Citation: Man Zhang, Pu Chen, Jiemian Chen, Yingchen Xu, Chenyu Yang, Xufang Hu, Chunhui Deng, Shuai Jiang, Nianrong Sun. Engineered incremental architecture enhances AI-driven metabolic profile-based tumor identification in urological cancers[J]. Chinese Chemical Letters, ;2026, 37(11): 112649. doi: 10.1016/j.cclet.2026.112649 shu

Engineered incremental architecture enhances AI-driven metabolic profile-based tumor identification in urological cancers

Man Zhang ^{a, 1} ,
Pu Chen ^{b, 1} ,
Jiemian Chen ^a ,
Yingchen Xu ^a ,
Chenyu Yang ^a ,
Xufang Hu ^{c, *,} ,
Chunhui Deng ^{d, *,} ,
Shuai Jiang ^{e, *,} ,
Nianrong Sun ^{f, *,}

a.
Department of Chemistry, Fudan University, Shanghai 200433, China
b.
Department of Laboratory Medicine, Zhongshan Hospital, Fudan University, Shanghai 200032, China
c.
School of Chemical Science and Technology, National Demonstration Center for Experimental Chemistry and Chemical Engineering Education, Yunnan University, Kunming 650091, China
d.
Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Department of Chemistry, Institutes of Biomedical Sciences, Fudan University, Shanghai 201399, China
e.
Department of Urology, Zhongshan Hospital, Fudan University, Shanghai 200032, China
f.
Department of Gastroenterology and Hepatology, Zhongshan Hospital, Fudan University, Shanghai 200040, China

huxufang@ynu.edu.cn

chdeng@fudan.edu.cn

jiang.shuai@zs-hospital.sh.cn

sunnianrong@fudan.edu.cn

Received Date: 25 July 2025
Revised Date: 15 March 2026
Accepted Date: 19 March 2026
Available Online: 19 March 2026

Figures(5)

Urological cancers with high mortality are often diagnosed incidentally or through biopsies. Developing a universal, non-invasive molecular diagnostic tool for these cancers is both urgent and challenging. Herein, we engineer a two-dimensional incremental architecture involving multiple optimizations in both composition and structure, based on cobalt-iron oxides and noble metals. In brief, the first-dimensional increment was achieved by precisely tuning the density of sharp-tipped iron-nanorods on the cobalt-based architecture, resulting in a two-fold amplification of the MS signal. The second incremental step was achieved by incorporating the surface plasmon resonance effects of noble metals, which offer enhanced light absorption and photocurrent response, yielding about three-fold signal increase. Consequently, the engineered product enables the direct efficient extraction of serum metabolic profiles from healthy control and various urological cancers, including prostate cancer (PCa), bladder cancer (BCa), and renal cancer (RCa). In the end, the artificial intelligence (AI)-driven direct four-category diagnostic strategy, based on three key features, achieves an impressive average the area under the curve (AUC) value of 0.979 and an average accuracy of 90.9%. This work underscores the need for incremental engineering and the feasibility of non-invasive, high-performance diagnostic tools.
- Diagnosis,
- Nanomaterials,
- Metal oxide,
- Metabolomics,
- LDI MS
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