Issue 31, 2024

An integrated platform for decoding hydrophilic peptide fingerprints of hepatocellular carcinoma using artificial intelligence and two-dimensional nanosheets

Abstract

Hydrophilic peptides (HPs) play a critical role in the pathogenesis of hepatocellular carcinoma (HCC). However, the comprehensive and in-depth high-throughput analysis of specific changes in HPs associated with HCC remains unrealized, due to the complex nature of biological fluids and the challenges of mining complex patterns in large data sets. The clinical diagnosis of HCC still lacks a non-destructive and accurate classification method, given the limited specificity of widely used biomarkers. To address these challenges, we have established a multifunctional platform that integrates artificial intelligence computation, hydrophilic interaction extraction of HPs, and MALDI-MS testing. This platform aims to achieve highly sensitive HP fingerprinting for accurate diagnosis of HCC. The method not only facilitates efficient detection of HPs, but also achieves a remarkable 100.00% diagnostic accuracy for HCC in a test cohort, supported by machine learning algorithms. By constructing a panel of HPs with 10 characteristic features, we achieved 98% accuracy in the test cohort for rapid diagnosis and identified 62 HPs deeply involved in pathways related to liver diseases. This integrated strategy provides new research directions for future biomarker studies as well as early diagnosis and individualized treatment of HCC.

Graphical abstract: An integrated platform for decoding hydrophilic peptide fingerprints of hepatocellular carcinoma using artificial intelligence and two-dimensional nanosheets

Supplementary files

Article information

Article type
Paper
Submitted
02 апр 2024
Accepted
01 юли 2024
First published
03 юли 2024

J. Mater. Chem. B, 2024,12, 7532-7542

An integrated platform for decoding hydrophilic peptide fingerprints of hepatocellular carcinoma using artificial intelligence and two-dimensional nanosheets

Z. Li, B. Ma, S. Shui, Z. Tu, W. Peng, Y. Chen, J. Zhou, F. Lan, B. Ying and Y. Wu, J. Mater. Chem. B, 2024, 12, 7532 DOI: 10.1039/D4TB00700J

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