Identification of novel serum lipid metabolism potential markers and metabolic pathways for oral cancer: a population-based study
Abstract Objective This study aims to identify potential lipid biomarkers and metabolic pathways associated with oral cancer (OC). Then to establish and evaluate disease classification models capable of distinguishing OC patients from healthy controls. Methods A total of 41 OC patients and 41 contro...
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2025-01-01
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| Online Access: | https://doi.org/10.1186/s12885-025-13561-x |
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| author | Na Wang Yujia Chen Jianli Lin Yulan Lin Haoyuan Song Weihai Huang Liling Shen Fa Chen Fengqiong Liu Jing Wang Yu Qiu Bin Shi Ling Li Lisong Lin Lizhen Pan Baochang He |
| author_facet | Na Wang Yujia Chen Jianli Lin Yulan Lin Haoyuan Song Weihai Huang Liling Shen Fa Chen Fengqiong Liu Jing Wang Yu Qiu Bin Shi Ling Li Lisong Lin Lizhen Pan Baochang He |
| author_sort | Na Wang |
| collection | DOAJ |
| description | Abstract Objective This study aims to identify potential lipid biomarkers and metabolic pathways associated with oral cancer (OC). Then to establish and evaluate disease classification models capable of distinguishing OC patients from healthy controls. Methods A total of 41 OC patients and 41 controls were recruited from a hospital in Southeast China to examine the serum lipidomics by Ultra-high Performance Liquid Chromatography Q Exactive Mass Spectrometry (UHPLC-QE-MS). Results The total serum lipid profile showed that triglycerides accounted for the highest proportion of total metabolites, reaching 35.90% of the total. A total of 74 different metabolites were screened (12 up-regulated and 62 down-regulated), mainly enriched in the glycerophospholipid metabolism pathway. The three most significant changes in lipid metabolites were phosphatidylcholine (PC(18:3e/17:2)), acylcarnitine (ACar(14:2)), and glucuronosyldiacylglycerol (GlcADG(14:1/14:1)). The disease classification model, constructed using a KNN algorithm with 13 metabolites selected through LASSO screening, achieved the best performance, with an AUC of 0.978 (0.955-1.000). Conclusion Lipid metabolic biomarkers identified in this study exhibit potential as candidate biomarkers for OC diagnosis. Further validation through prospective studies is required to confirm their clinical utility in early detection. |
| format | Article |
| id | doaj-art-f2cc3ec0247844dda67c82dc3b5fd9ff |
| institution | Kabale University |
| issn | 1471-2407 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cancer |
| spelling | doaj-art-f2cc3ec0247844dda67c82dc3b5fd9ff2025-02-02T12:28:42ZengBMCBMC Cancer1471-24072025-01-0125111110.1186/s12885-025-13561-xIdentification of novel serum lipid metabolism potential markers and metabolic pathways for oral cancer: a population-based studyNa Wang0Yujia Chen1Jianli Lin2Yulan Lin3Haoyuan Song4Weihai Huang5Liling Shen6Fa Chen7Fengqiong Liu8Jing Wang9Yu Qiu10Bin Shi11Ling Li12Lisong Lin13Lizhen Pan14Baochang He15Department of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical UniversityDepartment of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical UniversityDepartment of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical UniversityDepartment of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical UniversityDepartment of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical UniversityDepartment of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical UniversityDepartment of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical UniversityDepartment of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical UniversityDepartment of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical UniversityLaboratory Center, School of Public Health, The Major Subject of Environment and Health of Fujian Key Universities, Fujian Medical UniversityDepartment of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Fujian Medical UniversityDepartment of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Fujian Medical UniversityInternational Nursing School, Hainan Medical UniversityDepartment of Oral and Maxillofacial Surgery, the First Affiliated Hospital of Fujian Medical UniversityDepartment of Nursing, the First Affiliated Hospital of Fujian Medical UniversityDepartment of Epidemiology and Health Statistics, Fujian Provincial Key Laboratory of Environment Factors and Cancer, School of Public Health, Fujian Medical UniversityAbstract Objective This study aims to identify potential lipid biomarkers and metabolic pathways associated with oral cancer (OC). Then to establish and evaluate disease classification models capable of distinguishing OC patients from healthy controls. Methods A total of 41 OC patients and 41 controls were recruited from a hospital in Southeast China to examine the serum lipidomics by Ultra-high Performance Liquid Chromatography Q Exactive Mass Spectrometry (UHPLC-QE-MS). Results The total serum lipid profile showed that triglycerides accounted for the highest proportion of total metabolites, reaching 35.90% of the total. A total of 74 different metabolites were screened (12 up-regulated and 62 down-regulated), mainly enriched in the glycerophospholipid metabolism pathway. The three most significant changes in lipid metabolites were phosphatidylcholine (PC(18:3e/17:2)), acylcarnitine (ACar(14:2)), and glucuronosyldiacylglycerol (GlcADG(14:1/14:1)). The disease classification model, constructed using a KNN algorithm with 13 metabolites selected through LASSO screening, achieved the best performance, with an AUC of 0.978 (0.955-1.000). Conclusion Lipid metabolic biomarkers identified in this study exhibit potential as candidate biomarkers for OC diagnosis. Further validation through prospective studies is required to confirm their clinical utility in early detection.https://doi.org/10.1186/s12885-025-13561-xOral cancerLipid metabolismCase-control studySerumBiomarkers |
| spellingShingle | Na Wang Yujia Chen Jianli Lin Yulan Lin Haoyuan Song Weihai Huang Liling Shen Fa Chen Fengqiong Liu Jing Wang Yu Qiu Bin Shi Ling Li Lisong Lin Lizhen Pan Baochang He Identification of novel serum lipid metabolism potential markers and metabolic pathways for oral cancer: a population-based study BMC Cancer Oral cancer Lipid metabolism Case-control study Serum Biomarkers |
| title | Identification of novel serum lipid metabolism potential markers and metabolic pathways for oral cancer: a population-based study |
| title_full | Identification of novel serum lipid metabolism potential markers and metabolic pathways for oral cancer: a population-based study |
| title_fullStr | Identification of novel serum lipid metabolism potential markers and metabolic pathways for oral cancer: a population-based study |
| title_full_unstemmed | Identification of novel serum lipid metabolism potential markers and metabolic pathways for oral cancer: a population-based study |
| title_short | Identification of novel serum lipid metabolism potential markers and metabolic pathways for oral cancer: a population-based study |
| title_sort | identification of novel serum lipid metabolism potential markers and metabolic pathways for oral cancer a population based study |
| topic | Oral cancer Lipid metabolism Case-control study Serum Biomarkers |
| url | https://doi.org/10.1186/s12885-025-13561-x |
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