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Quantitative Detection of Gastrointestinal Tumor Markers Using a Machine Learning Algorithm and Multicolor Quantum

Gaowa Saren1, Linlin Zhu2, Yue Han3

  • 1The Department of Medical Nursing, Hulunbuir Vocational Technical College, Hulunbuir 021000, China.

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Summary

This study introduces an improved principal component analysis (PCA) model for gastrointestinal tumor detection. The novel approach enhances accuracy in identifying tumor gene features and classifying gastrointestinal tumors using quantum dots (QDs) immunobiosensors.

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Area of Science:

  • Biomedical Engineering
  • Molecular Biology
  • Computational Biology

Background:

  • Gastrointestinal tumors pose a significant health challenge, necessitating improved diagnostic tools.
  • Accurate detection and classification of gastrointestinal tumors rely on identifying specific gene features and tumor markers.
  • Existing diagnostic methods may lack the sensitivity and specificity required for early and precise tumor detection.

Purpose of the Study:

  • To explore the application value of a gene feature selection model based on principal component analysis (PCA) and multicolor quantum dots (QDs) immunobiosensors for gastrointestinal tumor detection.
  • To develop and evaluate an improved PCA model, termed OPCA, incorporating the neighborhood rough set algorithm for enhanced classification accuracy.
  • To assess the performance of QDs-based immunobiosensors for detecting key gastrointestinal tumor markers: carbohydrate antigen 125 (CA125), cancer antigen 19-9 (CA19-9), anticancer embryonic antigen (CEA), and alpha-fetoprotein (AFP).

Main Methods:

  • Fabrication of four coupled QD immunobiosensors using QDs with specific wavelengths (525nm, 605nm, 645nm, 565nm) conjugated with monoclonal antibodies against CA125, CA19-9, CEA, and AFP, respectively.
  • Development of the Optimized Principal Component Analysis (OPCA) model by integrating the neighborhood rough set algorithm with PCA for tumor gene feature selection and classification.
  • Characterization and quantification of QD-antibody conjugates using fluorescence spectroscopy and ultraviolet absorption spectroscopy; evaluation of OPCA model performance on colon tumor and gastric cancer datasets.

Main Results:

  • The OPCA model demonstrated high classification precision, achieving 99.52% for colon tumors and 99.03% for gastric cancer, with classification accuracies of 94.86% and 94.2%, respectively.
  • Optimal conjugation concentrations for QD-antibody conjugates were determined: 25 µg/mL for AFP McAb, 20 µg/mL for CEA McAb, 30 µg/mL for CA19-9 McAb, and 30 µg/mL for CA125 McAb.
  • The alpha-fetoprotein (AFP) marker showed the highest recovery rate (98.51%) and significantly higher fluorescence intensity (35.78 ± 2.99) compared to other antigens (P < 0.001).

Conclusions:

  • The OPCA model effectively reduces feature gene sets and enhances the accuracy of sample classification in gastrointestinal tumor detection.
  • Intelligent immunobiosensors utilizing machine learning algorithms and QDs show significant potential for gastrointestinal gene feature selection and tumor marker detection.
  • This approach offers a novel strategy for the clinical diagnosis of gastrointestinal tumors, improving upon existing diagnostic paradigms.