Untargeted serum metabolomics reveals potential biomarkers and metabolic pathways associated with the progression of gastroesophageal cancer
© 2023. The Author(s)..
BACKGROUND: Previous metabolic studies in upper digestive cancer have mostly been limited to cross-sectional study designs, which hinders the ability to effectively predict outcomes in the early stage of cancer. This study aims to identify key metabolites and metabolic pathways associated with the multistage progression of epithelial cancer and to explore their predictive value for gastroesophageal cancer (GEC) formation and for the early screening of esophageal squamous cell carcinoma (ESCC).
METHODS: A case-cohort study within the 7-year prospective Esophageal Cancer Screening Cohort of Shandong Province included 77 GEC cases and 77 sub-cohort individuals. Untargeted metabolic analysis was performed in serum samples. Metabolites, with FDR q value < 0.05 and variable importance in projection (VIP) > 1, were selected as differential metabolites to predict GEC formation using Random Forest (RF) models. Subsequently, we evaluated the predictive performance of these differential metabolites for the early screening of ESCC.
RESULTS: We found a distinct metabolic profile alteration in GEC cases compared to the sub-cohort, and identified eight differential metabolites. Pathway analyses showed dysregulation in D-glutamine and D-glutamate metabolism, nitrogen metabolism, primary bile acid biosynthesis, and steroid hormone biosynthesis in GEC patients. A panel of eight differential metabolites showed good predictive performance for GEC formation, with an area under the receiver operating characteristic curve (AUC) of 0.893 (95% CI = 0.816-0.951). Furthermore, four of the GEC pathological progression-related metabolites were validated in the early screening of ESCC, with an AUC of 0.761 (95% CI = 0.716-0.805).
CONCLUSIONS: These findings indicated a panel of metabolites might be an alternative approach to predict GEC formation, and therefore have the potential to mitigate the risk of cancer progression at the early stage of GEC.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:23 |
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| Enthalten in: |
BMC cancer - 23(2023), 1 vom: 15. Dez., Seite 1238 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Che, Jiajing [VerfasserIn] |
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| Links: |
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| Themen: |
Biomarker. |
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| Anmerkungen: |
Date Completed 18.12.2023 Date Revised 18.12.2023 published: Electronic Citation Status MEDLINE |
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| doi: |
10.1186/s12885-023-11744-y |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM36593237X |
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| 245 | 1 | 0 | |a Untargeted serum metabolomics reveals potential biomarkers and metabolic pathways associated with the progression of gastroesophageal cancer |
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| 520 | |a © 2023. The Author(s). | ||
| 520 | |a BACKGROUND: Previous metabolic studies in upper digestive cancer have mostly been limited to cross-sectional study designs, which hinders the ability to effectively predict outcomes in the early stage of cancer. This study aims to identify key metabolites and metabolic pathways associated with the multistage progression of epithelial cancer and to explore their predictive value for gastroesophageal cancer (GEC) formation and for the early screening of esophageal squamous cell carcinoma (ESCC) | ||
| 520 | |a METHODS: A case-cohort study within the 7-year prospective Esophageal Cancer Screening Cohort of Shandong Province included 77 GEC cases and 77 sub-cohort individuals. Untargeted metabolic analysis was performed in serum samples. Metabolites, with FDR q value < 0.05 and variable importance in projection (VIP) > 1, were selected as differential metabolites to predict GEC formation using Random Forest (RF) models. Subsequently, we evaluated the predictive performance of these differential metabolites for the early screening of ESCC | ||
| 520 | |a RESULTS: We found a distinct metabolic profile alteration in GEC cases compared to the sub-cohort, and identified eight differential metabolites. Pathway analyses showed dysregulation in D-glutamine and D-glutamate metabolism, nitrogen metabolism, primary bile acid biosynthesis, and steroid hormone biosynthesis in GEC patients. A panel of eight differential metabolites showed good predictive performance for GEC formation, with an area under the receiver operating characteristic curve (AUC) of 0.893 (95% CI = 0.816-0.951). Furthermore, four of the GEC pathological progression-related metabolites were validated in the early screening of ESCC, with an AUC of 0.761 (95% CI = 0.716-0.805) | ||
| 520 | |a CONCLUSIONS: These findings indicated a panel of metabolites might be an alternative approach to predict GEC formation, and therefore have the potential to mitigate the risk of cancer progression at the early stage of GEC | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Biomarker. | |
| 650 | 4 | |a Cancer progression | |
| 650 | 4 | |a Esophageal squamous cell carcinoma | |
| 650 | 4 | |a Gastroesophageal cancer | |
| 650 | 4 | |a Metabolomics | |
| 650 | 7 | |a Biomarkers |2 NLM | |
| 700 | 1 | |a Zhao, Yongbin |e verfasserin |4 aut | |
| 700 | 1 | |a Gu, Bingbing |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Shuting |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Yunfei |e verfasserin |4 aut | |
| 700 | 1 | |a Pan, Keyu |e verfasserin |4 aut | |
| 700 | 1 | |a Sun, Tiantian |e verfasserin |4 aut | |
| 700 | 1 | |a Han, Xinyue |e verfasserin |4 aut | |
| 700 | 1 | |a Lv, Jiali |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Shuai |e verfasserin |4 aut | |
| 700 | 1 | |a Fan, Bingbing |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Chunxia |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Cheng |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Jialin |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Tao |e verfasserin |4 aut | |
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