Evidence map›Paper›PMID 40844682›Full record

ArticleDiscover oncology2025

KYNU is a potential metabolic-related biomarker for nasopharyngeal carcinoma by Raman spectroscopy, metabolomics, and transcriptomics analysis.

Ziman Wu, Haiyan Yang, Yafei Xu, Xiang Ji, Dayang Chen, Chuang Zhang, Mingjie Liang, Xinying Li, Xiuming Zhang, Dan Xiong

Abstract read
In one paragraph

Article in Discover oncology, 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.

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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.

Ziman Wu *School of Medical Technology, Xinxiang Medical University, Xinxiang, 453003, China.
Haiyan Yang *Shantou University Medical College, Shantou, 515041, China.
Yafei XuDepartment of Cell Biology and Genetics, Shenzhen University Health Science Center, Shenzhen, 518060, China.
Xiang JiDepartment of Clinical Laboratory Medicine, The Third Affiliated Hospital of Shenzhen University, Shenzhen, 518001, China.
Dayang ChenDepartment of Clinical Laboratory Medicine, The Third Affiliated Hospital of Shenzhen University, Shenzhen, 518001, China.
Chuang ZhangSchool of Medicine, Anhui University of Science and Technology, Huainan, 232000, China.
Mingjie LiangShantou University Medical College, Shantou, 515041, China.
Xinying LiSchool of Medicine, Anhui University of Science and Technology, Huainan, 232000, China.
Xiuming ZhangSchool of Medical Technology, Xinxiang Medical University, Xinxiang, 453003, China. zhangxiuming0760@163.com.
Dan XiongShantou University Medical College, Shantou, 515041, China. sunny543@126.com.

Funding

the National Natural Science Foundation of China No. 81772921the Science and Technology Planning Project of Shenzhen Municipality, China JCYJ20230807142806014the Shenzhen Key Medical Discipline No. SZXK054
6 · The paper itself

Abstract

backgroundNasopharyngeal carcinoma (NPC) is a malignant tumor with high incidence in Southeast Asia and Southern China, characterized by difficulties in early diagnosis and high recurrence rates after treatment. Metabolic reprogramming plays a crucial role in the development and progression of tumors. In-depth studies on the metabolic characteristics and molecular mechanisms of NPC are essential to identify novel diagnostic and therapeutic targets.

objectivesThis study aimed to systematically reveal the metabolic characteristics and molecular mechanisms of NPC cell lines by integrating untargeted metabolomics, transcriptomics, and confocal micro-Raman spectroscopy (CMRS), and to explore potential biomarkers for prognostic evaluation and precision treatment of NPC.

methodsWe performed an integrated analysis of transcriptomic, metabolomic, and Raman spectral data on five NPC cell lines (CNE1, CNE2, 5-8 F, 6-10B, and SUNE1) and the immortalized nasopharyngeal epithelial cell line NPEC1-BMI1. The analysis included association analysis of differentially expressed metabolites (DEMs) and differentially expressed genes (DEGs), pathway enrichment analysis, and network analysis to elucidate the interplay between gene expression and metabolic alterations. Furthermore, we employed machine learning models to achieve efficient discrimination between NPC cell lines and NPEC1-BMI1 using Raman spectroscopy. Finally, we validated the expression levels of selected DEGs using quantitative polymerase chain reaction (qPCR), Western blotting (WB), and immunohistochemistry (IHC).

resultsSignificant differences in metabolic and gene expression profiles were observed between NPC cells and normal cells. CMRS analysis, combined with a multilayer perceptron (MLP) model, achieved high-precision discrimination between NPC cells and normal cells (accuracy 99.3%, AUC = 1.00). Further integrated analysis revealed significant correlations between KYNU and other DEGs, multiple DEMs, and specific Raman spectral features, suggesting their potential as diagnostic and prognostic biomarkers. Validation using the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases showed high KYNU expression in head and neck squamous cell carcinoma (HNSCC) and NPC tissues. Consistent high expression of KYNU was confirmed in NPC cell lines and tissues by qPCR, WB, and IHC.

conclusionsThis study elucidated the unique metabolic characteristics and molecular signatures of NPC, clarified how molecular changes regulate gene expression, and provided new potential targets for prognostic evaluation and precision treatment of NPC.

Indexed as

Confocal micro-Raman spectroscopyMachine learningMetabolomicsMulti-omicsNasopharyngeal carcinomaTranscriptomics

Identifiers

PMID40844682
PMCPMC12373589

What Socratic holds

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LicenceCC BY-NC-ND
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Registered trials

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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 Socratic graph.