Ultrafast and Ultrasensitive Simultaneous Molecular Recognition and Quantification of CA12-5, CA72-4, HER1, and AFP in Biological Samples

Abstract

Simultaneous molecular recognition and quantification of at least four biomarkers in biological samples may contribute to early and fast diagnosis of illnesses such as cancer. The electrodes able to reliably perform on-site these tests are the stochastic sensors. Therefore, three novel 3D stochastic sensors employing carbon-based powders (graphite, graphene, nanographene) treated with N-(2-mercapto-1H-benzo[d]imidazole-5-yl) oleamide solution were used for screening tests of whole blood, gastric tumoral tissue, urine, and saliva for molecular recognition and quantification of CA12-5, CA72-4, HER1, and AFP. The best performance was achieved for the sensor based on graphene, when the highest sensitivities were recorded, on wide working concentration ranges of: 8.37 × 10⁻¹⁴–8.37 U mL⁻¹ for CA12-5, 4.00 × 10⁻¹¹–4.00 × 10⁻³ U mL⁻¹ for CA72-4, 3.90 × 10⁻¹⁶–3.90 × 10⁻⁶ g mL⁻¹ for HER1, and 3.00 × 10⁻²⁰–3.00 × 10⁻⁶ g mL⁻¹ for AFP. The wide linear concentration ranges cover levels of biomarkers found in gastric cancer patients from early to late stages. The recovery values were higher than 98.00 with %, RSD lower than 1.00%.

1. Introduction

Structures generated by cancer tumors or by the body in reaction to cancer are known as tumor markers. Additionally, these structures might be manufactured to combat inflammatory diseases and other neoplastic illnesses. Tissues and bodily fluids like blood and urine may include tumor indicators that include hormones and other categories such as glycoproteins, including enzymes, receptors, and oncofetal antigens [1,2,3,4]. As a result of the high number of patients who passed due to gastric cancer, it is clear that the primary cause of mortality is the failure to discover the disease in a timely manner and the absence of therapy. It is essential to diagnose gastric cancer at an early stage in order to improve both identification and preventative efforts.

By analyzing tumor markers, one may get a basic knowledge of the tumor’s biology and track its evolution, identifying carcinogenesis, cell differentiation, and property changes [5,6,7]. Auxiliary diagnosis, tumor categorization and identification, prognosis monitoring, recurrence assessment, and directing therapeutic therapy are all areas where it might be useful. Research into developing more sensitive and accurate tumor marker detectors is warranted since early and precise identification of tumor markers may significantly enhance the efficacy of cancer prevention and therapy [8].

In addition to being connected to malignancies of the uterus, cervix, pancreas, liver, colon, breast, lung, and digestive system, elevated cancer antigen (CA) 12-5 is most typically associated with ovarian cancer. Increased levels of CA 12-5 have been used as a means of determining the transition of benign cells into malignant cells [9,10]. Overall, CA12-5 is a useful biomarker that may be used not just for the diagnosis of cancer but also for a variety of other aspects of cancer therapy and progression. Carbohydrate antigen 72-4, also known as CA72-4, is a glycoprotein with a large molecular weight that is considered to be one of the most effective tumor indicators for the disease of gastric cancer. The CA72-4 has a high degree of specificity for gastric cancer and has a strong application value in the treatment of malignant tumors of the digestive system [11,12,13]. The epidermal growth factor receptor (HER-1), often known as EGFR, is a transmembrane glycoprotein that has tyrosine kinase activity [14,15]. While it is attached to its particular ligands, it has the ability to activate certain genes, which in turn promotes cell division and proliferation [16]. However, overexpression of EGFR is seen in a number of carcinomas, including gastric, breast, ovarian, and colorectal cancers [17]. The normal range of EGFR concentration in humans is between 1 and 25 ng mL⁻¹ [18]. Alpha-fetoprotein, often known as AFP, is a glycoprotein that is classified as a member of the albumin family. Its molecular weight is around 70 kRa. Most of its production takes place in the gastrointestinal system, the yolk sac, and the liver of the fetus [19]. It has been shown that the amount of AFP in serum is connected to the proliferation of tumor cells [20]. Additionally, AFP may be directly employed as a key target of tumor therapy to assess curative impact, recrudescence, and/or metastasis [21,22]. This is because the identification of tumor markers [23] that indicate the existence or recurrence of cancer is essential for effective early diagnosis and treatment of cancer due to the fact that it is essential for successful early diagnosis and treatment of cancer.

To date, the assay of tumor markers has been accomplished by the use of a variety of techniques, including chemiluminescence [24], enzyme-linked immunosorbent assay (ELISA) [25], mass spectrometry [26], fluorescence [27], and immunoradiometric assay [28]. These approaches, on the other hand, are not suitable for applications that are not directed at the point of care since they are time-consuming, costly, labor-intensive, complex, and tedious. Consequently, the identification of tumor markers is of utmost importance in the process of developing and implementing detection systems that are high-sensitivity, rapid, and cost-effective. Electrochemical sensors [29,30,31,32] are of significant interest because of their outstanding characteristics, which include high sensitivity, fast return, cheap cost, simplicity, simple mobility, downsizing, the ability to be easily transported, and inexpensive. Studies have shown that the detection of numerous markers at the same time may significantly increase the accuracy of the diagnosis of gastric cancer [33]. Multiplexed sensors can simultaneously detect multiple analytes from a single sample, and they are becoming relevant not only for research purposes, but also for clinical use. This approach emerged in response to the evident finding that more than one biomarker is required to confirm a disease diagnosis [34].

The novelty of this article is represented by the utilization of three stochastic sensors based on carbon (graphite, graphene, nanographene) type of matrices modified with N-(2-mercapto-1H-benzo[d]imidazole-5-yl) oleamide (OL) for the simultaneous molecular recognition and quantification of CA12-5, AFP, HER-1, and CA72-4 in whole blood, gastric tumor tissue, urine, and saliva (Scheme 1).

As a result of their superior electric and mechanical characteristics, large specific surface area, and biocompatibility, carbon materials provide a number of promising options for the enhancement of sensor performance. Graphene (GR), graphite (G), and nanographene (nGR) are excellent materials for the design of sensors due to their sp²-bonded carbon atoms structure that provides advantages such as superior electrical conductivity, good chemical and physical stability, low toxicity, flexibility, lightweight, and ease of functionalization [35]. The oleamide through its “V” shape, can provide the channels that are required for stochastic sensing. Because it enhances the sensor’s electrochemical performance, the incorporation of oleamide into the design is a valuable choice. Moreover, these sensors proved to be very sensitive and selective with rapid response times. They are operated using small, portable potentiostats and could be integrated into point-of-care platforms, which would allow low-cost, real-time monitoring with minimal sample volumes. They could be regarded as next-generation instruments for accessible and individualized cancer diagnostics since they are compatible with multiplexed detection and wireless or smartphone-based readouts.

2. Materials and Methods

2.1. Materials and Reagents

CA12-5, CA72-4, HER1, AFP, sodium phosphate monobasic, and sodium phosphate dibasic heptahydrate were bought from Sigma Aldrich (Milwaukee, WI, USA), while the paraffin oil was bought from Fluka. N-(2-mercapto-1H-benzo[d]imidazole-5-yl) oleamide (OL) was synthesized in-house accordingly with the procedure published earlier by Cioates-Negut et al. [36]. Sodium phosphate dibasic heptahydrate and sodium phosphate monobasic monohydrate were mixed to produce PBS. The two solutions were mixed in varying quantities until a pH of 7.4 was achieved. To change the pH, either hydrochloric acid or a sodium hydroxide solution with a concentration of 0.1 mol L⁻¹ was added. The calibration of the sensor was performed using solutions prepared via serial dilution method in PBS pH = 7.4. The biomarker solutions were stored in the refrigerator at 2–8 °C when they were not being used.

2.2. Apparatus

The electrochemical investigations were conducted using an EmStat4S potentiostat (PalmSens BV, Houten, The Netherlands) coupled to a laptop running PSTrace program version 5.8. A 3D stochastic microsensor served as the working electrode, while a platinum wire was used as a counter electrode and a Ag/AgCl wire had the role of a reference electrode (0.1 mol L⁻¹ KCl), making up the 3D electrochemical system. It was possible to develop 3D tube form sensors in the lab using a 3D Stratasys Objet 24 printer (Stratasys, Rehovot, Israel).

The surface morphology of the pastes was examined using scanning electron microscopy (SEM) (Inspect S, FEI Company, Eindhoven, The Netherlands). To achieve high image resolution, the samples were evaluated using the LFD detector in low vacuum mode, a spot value of 3, and a high voltage (HV) of 30 kV. The results for X-ray diffraction (XRD) were obtained in the 2θ range of 10–80° utilizing a Cu-Kα radiation source from PANalytical’s X’Pert PRO MPD diffractometer (PANalytical, Almelo, The Netherlands).

Deionized water from a Direct-Q 3 Water Purification System (Millipore, Molsheim, France) was used to prepare the solutions. The pH adjustments were performed using a Mettler Toledo pH meter. The investigations were conducted at room temperature.

2.3. Design of the Stochastic Sensors

To prepare the sensors, 100 mg of each of the powders (graphene, graphite, or graphene nanopowder) were mixed with paraffin oil to obtain homogenous pastes. Subsequently, the pastes were chemically modified using a 1.0 × 10⁻³ mol L⁻¹ OL solution. Figure 1 presents the chemical structure of OL. Every modified paste was inserted into a non-conducting 3D-printed plastic tube with a 30 µm internal diameter with a silver wire as an electrical contact (Scheme 2). The active surface of each sensor was polished using alumina paper before use. After each measurement, the active surface was cleansed using deionized water and dried using paper tissues. When not used, the sensors were stored in the refrigerator at 2–8 °C.

2.4. Stochastic Method

Chronoamperometry was applied to determine the t_on and t_off parameters necessary for stochastic analysis. After optimizing the potential at which the current should be measured, a constant potential of 125 mV vs. Ag/AgCl was selected. This specific value was chosen since it was observed that the analytes’ signatures (t_off parameter) can be easily identified and interpreted at this potential. In the diagrams obtained after the screening, the four biomarkers were identified based on their signatures (molecular recognition phase), while their concentration was determined using the values of the t_on parameter (read in between two consecutive t_off). The concentrations of the biomarkers in the samples were calculated using the calibration equation: 1/t_on = a + b × C_biomarker, where a is the intercept, b is the slope (or sensitivity), and C_biomarker is the unknown concentration of the biomarker. The t_off parameter is commonly known as the signature of the analyte. It indicates the duration it takes for the analyte to enter the pore and obstruct the current flowing through it. This parameter is utilized in qualitative analysis and is directly associated with the molecular identification of the analyte. It is influenced by specific factors, including the analyte’s size, shape, and unfolding capacity, thus explaining why it is unique for each analyte. While the analysis of biological samples was carried out within a time period of 1200 s, the analysis of solutions that included varying concentrations of biomarkers was carried out within a time interval of 360 s.

2.5. Samples

A total of forty samples, comprising tumoral tissue, whole blood, saliva, and urine, were taken from individuals who had been diagnosed with gastric cancer. Without undergoing any kind of preparation before the analysis, the samples were utilized for the measurements. No one of the patients was receiving any kind of therapy for cancer at the time that the samples were being collected. Both the Emergency Clinical Hospital of Targu-Mures County and the Clinical Hospital of Targu-Mures County, which were both authorized by the Ethics Committee to conduct the investigation under the respective reference numbers 32647/14 December 2018 and 3206/28 February 2019, provided the samples that were used in the investigation. All of the patients in the study gave their informed consent.

3. Results

3.1. Morphology of the Active Surface of the Stochastic Sensors

A scanning electron microscope (SEM) is an adequate instrument to analyze surface morphology. Figure 2 displays the typical SEM micrographs of OL-G (a), OL-GR (b), and OL-nGR (c), at a scale of 100 µm. As can be seen in Figure 2a,b, in the case of G and GR-based pastes, the surface morphology reveals agglomerated particles in asymmetric formations. Moreover, a well-defined nanoporous structure can be observed when nGR is used (Figure 2c).

3.2. Electrochemical Characterization of the Stochastic Sensors

The characterization techniques used in this study included cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). These techniques were used to analyze and evaluate the performance of the unmodified sensors as well as three modified sensors: OL-nGR, OL-G, and OL-GR.

CV experiments (Figure 3a) were carried out with OL-nGR, OL-G, and OL-GR as working electrodes. Experiments were performed in a solution containing 5.0 × 10⁻³ mol L⁻¹ potassium ferrocyanide (K₃[Fe(CN)₆]) and 0.1 mol L⁻¹ potassium chloride (KCl), with applied potentials ranging from −1.0 V to 1.0 V.

It can be assumed that the modification was successfully implemented leading to an improved electrochemical result. The aim of the EIS investigation was to examine the interaction between sensors in a frequency range between 1.0 × 10⁵ and 1.0 × 10⁻¹ Hz. Each EIS investigation was carried out using a 5.0 × 10⁻³ mol L⁻¹ solution of K₃[Fe(CN)₆] in a 0.1 mol L⁻¹ KCl solution. Figure 3b illustrates the Nyquist plots and the fact that the carbon powders exhibit a distinct semicircular shape at low frequencies. Furthermore, modification of the three carbon-based powder sensors with oleamide revealed increasingly smaller semicircular shapes at the same frequencies used for the unmodified sensors. The EIS results for a solution containing K₃[Fe(CN)₆] at a concentration of 5.0 × 10⁻³ mol L⁻¹ (with a KCl concentration of 0.1 mol L⁻¹) showed satisfactory agreement with the CV results.

3.3. Response Characteristics of the Stochastic Sensors

The stochastic detection process, which occurs in two stages, is based on the interactions between the biomarker and a conductive channel. In the initial stage, the analyte moves through the channel at a constant potential, the current drops to 0 when the molecule blocks the channel. The duration of this step is read in the diagram as t_off and is named the biomarker’s signature. In the second stage, the biomarker undergoes redox processes inside the channel, and the length of time these processes take is known as t_on parameter which is used to quantitatively assess the analyte. The response characteristics of the proposed stochastic sensors are presented in

Table 1. Response characteristics of the proposed stochastic sensors.

Sensor Based on Oleamide OL and	Biomarker	toff (s)	Calibration Equation; Correlation Coefficient (r)	Sensitivity	LOQ	Working Concentration Range
GR	CA125 *	3.4	1/ton = 0.05 + 5.25 × 106 × Cr = 0.9996	5.25 × 106	8.37 × 10−13	8.37 × 10−13–8.37
	CA72-4 *	2.0	1/ton = 0.03 + 1.37 × 109 × Cr = 0.9995	1.37 × 109	4.00 × 10−16	4.00 × 10−16–4.00 × 10−1
	HER1 **	2.9	1/ton = 0.02 + 5.48 × 106 × Cr = 0.9999	5.48 × 106	3.90 × 10−15	3.90 × 10−15–3.90 × 10−5
	AFP **	2.7	1/ton = 0.02 + 1.98 × 108 × Cr = 0.9999	1.98 × 108	3.00 × 10−20	3.00 × 10−20–3.00 × 10−4
G	CA125 *	2.3	1/ton = 0.06 + 8.95 × 108 × Cr = 0.9993	8.95 × 108	8.37 × 10−14	8.37 × 10−14–8.37
	CA72-4 *	1.6	1/ton = 0.03 + 7.57 × 106 × Cr = 0.9999	7.57 × 106	4.00 × 10−11	4.00 × 10−11–4.00 × 10−3
	HER1 **	3.4	1/ton = 0.01 + 1.65 × 1012 × Cr = 0.9999	1.65 × 1012	3.90 × 10−16	3.90 × 10−16–3.90 × 10−6
	AFP **	1.9	1/ton = 0.13 + 2.77 × 1010 × Cr = 0.9999	2.77 × 1010	3.00 × 10−20	3.00 × 10−20–3.00 × 10−6
nGR	CA125 *	2.2	1/ton = 0.08 + 3.71 × 106 × Cr = 0.9995	3.71 × 106	8.37 × 10−11	8.37 × 10−11–8.37 × 10−3
	CA72-4 *	3.3	1/ton = 0.04 + 6.96 × 109 × Cr = 0.9950	6.96 × 109	4.00 × 10−14	4.00 × 10−14–4.00 × 10−2
	HER1 **	6.6	1/ton = 0.01 + 3.90 × 1010 × Cr = 0.9971	3.90 × 1010	3.90 × 10−15	3.90 × 10−15–3.90 × 10−6
	AFP **	1.5	1/ton = 0.04 + 3.63 × 109 × Cr = 0.9999	3.63 × 109	3.00 × 10−20	3.00 × 10−20–3.00 × 10−3

* <C> = U mL⁻¹, <Sensitivity> = s⁻¹ U⁻¹ mL; ** <C> = g mL⁻¹, <Sensitivity> = s⁻¹ g⁻¹ mL; <t_on> = s.

.

The utilization of the same sensor resulted in distinct signatures for the four biomarkers, demonstrating the sensors’ applicability for the simultaneous determination of the selected biomarkers. All sensors exhibited high sensitivities and low limits of quantification, while the concentration ranges were wide, allowing for the analysis of the biomarkers in patients confirmed with gastric cancer.

For the assay of the CA12-5 biomarker, the best results were obtained using the OL-GR stochastic sensor, with an LOQ value of 8.37 × 10⁻¹⁴ U mL⁻¹, and a sensitivity of 8.95 × 10⁸ s⁻¹ U⁻¹ mL. Similar results were obtained for CA72-4, an LOQ value of 4.00 × 10⁻¹⁶ U mL⁻¹, and a sensitivity of 1.37 × 10⁹ s⁻¹ U⁻¹ mL, when the OL-G sensor was utilized. While the LOQ values were the same when all three sensors were used, the highest sensitivity (2.77 × 10¹⁰ s⁻¹ g⁻¹ mL) for AFP was achieved using the OL-GR sensor. In the case of HER-1 biomarkers, the best results, in terms of sensitivity and LOQ, with values of 1.65 × 10¹² s⁻¹ g⁻¹ mL and 3.90 × 10⁻¹⁶ g mL⁻¹, respectively, the OL-GR sensor was better compared with the other two sensors.

The sensor of choice is the one based on OL-GR due to its response characteristics which are better than those recorded for the other two sensors.

3.4. Simultaneous Molecular Recognition and Determination of the Selected Biomarkers in Biological Samples

In order to determine the values of the t_on and t_off parameters, the chronoamperometric method was used. The t_off value is the signature of the analyte, while the t_on value is the quantitative parameter. In order to conduct qualitative and quantitative assessments of the CA12-5, CA72-4, HER-1, and AFP from actual samples, the stochastic mode was used. At a constant potential of 0.125 V, the measurements were carried out in a time span of 360 s for the calibration measurements of each analyte and 1200 s for the measurements of the biological sample. The unique t_off value of each biomarker, which was calculated from the calibration curve, allowed for the identification of the biomarkers in the diagram that was recorded for the biological samples, and it also allowed for the measurement of the parameter t_on value. With the help of the equation of calibration 1/t_on = f (conc biomarker), which was recorded for each stochastic sensor, the unknown levels of the biomarkers were assessed.

The developed sensors based on oleamide OL and various carbonaceous materials were applied to biological samples (whole blood, saliva, urine, and tissue) promptly following their collection from the patients. The biomarkers were initially identified based on their signature in the recorded diagrams. Subsequently, utilizing the t_on values determined between two t_off, the concentrations of the four biomarkers in the samples were calculated using the previously established calibration equations. The data that were obtained from the simultaneous test of CA125, CA72-4, HER1, and AFP are shown in Figure 4, Figure 5, Figure 6 and Figure 7, respectively.

Table 2. Determination of CA12-5, CA72-4, HER-1, and AFP in biological samples using OL/graphite stochastic sensor (N = 5).

Sample	CA125 (nU mL−1)	CA72-4 (nU mL−1)	HER-1 (ng mL−1)	AFP (ng mL−1)
Whole blood
1	75.70 ± 0.04	47.36 ± 0.03	22.24 ± 0.01	26.03 ± 0.02
2	5.70 ± 0.01	2.37 ± 0.04	6.34 ± 0.02	6.71 ± 0.01
3	5.03 ± 0.02	8.69 ± 0.01	4.38 ± 0.02	2.03 ± 0.04
4	20.12 ± 0.01	1.78 ± 0.03	4.43 ± 0.03	217.79 ± 0.02
5	81.54 ± 0.02	8.99 ± 0.04	6.17 ± 0.02	4.99 ± 0.03
6	5.77 ± 0.01	6.68 ± 0.02	14.17 ± 0.01	29.6 ± 0.02
7	21.34 ± 0.02	6.06 ± 0.04	5.19 ± 0.01	2.91 ± 0.03
8	29.20 ± 0.01	9.99 ± 0.03	35.45 ± 0.02	4.67 ± 0.04
9	39.94 ± 0.02	67.38 ± 0.01	8.62 ± 0.01	714.69 ± 0.05
10	9.79 ± 0.01	41.99 ± 0.02	21.04 ± 0.03	2.15 ± 0.03
Gastric tissue tumor
1	11.28 ± 0.04	1.47 ± 0.03	11.91 ± 0.03	9.33 ± 0.02
2	15.73 ± 0.02	5.40 ± 0.02	6.15 ± 0.02	27.05 ± 0.04
3	75.69 ± 0.04	2.40 ± 0.01	3.50 ± 0.01	55.56 ± 0.01
4	3.41 ± 0.01	8.58 ± 0.02	12.96 ± 0.02	4.15 ± 0.02
5	4.44 ± 0.02	12.85 ± 0.03	6.68 ± 0.01	26.46 ± 0.01
6	41.30 ± 0.02	10.65 ± 0.01	3.84 ± 0.02	2.65 ± 0.02
7	18.41 ± 0.03	1.38 ± 0.03	6.93 ± 0.01	2.30 ± 0.01
8	28.38 ± 0.01	3.13 ± 0.02	6.76 ± 0.02	755.95 ± 0.03
9	75.89 ± 0.02	21.03 ± 0.01	3.75 ± 0.01	179.50 ± 0.03
10	5.78 ± 0.03	3.04 ± 0.03	1.27 ± 0.03	15.13 ± 0.01
Saliva
1	4.90 ± 0.03	13.22 ± 0.03	16.20 ± 0.03	6.85 ± 0.03
2	123.95 ± 0.02	4.30 ± 0.02	9.96 ± 0.02	2.60 ± 0.02
3	7.98 ± 0.03	7.48 ± 0.01	26.96 ± 0.01	0.96 ± 0.02
4	4.44 ± 0.02	6.51 ± 0.02	8.71 ± 0.01	64.68 ± 0.03
5	5.89 ± 0.01	5.94 ± 0.03	3.25 ± 0.01	723.04 ± 0.01
6	93.48 ± 0.01	22.40 ± 0.01	11.15 ± 0.02	17.17 ± 0.04
7	4.35 ± 0.02	9.40 ± 0.02	2.83 ± 0.04	68.67 ± 0.01
8	2.28 ± 0.03	22.93 ± 0.02	7.27 ± 0.03	1.48 ± 0.03
9	2.20 ± 0.01	3.73 ± 0.01	7.95 ± 0.01	318.15 ± 0.01
10	17.57 ± 0.02	10.30 ± 0.03	16.82 ± 0.02	1.04 ± 0.03
Urine
1	9.79 ± 0.01	1.94 ± 0.02	6.58 ± 0.01	2.96 ± 0.03
2	3.31 ± 0.02	3.31 ± 0.03	3.06 ± 0.02	17.15 ± 0.02
3	11.67 ± 0.01	4.76 ± 0.02	5.95 ± 0.03	2.18 ± 0.02
4	14.15 ± 0.02	15.23 ± 0.01	8.77 ± 0.02	24.15 ± 0.01
5	10.19 ± 0.01	11.12 ± 0.02	18.91 ± 0.03	4.84 ± 0.03
6	1.65 ± 0.02	6.29 ± 0.01	2.38 ± 0.02	20.46 ± 0.01
7	4.54 ± 0.02	22.40 ± 0.02	18.04 ± 0.01	29.65 ± 0.03
8	4.70 ± 0.03	26.56 ± 0.03	9.28 ± 0.03	4.32 ± 0.02
9	9.44 ± 0.02	15.31 ± 0.02	1.27 ± 0.02	39.40 ± 0.01
10	4.54 ± 0.01	81.26 ± 0.02	28.01 ± 0.01	73.81 ± 0.02

,

Table 3. Determination of CA12-5, CA72-4, HER-1, and AFP in biological samples using OL/graphene stochastic sensor (N = 5).

Sample	CA125 (nU mL−1)	CA72-4 (nU mL−1)	HER-1 (ng mL−1)	AFP (ng mL−1)
Whole blood
1	75.02 ± 0.03	46.54 ± 0.01	22.35 ± 0.02	26.24 ± 0.03
2	5.80 ± 0.02	2.89 ± 0.01	6.69 ± 0.01	6.72 ± 0.01
3	6.00 ± 0.01	8.52 ± 0.03	4.99 ± 0.01	2.52 ± 0.02
4	22.94 ± 0.03	1.40 ± 0.01	4.86 ± 0.01	216.97 ± 0.02
5	81.74 ± 0.02	8.77 ± 0.01	6.18 ± 0.03	4.91 ± 0.01
6	5.68 ± 0.01	6.02 ± 0.01	14.53 ± 0.01	29.63 ± 0.02
7	21.02 ± 0.03	6.17 ± 0.03	5.78 ± 0.03	2.90 ± 0.01
8	29.47 ± 0.02	10.15 ± 0.02	35.76 ± 0.02	4.08 ± 0.02
9	39.46 ± 0.03	67.44 ± 0.01	8.01 ± 0.03	716.21 ± 0.01
10	10.04 ± 0.02	41.63 ± 0.02	21.83 ± 0.01	2.20 ± 0.01
Gastric tissue tumor
1	11.92 ± 0.01	1.40 ± 0.02	12.21 ± 0.01	9.40 ± 0.01
2	15.17 ± 0.03	5.29 ± 0.01	6.10 ± 0.03	27.51 ± 0.01
3	75.78 ± 0.02	2.29 ± 0.03	3.48 ± 0.01	55.89 ± 0.02
4	3.47 ± 0.02	8.65 ± 0.01	12.45 ± 0.01	4.51 ± 0.01
5	4.24 ± 0.01	12.88 ± 0.02	6.60 ± 0.03	27.51 ± 0.01
6	41.01 ± 0.03	10.62 ± 0.01	3.60 ± 0.04	2.60 ± 0.01
7	18.57 ± 0.01	1.59 ± 0.01	6.39 ± 0.01	2.47 ± 0.03
8	28.86 ± 0.02	3.20 ± 0.02	6.80 ± 0.02	756.52 ± 0.01
9	75.78 ± 0.01	21.35 ± 0.01	3.79 ± 0.03	180.01 ± 0.03
10	5.74 ± 0.02	3.87 ± 0.02	1.95 ± 0.02	15.89 ± 0.04
Saliva
1	4.78 ± 0.02	13.10 ± 0.01	16.22 ± 0.02	6.88 ± 0.01
2	123.00 ± 0.03	4.69 ± 0.02	9.50 ± 0.01	2.64 ± 0.02
3	7.90 ± 0.01	7.53 ± 0.03	26.56 ± 0.01	1.01 ± 0.03
4	4.26 ± 0.01	6.19 ± 0.01	8.32 ± 0.03	64.20 ± 0.02
5	5.64 ± 0.03	5.06 ± 0.01	3.30 ± 0.02	722.93 ± 0.01
6	93.70 ± 0.02	22.37 ± 0.02	11.69 ± 0.01	17.92 ± 0.02
7	4.39 ± 0.03	9.43 ± 0.03	1.94 ± 0.03	68.50 ± 0.01
8	2.38 ± 0.01	22.37 ± 0.01	7.20 ± 0.01	1.24 ± 0.02
9	1.92 ± 0.03	3.08 ± 0.02	7.60 ± 0.02	319.47 ± 0.01
10	17.15 ± 0.01	10.15 ± 0.01	15.12 ± 0.03	1.01 ± 0.02
Urine
1	9.23 ± 0.03	1.95 ± 0.01	6.48 ± 0.02	2.69 ± 0.03
2	3.91 ± 0.02	3.08 ± 0.02	3.48 ± 0.01	17.42 ± 0.01
3	11.67 ± 0.03	4.41 ± 0.01	5.01 ± 0.03	2.69 ± 0.03
4	15.03 ± 0.04	15.95 ± 0.03	8.81 ± 0.02	24.21 ± 0.02
5	10.13 ± 0.01	11.30 ± 0.02	17.99 ± 0.02	4.72 ± 0.01
6	1.85 ± 0.02	6.18 ± 0.02	2.16 ± 0.01	20.15 ± 0.02
7	4.50 ± 0.03	22.37 ± 0.01	17.34 ± 0.02	29.45 ± 0.01
8	4.60 ± 0.01	26.67 ± 0.02	9.17 ± 0.01	4.70 ± 0.02
9	9.40 ± 0.01	15.12 ± 0.01	1.50 ± 0.02	40.02 ± 0.01
10	4.90 ± 0.02	81.58 ± 0.02	28.03 ± 0.01	73.19 ± 0.02

and

Table 4. Determination of CA12-5, CA72-4, HER-1, and AFP in biological samples using OL/nano-graphene sensor.

Sample	CA12-5 (nU mL−1)	CA72-4 (nU mL−1)	HER-1 (ng mL−1)	AFP (ng mL−1)
Whole blood
1	74.98 ± 0.01	46.60 ± 0.02	22.20 ± 0.01	26.95 ± 0.04
2	5.78 ± 0.02	2.60 ± 0.04	6.63 ± 0.04	7.20 ± 0.03
3	5.45 ± 0.04	8.34 ± 0.01	4.34 ± 0.03	2.64 ± 0.02
4	22.36 ± 0.03	1.44 ± 0.02	4.88 ± 0.02	217.05 ± 0.03
5	81.61 ± 0.03	8.91 ± 0.03	6.26 ± 0.01	4.18 ± 0.02
6	5.20 ± 0.02	6.40 ± 0.02	14.51 ± 0.03	30.62 ± 0.01
7	21.27 ± 0.04	6.26 ± 0.04	5.30 ± 0.02	2.31 ± 0.02
8	28.59 ± 0.03	10.19 ± 0.02	35.95 ± 0.03	4.11 ± 0.03
9	39.38 ± 0.02	67.40 ± 0.01	8.85 ± 0.02	714.00 ± 0.04
10	9.18 ± 0.03	42.82 ± 0.02	21.70 ± 0.01	2.36 ± 0.03
Gastric tissue tumor
1	11.88 ± 0.04	1.48 ± 0.01	11.48 ± 0.03	9.43 ± 0.02
2	15.97 ± 0.02	5.30 ± 0.02	6.23 ± 0.01	27.48 ± 0.03
3	75.00 ± 0.04	2.37 ± 0.04	3.07 ± 0.02	56.01 ± 0.04
4	3.31 ± 0.03	8.93 ± 0.03	12.35 ± 0.02	4.87 ± 0.02
5	4.94 ± 0.04	12.89 ± 0.01	6.93 ± 0.01	26.14 ± 0.03
6	41.40 ± 0.02	10.39 ± 0.04	3.56 ± 0.02	2.53 ± 0.02
7	18.48 ± 0.03	1.24 ± 0.03	6.26 ± 0.03	2.42 ± 0.01
8	28.25 ± 0.01	3.01 ± 0.04	6.82 ± 0.02	756.00 ± 0.03
9	75.15 ± 0.03	21.50 ± 0.03	3.70 ± 0.02	179.32 ± 0.02
10	5.70 ± 0.02	3.36 ± 0.01	1.75 ± 0.02	15.21 ± 0.01
Saliva
1	4.70 ± 0.01	13.15 ± 0.04	16.33 ± 0.04	6.80 ± 0.02
2	123.58 ± 0.02	4.90 ± 0.03	9.80 ± 0.01	2.57 ± 0.03
3	8.02 ± 0.01	6.73 ± 0.05	26.49 ± 0.03	1.12 ± 0.03
4	4.94 ± 0.03	6.64 ± 0.01	8.54 ± 0.04	64.58 ± 0.01
5	5.18 ± 0.02	5.28 ± 0.03	3.28 ± 0.02	723.15 ± 0.04
6	93.95 ± 0.04	22.31 ± 0.04	11.23 ± 0.03	17.87 ± 0.03
7	4.47 ± 0.01	9.57 ± 0.01	2.11 ± 0.03	69.59 ± 0.04
8	2.47 ± 0.02	22.94 ± 0.03	7.11 ± 0.02	1.59 ± 0.01
9	2.68 ± 0.01	3.85 ± 0.01	7.73 ± 0.02	318.20 ± 0.02
10	17.03 ± 0.02	10.66 ± 0.02	16.49 ± 0.01	1.00 ± 0.01
Urine
1	9.89 ± 0.03	1.93 ± 0.02	6.49 ± 0.01	2.90 ± 0.02
2	3.35 ± 0.01	3.65 ± 0.02	3.66 ± 0.02	17.13 ± 0.03
3	11.70 ± 0.02	4.66 ± 0.01	5.27 ± 0.01	2.73 ± 0.01
4	14.49 ± 0.01	16.56 ± 0.02	8.65 ± 0.02	24.03 ± 0.02
5	10.53 ± 0.01	10.91 ± 0.03	18.47 ± 0.01	4.73 ± 0.01
6	1.70 ± 0.03	6.17 ± 0.02	2.40 ± 0.03	20.19 ± 0.03
7	4.20 ± 0.01	22.68 ± 0.01	17.34 ± 0.02	29.10 ± 0.01
8	4.49 ± 0.02	26.98 ± 0.03	9.27 ± 0.01	4.20 ± 0.02
9	9.81 ± 0.01	15.07 ± 0.01	1.69 ± 0.03	39.74 ± 0.03
10	4.96 ± 0.02	81.40 ± 0.03	28.73 ± 0.01	73.73 ± 0.01

show the results obtained for 40 biological samples, including whole blood, urine, saliva, and tumoral tissue. The samples were analyzed accordingly with the stochastic method described above using the 3D stochastic sensors. The data that were acquired with the 3D stochastic sensors were found to have extremely strong correlations with one another across all of the different types of samples. The paired t-test was carried out with a confidence level of 99.00% (the theoretical t-value that was calculated was 4.032). The fact that all of the t-values that were generated were lower than 3, demonstrates that there is no statistically significant difference between the findings that were achieved by employing the stochastic sensors that were presented.

By correlating the obtained results and calculated statistical parameters, it can be observed that the proposed stochastic sensors can be reliably employed to simultaneously determine CA12-5, CA72-4, HER-1, and AFP from biological samples without requiring any sample processing. Therefore, they can be reliably utilized in gastric cancer screening tests.

In addition, further validation tests were carried out, including recovery testing of CA12-5, CA72-4, HER-1, and AFP in whole blood, saliva, urine, and tumoral tissue samples. These tests were carried out using the 3D stochastic sensors. After determining the initial concentrations of HER-1, CA72-4, AFP, and CA12-5 in samples of tumoral tissue, whole blood, saliva, and urine, different amounts of these substances were added to each type of sample. These amounts ranged from very small to higher amounts, and they were calculated to be located within the working concentration range of each of the sensors. Following this, new measurements were carried out. A comparison was made between the quantity that was added and the amount of HER-1, CA72-4, AFP, and CA12-5 that was discovered in samples of tumoral tissue, saliva, and urine, as well as whole blood.

Table 5. Recovery of CA12-5, CA72-4, AFP, and HER-1 in biological samples using the 3D stochastic sensors.

Sensor	Sample	% Recovery
		CA12-5	CA72-4	AFP	HER-1
OL/nGR	Whole blood	99.85 ± 0.01	99.99 ± 0.02	99.76 ± 0.02	99.83 ± 0.04
OL/GR		99.90 ± 0.01	99.97 ± 0.01	99.95 ± 0.02	99.90 ± 0.02
OL/G		99.81 ± 0.02	99.80 ± 0.01	99.91 ± 0.01	99.87 ± 0.03
OL/nGR	Urine	99.80 ± 0.01	99.76 ± 0.02	99.83 ± 0.03	99.76 ± 0.01
OL/GR		99.91 ± 0.01	99.93 ± 0.03	99.90 ± 0.02	99.95 ± 0.01
OL/G		99.87 ± 0.02	99.90 ± 0.01	99.15 ± 0.02	99.07 ± 0.01
OL/nGR	Saliva	99.23 ± 0.01	99.03 ± 0.02	99.14 ± 0.01	99.27 ± 0.02
OL/GR		99.15 ± 0.02	99.81 ± 0.01	99.03 ± 0.04	99.02 ± 0.03
OL/G		98.98 ± 0.01	98.17 ± 0.03	98.75 ± 0.03	99.13 ± 0.01
OL/nGR	Tumoral tissue	99.19 ± 0.02	98.97 ± 0.01	99.00 ± 0.03	98.95 ± 0.01
OL/GR		99.03 ± 0.01	99.85 ± 0.02	99.37 ± 0.02	99.40 ± 0.01
OL/G		99.03 ± 0.01	99.90 ± 0.02	98.99 ± 0.02	99.13 ± 0.02

contains the findings that were obtained.

When the test of CA12-5, CA72-4, HER-1, and AFP was performed, very high values were recorded. These values were higher than 99.50% when determined in whole blood, higher than 98.00% when recorded from saliva, and higher than 99.00% when determined from urine and from tumoral tissue.

In every case, the RSD values were less than 0.10%. One may get the conclusion that CA12-5, CA72-4, HER-1, and AFP can be identified with a high degree of accuracy and precision from biological materials such as whole blood, saliva, urine, and tumoral tissue by using 3D stochastic sensors. This conclusion is based on the paired t-test as well as the recovery test.

The following equations were used in order to reach the LOQ and LOD values that were computed in accordance with the criteria provided by the ICH guidelines: LOQ is equal to 10 s/m, and LOD is equal to 3 s/m. In these equations, s represents the standard deviation of the peak current of the blank (five measurements), and m illustrates the slope of the calibration curve. The analytical response of the sensors is similar or even better compared to that of other analytical techniques (

Table 6. Comparison of the proposed method with previously reported analytical methods for the detection of CA12-5, CA72-4, AFP, and HER-1.

Sensor	Analyte	Methode	LOD	Reference
nanoMIPs/SPEs	cTnI	HTM	1.0 × 10−11 g L−1	[37]
MIP@AuSPE	CA12-5	SWV	0.1 U mL−1	[38]
Ru(bpy)32+/Apt@GO	CA12-5	EC	9.1 × 10−2 U mL−1	[39]
NiPc@PS	CA12-5	(FS)	1.0 × 10−4 U mL−1	[40]
PEC biosensor	AFP	PEC	6.22 × 10−12 g mL−1	[41]
EMIP	AFP	-	7.5 × 10−14 g mL−1	[42]
rGO-AuNPs@MXene	AFP	DPV	1.391 × 10−14 g mL−1	[43]
ATG-CEA-Ab2&AFW-CA72-4- Ab2/CEA-Ag&CA72-4-Ag/CEA-Ab1&CA72-4-Ab1/IMBs/MGCE	CA72-4	DPV	1.6 × 10−2 U mL−1	[44]
rGO-TEPA/ZIF67@ZIF8/Au and the AuPdRu	CA72-4	Chronoamperometry (I-T)	1.8 × 10−5 U mL−1	[45]
GIP	CA72-4	DPV	4.1 × 10−3 U mL−1	[46]
AuNP-Ab-HRP/SPGE	HER1	Amperometry	3.00  × 10−11 g mL−1	[47]
IDCE	HER1	immunofluorescence	5.00  × 10−12 g mL−1	[48]
NH2-GO/THI/AuNP/SPE	HER1	DPV	5.00  × 10−12 g mL−1	[49]
OL/GR	CA12-5	Chronoamperometry	2.79 × 10−13 U mL−1	This work
OL/G	CA12-5	Chronoamperometry	2.79 × 10−14 U mL−1	This work
OL/nGR	CA12-5	Chronoamperometry	2.79 × 10−11 U mL−1	This work
OL/GR	AFP	Chronoamperometry	1.00 × 10−20 g mL−1	This work
OL/G	AFP	Chronoamperometry	1.00 × 10−20 g mL−1	This work
OL/nGR	AFP	Chronoamperometry	1.00 × 10−20 g mL−1	This work
OL/GR	CA72-4	Chronoamperometry	1.33 × 10−16 U mL−1	This work
OL/G	CA72-4	Chronoamperometry	1.33 × 10−11 U mL−1	This work
OL/nGR	CA72-4	Chronoamperometry	1.33 × 10−14 U mL−1	This work
OL/GR	HER1	Chronoamperometry	1.30 × 10−15 g mL−1	This work
OL/G	HER1	Chronoamperometry	1.30 × 10−16 g mL−1	This work
OL/nGR	HER1	Chronoamperometry	1.30 × 10−15 g mL−1	This work

nanoMIPs/SPEs = molecularly imprinted polymer nanoparticles/screen-printed graphite electrodes; cTnI = cardiac biomarker troponin I; HTM = heat-transfer method; SWV = square wave voltammetry; FS = fluorescence spectroscopy; PEC = photoelectrochemical bioanalysis; EMIP = epitope molecularly imprinted electrochemical sensor; rGO-AuNPs@MXene= reduced graphene oxide-gold nanoparticle-MXene composite, ATG-CEA-Ab₂&AFW-CA72-4-Ab₂/CEA-Ag&CA72-4-Ag/CEA-Ab₁&CA72-4-Ab₁/IMBs/MGCE = gold nanoparticles–toluidine blue–graphene oxide-Carcinoembryonic antigen-secondary antibody& gold nanoparticles–carboxyl ferrocene–tungsten disulfide-Carbohydrate antigen 72-4 secondary antibody/CEA antigen& CA72-4 antigen/Anti-CEA primary antibody&biotinylated Anti-CA72-4 primary antibody/biotin immune magnetic beads/magnetic glassy carbon electrode; rGO-TEPA/ZIF67@ZIF8/Au and the AuPdRu = reduced graphene-tetraethylenepentamine/zeolite-imidazolic acid/gold nanoparticles&gold platinum ruthenium trimetal nanoparticles; DPV = differential pulse voltammetry; GIP = Glycosyl-imprinted electrochemical sensor, AuNP-Ab-HRP/SPGE = gold nanoparticles- monoclonal antibodies- horse radish peroxidase/screen-printed gold electrode; IDCE = interdigitated capacitor electrode; NH2-GO/THI/AuNP/SPE = Amino-functionalized graphene/thionine/gold particle nanocomposites/screen-printed electrode; OL = oleamide; GR = graphene; G = graphite; nGR = nano graphene.

).

4. Conclusions

The proposed stochastic sensors have shown high reliability for molecular recognition and quantification of CA12-5, CA72-4, HER-1, and AFP in whole blood, tumor tissue, urine, and saliva from patients with stomach cancer. The sensors exhibited high sensitivities, low limits of quantification, and wide concentration ranges. Conducting an investigation of several biomarkers, especially those associated with illnesses or malignancies, is crucial for initiating therapeutic therapy and early detection of diseases. The main features of the sensors are to introduce them into clinics’ routine analysis and protocols, after their validation in hospitals with more than 200 samples.