{"title":"血清代谢特征与结直肠腺瘤恶化的关系。","authors":"Ze Dai, Tong Li, Kecong Lai, Xiaomei Wang, Peng Zhou, Kefeng Hu, Yuping Zhou","doi":"10.1038/s41598-025-91444-8","DOIUrl":null,"url":null,"abstract":"<p><p>Colorectal cancer (CRC) can evolve from colorectal adenomas, which can be further classified into non-advanced adenomas (NAAs) and advanced adenomas (AAs) based on their clinical characteristics. Their prognoses are vastly different, with patients with NAAs having significantly lower recurrence and CRC-related mortality rates than those with AA or CRC. Although serum metabolomics has shown promise for the early diagnosis of CRC, the differences in serum metabolite composition between NAA and AA still need to be further elucidated. This study aimed to explore the mechanism of CRC occurrence and development based on the unique serum metabolic fingerprints of different stages of CRC and to discover a new non-invasive diagnostic method based on serum metabolomics. A clinical CRC progression cohort containing healthy control (NC; n = 40), NAA (n = 40), AA (n = 40), and CRC (n = 22) groups was constructed, and untargeted metabolomic analysis based on liquid chromatography/mass spectrometry was performed to analyze the serum metabolite characteristics of each group. A semi-quantitative analysis of intergroup metabolite differences was conducted, focusing on specific metabolites that differed in the NAA and AA groups. Finally, variable metabolites were selected based on least absolute shrinkage and selection operator (LASSO) regression analysis, and receiver operating characteristic curves were plotted to evaluate the efficacy of the serum metabolite-based diagnostic model in distinguishing NC/NAA populations from AA/CRC populations. Metabolomic analysis revealed significant differences in the composition of metabolites in the NC and NAA groups compared to the CRC group, whereas the serum metabolites of the AA group were similar to those of the CRC group. The levels of 33 metabolites were significantly different in the serum of AA/CRC patients compared to that of NAA patients, and their functions included glycerophospholipid, sphingolipid, and caffeine metabolism. LASSO regression analysis identified 57 differential metabolite variables between the NC/NAA and AA/CRC groups. The diagnostic model constructed using the random forest algorithm had the best discrimination effect, with areas under the curve of 1.000 (95% confidence interval [CI] 1.000-1.000) and 0.685 (95% CI 0.540-0.830) for the training and testing sets, respectively. The composition of serum metabolites is specific to the different stages of CRC development. The serum metabolite composition of patients with AAs was similar to that of patients with CRC. Auxiliary diagnostic measures based on serum metabolites have the potential to guide the follow-up and treatment of patients with adenoma.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"6845"},"PeriodicalIF":3.9000,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11861597/pdf/","citationCount":"0","resultStr":"{\"title\":\"Serum metabolic characteristics associated with the deterioration of colorectal adenomas.\",\"authors\":\"Ze Dai, Tong Li, Kecong Lai, Xiaomei Wang, Peng Zhou, Kefeng Hu, Yuping Zhou\",\"doi\":\"10.1038/s41598-025-91444-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Colorectal cancer (CRC) can evolve from colorectal adenomas, which can be further classified into non-advanced adenomas (NAAs) and advanced adenomas (AAs) based on their clinical characteristics. Their prognoses are vastly different, with patients with NAAs having significantly lower recurrence and CRC-related mortality rates than those with AA or CRC. Although serum metabolomics has shown promise for the early diagnosis of CRC, the differences in serum metabolite composition between NAA and AA still need to be further elucidated. This study aimed to explore the mechanism of CRC occurrence and development based on the unique serum metabolic fingerprints of different stages of CRC and to discover a new non-invasive diagnostic method based on serum metabolomics. A clinical CRC progression cohort containing healthy control (NC; n = 40), NAA (n = 40), AA (n = 40), and CRC (n = 22) groups was constructed, and untargeted metabolomic analysis based on liquid chromatography/mass spectrometry was performed to analyze the serum metabolite characteristics of each group. A semi-quantitative analysis of intergroup metabolite differences was conducted, focusing on specific metabolites that differed in the NAA and AA groups. Finally, variable metabolites were selected based on least absolute shrinkage and selection operator (LASSO) regression analysis, and receiver operating characteristic curves were plotted to evaluate the efficacy of the serum metabolite-based diagnostic model in distinguishing NC/NAA populations from AA/CRC populations. Metabolomic analysis revealed significant differences in the composition of metabolites in the NC and NAA groups compared to the CRC group, whereas the serum metabolites of the AA group were similar to those of the CRC group. The levels of 33 metabolites were significantly different in the serum of AA/CRC patients compared to that of NAA patients, and their functions included glycerophospholipid, sphingolipid, and caffeine metabolism. LASSO regression analysis identified 57 differential metabolite variables between the NC/NAA and AA/CRC groups. The diagnostic model constructed using the random forest algorithm had the best discrimination effect, with areas under the curve of 1.000 (95% confidence interval [CI] 1.000-1.000) and 0.685 (95% CI 0.540-0.830) for the training and testing sets, respectively. The composition of serum metabolites is specific to the different stages of CRC development. The serum metabolite composition of patients with AAs was similar to that of patients with CRC. Auxiliary diagnostic measures based on serum metabolites have the potential to guide the follow-up and treatment of patients with adenoma.</p>\",\"PeriodicalId\":21811,\"journal\":{\"name\":\"Scientific Reports\",\"volume\":\"15 1\",\"pages\":\"6845\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-02-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11861597/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Reports\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41598-025-91444-8\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Reports","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41598-025-91444-8","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Serum metabolic characteristics associated with the deterioration of colorectal adenomas.
Colorectal cancer (CRC) can evolve from colorectal adenomas, which can be further classified into non-advanced adenomas (NAAs) and advanced adenomas (AAs) based on their clinical characteristics. Their prognoses are vastly different, with patients with NAAs having significantly lower recurrence and CRC-related mortality rates than those with AA or CRC. Although serum metabolomics has shown promise for the early diagnosis of CRC, the differences in serum metabolite composition between NAA and AA still need to be further elucidated. This study aimed to explore the mechanism of CRC occurrence and development based on the unique serum metabolic fingerprints of different stages of CRC and to discover a new non-invasive diagnostic method based on serum metabolomics. A clinical CRC progression cohort containing healthy control (NC; n = 40), NAA (n = 40), AA (n = 40), and CRC (n = 22) groups was constructed, and untargeted metabolomic analysis based on liquid chromatography/mass spectrometry was performed to analyze the serum metabolite characteristics of each group. A semi-quantitative analysis of intergroup metabolite differences was conducted, focusing on specific metabolites that differed in the NAA and AA groups. Finally, variable metabolites were selected based on least absolute shrinkage and selection operator (LASSO) regression analysis, and receiver operating characteristic curves were plotted to evaluate the efficacy of the serum metabolite-based diagnostic model in distinguishing NC/NAA populations from AA/CRC populations. Metabolomic analysis revealed significant differences in the composition of metabolites in the NC and NAA groups compared to the CRC group, whereas the serum metabolites of the AA group were similar to those of the CRC group. The levels of 33 metabolites were significantly different in the serum of AA/CRC patients compared to that of NAA patients, and their functions included glycerophospholipid, sphingolipid, and caffeine metabolism. LASSO regression analysis identified 57 differential metabolite variables between the NC/NAA and AA/CRC groups. The diagnostic model constructed using the random forest algorithm had the best discrimination effect, with areas under the curve of 1.000 (95% confidence interval [CI] 1.000-1.000) and 0.685 (95% CI 0.540-0.830) for the training and testing sets, respectively. The composition of serum metabolites is specific to the different stages of CRC development. The serum metabolite composition of patients with AAs was similar to that of patients with CRC. Auxiliary diagnostic measures based on serum metabolites have the potential to guide the follow-up and treatment of patients with adenoma.
期刊介绍:
We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections.
Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021).
•Engineering
Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live.
•Physical sciences
Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics.
•Earth and environmental sciences
Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems.
•Biological sciences
Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants.
•Health sciences
The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.