国际口腔医学杂志

国际口腔医学杂志 ›› 2026, Vol. 53 ›› Issue (1): 107-115.doi: 10.7518/gjkq.2026111

• 综述 • 上一篇    下一篇

拉曼光谱技术在口腔鳞状细胞癌和口腔潜在恶性疾病诊断和治疗中的应用进展

张琳涵(),汤亚玲()   

  1. 口腔疾病防治全国重点实验室 国家口腔医学中心 口腔疾病国家临床医学研究中心四川大学华西口腔医院口腔病理科 成都 610041
  • 收稿日期:2025-03-11 修回日期:2025-07-15 出版日期:2026-01-01 发布日期:2025-12-31
  • 通讯作者: 汤亚玲
  • 作者简介:张琳涵，硕士，Email：rachel954@163.com
  • 基金资助:
    四川大学华西口腔医院临床研究项目(LCYJ-MS-202308)

Advancements in the application of Raman spectroscopy for the diagnosis and treatment of oral squamous cell carcinoma and oral potentially malignant disorders

Linhan Zhang(),Yaling Tang()   

  1. State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases & Dept. of Oral Pathology, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
  • Received:2025-03-11 Revised:2025-07-15 Online:2026-01-01 Published:2025-12-31
  • Contact: Yaling Tang
  • Supported by:
    Clinical Research Project Funded by West China Hospital of Stomatology, Sichuan University(LCYJ-MS-202308)

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摘要：

拉曼光谱（RS）能够提供反应物质特定分子“指纹”的特征光谱信息，可用于识别生物组织微小的生化变异，该技术具有快速、实时、非侵入、无需样本预处理等优势，在口腔鳞状细胞癌（OSCC）及口腔潜在恶性疾病（OPMD）诊治中展现出广阔的应用前景。本文就RS技术基于组织及体液样本诊断OSCC及OPMD的研究进展作一综述，并简要总结RS技术在OSCC手术切缘判定、相关分子机制研究以及人工智能辅助诊断等领域中的应用。

关键词: 拉曼光谱, 病理诊断, 口腔鳞状细胞癌, 口腔潜在恶性疾病, 人工智能

Abstract:

Raman spectroscopy (RS) provides characteristic spectral information that represents specific molecular “fingerprints” of substances. This technique enables the identification of subtle biochemical alterations in biological tissues, offering advantages such as rapid analysis, real-time capability, noninvasiveness, and minimal sample preparation requirements. It demonstrates considerable promise in the diagnosis and management of oral squamous cell carcinoma (OSCC) and oral potentially malignant disorders (OPMDs). This review summarizes recent advancements in RS-based diagnosis of OSCC and OPMDs using tissue and biofluid samples. The applications of RS in assessing surgical margins du-ring OSCC resection, investigating relevant molecular mechanisms, and enabling artificial intelligence-assisted diagnosis are briefly outlined.

Key words: Raman spectroscopy, pathological diagnosis, oral squamous cell carcinoma, oral potentially malignant disorders, artificial intelligence

中图分类号: 

  • R739.8

表 1

OSCC及OPMD、口腔异常增生组织中特征性RS改变及其含义"

组织来源/细胞系拉曼检测波数范围（峰位）/cm-1变化（相对于正常组织）生化改变生物学含义参考文献
OSCC500~2 200（747、897、930、1 060、1 092、1 125、1 610、1 666）增强DNA、蛋白质及脂类合成增强高增殖活性[21]
OSCC3 250减弱蛋白质N-H伸缩震动降低高增殖活性
OLK751、780、1 071增强色氨酸、DNA、脂质含量升高高增殖活性
OSCC1 653 （酰胺Ⅰ）减弱蛋白质结构变化正常上皮组织中蛋白质结构与恶性肿瘤组织不同[22]
OSCC1 670 （酰胺Ⅰ）增强胶原蛋白恶性肿瘤组织中胶原蛋白含量增加
OSCC1 246 （酰胺 Ⅲ）增强胶原蛋白恶性肿瘤组织中胶原蛋白含量增加
OSCC1 472、1 242、812增强DNA恶性肿瘤组织中DNA含量增加
OSCC850~830减弱酪氨酸正常上皮组织中酪氨酸含量较高
OSCC1 004增强苯丙氨酸、其他蛋白质恶性肿瘤组织中非胶原蛋白蛋白质含量增加
OSCC1 400、1 336、1 318、1 240、1 208、1 174~1 152、1 123、1 078增强免疫球蛋白恶性肿瘤组织中免疫球蛋白含量增加
OSCC1 750减弱脂质OSCC脂质减少[23]
OSCC1 451减弱蛋白质OSCC中蛋白质结构变化
OSCC/异常增生730增强DNA中的基本基团DNA含量增加，与细胞增殖能力增加有关[24]
OSCC/异常增生884增强脱氧核糖的磷酸链振动反映DNA结构的变化，可能与肿瘤发展相关
OSCC/异常增生1 054增强DNA中的C-O伸缩振动与DNA含量和结构变化相关
OSCC/异常增生1 090增强DNA的O-P-O主链伸缩与DNA结构的完整性和稳定性相关
OSCC/异常增生1 116增强蛋白质中的C-N伸缩振动蛋白质含量增加，可能与肿瘤细胞功能改变有关
OSCC/异常增生1 911增强酪氨酸中的ν（C-O）伸缩振动与蛋白质结构特别是氨基酸的变化相关
SCC-43 470、3 550增强OH伸缩振动未结合水含量增加，可能影响细胞代谢和肿瘤发展[25]
SCC-42 870增强CH3的不对称伸缩振动肿瘤细胞内更多蛋白质成分
[1] Tan YH, Wang ZH, Xu MT, et al. Oral squamous cell carcinomas: state of the field and emerging directions[J]. Int J Oral Sci, 2023, 15(1): 44.
[2] Zhu SJ, Zhang FX, Zhao GC, et al. Trends in the global burden of oral cancer joint with attributable risk factors: results from the global burden of di-sease study 2019[J]. Oral Oncol, 2022, 134: 106189.
[3] Raman CV, Krishnan KS. A new type of secondary radiation[J]. Nature, 1928, 121(3048): 501-502.
[4] Butler HJ, Ashton L, Bird B, et al. Using Raman spectroscopy to characterize biological materials[J]. Nat Protoc, 2016, 11(4): 664-687.
[5] 罗艺, 秦齐, 王沛豪, 等. 人工智能结合光谱诊断技术在肺癌组织鉴别领域的应用与研究进展[J]. 首都医科大学学报, 2023, 44(6): 1095-1102.
Luo Y, Qin Q, Wang PH, et al. Application and research progress of artificial intelligence combined withspectral diagnosis technology in the identification of lung cancer tissues[J]. J Cap Med Univ, 2023, 44(6): 1095-1102.
[6] Yang JQ, Xu P, Wu SY, et al. Raman spectroscopy for esophageal tumor diagnosis and delineation u-sing machine learning and the portable Raman spectrometer[J]. Spectrochim Acta A Mol Biomol Spectrosc, 2024, 317: 124461.
[7] Maiman TH. Stimulated optical radiation in ruby[J]. Nature, 1960, 187(4736): 493-494.
[8] Bi XY, Czajkowsky DM, Shao ZF, et al. Digital colloid-enhanced Raman spectroscopy by single-molecule counting[J]. Nature, 2024, 628(8009): 771-775.
[9] Horta-Velázquez A, Arce F, Rodríguez-Sevilla E, et al. Toward smart diagnostics via artificial intelligence-assisted surface-enhanced Raman spectroscopy[J]. Trac Trends Anal Chem, 2023, 169: 117378.
[10] Chang XH, Yu MX, Liu RY, et al. Deep learning methods for oral cancer detection using Raman spectroscopy[J]. Vib Spectrosc, 2023, 126: 103522.
[11] Lu DC, Shangguan ZK, Su ZH, et al. Artificial intelligence-based plasma exosome label-free SERS profiling strategy for early lung cancer detection[J]. Anal Bioanal Chem, 2024, 416(23): 5089-5096.
[12] Kim UJ, Lee S, Kim H, et al. Drug classification with a spectral barcode obtained with a smartphone Raman spectrometer[J]. Nat Commun, 2023, 14(1): 5262.
[13] Wang W, Ma PY, Song DQ. Applications of surface-enhanced Raman spectroscopy based on portable Raman spectrometers: a review of recent developments[J]. Luminescence, 2022, 37(11): 1822-1835.
[14] O’Grady A, Dennis AC, Denvir D, et al. Quantitative Raman spectroscopy of highly fluorescent samples using pseudosecond derivatives and multiva-riate analysis[J]. Anal Chem, 2001, 73(9): 2058-2065.
[15] Aragon FFH, Haeck CM, Morais PC, et al. Polymorphism characterization of segesterone acetate: a comprehensive study using XRPD, FT-IR and Raman spectroscopy[J]. Int J Pharm, 2021, 596: 120234.
[16] Grassi E, Laptenok SP, Genchi LC, et al. Frequency-modulation stimulated Raman scattering microscopy with an acousto-optic tunable filter[J]. Opt Express, 2023, 31(11): 18290-18299.
[17] Manganelli Conforti P, D’Acunto M, Russo P. Deep learning for chondrogenic tumor classification through wavelet transform of Raman spectra[J]. Sensors (Basel), 2022, 22(19): 7492.
[18] 曾琦, 刘瑞, 王楠, 等. 拉曼光谱技术在医学检验领域中的研究进展(特邀)[J]. 光子学报, 2021, 50(10): 300-311.
Zeng Q, Liu R, Wang N, et al. Research progress of Raman spectroscopy in medical laboratory science(invited)[J]. Acta Photonica Sin, 2021, 50(10): 300-311.
[19] Norton RD, Phan HT, Gibbons SN, et al. Quantitative surface-enhanced spectroscopy[J]. Annu Rev Phys Chem, 2022, 73: 141-162.
[20] Hu JQ, Chen GJ, Xue CL, et al. RSPSSL: a novel high-fidelity Raman spectral preprocessing scheme to enhance biomedical applications and chemical resolution visualization[J]. Light Sci Appl, 2024, 13(1): 52.
[21] 李一, 文志宁, 李龙江, 等. 口腔鳞状细胞癌近红外拉曼光谱特征及其诊断价值研究[J]. 华西口腔医学杂志, 2010, 28(1): 61-64.
Li Y, Wen ZN, Li LJ, et al. Near infrared Raman spectral character and diagnostic value of squamous cell carcinoma of oral mucosa[J]. West China J Stomatol, 2010, 28(1): 61-64.
[22] Krishna CM, Sockalingum GD, Kurien J, et al. Micro-Raman spectroscopy for optical pathology of oral squamous cell carcinoma[J]. Appl Spectrosc, 2004, 58(9): 1128-1135.
[23] Matthies L, Gebrekidan MT, Tegtmeyer JF, et al. Optical diagnosis of oral cavity lesions by label-free Raman spectroscopy[J]. Biomed Opt Express, 2021, 12(2): 836-851.
[24] Li B, Gu ZY, Yan KX, et al. Evaluating oral epithe-lial dysplasia classification system by near-infrared Raman spectroscopy[J]. Oncotarget, 2017, 8(44): 76257-76265.
[25] Carvalho LFCS, Bonnier F, Tellez C, et al. Raman spectroscopic analysis of oral cells in the high wavenumber region[J]. Exp Mol Pathol, 2017, 103(3): 255-262.
[26] Shrivastava PK, Kumar A, Aggarwal Y, et al. Diagnostic accuracy of Vibrational spectroscopy in the diagnosis of oral potentially malignant and malignant disorders: a systematic review and meta-analysis[J]. J Cancer Res Ther, 2023, 19(2): 151-158.
[27] Steybe D, Poxleitner P, Metzger MC, et al. Stimula-ted Raman histology for histological evaluation of oral squamous cell carcinoma[J]. Clin Oral Investig, 2023, 27(8): 4705-4713.
[28] Jeng MJ, Sharma M, Sharma L, et al. Novel quantitative analysis using optical imaging (VELscope) and spectroscopy (Raman) techniques for oral cancer detection[J]. Cancers (Basel), 2020, 12(11): 3364.
[29] Chundayil Madathil G, Iyer S, Thankappan K, et al. A novel surface enhanced Raman catheter for rapid detection, classification, and grading of oral cancer[J]. Adv Healthc Mater, 2019, 8(13): e1801557.
[30] Tan YY, Yan B, Xue LL, et al. Surface-enhanced Raman spectroscopy of blood serum based on gold nanoparticles for the diagnosis of the oral squamous cell carcinoma[J]. Lipids Health Dis, 2017, 16(1): 73.
[31] Moisoiu V, Stefancu A, Gulei DA, et al. SERS-based differential diagnosis between multiple solid malignancies: breast, colorectal, lung, ovarian and oral cancer[J]. Int J Nanomedicine, 2019, 14: 6165-6178.
[32] Han RY, Lin N, Huang J, et al. Diagnostic accuracy of Raman spectroscopy in oral squamous cell carcinoma[J]. Front Oncol, 2022, 12: 925032.
[33] 何传箴, 许露云, 林敏琪, 等. 表面增强拉曼光谱液体活检技术研究进展[J]. 光散射学报, 2023, 35(2): 160-173.
He CZ, Xu LY, Lin MQ, et al. Research progress on surface-enhanced Raman spectroscopy liquid biopsy technology[J]. J Light Scatt, 2023, 35(2): 160-173.
[34] 黄笑天, 李斌, 莫天录, 等. 基于表面增强拉曼光谱技术的肿瘤标志物检测进展[J]. 科学通报, 2023, 68(14): 1787-1798.
Huang XT, Li B, Mo TL, et al. Advances in tumor marker detection using surface-enhanced Raman spectroscopy[J]. Chin Sci Bull, 2023, 68(14): 1787-1798.
[35] Wei QB, Zhou LP, Sun J, et al. Rapid detection of drugs in blood using “molecular hook” surface-enhanced Raman spectroscopy and artificial intelligence technology for clinical applications[J]. Biosens Bioelectron, 2025, 267: 116855.
[36] Xu GY, Yu JH, Liu SY, et al. In situ surface-enhanced Raman spectroscopy for membrane protein analysis and sensing[J]. Biosens Bioelectron, 2025, 267: 116819.
[37] Faur CI, Dinu C, Toma V, et al. A new detection method of oral and oropharyngeal squamous cell carcinoma based on multivariate analysis of surface enhanced Raman spectra of salivary exosomes[J]. J Pers Med, 2023, 13(5): 762.
[38] Koster HJ, Guillen-Perez A, Gomez-Diaz JS, et al. Fused Raman spectroscopic analysis of blood and saliva delivers high accuracy for head and neck cancer diagnostics[J]. Sci Rep, 2022, 12(1): 18464.
[39] Brindha E, Rajasekaran R, Aruna P, et al. High wavenumber Raman spectroscopy in the characte-rization of urinary metabolites of normal subjects, oral premalignant and malignant patients[J]. Spectrochim Acta A Mol Biomol Spectrosc, 2017, 171: 52-59.
[40] Saha P, Sawant S, Deshmukh A, et al. Serum Raman spectroscopy: prognostic applications in oral cancers[J]. Head Neck, 2023, 45(5): 1244-1254.
[41] Amber A, Nawaz H, Bhatti HN, et al. Surface-enhanced Raman spectroscopy for the characterization of different anatomical subtypes of oral cavity cancer[J]. Photodiagnosis Photodyn Ther, 2023, 42: 103607.
[42] Liu YJ, Kyne M, Wang S, et al. A user-friendly platform for single-cell Raman spectroscopy analysis[J]. Spectrochim Acta A Mol Biomol Spectrosc, 2022, 282: 121686.
[43] Rebrosova K, Samek O, Kizovsky M, et al. Raman spectroscopy-a novel method for identification and characterization of microbes on a single-cell level in clinical settings[J]. Front Cell Infect Microbiol, 2022, 12: 866463.
[44] Fang T, Shang WH, Liu C, et al. Single-cell multimodal analytical approach by integrating Raman optical tweezers and RNA sequencing[J]. Anal Chem, 2020, 92(15): 10433-10441.
[45] Lee S, Kim JK. Label-free Raman spectroscopic techniques with morphological and optical characterization for cancer cell analysis[J]. Adv Exp Med Biol, 2021, 1310: 385-399.
[46] Auner GW, Koya SK, Huang CH, et al. Applications of Raman spectroscopy in cancer diagnosis[J]. Cancer Metastasis Rev, 2018, 37(4): 691-717.
[47] Adamczyk A, Orzechowska S, Nowakowska AM, et al. Stimulated Raman scattering microscopy in the analysis of cancer cells-a review and own research[J]. Trac Trends Anal Chem, 2023, 169: 117366.
[48] Notarstefano V, Sabbatini S, Pro C, et al. Exploiting Fourier transform infrared and Raman microspectroscopies on cancer stem cells from oral squamous cells carcinoma: new evidence of acquired cisplatin chemoresistance[J]. Analyst, 2021, 145(24): 8038-8049.
[49] Sahu A, Gera P, Pai V, et al. Raman exfoliative cytology for oral precancer diagnosis[J]. J Biomed Opt, 2017, 22(11): 1-12.
[50] 朱智慧. 基于光纤拉曼光谱技术检测口腔癌组织及舌鳞癌动物模型的研究[D]. 北京: 中国医学科学院北京协和医学院, 2019.
Zhu ZH. A study on the detection of oral cancer tissues and animal models of tongue squamous cell carcinoma based on fiber optic Raman spectroscopy[D]. Beijing: Chinese Academy of Medical Sciences & Peking Union Medical College, 2019.
[51] Smits RWH, van Lanschot CGF, Aaboubout Y, et al. Intraoperative assessment of the resection specimen facilitates achievement of adequate margins in oral carcinoma[J]. Front Oncol, 2020, 10: 614593.
[52] Aaboubout Y, Nunes Soares MR, Bakker Schut TC, et al. Intraoperative assessment of resection margins by Raman spectroscopy to guide oral cancer surgery[J]. Analyst, 2023, 148(17): 4116-4126.
[53] Sharma M, Li YC, Manjunatha SN, et al. Identification of healthy tissue from malignant tissue in surgical margin using Raman spectroscopy in oral cancer surgeries[J]. Biomedicines, 2023, 11(7): 1984.
[54] Zhao ZL, Wang SR, Dong G, et al. Microwave ablation versus surgical resection for US-detected multifocal T1N0M0 papillary thyroid carcinoma: a 10-center study[J]. Radiology, 2024, 311(1): e230459.
[55] Barroso EM, Smits RWH, van Lanschot CGF, et al. Water concentration analysis by Raman spectroscopy to determine the location of the tumor border in oral cancer surgery[J]. Cancer Res, 2016, 76(20): 5945-5953.
[56] Bury D, Morais CLM, Ashton KM, et al. Ex vivo Raman spectrochemical analysis using a handheld probe demonstrates high predictive capability of brain tumour status[J]. Biosensors (Basel), 2019, 9(2): 49.
[57] Shu C, Zheng W, Lin K, et al. Real-time in vivo cancer staging of nasopharyngeal carcinoma patients with rapid fiberoptic Raman endoscopy[J]. Talanta, 2023, 259: 124561.
[58] 周苏伟, 郭宇, 吴进锦, 等. 光纤拉曼探头的研究进展及生物医学应用[J]. 激光与光电子学进展, 2024, 61(21): 2100003.
Zhou SW, Guo Y, Wu JJ, et al. Development and biomedical applications of fiber optic raman probes[J]. Laser Optoelectron Prog, 2024, 61(21): 2100003.
[59] van der Velden BHM, Kuijf HJ, Gilhuijs KGA, et al. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis[J]. Med Ima-ge Anal, 2022, 79: 102470.
[60] Bi XY, Lin L, Chen Z, et al. Artificial intelligence for surface-enhanced Raman spectroscopy[J]. Small Methods, 2024, 8(1): e2301243.
[61] Yu MX, Yan H, Xia JB, et al. Deep convolutional neural networks for tongue squamous cell carcinoma classification using Raman spectroscopy[J]. Photodiagnosis Photodyn Ther, 2019, 26: 430-435.
[62] Xie X, Yu WR, Chen ZX, et al. Early-stage oral cancer diagnosis by artificial intelligence-based SERS using Ag NWs@ZIF core-shell nanochains[J]. Nanoscale, 2023, 15(32): 13466-13472.
[63] Li X, Li LY, Sun Q, et al. Rapid multi-task diagnosis of oral cancer leveraging fiber-optic Raman spectroscopy and deep learning algorithms[J]. Front Oncol, 2023, 13: 1272305.
[64] Weber A, Enderle-Ammour K, Kurowski K, et al. AI-based detection of oral squamous cell carcinoma with Raman histology[J]. Cancers (Basel), 2024, 16(4): 689.
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