Wenjun Zhang , Chuntao Dong , Zhaosheng Li , Huina Shi , Yijun Xu , Mingchen Zhu
{"title":"血清靶向代谢组学发现诊断肝内胆管癌的特异性氨基酸特征","authors":"Wenjun Zhang , Chuntao Dong , Zhaosheng Li , Huina Shi , Yijun Xu , Mingchen Zhu","doi":"10.1016/j.jpba.2024.116457","DOIUrl":null,"url":null,"abstract":"<div><p>Intrahepatic cholangiocarcinoma (iCCA) is a hepatobiliary malignancy which accounts for approximately 5–10 % of primary liver cancers and has a high mortality rate. The diagnosis of iCCA remains significant challenges owing to the lack of specific and sensitive diagnostic tests available. Hence, improved methods are needed to detect iCCA with high accuracy. In this study, we evaluated the efficacy of serum amino acid profiling combined with machine learning modeling for the diagnosis of iCCA. A comprehensive analysis of 28 circulating amino acids was conducted in a total of 140 blood samples from patients with iCCA and normal individuals. We screened out 6 differentially expressed amino acids with the criteria of |Log<sub>2</sub>(Fold Change, FC)| > 0.585, P-value < 0.05, variable importance in projection (VIP) > 1.0 and area under the curve (AUC) > 0.8, in which amino acids L-Asparagine and Kynurenine showed an increasing tendency as the disease progressed. Five frequently used machine learning algorithms (Logistic Regression, Random Forest, Supporting Vector Machine, Neural Network and Naïve Bayes) for diagnosis of iCCA based on the 6 circulating amino acids were established and validated with high sensitivity and good overall accuracy. The resulting models were further improved by introducing a clinical indicator, gamma-glutamyl transferase (GGT). This study introduces a new approach for identifying potential serum biomarkers for the diagnosis of iCCA with high accuracy.</p></div>","PeriodicalId":16685,"journal":{"name":"Journal of pharmaceutical and biomedical analysis","volume":"252 ","pages":"Article 116457"},"PeriodicalIF":3.1000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0731708524004977/pdfft?md5=662cb1d24e6f44b69c93e1da7cecaef4&pid=1-s2.0-S0731708524004977-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Serum targeted metabolomics uncovering specific amino acid signature for diagnosis of intrahepatic cholangiocarcinoma\",\"authors\":\"Wenjun Zhang , Chuntao Dong , Zhaosheng Li , Huina Shi , Yijun Xu , Mingchen Zhu\",\"doi\":\"10.1016/j.jpba.2024.116457\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Intrahepatic cholangiocarcinoma (iCCA) is a hepatobiliary malignancy which accounts for approximately 5–10 % of primary liver cancers and has a high mortality rate. The diagnosis of iCCA remains significant challenges owing to the lack of specific and sensitive diagnostic tests available. Hence, improved methods are needed to detect iCCA with high accuracy. In this study, we evaluated the efficacy of serum amino acid profiling combined with machine learning modeling for the diagnosis of iCCA. A comprehensive analysis of 28 circulating amino acids was conducted in a total of 140 blood samples from patients with iCCA and normal individuals. We screened out 6 differentially expressed amino acids with the criteria of |Log<sub>2</sub>(Fold Change, FC)| > 0.585, P-value < 0.05, variable importance in projection (VIP) > 1.0 and area under the curve (AUC) > 0.8, in which amino acids L-Asparagine and Kynurenine showed an increasing tendency as the disease progressed. Five frequently used machine learning algorithms (Logistic Regression, Random Forest, Supporting Vector Machine, Neural Network and Naïve Bayes) for diagnosis of iCCA based on the 6 circulating amino acids were established and validated with high sensitivity and good overall accuracy. The resulting models were further improved by introducing a clinical indicator, gamma-glutamyl transferase (GGT). This study introduces a new approach for identifying potential serum biomarkers for the diagnosis of iCCA with high accuracy.</p></div>\",\"PeriodicalId\":16685,\"journal\":{\"name\":\"Journal of pharmaceutical and biomedical analysis\",\"volume\":\"252 \",\"pages\":\"Article 116457\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0731708524004977/pdfft?md5=662cb1d24e6f44b69c93e1da7cecaef4&pid=1-s2.0-S0731708524004977-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of pharmaceutical and biomedical analysis\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0731708524004977\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"CHEMISTRY, ANALYTICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of pharmaceutical and biomedical analysis","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0731708524004977","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, ANALYTICAL","Score":null,"Total":0}
Serum targeted metabolomics uncovering specific amino acid signature for diagnosis of intrahepatic cholangiocarcinoma
Intrahepatic cholangiocarcinoma (iCCA) is a hepatobiliary malignancy which accounts for approximately 5–10 % of primary liver cancers and has a high mortality rate. The diagnosis of iCCA remains significant challenges owing to the lack of specific and sensitive diagnostic tests available. Hence, improved methods are needed to detect iCCA with high accuracy. In this study, we evaluated the efficacy of serum amino acid profiling combined with machine learning modeling for the diagnosis of iCCA. A comprehensive analysis of 28 circulating amino acids was conducted in a total of 140 blood samples from patients with iCCA and normal individuals. We screened out 6 differentially expressed amino acids with the criteria of |Log2(Fold Change, FC)| > 0.585, P-value < 0.05, variable importance in projection (VIP) > 1.0 and area under the curve (AUC) > 0.8, in which amino acids L-Asparagine and Kynurenine showed an increasing tendency as the disease progressed. Five frequently used machine learning algorithms (Logistic Regression, Random Forest, Supporting Vector Machine, Neural Network and Naïve Bayes) for diagnosis of iCCA based on the 6 circulating amino acids were established and validated with high sensitivity and good overall accuracy. The resulting models were further improved by introducing a clinical indicator, gamma-glutamyl transferase (GGT). This study introduces a new approach for identifying potential serum biomarkers for the diagnosis of iCCA with high accuracy.
期刊介绍:
This journal is an international medium directed towards the needs of academic, clinical, government and industrial analysis by publishing original research reports and critical reviews on pharmaceutical and biomedical analysis. It covers the interdisciplinary aspects of analysis in the pharmaceutical, biomedical and clinical sciences, including developments in analytical methodology, instrumentation, computation and interpretation. Submissions on novel applications focusing on drug purity and stability studies, pharmacokinetics, therapeutic monitoring, metabolic profiling; drug-related aspects of analytical biochemistry and forensic toxicology; quality assurance in the pharmaceutical industry are also welcome.
Studies from areas of well established and poorly selective methods, such as UV-VIS spectrophotometry (including derivative and multi-wavelength measurements), basic electroanalytical (potentiometric, polarographic and voltammetric) methods, fluorimetry, flow-injection analysis, etc. are accepted for publication in exceptional cases only, if a unique and substantial advantage over presently known systems is demonstrated. The same applies to the assay of simple drug formulations by any kind of methods and the determination of drugs in biological samples based merely on spiked samples. Drug purity/stability studies should contain information on the structure elucidation of the impurities/degradants.