Evidence map›Paper›PMID 41560801›Full record

ArticleMaterials today. Bio2026

Primary tissue metabolic fingerprinting for efficient diagnosis of lymph node metastasis and metabolic reprogramming mechanisms in colorectal cancer.

Hao Zhang, Juxiang Zhang, Meng Yan, Xiaohui Liu, Shouzhi Yang, Jiao Wu, Shan Huang, Xia Guo, Weidong Zhu, Jingyi Wang and 7 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Materials today. Bio, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Hao ZhangDepartment of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China.
Juxiang ZhangState Key Laboratory of Systems Medicine for Cancer School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China.
Meng YanDepartment of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China.
Xiaohui LiuState Key Laboratory of Systems Medicine for Cancer School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China.
Shouzhi YangState Key Laboratory of Systems Medicine for Cancer School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China.
Jiao WuState Key Laboratory of Systems Medicine for Cancer School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China.
Shan HuangDepartment of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China.
Xia GuoDepartment of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China.
Weidong ZhuDepartment of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China.
Jingyi WangDepartment of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China.
Zhe LeiDepartment of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China.
Ding ZhangAmoy Diagnostics Co., Ltd., Fujian, 361027, China.
Changbin ZhuAmoy Diagnostics Co., Ltd., Fujian, 361027, China.
Li RuanAmoy Diagnostics Co., Ltd., Fujian, 361027, China.
Zhiyong LiangDepartment of Pathology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Lingchuan GuoDepartment of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215006, China.
Yida HuangState Key Laboratory of Systems Medicine for Cancer School of Biomedical Engineering, Institute of Medical Robotics and Shanghai Academy of Experimental Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate detection of lymph node metastasis (LNM) is critical for colorectal cancer (CRC) staging and treatment planning, yet current histopathological assessment based on lymph nodes remains labor-intensive and operator-dependent. Here, we developed a tissue metabolic fingerprinting platform leveraging label-free ferric nanoparticle-enhanced laser desorption/ionization mass spectrometry (FELDI-MS) to directly acquire colorectal cancer tissue metabolic fingerprints (CRC-TMFs) from 276 primary CRC tissue samples (138 non-metastatic/LNM-, 138 metastatic/LNM+). Based on CRC-TMFs, we constructed a machine learning-based diagnostic model for LNM detection, achieving area under the curve (AUC) of 0.914. Furthermore, metabolic profiling revealed cysteine deficiency in LNM + tissues, concomitant with upregulation of glutamate-cysteine ligase catalytic subunit (GCLC), which catalyzes the rate-limiting step in glutathione biosynthesis from cysteine. Functional validation demonstrated that GCLC knockdown inhibited CRC cell proliferation and migration, underscoring its role in metastatic reprogramming. Our work not only introduces a rapid, operator-independent tool for precise LNM assessment but also highlights dysregulated cysteine-GCLC-glutathione metabolism as a key feature of metastatic reprogramming in CRC.

Indexed as

Colorectal cancerDiagnosisLymph node metastasisMetabolic reprogrammingMetabolomicsTissue

Identifiers

PMID41560801
PMCPMC12813119

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.