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Abstract
Early detection of cancer, can greatly reduce mortality and thus increase the life expectancy of patients. In this study, we introduce a plasmonic biosensor platform to detect relevant microRNAs for gastric cancer diagnosis. The proposed sensor uses the LSPR to detect RNA in the human blood. Different geometries of nanostructures were examined, and the results of their resonance peak were analyzed. The proposed nano-flower structure with five petals was considered as the original shape and then was examined in terms of changes, including substrate changes, the type of structure, the presence or absence of holes on the structure, and different thicknesses of the desired biomarkers. It shows the optimal wavelength of LSPR at 652 nm, which is suitable for physiological environments such as blood and plasma. The creation of several holes caused a shift to the wavelength of 663.63 nm, which was about 12.12 nm, but due to the reduction of the peak intensity, the optimization steps were performed without holes. Target miRNAs such as miR-21, miR-221, and miR-153 are selectively trapped on nanostructured surfaces and change λLSPR. The resonance peak of the LSPR found a 30 nm shift due to the presence of biomarkers.
© 2022 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement
Corrections
15 September 2022: A typographical correction was made to the author affiliations.
1. Introduction
Gastric cancer (GC) is one of the most common cancers in the world; despite, its decline in some countries in recent years, with new cases and high mortality per year [1], which reduces patient survival of gastric cancer. Therefore, to identify highly sensitive biomarkers [2], to diagnose, and monitor the progression of this disease and its therapeutic effect with effective and accurate screening tools [3], have been considered by researchers for this purpose, and any effort in early diagnosis, selection of appropriate treatment strategies and monitoring of effectiveness can play a key role in reducing mortality from this disease. Although gastric cancer is the second leading cause of cancer death, its early detection has not been well established due to its small cancer cells [4]. Despite significant advances in the diagnosis of gastric cancer over the past few decades, the disease is often diagnosed in advanced stages, and since this type of cancer has no symptoms until it progresses to advanced stages, the mortality rate due to this disease is still high, and the survival of patients is low. To diagnose the disease invasive methods and examination of tumor tissue are usually used, which are costly and, in some cases, can have serious complications for patients, even if the disease is asymptomatic, invasive methods are not used [5–7]. As a result, it is important to try rapid and early detection methods using low-cost and effective screening methods by sampling biological fluids to predict and diagnose GC. According to studies, the clinical results of gastric cancer are improving, but the prognosis of tumor stages and diagnosis in the early stages is poor. On the other hand, because metastasis fails the treatment, diagnosing it is a very important challenge for specialists. Given that researchers have shown that microRNAs detect the stages and recurrence of gastric cancer, they can be used as a good commercial biomarker to improve metastasis, prognosis, prediction, and diagnosis of cancer. Because according to research, increased or decreased levels of microRNAs have a significant relationship with survival, disease stage, and metastasis. Although researchers have found new biomarkers as diagnostic biomarkers of gastric cancer, only CEA, CA19-9, and HER2 are used in clinical diagnoses [5]. However, further studies indicate that miRNAs can be considered potentially important biomarkers for gastric cancer [6]. MiRNAs have been selected as experimental elements in this study because of their importance as biomarkers of gastric cancer in prognosis, diagnosis, and prediction. These miRNAs, miR-221, miR-153, and miR-21, are often regulated in the blood of GC patients and are associated with GC metastasis, and studies show that these miRNAs can serve as good markers for the diagnosis of GC. Therefore, in this study, the feasibility of three miRNAs as non-invasive biomarkers with a new structure for the diagnosis of GC has been evaluated, and these findings may provide valuable targets for the early detection of gastric cancer.
Although nanoplasmonic biosensors have been used in diagnostic and medical applications for more than two decades, they can help achieve hypersensitive plasmonic applications. Due to the preparation, biocompatibility and in addition, the ease of surface modification with a wide range of biomolecules through the formation of stable bonds to create selective bonds allows the development of new biosensor sensing systems with greater capability in various biological analyzes [7]. Examining advanced diagnostic techniques can help pave the way for global health access in the future. Combining a bioreceptor with a transducer in one device allows the detection of target biomarkers with high accuracy in a fast way. In addition, the biosensor platform ideally has a high ability to become portable and user-friendly devices that the patient can perform diagnostic screening without going outside [8]. The most appropriate method for clinical diagnoses is the use of biosensors, which are expected to use biosensors for early and accurate biological diagnosis. These biosensors hope for effective clinical screening tools designed to identify new biomarkers, as well as the potential for downsizing and integrating different parts into one device, making it smaller, and cheaper. Easier devices are possible that can accelerate the performance and analysis of biosensors [9]. Nevertheless, nanoplasmonic technologies have still rarely been considered real tools in biomedical applications. Due to the high advantages of optical biosensors over other biosensors, they can provide accurate, quantitative, low-response, unlabeled, and minimized screening capabilities.
These nanoplasmonic biosensors can detect biomarkers using appropriate biomarker receptors that are placed on the surface of the nanostructure and, thus with this function, it develops biosensors to upgrade high-sensitivity and real-time screening and medical devices [10]. In recent decades, appropriate techniques based on plasmonic biosensor to measure the intensity of the far-field spectrum for rapid and quantitative identification of biomarkers such as bacteria, viruses, and proteins in low biological samples has been done [11]. Plasmonic sensors can be used for medical diagnosis, including screening, in particular, to determine tumor biomarkers, a specific protein in a small volume of biological fluid. These sensors can be used to diagnose cancer in individuals, predict disease response to treatment, determine disease stage, and tumor size, predict patients’ chances of recovery and even diagnose cancer in patients who have not yet developed symptoms. The horizons and prospects that can be imagined for plasmonic biosensors are positive due to the use of plasmonic nanostructures in biological applications with high accuracy and resolution, which can be a significant step forward in medicine, especially in diagnosis and screening. The development of high-sensitivity sensors has become an important challenge in diagnosing this disease. Mass production of plasmonic biosensors is possible for use in high-resolution, high-sensitivity medical diagnostics, and requires a planar manufacturing technique that can produce highly reliable devices. The results of these studies could help design new sensory experiments that show that surface plasmon resonance sensors can be used to identify chemical and biological molecules.
In this paper, the flower-shaped nanostructure was chosen as the main geometry and then the structural engineering was done. By applying changes of the effective factors on the reflection resonance peak, an optimized structure was obtained. Finally, the shape of the main structure was presented as a nano-flower shape with five petals because it showed better results. At first, the work of different geometries was considered as an array in nanometer size, and after choosing the main structure, their size increased to about 500 micrometers. Necessary optimizations were made on the structure, which will be mentioned in other sections. Mathematical modeling is presented in section II. In this part, the Plasmonic principle operation is presented too. The numerical simulation of the proposed structure is discussed in section III. Simulation results are presented and discussed in this section. Finally, the paper ends with a short conclusion.
2. Mathematical modeling
In general, sensing techniques have two main types surface spectroscopy sensors and SPR sensors. The first type of sensors themselves include surface-enhanced Raman scattering (SERS), surface-enhanced fluorescence, and surface-enhanced infrared absorption, and the second type of sensors include surface plasmon resonance (SPR) sensors and local surface plasmon resonance (LSPR) sensors that this type has attracted the attention of many researchers. Both SPR and LSPR spectroscopy techniques can provide real-time data for connection processes [12,13]. Before introducing the mathematical modeling detail, physical phenomena, including LSPR, are presented and discussed.
For this purpose, according to Refs. [12,14,15], let us consider a spherical nanoparticle: a small-radius sphere, and z-polarized light with wavelength λ that radius is much smaller than the wavelength of light (a ≲ λ). As can be seen in Fig. 1, the polarity of the gold nanoparticles under the external field due to localized surface plasmons is shown.
Fig. 1. Schematic of the polarity of nanoparticles subjected to external field induced by local surface plasmons for a sphere.
To calculate the EM field outside the particle is used from Maxwell’s equations, and therefore, Eq.(1) is obtained:
In particular, here, ${\varepsilon _{in}}$ is the dielectric constant of a metal nanoparticle, and ${\varepsilon _{out}}$ represents the dielectric constant of the external medium, which plays an important role in the amount of EM field outside the particle, given that the dielectric constant of the metal is strongly dependent on the wavelength, the first parenthesis determines the dielectric resonance equation of the particle [12,14].
According to Mie's theory for a sphere, the electric field is obtained in the form of the following relation, Eq. (2), (${N_A}$) the real density of the nanostructure, ($a$) the radius of the nanostructure, (${\varepsilon _m}$) the dielectric coefficient of the environment around the nanoparticle, which is always considered positive, and ($\lambda $) the wavelength of the absorbed radiation, (${\varepsilon _i}$) the imaginary part of the nanoparticle dielectric function, (${\varepsilon _r}$) the real part of the nanoparticle function ($\chi $) represents the shape ratio (the shape ratio is for non-spherical shapes):
In addition, the maximum wavelength of LSPR spectroscopy or ${\lambda _{max}}$ is obtained through Eq.(3). Change in it is caused by the presence of adsorbents in the local environment [12,16].
Here, m is the bulk refractive index response of nanoparticles; $\Delta n$ is the change in the refractive index due to the adsorbent; d is the thickness of the adsorbent layer $ld$ is the characteristic of the EM-field-decay length [12]. This relationship is the basis of LSPR assays.
The Lorentz-Droud formula relates the optical properties of metals such as gold to frequency and wavelength. Thus, in many plasmonic problems, the equation is expressed as shown in Eq. (4), and the optical properties of metals can be described using the dielectric constant function of the metal($\varepsilon (\omega )$). According to this formula, by decreasing the damping coefficient, the resonant frequency or the same absorption peak is found towards lower frequencies and longer wavelengths (red change). The effect of particle size on the adsorption peak is applied by modifying the surface dispersion according to Eq. (5), which reduces the damping coefficient due to the increase in nanoparticle size independent of the nanoparticle shape [15,17,18].
Here, ${\varepsilon _\infty }$ is high-frequency permeability; ${\omega _P}$ is plasma frequency; $\gamma $ is damping constant and $\omega $ is angular frequency [19,20]. For gold nanoparticles, it is important to correct the model and account for their finite size. In this case, a difference in the dielectric constant arises from the fact that the mean free path of electrons in gold is much larger than the size of a particle. However, this problem can be solved simply by taking the contribution of electron surface scattering [15].
In these formulas, $\gamma (R )$ is the damping coefficient, and ${\gamma _0}$ is the damping coefficient for bulk metal, and (A) is a theory-dependent constant whose maximum value is 1, A ≈ 0.25-0.3 is a constant determined experimentally, Vf is the Fermi velocity of electrons in gold, and R is the particle radius, and ${\sigma _{abs}}$ is the cross-section area of the absorption and ${\omega _P}$ is the oscillation frequency of the plasmon in the bulk metal [17,18].
The necessary and hypothetical parameters for the Drude-Lorentz model for bulk gold and silver nanoparticles are given in Table 1. Gold and silver structures, especially gold, are most widely used in nanoscience or nanotechnology due to their special plasmonic properties, such as high optical conductivity and lack of chemical reaction in environmental conditions. The effective parameters which we accept for gold and silver are: ${\varepsilon _\infty }$= 9.84, and for silver [21,22]: ${\varepsilon _\infty }$ = 3.7 and According to Ref. [23], hypothetical parameters is proposed in this work for gold (${\hbar}$wp = 8.6 eV and ${\hbar \gamma}$ = 0.072 eV) and silver (${\hbar}$wp = 9.0 eV and ${\hbar \gamma}$ = 0.02 eV).
Table 1. Necessary and hypothetical Drude-Lorentz fitting parameters for gold and silver nanoparticles
To obtain the shape of the ${\lambda _{max}}$ function in terms of the dielectric function of the environment, which makes the optical properties of metals such as gold dependent on frequency and wavelength, the metal Drood model is used. The dielectric function of a metal is defined as Eq. (7).
According to the Lorentz-Drood relation in Refs. [16, 24], in the plasmon environment, Because of visible and near-infrared frequencies, ${\gamma}$<${\omega _p}$ is established, the above equation with the assumptions assumed for the light source and by ignoring the imaginary part can be expressed as Eq. (8), and consequently, the relationship between the resonant frequency and the dielectric factor is obtained, as you can see that the resonant frequency is inversely proportional to the dielectric ratio of the environment. Therefore, as the dielectric coefficient increases, the resonant frequency decreases, and the wavelength increases. The dielectric function $\varepsilon (\omega )$ is predominantly real:
Using Eq. (8) and by placing $\varepsilon $ in the relation, the resonance condition in the local surface plasmon is given as Eq. (10):
According to the relationship between wavelength and frequency and that ${\varepsilon _d} = $n2, we will have [24]:
Here ${\lambda _{max}}$ is the maximum wavelength LSPR wavelength, and $\lambda p$ is the wavelength of the plasma metal. According to Eq. (10), it can be seen that the relationship between the maximum wavelength of LSPR and the refractive index at optical frequencies changes almost linearly. This is one of the advantages of the Drude model, which can be easily used in solving Maxwell equations in a numerical way, especially FDTD, which is a time-domain method [24].
Calculation and analysis of optical properties of surface sensors biosensor resonance with noble metal nanostructure can be done through Maxwell equations and analytical, semi-analytical, numerical methods, and Mie theory. To use this theory, one must know the refractive index of the particle and the environment. In this work, to analyze the desired structure and obtain the resonance peak, a set of three-dimensional finite difference time domain (FDTD) solutions is used, which is one of the most powerful methods for solving Maxwell equations, because it is difficult to obtain analytical answers. To obtain the simulation results, transmission, reflection, and field displays have been used that can show the distribution of light fields in the time domain within the structure and evaluate the performance of the structure in response to the collision light in a spectral manner.
In the mesh settings, we set perfectly matched layer (PML) boundary conditions to split in the z-direction of 0.006u and antisymmetric and symmetric boundary conditions to split in the x and y direction of 0.5u (Δx=Δy= 0.5µ, Δz = 0.006µ). The source shape used for the simulation is a plane wave source in the infrared range from 0.4 to 0.7 µm.
2.1 Surface plasmon resonance technique
The surface plasmon resonance (SPR) method is one of the most powerful environmental techniques that enable rapid and high-sensitivity detection in medical diagnosis and environmental monitoring. In recent years, many advances have been made in the field of plasmonic biosensors due to their high speed and high-sensitivity detection in biological diagnostics, which can improve the quality of the results compared to older diagnostic methods. As shown in Fig. 2(a), the universal design used for the SPR measurement technique is the Kershmann geometry, which consists of a prism and a thin layer of noble metal coating and requires a sophisticated sensing system and precision equipment. [25,26].
The use of surface plasmon resonance sensors has increased due to their high sensitivity, fast operation, and labeling. The surface plasmon resonance method has a simple, durable, and robust structure compared to other methods and does not require complex arrangement; at the same time creates high sensitivity and does not cause electromagnetic interference. Therefore, this spectroscopy method is a reliable method for conducting biologically sensitive experiments [27]. Thus, in addition to the numerous challenges to nanoplasmonic sensor technologies, including the need for simple and low-cost reading systems, these scalable nanostructured chips are used not only for medical diagnosis but also in other fields that can deliver analytes rapidly and quantitatively.
2.2 Local surface plasmon resonance (LSPR) technique
As shown in Fig. 2(b), the LSPR structure is composed of a thin layer of metal, and when biomarkers are attached to the surface of a metal structure, the plasmon resonance peak is changed due to a change in the refractive index. Due to the special advantages of this method, LSPR is considered a useful and reliable analytical technique for small biomarker detection applications and is a suitable alternative method for SPR systems [9,28,29]. LSPR-based techniques are superior to unlabeled methods due to their sensitivity, high robustness, and simple detection and screening [25,30]. The special advantages of nanoplasmonic sensors, including their high sensitivity, have not yet been fully explored [31]. In the LSPR technique, nanostructures show peaks of strong absorption and scattering spectra that can be used to study biomolecular reactions, so these sensors can be used to quantify the binding of small biomarkers. The LSPR method in recent years has shown to be a promising and useful method with a powerful and quantitative probe for reactions such as protein-ligand, protein-protein, and protein-DNA, which are used as a tool in high-sensitivity and unlabeled biological applications [29,32]. However, surface plasmon resonance technologies are widely used today to measure biomolecular interactions, while the advantages of this technique, there are several challenges such as improving sensitivity and detection limit, selection in complex biological solutions, and sensitive detection of membrane-related species, and element compatibility. There is a sensor [25,30], that has limited the possibility of using this method in various applications. Despite the advantages of the LSPR technique, little effort has been made to advance the LSPR assay, so in this study, the assay method was chosen to assess biomarkers.
Among the works that have been done in the field of LSPR recently, it was the work of Chang and her colleagues in 2018 that designed a flexible sensor made from a reliable three-layer MIM disk embedded in a PDMS platform. This LSPR sensor showed high sensitivity due to the spatial overlap of the LSPR wave with the environment. In addition, a flexible MIM-LSPR sensor was used to detect A549 cancer cells. The absorption spectrum of the LSPR sensor of the MIM disk was shifted to 75 nm, and a distinction was easily made between PBS + solutions and A549 cancer cells [33]. In the same year, Wang et al. investigated metal nanoparticle-based LSPR in refractive index measurement and molecule identification. In this study, the non-local effect on the evolutionary hypothesis of different plasmon resonance modes in nanoparticle dimers and tumor antennas with a distance of up to 1 nm has been investigated [34]. Also, at the same time as this research in 2018, Lee and his colleagues used gold nanoparticles to develop various biosensors for molecular detection, because they are easily applied. It focuses on four different methods of plasmonic bioassay, including LSPR, SERS incremental Raman spectroscopy, fluorescence enhancement and plasmon-induced cooling, and GNP binding-based colorimetry [7].
2.3 Nanoplasmonic biosensors for clinical biomarker analysis
2.3.1 Cancer biomarkers
Detection of cancer biomarkers by plasmon biosensor is used as a tool for prediction, diagnosis, and prognosis, for rapid and simultaneous screening of different biomarkers. Some of the specific biomarkers of gastric cancer have been widely used as the most accepted and common biomarkers for diagnosing the stage, size, and recurrence of gastric cancer, despite their low accuracy. Still, due to the small amount of these biomarkers in the early stages, diagnosis is difficult. Matsuka and colleagues in [5] classified gastric cancer biomarkers as non-invasive commercial markers in biological body fluids, including blood or gastric juice that could accelerate treatment. This study aimed to classify gastric cancer biomarkers. CEA levels were also accurately predicted after tumor resection and showed that these predictive biomarkers are involved in improving the outcome of gastric cancer in advanced stages. Nakula and colleagues in [6] identified an association between elevated levels of several genes and GC that may be useful in the early detection of gastric cancer. Collection of samples from gastric lavage during endoscopy showed that cancer cells exit the mucosal layers more easily than normal cells in gastric juice and that DNA isolated from such tumor cells due to acidity is less destroyed. Further studies show that miRNAs can be considered potentially important markers for gastric pathology. Another possible marker for early detection and prognosis of GC is lncRNA because its level in gastric water was much higher than that of normal patients. Quinn and colleagues in [35] conducted a study to select tumor-related anti-tumor antibodies as biomarkers in the diagnostic safety of gastric tumors using the RPA recursive segmentation method. Anti-TAA antibodies were measured in the serum of all subjects by immunoassay. The optimized panel can distinguish GAC patients from normal people at an early stage. Different types of serum protein biomarkers have been used to diagnose and predict GC in clinics (CEA carcinoembryonic antigen, carbohydrate antigen CA19-9, CA72-4, and CA50).
2.3.2 Micro-ribonucleic biomarkers
Micro ribonucleic biomarkers are small, unencoded ribonucleic acids that regulate gene expression. Changes in miRNA expression lead to various diseases, especially cancer. This type of biomarker in tissue samples can be used as a biomarker to diagnose cancer. Still, diagnostic methods use tissue samples because access to sample Tissue is hard and invasively injures patients. Diagnosis with miRNA biomarkers is stable and reliable because access to biological samples, especially plasma and serum, is easy and non-invasive. Recently, several miRNA markers have been identified in serum or plasma samples for the detection of GC, many of which have carcinogenic roles and tumor suppressor functions in cancers [36]. MicRNAs are small, single-stranded RNA molecules with 19-24 nucleotides that are capable of regulating gene expression and are imperceptibly expressed in most cancers. MicRNAs have also been observed in the blood of cancer patients and can circulate as biomarkers [2,37]. In recent studies related to the diagnosis of gastric cancer, various biomarkers have been selected to identify this cancer. Still, for the reasons given, there is an urgent need for early detection of this cancer in the early stages, where miRNA biomarkers can play a key role in the onset and progression of cancer. MiRNAs have been used as new and potential biomarkers in clinical diagnoses. They have shown good results that can be hoped for more accurate and sensitive diagnoses of these biomarkers in the future. MiRNAs as diagnostic biomarkers of tumors, despite being more stable than other RNAs, are less stable than conventional proteins because they are more prone to degradation, which can be minimized by precautions [3]. Common techniques, such as polymerization chain reactions (PCR), detect RNA biomarkers, but because this method uses massive and expensive devices to measure, they have many limitations and low sensitivity. In addition, this method has disadvantages such as sample destruction or sample contamination, and the experimenter and patient may even be at risk due to the use of UV rays to bind to the biomarker. Although studies in this area are progressing and completed, identifying specific high-sensitivity microRNAs in gastric cancer cells is still challenging [4]. The discovery of miRNAs, transcriptional regulation, and translation in genome studies have changed the world. Research has shown that miRNAs can regulate a set of cellular functions. Abnormal changes in miRNA expression in most cases lead to inflammatory disorders and cancer [38].
2.3.3 Gastric cancer biomarkers
Among biomarkers, the most significant and commonly reported biomarkers for gastric cancer screening have been microRNAs, specially miRNA-21, miRNA-221, and miRNA-153 [3,39–41], these Microrna act as prognostic markers in gastric cancer, and their role in the diagnosis and treatment of gastric cancer. According to the research done in some studies [3], the results of the data indicate that the level of miR-21, miR-221, and miR-153 can be suitable biomarkers for GC and can be used in the initial diagnosis. These MicroRNAs were expressed in gastric cancer (GC) as potential diagnostic biomarkers for GC. Research shows that level 21 expression in the blood can detect gastric cancer in the early stages of the disease because it is one of the first biomarkers with a high chance of rapid diagnosis and prognosis of gastric cancer [3,39,40]. Table 2 shows several examples of GC biomarkers in the form of incremental and decremental expression.
Table 2. Some biomarkers involved in gastric cancer with altered expression levels
The gastric-specific microRNAs, despite their low accuracy and limited specificity, have been widely used as the most well-known biomarker in diagnosing gastric cancer. Simple, high-performance, cost-effective identification technologies that can quickly and quantitatively detect analytes in small sample volumes improve medical diagnosis and greatly aid early prognosis. Slowly, recent advances in plasmonics have led to high-precision and sensitive medical applications. Therefore, continuous efforts to design particles with strong, adjustable, compatible plasmon resonance using surface plasmon will lead to new advances in medical sensors; on the other hand, Due to the increase in various diseases, especially cancers, the use of biosensors is essential in health care monitoring, and biosensor based on local level plasmon resonance provides an optical spectroscopy method with fast, accurate and high-sensitivity detection. LSPR sensors have the potential for high sensitivity and unlabeled biological detection. Recent advances in design and fabrication techniques have led to several approaches to developing plasmonic biosensor devices based on local surface plasmon resonance. One of the unique advantages of plasmonic sensors for bioassay is easy preparation, label-free and environmental compatibility. Research in this area could open up new perspectives for the diagnosis and prognosis of diseases, especially cancer. Studies of existing sensors and various measurement techniques show that plasmon assay techniques for a variety of biochemical assays can be obtained in complex biological structures using low-cost substrates. The association of high levels of some molecules and changes in their levels in the biological fluids of patients with malignant tumors with cancer progression may help explain the relationship between miRNA and cancer and even decide whether this molecule can be used as a biomarker to diagnose cancer. In recent years, plasmonic-based methods have been used to quantify specific biomarkers that have had satisfactory and successful results. Analytical methods are commonly used to detect elements, but analytical methods to detect a particular element are all costly and time-consuming and require expensive equipment and experienced personnel. Other methods, such as electrochemical, require complex operations and equipment and do not. They can directly determine the concentration of the element in biomolecules. The solution to this problem is to use plasmonic biosensors that can detect quickly and sensitively. As a result, the Plasmon Biosensor Platform can be used to identify multiple cancer biomarkers and can also be a promising option for rapid, simple, inexpensive, unlabeled, and sensitive diagnosis and may be used in various contexts and achieve valuable results, especially in clinical applications.
2.3.4 History of plasmonic biosensors and detection of cancer biomarkers
In this section, some of the efforts that researchers have made in this field are included. In 2013, Skiaka et al. showed that using the SPR platform with an improved surface process for the correct orientation of antibodies, the correct diagnosis and regulation of apoE in cancer patients can be done quickly. In this work, researchers selected four biomarkers, miR-223, miR-218, miR-25, and miR-21, and investigated the effect of expression levels of these biomarkers to distinguish GC patients in the early stages from healthy individuals [39]. Also, in the same year, for the first time, the simultaneous detection of several biological analyzes was obtained using a fiber-optic biosensor based on SPR. The diagnosis was made by collecting plasmonic waves scattered by uneven metal coatings on multimode fiber optic sensor regions, and two biomarkers of gastric cancer, CLU and apoE, were detected at clinical concentrations each in a separate sensitive area [40]. In 2018, Zaveri et al. introduced a sensitive ammeter sensor available for simple and sensitive miRNA-21 determination. A new electrochemical platform for the determination of miRNAs has been described, which combines direct DNA-RNA hybridization with the benefits of nano-structured AuNPs platforms and the high specificity of hybrid anti-DNA-RNA antibodies [41]. In 2019, Lai et al. reviewed the unlabeled microRNA biosensors and nanomaterials used to improve the performance of optical sensors. In this study, LSPR sensors have been studied in particular. Gold nanoparticles were fabricated on a glass platform followed by chemical function with poly (ethylene glycol) -6-thiol (PEG6-SH) and complementary probes (-C6-ssDNA). PEG6-SH and ssDNA formed a monolayer of biological elements that provide the space required for hybridization with complementary miRNAs. After direct hybridization of miR-21 and miR- b10 target miRNAs to their complementary ssDNA, a change in ${\lambda _{LSPR}}$ to a longer wavelength was observed in the biosensor response. The calculated LODs for 21miR- in three different biological samples are between 23-35 FM [42].
2.3.5 Proposed work
Due to the above and the importance of low-volume biological diagnostics, in this work, an attempt has been made to detect cancerous biomarkers using LSPR plasmonic sensors. Since the work that has been done before has been more on SPR sensors, this method, as mentioned in the previous sections, has a high volume due to the use of the prism in its structure, so the technique LSPR has been used in this work. Since the response of this method changes with the surface morphology, by selecting and designing a structure with high field strength and low volume, it is possible to have a suitable identification of biomarkers by having a sensitive and stronger probe. In this regard, gastric cancer protein biomarkers were first used among the markers obtained by PCR in gastrointestinal journals.
The predominant protein biomarkers used in previous work for the detection and prediction of GC with plasmonic biosensors are usually the cancer antigen CEA, the carbohydrate antigen CA19-9, CA72-4, and CA50 [6,8], which are sensitive biomarkers and despite being discovered. Newer GC biomarkers, but still only CEA, CA19-9, and HER2 biomarkers are used in clinical applications, but these biomarkers are detected in the late stages due to their low concentration in the early stages of cancer, which is less promising to treat patients. For this purpose, miRNA-sensitive biomarkers have been considered to select a structure with hot spots, sensitivity, and high resolution. These protein biomarkers can be detected at lower concentrations, and we can also increase the field strength to higher wavelengths so that healthy cells in the human body are not damaged, new biomarkers can be used to open new opportunities for the diagnosis of gastric cancer to make treatment easier and more timely. Therefore, according to the features mentioned for diagnostic methods, the proposed plasmonic method has been selected to identify microRNA biomarkers that are very sensitive and enter the body's biological fluids such as blood, urine, tears, and saliva directly through cancerous tissues. Therefore, this study aimed to identify the proposed miRNA biomarkers in body fluid (blood) using the LSPR method, which could be closely related to tumorigenesis or GC metastasis, and it was considered a potential biomarker for the diagnosis of GC, especially in the early stages of cancer, to distinguish healthy individuals from patients. In addition, since a biomarker is a marker of multiple cancers, the diagnosis of a biomarker is not suitable for better diagnosis, so it is necessary to consider several biomarkers to diagnose the desired cancer because when a biomarker is used, environmental conditions and physiologically affects it and thus leads to incorrect results. Since the spectral properties of local surface plasmons are highly dependent on the geometry of metal structures, it is possible to select the optical response in a wide range from visible to infrared by choosing the appropriate geometry of these structures and obtaining the desired spectrum. Because previous reports of optical plasmon diagnosis for gastric cancer biomarkers have been less common, gastric cancer is one of the deadliest cancers. Early detection of gastric cancer is very important to improve the therapeutic effect and increase patient survival. Work in this area is a priority. Therefore, in this paper, a substrate for optical biosynthesis was used to diagnose gastric cancer based on the LSPR principle by various structures to optimize the diagnosis. To discover that specific biomarkers in the biological fluids can distinguish gastric cancer patients from healthy individuals, especially in the early stages of cancer, specific disease-specific biomarkers have been identified.
By particle size and type engineering, the plasmon resonance peak can be adjusted in the infrared range, and a nanostructure with high adjustability and high sensitivity can be provided. Because biological plasmon sensors are more commonly used in medical applications, the body's cells should not be severely damaged. For this purpose, with the development of adjustable plasmon nanostructures, environmentally friendly and with high sensitivity and resolution, high-performance nanostructures can be provided for biological applications. Sensitivity, resolution, and figures of merit can be used to compare the detection capability and performance of sensors, which are standard parameters. For better performance, high accuracy, good sensitivity, and high resolution, the absorption peak should be located in the visible area, and their absorption intensity should be increased. Therefore, in this study, to obtain the sensitivity and figures of merit of the structure, the amount of spectral shift in the refractive index change unit was investigated because LSPR sensors work based on refractive index changes and are strongly dependent on geometry, size, and environment. Since the goal is to identify a biomarker for gastric cancer, the biological receptor for that biomarker must first be placed on the surface of the structure to trap tumor markers, and then due to changes in refractive index due to the biomarker reaction of surface molecules, shifts occur at resonance, and detection of biomarker concentrations is obtained by considering the magnitude of the resonance change to the visible region. Because the optical properties of this measurement method strongly depend on the geometry of metal nanostructures, we make changes in the structure and observe the results. It is also possible to create resonance by applying an electromagnetic field at specific wavelengths. By engineering and changing the size, shape, and environmental parameters of the system, controlling and adjusting the direction, the surface plasmon resonance can be created for biological applications. Thus, with this control and adjustment of the optical response, it will show a high ability to adjust to the geometry of the particles. Therefore, in this case, an adjustable sensor will be obtained, which is useful in biological applications to detect nanoparticles or molecules. Nanostructures that have higher sharp points are preferred for use as biosensors because they often accumulate at sharp points and create a strong field resulting in high absorption. Therefore, probes are very accurate and sensitive to detect small changes in the dielectric environment around structures. As a result, when the biomolecule is close to the surface of the noble metal structure, the refractive index of the environment around the nanostructure increases. Therefore, biological reactions at the surface of structures lead to changes in the local refractive index. The material of the studied structures is gold because gold for chemical applications in the face of living cells shows little chemical contamination and is biocompatible.
3. Method of simulation
To simulate incoming light, a plane wave source with a wavelength commensurate with the nanostructure is considered because Mie theory occurs when particles are the same size or slightly larger than the wavelength of the light they collide with. To take advantage of Mie's theory, the refractive index of the particle and the environment are required. There are precise analytical methods and solutions for solving Maxwell time-dependent equations for specific geometries. Still, with advances in the construction of structures, the development of various structures is diverse and complex. But because analytical solutions to these structures are weak, numerical simulation methods are an important tool for solving Maxwell's time-dependent equations. Therefore, due to the complexity of the geometry of the structures, the FDTD (Finite Difference Time Domain) method is used to calculate the surface plasmon absorption. The FDTD method is one of the computational methods used to solve time-dependent Maxwell equations and calculates electric and magnetic fields in simulation space and time steps. To simulate and obtain the spectral response of transmission and dispersion, different forms of gold have been studied, then the structure and conditions affecting the resonance peak have been optimized. Environments such as the skin were also examined, where the skin dielectric coefficient was approximately between 1.41 and 1.49, depending on the thickness of the skin and the frequency of incoming light, and the dielectric coefficient for the innermost layer of the skin (dermis) was approximately between 1.36 and 1.41. Since the goal is to design a biosensor, it is best to consider the innermost layer of the skin as a dielectric. We consider the refractive index of 1.41 for the skin in the visible light range and perform the simulations based on it. Radiant light was also placed on a plane source in the range of visible light with a wavelength between 400 and 700 nm.
3.1 Numerical simulation
Since the plasmonic resonance peak is strongly dependent on the geometry of the structure, some structures (nano-hole array, nano-triangle, nano-star, nano-polygon, etc.) are simulated as 3 × 3 arrays on the substrate with a refractive index of 1.4. These can be seen in Fig. 3(a-f) and the results of the reflection spectra of these structures have been obtained. In the proposed flower-shaped nanostructure, the sharp points have been increased so that with the increase of the sharp edges, the points of electron accumulation increase, and the field in these points increases, causing a probe with a higher field intensity to be created.
Fig. 3. Different types of structures in the form of a 3 × 3 array of gold on the substrate. a) Nano-hole, b) Nano-triangle, c) Nano-polygon, d) Nano-star, e) Nano-flower, f) Nano-proposed structure.
The results of the reflection of these structures have been obtained. As shown in Fig. 4, the results show that the structure with the proposed flower pattern shows a better result, and the resonance peak goes to higher wavelengths. Also, the dimensions of these presentations are nanometer size. As can be seen in the resonance peak results of different geometries, the diagram with blue color and dot form which belongs to the proposed array has a better resonance wavelength. In addition, the results show that by selecting a structure with higher sharp points, the field strength, which acts as a sensitive and strong probe to accurately detect biomarkers, increases and can be effective in detecting small biomarkers, especially miRNAs.
Figure 5 shows the three-dimensional flower-shaped structure with the number of different petals that are being compared from four to nine petals. As shown, to optimize the result of the reflection simulation for the proposed structure, the number of petals in the design was changed to select the best result to continue the work.
As shown, in Fig. 6(a), the reflection result of six modes is investigated. According to the reflection results obtained from the simulation of the flower-shaped structure, if the structure has five petals as shown by the green color in the diagram, in terms of peak width and resonance intensity, and wavelength, compared to the number of other petals are more suitable and it can be concluded that the structure with five petals goes towards higher wavelengths. In cases where the number of petals is more or less than five petals, the resonance peak results in wavelengths shorter than five petals. Since the resonance peak at higher wavelengths is considered for work in the medical field, the five-petal flower structure has been chosen as a suitable option for this work.
Fig. 6. a) Reflection result of the proposed structure with the number of different petals. Simulated the electric field from (b) cross-view, (c) Simulated the electric field from the front view.
In Figs. 6(b) and (c), the simulation results of the front and cross-view electric field are shown for the flower-shaped structure with eight petals, which indicates that the field is stronger at the sharp points and can be a very strong and sensitive probe.
3.1 Simulation of the proposed plasmonic biosensor
3.1.1 Dimensions of the desired structure and the results of reflection simulation
Figure 7 shows a three-dimensional design of the structure from different angles. Since the optimized flower structure shows a better result than the usual flower structure, that structure is considered the main structure. The results of reflection and their field strength have been studied. The simulation was performed with a plane wave type source in the range of 0.4-0.7 µm with deg0 and with sio2 substrate with a thickness of 1 µ and RNA thickness of 25 nm. As shown in Table 3, the dimensions of different parts of this selected structure are included in micrometer dimensions.
Table 3. Dimensions of the proposed flower-shaped structure
As shown in Figs. 8(a), and (b), after optimizing the flower shape, the results of reflection and transmission simulation of the proposed structure were performed individually in nm size, and reflection and transmission resonance peaks were obtained at 671.7 nm and 667.7 nm, respectively. The results of reflection and transmission simulation are shown in this section by applying a type of miRNA (miRNA-21) biomarker. Graphs with blue color without applying any biomarker and orange colors in the presence of a type of miRNA biomarker. In the results of these two figures, the shift of resonance wavelength can be seen and its amount is about 30 nanometers. These resonance peak changes are due to the increase in the thickness of the structure along with the biomarker, which has changed the refractive index of the medium. This change in the refractive index has finally led to a change in the peak of the resonance wavelength, which can lead to the existence of a biomarker on the structure.
Fig. 8. Reflection and transmission result of the proposed structure with the number of 5 petals. a) Reflection b) Transmission
3.1.2 Optimization of the reflection coefficient
The resonance peak of the reflection results was performed in two modes without miRNA, and with a specific concentration of target miRNA (miRNA-21, miRNA-153, miRNA-221), and they created a resonance wavelength of 6.5151 $\mu $m, 6.5454 $\mu $m, respectively, with a resonance shift value of 30 nm, which is visible that show in Fig. 9. The three desired biomarkers were applied under the same conditions and in the same amount on the surface of the selected structure, and these miRNAs with different refractive indices caused the same shift, and this shows that it is possible to place the complementary receptors of these miRNAs (especially the complementary ssDNA of each of these miRNAs), the work of detecting these biomarkers was done.
In addition to the structure’s geometry, the peak resonance results depend on other factors such as the structure material, the refractive index of the substrate, and the environment around the structure, which can be seen in Fig. 10 and 11. As can be seen in the diagrams in Fig. 10, the simulation is performed with different substrates (PMMA, Si3N4, Sio2,…), and the results are visible. The results show that the resonance peak was better in substrates with a higher refractive index, and according to the obtained results, the dependence of the resonance wavelength peak on the refractive index of the substrate can be seen.
The results show that for the substrate with a refractive index of 1.48, the resonance peak shifts to higher wavelengths, and also according to the Fig. 11, reflection resonance peak simulation was done for the selected structure with different metals (Au, Ag, Al, Zn), and showed among the different types of noble metals for the structure, gold a better resonant response in terms of wavelength and resonance intensity. In the following work, as shown in Fig. 12(a), we observed the reflection results by creating several holes in the proposed structure. Figure 12(b) shows the structure with 10-hole and without holes. By applying the hole as shown in the figure, the shift was performed towards higher wavelengths, and the resonance peak was transferred from 651.51 nm to 663.63 nm, which is 12.12 nm.
Fig. 12. (a) Resonance peak of structure without and with hole. (b) Structure without and with 10-hole.
Also, different thicknesses of miRNA were considered as different concentrations of the biomarker, as shown in Fig. 13. In this figure, which is based on the thickness changes of miRNAs, no difference was observed in thicknesses smaller than 10 nm, but with the increase of thickness from 10 nm to above, changes were visible. In this simulation, we considered under 25 nm thickness, so the displacement of the peak is negligible.
4. Conclusions
In this paper, the aim was to design a plasmonic biosensor platform based on the LSPR principle with a flower-shaped structure for biological diagnoses, including the detection of gastric cancer biomarkers. In this work, by optimizing the desired shape, the proposed nano-flower structure with five petals showed a better result than the others in terms of reflection peak intensity and resonance wavelength and, the light response at an approximate wavelength of 652 nm was obtained. By applying changes such as the material of the structure, the refractive index of the substrate, and the surrounding environment of the structure, the selected structure with gold and in a substrate with a higher refractive index without creating holes on it showed a satisfactory result in the reflection wavelength peak. The creation of the hole caused a shift to higher wavelengths, from the resonance peak wavelength of 651.51 nm to 663.63 nm by about 12.12 nm, but the peak intensity decreased. The optical response of the proposed structure shifted to higher wavelengths of 30 nm after selecting target miRNA biomarkers such as miRNA-21, miRNA-221, and miRNA-153. Then, by sizing engineering and changes of the effective factors on the resonance peak, optimization of the proposed structure was performed.
Disclosures
The authors confirm that there is not conflict of interest.
Data availability
No data were generated in this work.
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