Your browser doesn't support javascript.
loading
Serum targeted metabolomics uncovering specific amino acid signature for diagnosis of intrahepatic cholangiocarcinoma.
Zhang, Wenjun; Dong, Chuntao; Li, Zhaosheng; Shi, Huina; Xu, Yijun; Zhu, Mingchen.
Afiliación
  • Zhang W; Department of Laboratory Medicine, Nanjing Drum Tower Hospital, Nanjing University Medical School, Nanjing 210008, China.
  • Dong C; Nanjing High-Tech Precision Medicine Technology Co., Ltd, Nanjing 210061, China.
  • Li Z; Department of Clinical Laboratory, The Affiliated Cancer Hospital of Nanjing Medical University and Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Nanjing 210009, China.
  • Shi H; Department of Clinical Laboratory, The Affiliated Cancer Hospital of Nanjing Medical University and Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Nanjing 210009, China.
  • Xu Y; Department of Gastroenterology, Nanjing First Hospital, Nanjing Medical University, Nanjing 210006, China.
  • Zhu M; Department of Clinical Laboratory, The Affiliated Cancer Hospital of Nanjing Medical University and Jiangsu Cancer Hospital and Jiangsu Institute of Cancer Research, Nanjing 210009, China. Electronic address: sjnh_4914@163.com.
J Pharm Biomed Anal ; 252: 116457, 2025 Jan 01.
Article en En | MEDLINE | ID: mdl-39241676
ABSTRACT
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.
Asunto(s)
Palabras clave
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de los Conductos Biliares / Biomarcadores de Tumor / Colangiocarcinoma / Metabolómica / Aprendizaje Automático / Aminoácidos Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: J Pharm Biomed Anal Año: 2025 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de los Conductos Biliares / Biomarcadores de Tumor / Colangiocarcinoma / Metabolómica / Aprendizaje Automático / Aminoácidos Límite: Aged / Female / Humans / Male / Middle aged Idioma: En Revista: J Pharm Biomed Anal Año: 2025 Tipo del documento: Article Pais de publicación: Reino Unido