Citation: Yiwen Lin, Yijie Chen, Chunhui Deng, Nianrong Sun. Integration of resol/block-copolymer carbonization and machine learning: A convenient approach for precise monitoring of glycan-associated disorders[J]. Chinese Chemical Letters, ;2024, 35(12): 109813. doi: 10.1016/j.cclet.2024.109813 shu

Integration of resol/block-copolymer carbonization and machine learning: A convenient approach for precise monitoring of glycan-associated disorders

Yiwen Lin ^a ,
Yijie Chen ^a ,
Chunhui Deng ^{a, c, *,} ,
Nianrong Sun ^{b, *,}

a.
Department of Chemistry, Department of Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai 200433, China
b.
Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
c.
School of Chemistry and Chemical Engineering, Nanchang University, Nanchang 330031, China

chdeng@fudan.edu.cn

sunnianrong@fudan.edu.cn

Received Date: 19 November 2023
Revised Date: 21 February 2024
Accepted Date: 21 March 2024
Available Online: 21 March 2024

Figures(3)

Ensuring the timely and precise monitoring of severe liver diseases is crucial for guiding effective therapies and significantly extending overall quality of life. However, this remains a worldwide challenge, given the high incidence rate and the presence of strong confounding clinical symptoms. Herein, we applied a convenient and high-yield method to prepare the magnetic mesoporous carbon (MMC-Fe), guided by a composite of resol and triblock copolymer. With the combination of MMC-Fe, high-throughput mass spectrometry, and a simple machine learning algorithm, we extracted N-glycan profiles from various serum samples, including healthy controls, liver cirrhosis, and liver cancer, and from which we screened specific N-glycans. Specifically, the selected N-glycans demonstrate exceptional performance with area under the curve (AUC) values ranging from 0.948 to 0.993 for the detection of liver diseases, including alpha fetoprotein (AFP)-negative liver cancer. Among them, five N-glycans holds potential in monitoring distinctions between liver cirrhosis and AFP-negative liver cancer (AUC values of 0.827–0.842). This study is expected to promote the glycan-based precise monitoring of diseases, not limited to liver disease.
- N-Glycan,
- Mesoporous carbon,
- MALDI-TOF MS,
- Machine learning model,
- Liver disease
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