29T05:21:18ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011510.3389/fonc.2025.15091701509170Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma: a SEER database analysis with external validationRuixin Wu0Ruixin Wu1Sihao Chen2Sihao Chen3Yi He4Yi He5Ya Li6Song Mu7Aishun Jin8Aishun Jin9Department of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, ChinaChongqing Key Laboratory of Tumor Immune Regulation and Immune Intervention, Chongqing, ChinaDepartment of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, ChinaChongqing Key Laboratory of Tumor Immune Regulation and Immune Intervention, Chongqing, ChinaDepartment of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, ChinaChongqing Key Laboratory of Tumor Immune Regulation and Immune Intervention, Chongqing, ChinaDepartment of Gastrointestinal Surgery, the First Affiliated Hospital of Chongqing Medical University, Chongqing, ChinaDepartment of Colorectal Surgery, The Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou, ChinaDepartment of Immunology, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, ChinaChongqing Key Laboratory of Tumor Immune Regulation and Immune Intervention, Chongqing, ChinaBackgroundHigh-grade colorectal neuroendocrine carcinoma (HCNEC) is a rare but aggressive subset of neuroendocrine tumors. This study was designed to construct a risk model based on comprehensive clinical and mutational genomics data to facilitate clinical decision making.MethodsA retrospective analysis was conducted using data from the Surveillance, Epidemiology, and End Results (SEER) database, spanning 2000 to 2019. The external validation cohort was sourced from two tertiary hospitals in Southwest China. Independent factors influencing both overall survival (OS) and cancer-specific survival (CSS) were identified using LASSO, Random Forest, and XGBoost regression techniques. Molecular data with the most common mutations in CNEC were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database.ResultsIn this prognostic analysis, the data from 714 participants with HCNEC were evaluated. The median OS for the cohort was 10 months, whereas CSS was 11 months. Six variables (M stage, LODDS, Nodes positive, Surgery, Radiotherapy, and Chemotherapy) were screened as key prognostic indicators. The machine learning model showed reliable performance across multiple evaluation dimensions. The most common mutations of CNEC identified in the COSMIC database were TP53, KRAS, and APC.ConclusionsIn this study, a refined machine learning predictive model was developed to assess the prognosis of HCNEC accurately and we briefly analyzed its genomic features, which might offer a valuable tool to address existing clinical challenges.https://www.frontiersin.org/articles/10.3389/fonc.2025.1509170/fullhigh-grade colorectal neuroendocrine carcinoma (HCNEC)machine learningprognosisSEERCOSMICgenetic mutation landscape |
| spellingShingle | Ruixin Wu Ruixin Wu Sihao Chen Sihao Chen Yi He Yi He Ya Li Song Mu Aishun Jin Aishun Jin Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma: a SEER database analysis with external validation Frontiers in Oncology high-grade colorectal neuroendocrine carcinoma (HCNEC) machine learning prognosis SEER COSMIC genetic mutation landscape |
| title | Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma: a SEER database analysis with external validation |
| title_full | Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma: a SEER database analysis with external validation |
| title_fullStr | Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma: a SEER database analysis with external validation |
| title_full_unstemmed | Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma: a SEER database analysis with external validation |
| title_short | Machine learning based predictive model and genetic mutation landscape for high-grade colorectal neuroendocrine carcinoma: a SEER database analysis with external validation |
| title_sort | machine learning based predictive model and genetic mutation landscape for high grade colorectal neuroendocrine carcinoma a seer database analysis with external validation |
| topic | high-grade colorectal neuroendocrine carcinoma (HCNEC) machine learning prognosis SEER COSMIC genetic mutation landscape |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1509170/full |
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