Skip to main content

Nanotechnology: a promising method for oral cancer detection and diagnosis

Abstract

Oral cancer is a common and aggressive cancer with high morbidity, mortality, and recurrence rate globally. Early detection is of utmost importance for cancer prevention and disease management. Currently, tissue biopsy remains the gold standard for oral cancer diagnosis, but it is invasive, which may cause patient discomfort. The application of traditional noninvasive methods-such as vital staining, exfoliative cytology, and molecular imaging-is limited by insufficient sensitivity and specificity. Thus, there is an urgent need for exploring noninvasive, highly sensitive, and specific diagnostic techniques. Nano detection systems are known as new emerging noninvasive strategies that bring the detection sensitivity of biomarkers to nano-scale. Moreover, compared to current imaging contrast agents, nanoparticles are more biocompatible, easier to synthesize, and able to target specific surface molecules. Nanoparticles generate localized surface plasmon resonances at near-infrared wavelengths, providing higher image contrast and resolution. Therefore, using nano-based techniques can help clinicians to detect and better monitor diseases during different phases of oral malignancy. Here, we review the progress of nanotechnology-based methods in oral cancer detection and diagnosis.

Background

Cancer is a critical public health problem worldwide that has brought great burden to society. In 2016, an estimated 1,685,210 new cases and 595,690 cancer deaths occurred in the United States alone [1]. Oral cancer is the sixth most common cancer globally and has a 5-year survival rate of around 50% [2]. According to US cancer statistics, approximately 31,910 new cases of oral cancer and 6490 oral cancer deaths occurred in 2016 [3]. Oral cancer is an aggressive cancer that mainly affects oral epithelial cells, may develop metastasis, and even results in death [4]. The major type of malignancy is oral squamous cell carcinomas (OSCC), which accounts for more than 90% of all oral cancers [5]. These tumors may invade the mucosa of the tongue, buccal, floor of mouth, alveolar and the hard palate, and the tongue is reported to be the most common subsite, with poor prognosis [1, 6]. Oral carcinogenesis is often due to long-term exposure to various potential risk factors, which may lead to accumulation of multiple genetic mutations [4]. Several major risk factors for oral cancer, including smoking, alcohol consumption, and human papillomavirus infection, with smoking acting as the leading cause of cancer death [3, 7]. Besides, habitual use of the areca nut is another risk factor that closely associated with oral cancer, especially in Indian subcontinent [8].

The formation of oral cancer is a multifactorial and multistep process [6]. Oral leukoplakia, oral erythroplakia, oral lichen planus, oral submucous fibrosis, actinic keratosis, and discoid lupus erythematosus are common oral potentially malignant disorders (OPMD) that are known to have the potential for malignant transformation [8, 9]. Thus, early detection of OPMD and oral cancer is critical for the prognosis of diseases [5]. To date, scalpel biopsy and histopathological examinations are still the standard diagnostic procedures applied to ascertain the oral potentially malignant and malignant lesions [17, 18]. However, the biopsy procedure is often invasive, which may cause patients anxiety and discomfort [10]. The selection of resection margins depends largely on the histopathological assessments, and the results can be affected by the quality of the specimens and pathologists’ subjective judgments [11, 12]. In addition, the assessments are unable to detect small numbers of genetically abnormal cells at the margins, thus leaving the risk of recurrence [13, 14].

In the past few decades, a variety of pain-free diagnostic strategies have been developed. Non-invasive visual tools such as toluidine blue (TB) staining, autofluorescence (VELscope) and chemiluminescence (ViziLite) have been used solely or in combination as adjuvant tests to detect potentially malignant lesions [15,16,17,18,19]. In oral epithelial dysplasia cases, the sensitivity and specificity of TB, VELscope and ViziLite are reported to be 84.1% and 15.3, 77.3 and 27.8, 56.8 and 65.8%, respectively [15]. Exfoliated cells, serum, and saliva are the most commonly used non-invasive samples for oral cancer detection since they are easily accessible, convenient, and cost-effective [11, 20]. For oral cancer diagnosis, the sensitivity and specificity of exfoliative cytology is reported to be 93.5 and 50.6%, respectively [21]. The biomarker with high sensitivity and specificity in serum is combined detection of Cyclin D1 and epidermal growth factor receptor (EGFR), while the reliable marker in saliva is CD44 [22, 23]. Imaging techniques are used as diagnostic adjuncts to the histopathological assessments since they are noninvasive and done in real-time [24]. Radiographic imaging modalities-including magnetic resonance imaging (MRI), computed tomography (CT), cone beam computed tomography (CBCT), and positron emission tomography (PET)-are commonly used for clinical establishment of oral cancer stages and treatment plans [24, 25]. Raman spectroscopy, elastic scattering spectroscopy, diffuse reflectance spectroscopy, narrow-band imaging, and confocal reflectance microscopy are common optical diagnostic methods that distinguish malignant lesions from normal oral mucosa by reflecting changes within tissues through returned optical signals [11, 26,27,28,29,30,31,32].

However, these noninvasive methods still have some limitations [12]. The visual tools are highly subjective and depend on the expertise of the investigators [16,17,18]. The main deficiency of exfoliative cytology technology, which is based on the quantitative cytomorphometry and DNA aneuploidy, is the low detection specificity, resulting from the collection of disaggregated cells [12, 33, 34]. Moreover, the sensitivity for traditional detection methods is limited as the biomarkers with low concentrations in the tissue samples or body fluids may not be detected [35]. Although the imaging methods have provided real-time cancer cell morphology, their sensitivity for detecting small, earlier intraepithelial lesions are insufficient [36]. Thus, novel detection methods need to be explored to bring clinical benefits, including (1) accurately predicting the malignant risk of OPMDs, (2) specifically detecting oral cancer based on molecular targeting, (3) providing ultrasensitive detection strategies at nano-scale, (4) making real-time suggestions for the extent of surgical resection margins, and (5) monitoring oral cancer prognosis in a convenient way after treatment.

According to the US National Nanotechnology Initiative, nanotechnology refers to the manipulation of matter with the length scale of 1–100 nm in at least one dimension [37, 38]. In the past few decades, nanotechnologies have been applied in various fields, especially in the medical field [39]. One of the most hotly researched subfield of nanotechnology is nanomedicine, which increases the possibility of specific targeted cancer therapy [40]. Moreover, nanotechnology is also a useful tool for cancer detection, and monitoring the disease as it metastasizes [41,42,43,44]. To date, nanotechnology has been applied in the detection and diagnosis of various cancers, such as cervical cancer, lung cancer, breast cancer, gastric cancer, nasopharyngeal cancer, and oral cancer [45,46,47,48,49,50,51,52]. As far as we know, the application of nano-based detection methods for oral cancer has not been systematically reviewed. In this review, we highlighted the various nanotechnologies that have been developed for oral cancer detection and diagnosis. The application of nanotechnology for in vitro and in vivo bioimaging of oral cancer was shown in Fig. 1.

Fig. 1
figure 1

The application of nanotechnology for in vitro and in vivo bioimaging of oral cancer

Nanotechnology-based detection and diagnostic methods

Nano-based molecular imaging

Magnetic resonance imaging

Magnetic resonance imaging (MRI) is reported to be suitable for the assessment of the primary tumor and bone invasion, as well as the outlining of the actual tumor borders during surgery [25, 53]. Commonly used positive MRI contrast agents-Gd3+ complexed with diethyltriamine-pentaacetic acid (Gd-DTPA) or tetra azacyclododecane-1,4,7,10-tetraacetic acid (Gd-DOTA)-can shorten tissue longitudinal relaxation times (T1) [54]. However, the contrast agents distribute throughout the entire body after being intravenously injected, but do not specifically accumulate in tumors. In addition, the blood circulation life time for Gd-DTPA or Gd-DOTA is very short, approximately only 1–1.5 h [55]. The contrast agents usually consist of superparamagnetic nanoparticles with coating layers [56].

With the advancement in nanotechnology, various types of nanoparticles have been applied as specific MRI contrast agents for cancer screening [54]. Nano-contrast agents have the ability to recognize unique cell surface markers and prolonged blood circulation half-life, exhibiting better MRI contrast properties [57]. The most commonly studied superparamagnetic iron oxide (SPIO) and ultrasmall superparamagnetic iron oxide (USPIOs) nanoparticles, which can shorten T2 and T2*, have already been used as negative contrast agents for detecting liver and spleen diseases [58].

Nano-contrast agents have also been studied in oral cancers. For example, Asifkhan et al. combined the folate preconjugated chitosan and magnetic poly (lactide-co-glycolide) (PLGA) nanoparticles to create an MRI contrast agent (Fig. 2) [59]. The overall T2 relaxation time was shortened, and the nanoparticle relaxivity was enhanced thereby providing better imaging contrast [59]. Meanwhile, the folate receptor positive KB oral cancer cells showed increased nanoparticle uptake and caused significant enhancement in cytotoxicity [59]. This nano agent not only provided high contrast cancer imaging but also simultaneously provided cancer therapy. Another novel magnetic nano-contrast agent was developed based on Gd3+ doped amorphous TiO2 and was suitable for T1 weighted MRI [60]. The size of this agent was reported to be about 25 nm, which is much smaller than SPIO (50 nm) [58]. The potential of inducing hemolysis, platelet aggregation, and plasma coagulation was studied, and no adverse reaction was reported [60]. As a consequent, the folic acid conjugated nanoparticles were specifically aggregated on the surface of folate receptor positive oral cancer KB cells, leaving normal L929 cells unstained [60]. Notably, this nano-contrast agent showed enhanced longitudinal relaxivity, magnetic resonance, and excellent biocompatibility for MRI.

Fig. 2
figure 2

(Reprinted with permission from [59]. Copyright 2017 Journal of Colloid and Interface Science)

Representation of the magnetic core–shell hybrid nanoparticles for receptor targeted MRI

Optical coherence tomography

Optical coherence tomography (OCT) is a direct simulation of ultrasound. It produces cross-sectional architectural images of subsurface tissues, such as epithelial layers and basement membranes, using infrared light with a penetration depth of about 2 mm, and is suitable for early oral cancer detection and oral dysplasia monitoring [61]. The resolution of OCT is reported to be around 10 μm which is higher than that of other noninvasive diagnostic techniques, such as CT, MRI, and ultrasound [50, 62]. Although OCT is a non-invasive and real-time clinical diagnostic method for cell and stromal morphology imaging, the contrast remains insufficient, especially between neoplastic and normal tissues [63].

Gold nanoparticles are promising OCT contrast agents. They are biocompatible, easy to synthesize, and can provide localized surface plasmon resonances at near-infrared wavelengths that avoid predominant absorption in tissues [64]. For example, the EGFR monoclonal antibodies conjugated Au nanoparticles with a diameter of 71 nm have been applied to enhance the contrast of OCT images of oral dysplasia in a hamster model [65]. Meanwhile, microneedles and ultrasound were utilized to overcome the obstacle for Au NP delivery. This multimodal delivery was demonstrated to be effective in improving OCT penetration depth and resulted in an approximately 150% increased contrast level in oral carcinogenesis [65].

Photoacoustic imaging

Photoacoustic imaging is a new emerging optical diagnostic technology. By using a short laser pulse, it generates ultrasound transients from tissues, thereby causing transient thermoelastic expansions after optical absorption [66,67,68]. These photoacoustic waves are being then transformed into photoacoustic images according to their arrival times after collected by an ultrasound transducer [69, 70]. The ultrasound provides high spatial resolution for structural phenotyping and is a useful tool for assessing lymph nodes following a radical surgery [71, 72]. Consequently, the optical contrast can be significantly improved while maintaining the high spatial resolution of ultrasound [73]. Compared to conventional optical imaging, photoacoustic imaging has improved imaging depth, about 6 cm [69]. Though various exogenous contrast agents-such as methylene blue, ICG, and GNs-have been used to enhance the photoacoustic imaging contrast, the gold nanoparticles are considered a more attractive contrast agent due to their ability to conjugate biomolecules and their production of stronger photoacoustic imaging signals [67, 69, 74]. To date, photoacoustic imaging has demonstrated great potential in brain, breast, and prostate cancer diagnosis [67, 73, 75, 76].

Luke et al. introduced ultrasound-guided spectroscopic photoacoustic imaging technology for detecting lymph node micrometastases in a metastatic murine model of OSCC (Fig. 3) [77]. Using anti-EGFR antibody conjugated molecularly activated plasmonic nanosensors (MAPS), the study showed that the MAPS shifted their absorption spectrum to the near-infrared region [77]. In addition, large ultrasound-guided spectroscopic photoacoustic signals appeared in micrometastases as small as 50 mm within 30 min after MAPS injection [77]. These findings offer an alternate to sentinel lymph node biopsy analysis of oral cancer resection.

Fig. 3
figure 3

(Reprinted with permission from [77]. Copyright 2014 Cancer Research)

Representation of the photoacoustic imaging using anti-EGFR antibody conjugated molecularly activated MAPS. a A schematic of the EGFR-targeted MAPS; b optical spectra obtained hyperspectral dark-field microscopy; c, f cancer cells in the absence of gold nanoparticles; d, g cells in the presence of nonspecific AuNPs; e, h cells labeled with MAPS

Surface plasmon resonance scattering

Surface plasmon waves are formed by collective oscillation of conduction electrons in noble metals [78]. Recently, gold nanoparticles have been commonly applied for surface plasmon resonance scattering since they can resonantly scatter visible and near-infrared light due to their surface plasmon oscillation [78]. In addition, they are easy to prepare, readily bioconjugated, and have low cytotoxicity, making them suitable for biomolecular labeling and targeting [79]. It is reported that the conjugated nanoparticles tended to aggregate together, inducing a greatly enhanced surface plasmon resonance scattering compared to unconjugated nanoparticles [80].

El-Sayed et al. recorded surface plasmon resonance scattering images and surface plasmon resonance absorption spectra after cell incubation [81]. Light-scattering images showed that the EGFR conjugated nanoparticles bind specifically to the surface of the cancer cells with high concentration, while the binding to noncancerous cells was nonspecific and random [81]. Micro absorption spectra showed that the absorption maximum for conjugated nanoparticles was 545 nm, without aggregation tendency, while unconjugated colloidal gold nanoparticles accumulated inside cells and aggregated with an absorption maximum around 552 nm [81]. As a result, the anti-EGFR antibody conjugated nanoparticles showed 600% greater affinity to malignant oral epithelial cell lines HOC 313 clone 8 and HSC 3 than to the nonmalignant cell line HaCaT [81]. In addition, the surface plasmon resonance property of gold nanoparticles was shown to have the ability to increase Raman scattering in saliva samples of oral cancer patients [63, 78]. High optical signals were produced by enhanced surface plasmon resonance when the gold nanoparticles gathered around the target cancerous cells, due to their conjugation with anti-EGFR [63]. The sensitivity was observed to be around 70% of the current technique, which needs to be further improved [63].

Surface-enhanced Raman spectroscopy

Raman spectroscopy is a vibrational spectroscopic technique based on inelastic interactions between light and matter [82]. The normal, premalignant, or malignant lesions are distinguished by inelastic scattering of light, which can be a laser in the visible, near-infrared, or near-ultraviolet range [83]. The signals in normal tissues are homogeneous but heterogeneous in malignant cells, reflecting the changes in chemical characterization and molecular structure of the lesions [84]. Raman spectroscopy is a near-field effect and has a low penetration depth. Its clinical application has been limited by the weak Raman signal intensity and the slow speed of spectrum acquisitions [78, 83].

Recently, nanoparticles have been applied as exogenous contrast agents, in order to acquire Raman signal with high speed and resolution [85,86,87]. After directly adsorbed on the nanoparticle surface, the molecules emit an amplified Raman scattering intensity, known as surface-enhanced Raman scattering (SERS) [83, 88]. A study introduced small, spherical, near-infrared region sensitive and SERS active gold nanoparticles with highly narrow intra-nanogap structures for single oral cancer cell HSC-3 imaging (Fig. 4) [89]. The gold nanoparticles can selectively target intracellular organelles and were specifically distributed in cytoplasm, mitochondria, and nuclei. Finally, high speed Raman imaging was achieved within 30 s with a high resolution of 50 × 50 pixels [89].

Fig. 4
figure 4

(Reprinted with permission from [89]. Copyright 2015 Nano Letters)

Graphical representation of the SERS active gold nanoparticles for oral cancer cell HSC-3 imaging. a synthetic scheme of Raman dye (44DP)-coded Au-NNPs using four different kinds of DNA-AuNPs as core particles. b the solution color and HR-TEM image of 44DP-coded Au-NNPs. c, d Raman spectra of 44DP-coded Au-NNP solution prepared from four different spacer DNA with an excitation of 633 (c) and 785 nm (d)

Nanospheres, nanorods, nanocubes, nanobranches, and nanobipyramids are different shapes of gold nanoparticles [90, 91]. Gold nanorods (GNRs) have received much attention for molecular imaging because of their advantage of higher index sensitivity over spherical and cubic gold nanoparticles, which means minor changes in the surrounding environment of GNRs can result in significant longitudinal surface plasmon resonance (LSPR) peak wavelength variation [90, 92]. Since the index sensitivities and longitudinal plasmon wavelengths of nanorods increase with aspect ratios, the use of nanorods with large aspect ratios can provide near-infrared region plasmon wavelengths and high index sensitivity for optical techniques [90, 91].

Wang et al. conjugated GNRs with rose bengal (RB), a specific probe for oral cancer cell target, and monitored optical absorption in the near-infrared region [93]. The RB molecules have the ability to bind with the protein or nucleic acid of cancer cell lysate, whereafter the RB-GNR probes aggregated, inducing red-shift in the near-infrared absorption wavelength [93]. This RB-GNR platform provided a specific and quantitative method for oral cancer cell lysate analysis with a detection sensitivity of 2000 cells/ml [93]. Liu et al. described a paper-based SERS technology in combination with exfoliative cytology for screening of exfoliated cells from oral cancer patients and healthy individuals [94]. Cells were placed on a plasmonic paper with GNRs adsorbed on it, and spectra were acquired afterward. Sensitivity and specificity were both 100% for distinguishing exfoliated cells from normal and cancer tissues, based on the I1600/1440 and I1440/1340 peak ratios of the spectra values [94]. This paper-based SERS platform has overcome the drawbacks of traditional exfoliative cytology, such as low sensitivity and subjective cytologic interpretation [94].

Diffusion reflection imaging

In diffusion reflection imaging, a small portion of the white light entering the tissue is absorbed or transmitted, while the rest undergoes multiple elastic scattering and gets diffusely reflected [95]. The reflected light is greatly affected by cytologic and morphologic changes during epithelial tissue cancerization, including nuclear size, collagen content, extracellular matrix structure, epithelial thickness, and blood flow variation [28, 96]. It is reported that recording diffuse reflectance images can help to determine surgical margins and is a useful tool to differentiate normal mucosa, OPMD, and oral cancer [96,97,98].

In oral cancer, 14.3% of tumor margins after surgical excision were identified to have residual carcinoma [99]. Accurate determination of tumor margins is critical for complete surgical resection of residual diseases in oral cancer and may reduce the high rate of recurrence [100]. The accuracy of routine microscopic examination after frozen sections is limited by the 30.7–47.3% shrinkage of the frozen tissues [101]. Meanwhile, for the paraffin-embedded tissue section, results are only available after the operation, making the intraoperative identification challenging [101]. Thus, efforts should be made to achieve a real-time and high sensitive way for more complete tumor resections.

Ankri et al. conjugated GNRs to monoclonal antibodies against EGFR and evaluated the margins of human OSCC specimens by diffusion reflection imaging [102]. Air scanning electron microscopy was used to visualize the nanorods in tissues, showing the GNRs-EGFR spread a distance of 1 mm between the tumor and the healthy regions. Diffusion reflection imaging was then performed in a resolution of 1 mm, suggesting that the tumor edge is in the region of 4–5 mm, which is consistent with the commonly used cutoff of 5 mm for a close margin [100]. This study group has also tested diffusion reflection imaging of GNRs-EGFR on a mice OSCC model induced by 4-nitroquinoline-N-oxide [103]. GNRs specifically attached to areas histologically identified as OSCC, with high reflectance at 780 nm over 17 intensity units. The overall specificity and sensitivity was 97 and 87%, respectively [103]. Moreover, the reflectance spectrum at 780 nm was found to be moderate in areas of carcinoma in situ, but absent in normal epithelium. The optical properties showed significant changes-more than 80% of the invasive cancer and more than 30% of carcinoma in situ [103]. The group has also found that this modality is suitable for discriminating benign from malignant oral lesions since the reflectance intensity increased as the dysplastic changes increased [104]. Thus, the group has demonstrated that diffusion reflection imaging is a promising technique for the screening of malignant oral lesions and detecting residual disease during operation.

Quantum dots imaging

Quantum dots are nanometer-sized semiconductor crystals that luminesce through quantum confinement effects [105, 106]. Quantum dots have several advantages that could overcome the limitations of conventional fluorescent dyes, such as size-tunable emission, wide excitation spectra, strong luminescence and excellent stability against photobleaching [106,107,108]. In addition, changing the size and composition of quantum dots allows for obtaining a wide range of spectrum, from ultraviolet to the near infrared [109, 110].

Currently, quantum dots have been applied in the molecular and cell imaging of OSCC both in vitro and in vivo. It has been demonstrated that quantum dots have high fluorescence intensity, low nonspecific binding, and good stability against photobleaching for the in vitro imaging of human oral cancer cells Tca8113, SCC-25 and BcaCD885 [111,112,113,114]. Most of the quantum dots used for in vivo imaging were linked to molecules with the ability to target cancer cells [115]. Recently, it was reported that the near-infrared quantum dots with an emission wavelengths range of 700–900 nm have strong tissue penetration and are not harmful in vivo [114, 115]. Meanwhile, quantum dots with emission wavelengths between 400 and 600 nm are able to avoid the interference of tissue autofluorescence, making them suitable for bioimaging [116, 117]. Studies have proven that quantum dots with an emission wavelength of 800 nm conjugated with EGFR monoclonal antibodies or arginine–glycine–aspartic acid sequence can generate high quality images of OSCC (Fig. 5) [117,118,119]. The technique also offers great potential in personalized therapy for OSCC [117,118,119].

Fig. 5
figure 5

(Reprinted with permission from [117]. Copyright 2017 Small)

Schematic illustration of surface modification, bioconjugation, and theranostic application of Ag2Se QDs coupled with cetuximab

Nano-based ultrasensitive biomarker detection

Currently, plenty of novel proteomic, genomic, and transcriptomic biomarkers are being researched. Exploration of tumor molecular biomarkers-such as tumor necrosis factor-alpha (TNF-α), vascular endothelial growth factor (VEGF), EGFR, and interleukin 6 (IL 6)-holds great promise for early cancer detection and diagnosis [22, 120, 121]. Routine measurement methods-including enzyme-linked immunosorbent assay (ELISA), immunohistochemistry, Western Blot, and polymerase chain reaction-still bear a limited detection sensitivity ranging from pM to fM (10−12 to 10−15 M) concentration levels [22, 23, 35]. The application of nanotechnology may enhance the detection sensitivity for biomarkers with low concentrations in the tissue samples or body fluids [122, 123].

The saliva peptide finger print technique is a useful tool for salivary proteomics analysis and can predict potential biomarkers valuable for cancer diagnosis [124]. A study utilized matrix-assisted laser-desorption ionization-time-of-flight mass spectrometry (MALDI-TOF–MS) for analyzing the expression spectrum of salivary peptides in 40 OSCC patients and 23 normal controls [125]. Nanomaterial-based magnetic beads were used for selective enrichment of low-molecular-mass peptides. It is noteworthy that 50 proteins expression levels were significantly different between OSCC patients and healthy controls. As a result, the mass peaks of 1285.6 and 1432.2 Da, which were both identified as histatin-3, were correlated with OSCC progression. This study introduced a novel high-throughput, non-invasive strategy for valuable oral cancer biomarkers screening [125]. The specific advantages of magnetic beads constructed on nanomaterial over other types of separation beads have not yet been illustrated.

A nano-based single biomarker detection method has also been utilized for oral cancer detection. A study detected TNF-α by gold protein chip method using a total internal reflection fluorescence microscopy (TIRFM) [35]. A 4 × 5 nanoarray incorporating 500 nm diameter gold spots was achieved on 10 mm square glass substrates. The TNF-α detection sensitivity was reported to be at the attomolar (aM) concentration level (× 10−18), enabling ultra-sensitive oral cancer detection [35]. However, this method could not be used for precise quantitative analysis. Another study described the analysis of oral cancer bio marker EGFR with exfoliative cytology specimens of 41 OPMD or OSCC patients and 11 healthy volunteers, using a nano-bio-chip sensor technique [126]. A total of 51 measurement parameters were collected, and biochemical and morphologic changes were further analyzed. The EGFR expression level-along with nuclear area, nuclear diameter, and nuclear-to-cytoplasmic ratio-was significantly altered in oral lesions with diagnosed squamous cell carcinoma or dysplasia [126]. Using ultra-sensitive atomic force microscopy (AFM) and field emission scanning electron microscopy (FESEM) with high resolution (~ 1 nm), another study exhibited the substructure of single human saliva exosomes and interpreted the nanoscale structures of exosomes under varying forces, revealing reversible mechanical deformation [127]. Further, cell-type specific marker CD63 was detected by using 10 nm gold beads on individual exosomes. The nanoscale biomechanical, morphological, and surface biomolecular properties of saliva exosomes are found to be critical for the oral cancer diagnosis [127]. Although these two systems have made it possible for the quantitative analysis of cellular biomarkers, the systems described above can only be used for single biomarker analysis.

It is well-known that single oral cancer biomarkers cannot provide reliable diagnoses [128]. Multiplexed biomarker detection can minimize false positives and negatives arising from single biomarker analysis [128]. A multiplexed biomarker detection approach measured a four-protein panel of biomarkers using an ultrasensitive electrochemical microfluidic array [129]. The microfluidic device contained an array of nanostructured sensors, and plenty of magnetic beads were labeled. The four-protein panel-including interleukin-6, interleukin-8, VEGF, and VEGF-C-was analyzed in 78 oral cancer patient serum samples and 49 controls, and showed a clinical diagnostic sensitivity and specificity for 89 and 98%, respectively [129]. The study provided a low-cost, easily fabricated method for accurate clinical oral cancer diagnosis. Another study analyzed proteins biomarkers in conditioned media of oral squamous cell lines HN12, HN13, OSCC-3 and CAL27 by utilizing a nano ultra-performance liquid chromatography (nano-UPLC) ion-mobility mass spectrometry [130]. A total of approximately 952 proteins-including known cancer biomarker proteins IL-6, IL-8, VEGF-A, and VEGF-C were identified. This nano-UPLC-Q-TOF assay provided a high-throughput approach to quantify proteins and compare protein expression levels across different samples, without the need for stable isotope labeling. The identification of peptides was unlimited with the fragmentation technique [130].

Conclusion and perspective

Ranking as one of the top 10 cancers worldwide, oral cancer has a poor prognosis and a high recurrence rate, and the time and accuracy of diagnosis directly affects disease outcomes [131]. In the past few decades, nanotechnology has brought new techniques to cancer diagnosis [36, 38, 132, 133]. The performance parameters of nanoparticles-such as biocompatibility, function-specific size and shape, blood circulation half-life, and targeting to specific cell surface molecules-can be controlled by modulating their fabrication materials, methods or surface chemistry, making nanoparticles a promising diagnostic material [79]. The present review article has critically introduced nano-based detection strategies for oral cancers, and summarized various kinds of nanomaterials, sample types, and the characteristic of each technique in Table 1. The pros and cons of each nanotechnology for bioimaging and molecular detection of oral cancer were shown in Fig. 6. In the oral cavity, the use of nanoparticles has not only achieved noninvasive real-time diagnosis with high sensitivity and specificity but also assisted with accurately identifying surgical margins, indicating the potential to reduce the reliance on tissue biopsy and histopathological assessment in many cases.

Table 1 Summary of nanotechnology based methods for oral cancer detection and diagnosis
Fig. 6
figure 6

The pros and cons of different nanotechnology for bioimaging and biomarker detection of oral cancer

Nano-based contrast agents for MRI, OCT and photoacoustic imaging have lower toxicity, prolonged blood circulation half-life, and the ability to target unique cell surface molecules. Compared to routine contrast agents, nano agents exhibit better image contrast properties and improved penetration depth. In optical imaging, nanoparticles enable sufficient signals and sub-cellular spatial resolution. They can generate surface plasmon resonance at near-infrared wavelengths, gathered around the targeted cell surface, and the optical resonance properties of nanorods can be regulated over a broad range by adjusting their sizes and shapes. Quantum dots with size-tunable emission, wide excitation spectrum, high intensity of luminescence, and excellent photochemical stability have overcome the disadvantages of traditional fluorescence markers. As for cancer biomarker detection, nano-based materials-such as nano beads, gold nanoarray, and nano-bio-chips-offer high throughput screening for potential biomarkers and have brought the level of detection sensitivity to the nanoscale. Therefore, the small and earlier intraepithelial lesions missed by common techniques can potentially be detected by nanotechnologies, making oral diseases more readily cured.

Nano-based diagnostic methods act as a promising tool to provide real-time, convenient, and cost-effective diagnosis for oral cancer detection and diagnosis. They can provide molecular targeted imaging, analyze biomarkers at nano-scale, enable intraoperative identification of surgical resection margins, and monitor oral cancer prognosis after treatment. Although these technologies have been studied in ex vivo studies of tissue and saliva samples and in vivo studies in animal models, further efforts should be employed before these strategies can be successfully applied in clinical diagnosis.

Abbreviations

OPMD:

oral potentially malignant disorders

OSCC:

oral squamous cell carcinomas

TB:

toluidine blue

EGFR:

epidermal growth factor receptor

MRI:

magnetic resonance imaging

CT:

computed tomography

CBCT:

cone beam computed tomography

PET:

positron emission tomography

Gd-DTPA:

Gd3+ complexed with diethyltriamine-pentaacetic acid

Gd-DOTA:

tetra azacyclododecane-1,4,7,10-tetraacetic acid

SPIO:

superparamagnetic iron oxide

USPIO:

ultrasmall superparamagnetic iron oxide

MAPS:

molecularly activated plasmonic nanosensors

SERS:

surface-enhanced Raman scattering

GNRs:

gold nanorods

LSPR:

longitudinal surface plasmon resonance

RB:

rose bengal

TNF-α:

tumor necrosis factor-alpha

VEGF:

vascular endothelial growth factor

IL 6:

interleukin 6

ELISA:

en-zyme-linked immunosorbent assay

MALDI-TOF–MS:

matrix-assisted laser-desorption ionization-time-of-flight mass spectrometry

TIRFM:

total internal reflection fluorescence microscopy

AFM:

atomic force microscopy

FESEM:

field emission scanning electron microscopy

PEG:

polyethylene glycol

UPLC:

ultra-performance liquid chromatography

PLGA:

poly lactide-co-glycolide

HUVEC:

human primary endothelial cells

PBMC:

peripheral blood mononuclear cells

References

  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016. CA Cancer J Clin. 2016;66:7–30.

    Article  PubMed  Google Scholar 

  2. Calixto G, Bernegossi J, Fonseca-Santos B, Chorilli M. Nanotechnology-based drug delivery systems for treatment of oral cancer: a review. Int J Nanomed. 2014;9:3719–35.

    Article  CAS  Google Scholar 

  3. Torre LA, Bray F, Siegel RL, Ferlay J, Lortet-Tieulent J, Jemal A. Global cancer statistics, 2012. CA Cancer J Clin. 2015;65:87–108.

    Article  PubMed  Google Scholar 

  4. Tanaka T, Ishigamori R. Understanding carcinogenesis for fighting oral cancer. J Oncol. 2011;2011:603740.

    PubMed  PubMed Central  Google Scholar 

  5. Warnakulasuriya S. Global epidemiology of oral and oropharyngeal cancer. Oral Oncol. 2009;45:309–16.

    Article  PubMed  Google Scholar 

  6. Ng JH, Iyer NG, Tan MH, Edgren G. Changing epidemiology of oral squamous cell carcinoma of the tongue: a global study. Head Neck. 2017;39:297–304.

    Article  PubMed  Google Scholar 

  7. Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin. 2011;61:69–90.

    Article  PubMed  Google Scholar 

  8. Arakeri G, Patil SG, Aljabab AS, Lin KC, Merkx MAW, Gao S, et al. Oral submucous fibrosis: an update on pathophysiology of malignant transformation. J Oral Pathol Med. 2017;46:413–7.

    Article  PubMed  Google Scholar 

  9. Khan Z, Khan S, Christianson L, Rehman S, Ekwunife O, Samkange-Zeeb F. Smokeless tobacco and oral potentially malignant disorders in south asia: a systematic review and meta-analysis. Nicotine Tob Res. 2017;20:12–21.

    PubMed  Google Scholar 

  10. Benergossi J, Calixto G, Fonseca-Santos B, Aida KL, de Cassia Negrini T, Duque C, et al. Highlights in peptide nanoparticle carriers intended to oral diseases. Curr Top Med Chem. 2015;15:345–55.

    Article  CAS  PubMed  Google Scholar 

  11. Liu D, Zhao X, Zeng X, Dan H, Chen Q. Non-invasive techniques for detection and diagnosis of oral potentially malignant disorders. Tohoku J Exp Med. 2016;238:165–77.

    Article  CAS  PubMed  Google Scholar 

  12. Mercadante V, Paderni C, Campisi G. Novel non-invasive adjunctive techniques for early oral cancer diagnosis and oral lesions examination. Curr Pharm Des. 2012;18:5442–51.

    Article  CAS  PubMed  Google Scholar 

  13. Wikner J, Grobe A, Pantel K, Riethdorf S. Squamous cell carcinoma of the oral cavity and circulating tumour cells. World J Clin Oncol. 2014;5:114–24.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Brennan JA, Mao L, Hruban RH, Boyle JO, Eby YJ, Koch WM, et al. Molecular assessment of histopathological staging in squamous-cell carcinoma of the head and neck. N Engl J Med. 1995;332:429–35.

    Article  CAS  PubMed  Google Scholar 

  15. Awan KH, Morgan PR, Warnakulasuriya S. Assessing the accuracy of autofluorescence, chemiluminescence and toluidine blue as diagnostic tools for oral potentially malignant disorders—a clinicopathological evaluation. Clin Oral Investig. 2015;19:2267–72.

    Article  CAS  PubMed  Google Scholar 

  16. Chainani-Wu N, Madden E, Cox D, Sroussi H, Epstein J, Silverman S Jr. Toluidine blue aids in detection of dysplasia and carcinoma in suspicious oral lesions. Oral Dis. 2015;21:879–85.

    Article  CAS  PubMed  Google Scholar 

  17. Balasubramaniam AM, Sriraman R, Sindhuja P, Mohideen K, Parameswar RA, Muhamed Haris KT. Autofluorescence based diagnostic techniques for oral cancer. J Pharm Bioallied Sci. 2015;7:S374–7.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Kammerer PW, Rahimi-Nedjat RK, Ziebart T, Bemsch A, Walter C, Al-Nawas B, et al. A chemiluminescent light system in combination with toluidine blue to assess suspicious oral lesions-clinical evaluation and review of the literature. Clin Oral Investig. 2015;19:459–66.

    Article  CAS  PubMed  Google Scholar 

  19. Giovannacci I, Vescovi P, Manfredi M, Meleti M. Non-invasive visual tools for diagnosis of oral cancer and dysplasia: a systematic review. Med Oral Patol Oral Cir Bucal. 2016;21:e305–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Ye X, Zhang J, Tan Y, Chen G, Zhou G. Meta-analysis of two computer-assisted screening methods for diagnosing oral precancer and cancer. Oral Oncol. 2015;51:966–75.

    Article  PubMed  Google Scholar 

  21. Sekine J, Nakatani E, Hideshima K, Iwahashi T, Sasaki H. Diagnostic accuracy of oral cancer cytology in a pilot study. Diagn Pathol. 2017;12:27.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Fernandez-Olavarria A, Mosquera-Perez R, Diaz-Sanchez RM, Serrera-Figallo MA, Gutierrez-Perez JL, Torres-Lagares D. The role of serum biomarkers in the diagnosis and prognosis of oral cancer: a systematic review. J Clin Exp Dent. 2016;8:e184–93.

    PubMed  PubMed Central  Google Scholar 

  23. Sannam Khan R, Khurshid Z, Akhbar S, Faraz Moin S. Advances of salivary proteomics in oral squamous cell carcinoma (OSCC) detection: an update. Proteomes. 2016;4:E41.

    Article  CAS  PubMed  Google Scholar 

  24. Keshavarzi M, Darijani M, Momeni F, Moradi P, Ebrahimnejad H, Masoudifar A, et al. Molecular imaging and oral cancer diagnosis and therapy. J Cell Biochem. 2017;118:3055–60.

    Article  CAS  PubMed  Google Scholar 

  25. Sarrion Perez MG, Bagan JV, Jimenez Y, Margaix M, Marzal C. Utility of imaging techniques in the diagnosis of oral cancer. J Craniomaxillofac Surg. 2015;43:1880–94.

    Article  PubMed  Google Scholar 

  26. Mian SA, Yorucu C, Ullah MS, Rehman IU, Colley HE. Raman spectroscopy can discriminate between normal, dysplastic and cancerous oral mucosa: a tissue-engineering approach. J Tissue Eng Regen Med. 2016;11:3253–62.

    Article  CAS  PubMed  Google Scholar 

  27. Green B, Cobb AR, Brennan PA, Hopper C. Optical diagnostic techniques for use in lesions of the head and neck: review of the latest developments. Br J Oral Maxillofac Surg. 2014;52:675–80.

    Article  PubMed  Google Scholar 

  28. Stephen MM, Jayanthi JL, Unni NG, Kolady PE, Beena VT, Jeemon P, et al. Diagnostic accuracy of diffuse reflectance imaging for early detection of pre-malignant and malignant changes in the oral cavity: a feasibility study. BMC Cancer. 2013;13:278.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Piazza C, Del Bon F, Paderno A, Grazioli P, Perotti P, Barbieri D, et al. The diagnostic value of narrow band imaging in different oral and oropharyngeal subsites. Eur Arch Otorhinolaryngol. 2016;273:3347–53.

    Article  PubMed  Google Scholar 

  30. Lucchese A, Gentile E, Romano A, Maio C, Laino L, Serpico R. The potential role of in vivo reflectance confocal microscopy for evaluating oral cavity lesions: a systematic review. J Oral Pathol Med. 2016;45:723–9.

    Article  PubMed  Google Scholar 

  31. Gentile E, Maio C, Romano A, Laino L, Lucchese A. The potential role of in vivo optical coherence tomography for evaluating oral soft tissue: a systematic review. J Oral Pathol Med. 2017;46:864–76.

    PubMed  Google Scholar 

  32. Sokolov K, Aaron J, Hsu B, Nida D, Gillenwater A, Follen M, et al. Optical systems for in vivo molecular imaging of cancer. Technol Cancer Res Treat. 2003;2:491–504.

    Article  CAS  PubMed  Google Scholar 

  33. Omar E. Future imaging alternatives: the clinical non-invasive modalities in diagnosis of oral squamous cell carcinoma (OSCC). Open Dent J. 2015;9:311–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Liu Y, Li Y, Fu Y, Liu T, Liu X, Zhang X, et al. Quantitative prediction of oral cancer risk in patients with oral leukoplakia. Oncotarget. 2017;118(10):3055–60.

    Google Scholar 

  35. Lee K, Lee S, Yu H, Kang SH. Ultra-sensitive detection of tumor necrosis factor-alpha on gold nano-patterned protein chip formed via E-beam nanolithography by total internal reflection fluorescence microscopy. J Nanosci Nanotechnol. 2010;10:3228–31.

    Article  CAS  PubMed  Google Scholar 

  36. Sharma P, Brown S, Walter G, Santra S, Moudgil B. Nanoparticles for bioimaging. Adv Colloid Interface Sci. 2006;123–126:471–85.

    Article  CAS  PubMed  Google Scholar 

  37. Ogle OE, Byles N. Nanotechnology in dentistry today. West Indian Med J. 2014;63:344–8.

    CAS  PubMed  Google Scholar 

  38. Jaishree V, Gupta PD. Nanotechnology: a revolution in cancer diagnosis. Indian J Clin Biochem. 2012;27:214–20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Ho D, Wang CH, Chow EK. Nanodiamonds: the intersection of nanotechnology, drug development, and personalized medicine. Sci Adv. 2015;1:e1500439.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Wang ZQ, Liu K, Huo ZJ, Li XC, Wang M, Liu P, et al. A cell-targeted chemotherapeutic nanomedicine strategy for oral squamous cell carcinoma therapy. J Nanobiotechnol. 2015;13:63.

    Article  CAS  Google Scholar 

  41. Gharat SA, Momin M, Bhavsar C. Oral squamous cell carcinoma: current treatment strategies and nanotechnology-based approaches for prevention and therapy. Crit Rev Ther Drug Carrier Syst. 2016;33:363–400.

    Article  PubMed  Google Scholar 

  42. Bao C, Conde J, Curtin J, Artzi N, Tian F, Cui D. Bioresponsive antisense DNA gold nanobeacons as a hybrid in vivo theranostics platform for the inhibition of cancer cells and metastasis. Sci Rep. 2015;5:12297.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Han Y, An Y, Jia G, Wang X, He C, Ding Y, et al. Theranostic micelles based on upconversion nanoparticles for dual-modality imaging and photodynamic therapy in hepatocellular carcinoma. Nanoscale. 2018;10:6511–23.

    Article  CAS  PubMed  Google Scholar 

  44. Halo TL, McMahon KM, Angeloni NL, Xu Y, Wang W, Chinen AB, et al. NanoFlares for the detection, isolation, and culture of live tumor cells from human blood. Proc Natl Acad Sci USA. 2014;111:17104–9.

    Article  CAS  PubMed  Google Scholar 

  45. Zdobnova TA, Lebedenko EN, Deyev Scapital Em C. Quantum dots for molecular diagnostics of tumors. Acta Nat. 2011;3:29–47.

    CAS  Google Scholar 

  46. Jaiswal JK, Mattoussi H, Mauro JM, Simon SM. Long-term multiple color imaging of live cells using quantum dot bioconjugates. Nat Biotechnol. 2003;21:47–51.

    Article  CAS  PubMed  Google Scholar 

  47. Lee MH, Lee DH, Jung SW, Lee KN, Park YS, Seong WK. Measurements of serum C-reactive protein levels in patients with gastric cancer and quantification using silicon nanowire arrays. Nanomedicine. 2010;6:78–83.

    Article  CAS  PubMed  Google Scholar 

  48. Adarsh N, Ramya AN, Maiti KK, Ramaiah D. Unveiling NIR aza–boron–dipyrromethene (BODIPY) dyes as Raman probes: surface-enhanced raman scattering (SERS)-guided selective detection and imaging of human cancer cells. Chemistry. 2017;23:14286–91.

    Article  CAS  PubMed  Google Scholar 

  49. Gonda K, Watanabe M, Tada H, Miyashita M, Takahashi-Aoyama Y, Kamei T, et al. Quantitative diagnostic imaging of cancer tissues by using phosphor-integrated dots with ultra-high brightness. Sci Rep. 2017;7:7509.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Pande P, Shrestha S, Park J, Gimenez-Conti I, Brandon J, Applegate BE, et al. Automated analysis of multimodal fluorescence lifetime imaging and optical coherence tomography data for the diagnosis of oral cancer in the hamster cheek pouch model. Biomed Opt Express. 2016;7:2000–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Jackson AW, Chandrasekharan P, Ramasamy B, Goggi J, Chuang KH, He T, et al. Octreotide functionalized nano-contrast agent for targeted magnetic resonance imaging. Biomacromol. 2016;17:3902–10.

    Article  CAS  Google Scholar 

  52. Kwon OS, Song HS, Conde J, Kim HI, Artzi N, Kim JH. Dual-color emissive upconversion nanocapsules for differential cancer bioimaging in vivo. ACS Nano. 2016;10:1512–21.

    Article  CAS  PubMed  Google Scholar 

  53. Hinni ML, Zarka MA, Hoxworth JM. Margin mapping in transoral surgery for head and neck cancer. Laryngoscope. 2013;123:1190–8.

    Article  PubMed  Google Scholar 

  54. Cheng W, Ping Y, Zhang Y, Chuang KH, Liu Y. Magnetic resonance imaging (MRI) contrast agents for tumor diagnosis. J Healthc Eng. 2013;4:23–45.

    Article  PubMed  Google Scholar 

  55. Bennett KM, Jo J, Cabral H, Bakalova R, Aoki I. MR imaging techniques for nano-pathophysiology and theranostics. Adv Drug Deliv Rev. 2014;74:75–94.

    Article  CAS  PubMed  Google Scholar 

  56. Villaraza AJ, Bumb A, Brechbiel MW. Macromolecules, dendrimers, and nanomaterials in magnetic resonance imaging: the interplay between size, function, and pharmacokinetics. Chem Rev. 2010;110:2921–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Aryal S, Key J, Stigliano C, Landis MD, Lee DY, Decuzzi P. Positron emitting magnetic nanoconstructs for PET/MR imaging. Small. 2014;10:2688–96.

    Article  CAS  PubMed  Google Scholar 

  58. Brigger I, Dubernet C, Couvreur P. Nanoparticles in cancer therapy and diagnosis. Adv Drug Deliv Rev. 2002;54:631–51.

    Article  CAS  PubMed  Google Scholar 

  59. Shanavas A, Sasidharan S, Bahadur D, Srivastava R. Magnetic core-shell hybrid nanoparticles for receptor targeted anti-cancer therapy and magnetic resonance imaging. J Colloid Interface Sci. 2017;486:112–20.

    Article  CAS  PubMed  Google Scholar 

  60. Chandran P, Sasidharan A, Ashokan A, Menon D, Nair S, Koyakutty M. Highly biocompatible TiO(2):Gd(3)(+) nano-contrast agent with enhanced longitudinal relaxivity for targeted cancer imaging. Nanoscale. 2011;3:4150–61.

    Article  CAS  PubMed  Google Scholar 

  61. Green B, Tsiroyannis C, Brennan PA. Optical diagnostic systems for assessing head and neck lesions. Oral Dis. 2016;22:180–4.

    Article  CAS  PubMed  Google Scholar 

  62. Troutman TS, Barton JK, Romanowski M. Optical coherence tomography with plasmon resonant nanorods of gold. Opt Lett. 2007;32:1438–40.

    Article  PubMed  Google Scholar 

  63. Kah JC, Kho KW, Lee CG, James C, Sheppard R, Shen ZX, et al. Early diagnosis of oral cancer based on the surface plasmon resonance of gold nanoparticles. Int J Nanomed. 2007;2:785–98.

    CAS  Google Scholar 

  64. Oldenburg AL, Hansen MN, Zweifel DA, Wei A, Boppart SA. Plasmon-resonant gold nanorods as low backscattering albedo contrast agents for optical coherence tomography. Opt Express. 2006;14:6724–38.

    Article  CAS  PubMed  Google Scholar 

  65. Kim CS, Wilder-Smith P, Ahn YC, Liaw LH, Chen Z, Kwon YJ. Enhanced detection of early-stage oral cancer in vivo by optical coherence tomography using multimodal delivery of gold nanoparticles. J Biomed Opt. 2009;14:034008.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Bayer CL, Wlodarczyk BJ, Finnell RH, Emelianov SY. Ultrasound-guided spectral photoacoustic imaging of hemoglobin oxygenation during development. Biomed Opt Express. 2017;8:757–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Zhang M, Kim HS, Jin T, Yi A, Moon WK. Ultrasound-guided photoacoustic imaging for the selective detection of EGFR-expressing breast cancer and lymph node metastases. Biomed Opt Express. 2016;7:1920–31.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Xu C, Chen F, Valdovinos HF, Jiang D, Goel S, Yu B, et al. Bacteria-like mesoporous silica-coated gold nanorods for positron emission tomography and photoacoustic imaging-guided chemo-photothermal combined therapy. Biomaterials. 2018;165:56–65.

    Article  CAS  PubMed  Google Scholar 

  69. Jiang Y, Pu K. Advanced photoacoustic imaging applications of near-infrared absorbing organic nanoparticles. Small. 2017;13:1700710.

    Article  CAS  Google Scholar 

  70. Bao C, Conde J, Pan F, Li C, Zhang C, Tian F, et al. Gold nanoprisms as a hybrid in vivo cancer theranostic platform for in situ photoacoustic imaging, angiography, and localized hyperthermia. Nano Res. 2016;9:1043–56.

    Article  CAS  Google Scholar 

  71. Palasz P, Adamski L, Gorska-Chrzastek M, Starzynska A, Studniarek M. Contemporary diagnostic imaging of oral squamous cell carcinoma—a review of literature. Pol J Radiol. 2017;82:193–202.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Bui NQ, Cho SW, Moorthy MS, Park SM, Piao Z, Nam SY, et al. In vivo photoacoustic monitoring using 700-nm region Raman source for targeting Prussian blue nanoparticles in mouse tumor model. Sci Rep. 2018;8:2000.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Weber J, Beard PC, Bohndiek SE. Contrast agents for molecular photoacoustic imaging. Nat Methods. 2016;13:639–50.

    Article  CAS  PubMed  Google Scholar 

  74. Liang S, Li C, Zhang C, Chen Y, Xu L, Bao C, et al. CD44v6 monoclonal antibody-conjugated gold nanostars for targeted photoacoustic imaging and plasmonic photothermal therapy of gastric cancer stem-like cells. Theranostics. 2015;5:970–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Fan Q, Cheng K, Yang Z, Zhang R, Yang M, Hu X, et al. Perylene–diimide-based nanoparticles as highly efficient photoacoustic agents for deep brain tumor imaging in living mice. Adv Mater. 2015;27:843–7.

    Article  CAS  PubMed  Google Scholar 

  76. Horiguchi A, Shinchi M, Nakamura A, Wada T, Ito K, Asano T, et al. Pilot study of prostate cancer angiogenesis imaging using a photoacoustic imaging system. Urology. 2017;108:212–9.

    Article  PubMed  Google Scholar 

  77. Luke GP, Myers JN, Emelianov SY, Sokolov KV. Sentinel lymph node biopsy revisited: ultrasound-guided photoacoustic detection of micrometastases using molecularly targeted plasmonic nanosensors. Cancer Res. 2014;74:5397–408.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Hou C, Galvan DD, Meng G, Yu Q. Long-range surface plasmon resonance and surface-enhanced Raman scattering on X-shaped gold plasmonic nanohole arrays. Phys Chem Chem Phys. 2017;19:24126–34.

    Article  CAS  PubMed  Google Scholar 

  79. Lee SH, Lee JB, Bae MS, Balikov DA, Hwang A, Boire TC, et al. Current progress in nanotechnology applications for diagnosis and treatment of kidney diseases. Adv Healthc Mater. 2015;4:2037–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Sokolov K, Follen M, Aaron J, Pavlova I, Malpica A, Lotan R, et al. Real-time vital optical imaging of precancer using anti-epidermal growth factor receptor antibodies conjugated to gold nanoparticles. Cancer Res. 2003;63:1999–2004.

    CAS  PubMed  Google Scholar 

  81. El-Sayed IH, Huang X, El-Sayed MA. Surface plasmon resonance scattering and absorption of anti-EGFR antibody conjugated gold nanoparticles in cancer diagnostics: applications in oral cancer. Nano Lett. 2005;5:829–34.

    Article  CAS  PubMed  Google Scholar 

  82. Yan B, Li B, Wen Z, Luo X, Xue L, Li L. Label-free blood serum detection by using surface-enhanced Raman spectroscopy and support vector machine for the preoperative diagnosis of parotid gland tumors. BMC Cancer. 2015;15:650.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Harmsen S, Wall MA, Huang RM, Kircher MF. Cancer imaging using surface-enhanced resonance Raman scattering nanoparticles. Nat Protoc. 2017;12:1400–14.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Guze K, Pawluk HC, Short M, Zeng H, Lorch J, Norris C, et al. Pilot study: Raman spectroscopy in differentiating premalignant and malignant oral lesions from normal mucosa and benign lesions in humans. Head Neck. 2015;37:511–7.

    Article  PubMed  Google Scholar 

  85. Galloway TA, Cabo-Fernandez L, Aldous IM, Braga F, Hardwick LJ. Shell isolated nanoparticles for enhanced Raman spectroscopy studies in lithium-oxygen cells. Faraday Discuss. 2017;205:469–90.

    Article  CAS  PubMed  Google Scholar 

  86. Wang YW, Reder NP, Kang S, Glaser AK, Yang Q, Wall MA, et al. Raman-encoded molecular imaging with topically applied SERS nanoparticles for intraoperative guidance of lumpectomy. Cancer Res. 2017;77:4506–16.

    Article  CAS  PubMed  Google Scholar 

  87. Conde J, Bao C, Cui D, Baptista PV, Tian F. Antibody-drug gold nanoantennas with Raman spectroscopic fingerprints for in vivo tumour theranostics. J Control Release. 2014;183:87–93.

    Article  CAS  PubMed  Google Scholar 

  88. Liu R, Zhao J, Han G, Zhao T, Zhang R, Liu B, et al. Click-functionalized SERS nanoprobes with improved labeling efficiency and capability for cancer cell imaging. ACS Appl Mater Interfaces. 2017;9:38222–9.

    Article  CAS  PubMed  Google Scholar 

  89. Kang JW, So PTC, Dasari RR, Lim DK. High resolution live cell Raman imaging using subcellular organelle-targeting SERS-sensitive gold nanoparticles with highly narrow intra-nanogap. Nano Lett. 2015;15:1766–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Chen H, Kou X, Yang Z, Ni W, Wang J. Shape- and size-dependent refractive index sensitivity of gold nanoparticles. Langmuir. 2008;24:5233–7.

    Article  CAS  PubMed  Google Scholar 

  91. El-Sayed MA. Some interesting properties of metals confined in time and nanometer space of different shapes. Acc Chem Res. 2001;34:257–64.

    Article  CAS  PubMed  Google Scholar 

  92. Tian F, Conde J, Bao C, Chen Y, Curtin J, Cui D. Gold nanostars for efficient in vitro and in vivo real-time SERS detection and drug delivery via plasmonic-tunable Raman/FTIR imaging. Biomaterials. 2016;106:87–97.

    Article  CAS  PubMed  Google Scholar 

  93. Wang JH, Wang B, Liu Q, Li Q, Huang H, Song L, et al. Bimodal optical diagnostics of oral cancer based on rose bengal conjugated gold nanorod platform. Biomaterials. 2013;34:4274–83.

    Article  CAS  PubMed  Google Scholar 

  94. Liu Q, Wang J, Wang B, Li Z, Huang H, Li C, et al. Paper-based plasmonic platform for sensitive, noninvasive, and rapid cancer screening. Biosens Bioelectron. 2014;54:128–34.

    Article  CAS  PubMed  Google Scholar 

  95. Chen C, Florian K, Rajesh K, Max R, Christian K, Florian S, et al. Recovering the superficial microvascular pattern via diffuse reflection imaging: phantom validation. Biomed Eng Online. 2015;14:87.

    Article  PubMed  PubMed Central  Google Scholar 

  96. Jayanthi JL, Nisha GU, Manju S, Philip EK, Jeemon P, Baiju KV, et al. Diffuse reflectance spectroscopy: diagnostic accuracy of a non-invasive screening technique for early detection of malignant changes in the oral cavity. BMJ Open. 2011;1:e000071.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Lalla Y, Matias M, Farah CS. Oral mucosal disease in an Australian urban Indigenous community using autofluorescence imaging and reflectance spectroscopy. Aust Dent J. 2015;60:216–24.

    Article  CAS  PubMed  Google Scholar 

  98. Miller DM, Jokerst NM. Flexible silicon sensors for diffuse reflectance spectroscopy of tissue. Biomed Opt Express. 2017;8:1512–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Varvares MA, Poti S, Kenyon B, Christopher K, Walker RJ. Surgical margins and primary site resection in achieving local control in oral cancer resections. Laryngoscope. 2015;125:2298–307.

    Article  PubMed  Google Scholar 

  100. Tasche KK, Buchakjian MR, Pagedar NA, Sperry SM. Definition of “Close Margin” in oral cancer surgery and association of margin distance with local recurrence rate. JAMA Otolaryngol Head Neck Surg. 2017;143:1166–72.

    Article  PubMed  PubMed Central  Google Scholar 

  101. Abbas SA, Ikram M, Tariq MU, Raheem A, Saeed J. Accuracy of frozen sections in oral cancer resections, an experience of a tertiary care hospital. J Pak Med Assoc. 2017;67:806–9.

    PubMed  Google Scholar 

  102. Ankri R, Ashkenazy A, Milstein Y, Brami Y, Olshinka A, Goldenberg-Cohen N, et al. Gold nanorods based air scanning electron microscopy and diffusion reflection imaging for mapping tumor margins in squamous cell carcinoma. ACS Nano. 2016;10:2349–56.

    Article  CAS  PubMed  Google Scholar 

  103. Fixler D, Ankri R, Kaplan I, Novikov I, Hirshberg A. Diffusion reflection: a novel method for detection of oral cancer. J Dent Res. 2014;93:602–6.

    Article  CAS  PubMed  Google Scholar 

  104. Hirshberg A, Allon I, Novikov I, Ankri R, Ashkenazy A, Fixler D. Gold nanorods reflectance discriminate benign from malignant oral lesions. Nanomedicine. 2017;13:1333–9.

    Article  CAS  PubMed  Google Scholar 

  105. Liu L, Miao Q, Liang G. Quantum dots as multifunctional materials for tumor imaging and therapy. Materials (Basel). 2013;6:483–99.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Bera D, Qian L, Tseng TK, Holloway PH. Quantum dots and their multimodal applications: a review. Materials. 2010;3:2260–345.

    Article  CAS  PubMed Central  Google Scholar 

  107. Rosenthal SJ, Chang JC, Kovtun O, McBride JR, Tomlinson ID. Biocompatible quantum dots for biological applications. Chem Biol. 2011;18:10–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Bakalova R, Zhelev Z, Kokuryo D, Spasov L, Aoki I, Saga T. Chemical nature and structure of organic coating of quantum dots is crucial for their application in imaging diagnostics. Int J Nanomed. 2011;6:1719–32.

    Article  CAS  Google Scholar 

  109. Michalet X, Pinaud FF, Bentolila LA, Tsay JM, Doose S, Li JJ, et al. Quantum dots for live cells, in vivo imaging, and diagnostics. Science. 2005;307:538–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Medintz IL, Uyeda HT, Goldman ER, Mattoussi H. Quantum dot bioconjugates for imaging, labelling and sensing. Nat Mater. 2005;4:435–46.

    Article  CAS  PubMed  Google Scholar 

  111. Li Z, Wang K, Tan W, Li J, Fu Z, Ma C, et al. Immunofluorescent labeling of cancer cells with quantum dots synthesized in aqueous solution. Anal Biochem. 2006;354:169–74.

    Article  CAS  PubMed  Google Scholar 

  112. Zhao JJ, Chen J, Wang ZP, Pan J, Huang YH. Double labeling and comparison of fluorescence intensity and photostability between quantum dots and FITC in oral tumors. Mol Med Rep. 2011;4:425–9.

    Article  CAS  PubMed  Google Scholar 

  113. Chen J, Pan J, Zhao J, Qiu X, Zheng J, Wang Z, et al. Quantum dot imaging for HSP70 and HSF1 kinetics in SCC25 cells with or without leucine deprivation following heat shock. Oncol Rep. 2013;29:2255–60.

    Article  CAS  PubMed  Google Scholar 

  114. Yang K, Cao YA, Shi C, Li ZG, Zhang FJ, Yang J, et al. Quantum dot-based visual in vivo imaging for oral squamous cell carcinoma in mice. Oral Oncol. 2010;46:864–8.

    Article  PubMed  Google Scholar 

  115. Yang K, Zhao C, Cao YA, Tang H, Bai YL, Huang H, et al. In vivo and in situ imaging of head and neck squamous cell carcinoma using near-infrared fluorescent quantum dot probes conjugated with epidermal growth factor receptor monoclonal antibodies in mice. Oncol Rep. 2012;27:1925–31.

    Article  CAS  PubMed  Google Scholar 

  116. Aswathy RG, Yoshida Y, Maekawa T, Kumar DS. Near-infrared quantum dots for deep tissue imaging. Anal Bioanal Chem. 2010;397:1417–35.

    Article  CAS  PubMed  Google Scholar 

  117. Zhu CN, Chen G, Tian ZQ, Wang W, Zhong WQ, Li Z, et al. Near-infrared fluorescent Ag2Se-cetuximab nanoprobes for targeted imaging and therapy of cancer. Small. 2017;13:1602309.

    Article  CAS  Google Scholar 

  118. Huang H, Bai YL, Yang K, Tang H, Wang YW. Optical imaging of head and neck squamous cell carcinoma in vivo using arginine–glycine–aspartic acid peptide conjugated near-infrared quantum dots. Onco Targets Ther. 2013;6:1779–87.

    PubMed  PubMed Central  Google Scholar 

  119. Yang K, Zhang FJ, Tang H, Zhao C, Cao YA, Lv XQ, et al. In-vivo imaging of oral squamous cell carcinoma by EGFR monoclonal antibody conjugated near-infrared quantum dots in mice. Int J Nanomed. 2011;6:1739–45.

    Article  CAS  Google Scholar 

  120. Saxena S, Sankhla B, Sundaragiri KS, Bhargava A. A review of salivary biomarker: a tool for early oral cancer diagnosis. Adv Biomed Res. 2017;6:90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Almangush A, Heikkinen I, Makitie AA, Coletta RD, Laara E, Leivo I, et al. Prognostic biomarkers for oral tongue squamous cell carcinoma: a systematic review and meta-analysis. Br J Cancer. 2017;117:856–66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Janissen R, Sahoo PK, Santos CA, da Silva AM, von Zuben AAG, Souto DEP, et al. InP nanowire biosensor with tailored biofunctionalization: ultrasensitive and highly selective disease biomarker detection. Nano Lett. 2017;17:5938–49.

    Article  CAS  PubMed  Google Scholar 

  123. Li X, Wei L, Pan L, Yi Z, Wang X, Ye Z, et al. Homogeneous immunosorbent assay based on single-particle enumeration using upconversion nanoparticles for the sensitive detection of cancer biomarkers. Anal Chem. 2018;90:4807–14.

    Article  CAS  PubMed  Google Scholar 

  124. Wei P, Kuo WP, Chen F, Hua H. Diagnostic model of saliva peptide finger print analysis of primary Sjogren’s syndrome patients by using weak cation exchange magnetic beads. Biosci Rep. 2013;33:e00051.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Jiang WP, Wang Z, Xu LX, Peng X, Chen F. Diagnostic model of saliva peptide finger print analysis of oral squamous cell carcinoma patients using weak cation exchange magnetic beads. Biosci Rep. 2015;35:e00211.

    PubMed  PubMed Central  Google Scholar 

  126. Weigum SE, Floriano PN, Redding SW, Yeh CK, Westbrook SD, McGuff HS, et al. Nano-bio-chip sensor platform for examination of oral exfoliative cytology. Cancer Prev Res (Phila). 2010;3:518–28.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Sharma S, Rasool HI, Palanisamy V, Mathisen C, Schmidt M, Wong DT, et al. Structural-mechanical characterization of nanoparticle exosomes in human saliva, using correlative AFM, FESEM, and force spectroscopy. ACS Nano. 2010;4:1921–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Munge BS, Stracensky T, Gamez K, DiBiase D, Rusling JF. Multiplex immunosensor arrays for electrochemical detection of cancer biomarker proteins. Electroanalysis. 2016;28:2644–58.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Malhotra R, Patel V, Chikkaveeraiah BV, Munge BS, Cheong SC, Zain RB, et al. Ultrasensitive detection of cancer biomarkers in the clinic by use of a nanostructured microfluidic array. Anal Chem. 2012;84:6249–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Nassar AF, Williams BJ, Yaworksy DC, Patel V, Rusling JF. Rapid label-free profiling of oral cancer biomarker proteins using nano-UPLC-Q-TOF ion mobility mass spectrometry. Proteomics Clin Appl. 2016;10:280–9.

    Article  CAS  PubMed  Google Scholar 

  131. Ernani V, Saba NF. Oral cavity cancer: risk factors, pathology, and management. Oncology. 2015;89:187–95.

    Article  CAS  PubMed  Google Scholar 

  132. Conde J, Oliva N, Artzi N. Implantable hydrogel embedded dark-gold nanoswitch as a theranostic probe to sense and overcome cancer multidrug resistance. Proc Natl Acad Sci USA. 2015;112:E1278–87.

    Article  CAS  PubMed  Google Scholar 

  133. Prigodich AE, Randeria PS, Briley WE, Kim NJ, Daniel WL, Giljohann DA, et al. Multiplexed nanoflares: mRNA detection in live cells. Anal Chem. 2012;84:2062–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Authors’ contributions

XJC designed the review and drafted the manuscript. XQZ, QL, JZ and GZ helped with the manuscript editing. GZ reviewed the manuscript drafts. All authors read and approved the final manuscript.

Acknowledgements

This work was supported by Grants from National Natural Science Foundation of China (No. 81771080, No. 81371147) to Professor Zhou Gang.

Competing interests

The authors declare that they have no competing interests.

Availability of data and materials

Not applicable.

Consent for publication

Not applicable.

Ethics approval and consent to participate

Not applicable.

Funding

Not applicable.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Xue-Qiong Zhang or Gang Zhou.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, XJ., Zhang, XQ., Liu, Q. et al. Nanotechnology: a promising method for oral cancer detection and diagnosis. J Nanobiotechnol 16, 52 (2018). https://doi.org/10.1186/s12951-018-0378-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12951-018-0378-6

Keywords