Evidence map›Paper›PMID 41318566›Full record

ArticleJournal of translational medicine2025

Spatial metabolomics atlas in the progression of oral squamous cell carcinoma.

Huiting Zhao, Wei Han, Chaowen Shi, Guanfa Luo, Wen Lu, Yujuan Fu, Qingang Hu, Xiaowei Song, Zhengjun Shang, Xihu Yang

Abstract read
In one paragraph

Article in Journal of translational medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
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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

10 authors.

Huiting Zhao *Department of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, China.
Wei Han *Department of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, 210008, China.
Chaowen ShiSchool of Life Sciences, Jiangsu University, Zhenjiang, Jiangsu, 212013, China.
Guanfa LuoDepartment of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, China.
Wen LuDepartment of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, China.
Yujuan FuDepartment of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, China.
Qingang HuDepartment of Oral and Maxillofacial Surgery, Nanjing Stomatological Hospital, Medical School of Nanjing University, Nanjing, 210008, China.
Xiaowei SongDepartment of Chemistry, Stanford University, Stanford, CA, 94305, USA.
Zhengjun ShangState Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan, 430079, Hubei, China. shangzhengjun@hotmail.com.
Xihu YangDepartment of Oral and Maxillofacial Surgery, Affiliated Hospital of Jiangsu University, Zhenjiang, 212001, China. yangxihu1981@126.com.

Funding

Project of National Natural Science Foundation Committee 82173380
6 · The paper itself

Abstract

backgroundOral squamous cell carcinogenesis is a complex biological process, although some progress has been made in predicting the risk of malignant transformation of oral squamous cell carcinoma (OSCC). We aimed to visualise the dynamic metabolic characteristics of the progression of OSCC using desorption electrospray ionisation mass spectrometry imaging (DESI-MSI).

methodsEight matched OSCC samples were analysed using DESI-MSI. MetaboAnalyst database was used to screen for differential metabolism, and perform metabolic pathway analysis. Key pathways and enzymes were validated using immunohistochemical (IHC) techniques in an additional 60 patients with OSCC. Knockdown OSCC cell lines were constructed and functional experiments were carried out to elucidate the effect of key enzyme on the proliferation and migration of OSCC.

resultsSpatial metabolomics revealed 55 differential metabolites between cancer and normal mucosal tissues, 59 between cancer and precancerous tissues, and 52 between precancerous and normal mucosal tissues. We further identified 30 metabolites that were either increased or decreased from normal tissues to precancerous lesions and then to cancer tissues. Kyoto Encyclopedia of Genes and Genomes (KEGG) metabolic pathway analysis confirmed that sphingolipid metabolism and biosynthesis of unsaturated fatty acids are the main metabolic pathways involved in OSCC. Dihydroceramide desaturase (DEGS1) is the key enzyme in sphingolipid metabolism and is involved in ceramide synthesis, which is closely related to tumour carcinogenesis. We further confirmed that high expression of DEGS1 predicts poor overall survival and disease-free survival, and is closely related to the pathological grade of the tumour. In multivariate COX regression analysis, high expression of DEGS1 in tumor cells was an independent risk factor for OS in OSCC patients. Moreover, we demonstrated that DEGS1 knockdown inhibited the proliferation and migration of OSCC cells in vitro.

conclusionOur study used DESI-MSI to uncover the progression of OSCC at the spatial metabolomics level, and discovered key metabolic molecules involved in the carcinogenesis of OSCC. Sphingolipid metabolism plays an important role in OSCC carcinogenesis, and its key metabolic enzyme, DEGS1, is expected to become a new therapeutic target for OSCC in the future.

Indexed as

Carcinoma, Squamous CellDisease ProgressionMetabolomicsMouth NeoplasmsCell Line, TumorCell MovementCell ProliferationFemaleHumansMaleMetabolic Networks and PathwaysMiddle AgedDESI-MSIMalignant transformationOSCCSphingolipid metabolism

Identifiers

PMID41318566
PMCPMC12772000

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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 OpenQuestion graph.