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Background <p>Nasopharyngeal 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.</p> Objectives <p>This 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. Methods: We performed an integrated analysis of transcriptomic, metabolomic, and Raman spectral data on five NPC cell lines (CNE1, CNE2, 5–8&#xa0;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).</p> Results <p>Significant 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.</p> Conclusions <p>This 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.</p> Graphical abstract <p></p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月22日

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

摘要

Background

Nasopharyngeal 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.

Objectives

This 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. Methods: We 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).

Results

Significant 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.

Conclusions

This 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.

Graphical abstract