Cancer is a major cause of death worldwide
[1]. Based on a World Health Organization (WHO) Report, new cancer cases are increasing at an alarming rate from 10 million new cases globally in 2000 to 20 million in 2021, with 10 million deaths
[2]. At present, more than 90% of cancer deaths result from the metastasis of primary cancer tumors, and failure in the early diagnosis of cancers is a direct cause of this high mortality rate
[3]. Hence, a significant challenge in molecular oncology is early diagnosis
[4]. Early and effective cancer detection is critical to facilitate timely treatments and improve the survival rate of patients, since most treatment strategies generate more successful results with smaller-size tumors. The development of enhanced detection approaches based on interdisciplinary research is critical to facilitating the development of new and improved early cancer detection technologies. The identification of biomarkers at ultra-low levels during the early stages of the disease and the development of molecular probes that bind to these biomarkers is a successful method for effective diagnosis and accurate pre-treatment staging of the cancer
[5]. To date, various methods have been applied for biomarker detection, such as electrochemistry
[5], electrochemiluminescence (ECL)
[6] and inexpensive detection techniques for biomarkers
[7][8][9][10][11]. Most of the sensors developed for biomarkers’ and various cancer cells’ detection rely on antigen–antibody interactions
[12]. It is well known that antibodies, as a major class of biomolecular probes, can specifically bind to tumor cell biomarkers, but immunogenicity and peptidase susceptibility limit their theranostic value
[13][14][15][16]. The development of a combinatorial chemistry-based assay termed the systematic evolution of ligands by exponential enrichment (SELEX) has provided an alternative, yielding oligonucleotides, called aptamer, which can be selected to specifically bind various target molecules
[17][18][19][20][21][22][23] as well as cell membranes through cell-SELEX
[24][25]. Owing to their significant advantages, such as high sensitivity, simplicity, rapid response, reusability, and low cost, aptamer-based electrochemical biosensors have received considerable attention as a promising approach for clinical diagnostics
[26]. Electrochemical detection methods are based on either redox indicators or label-free detection. Most of the methods based on redox indicators involve tedious modification or immobilization techniques, which are often time-consuming, costly, and, more importantly, may affect the affinity of the aptamer. Hence, label-free aptasensors present a promising strategy.
2. Redox-Active Molecules
A simple way to generate an electrochemical signal is through the use of redox-active labels
[27][28]. Using this strategy, aptamers can be incorporated to develop enhanced aptasensors. Aptamers can fold their flexible single-stranded chains into three-dimensional (3D) structures upon binding to a target molecule and can easily be immobilized on a conductive surface. These features enable redox-active molecules to be anchored to aptamers, allowing for the identification of the formation of aptamer–target complexes by probing the electron transfer features of the redox probes of rigidified complexes
[29]. Generally, redox-active molecule-based electrochemical aptasensors include two subclasses: “signal-on” or “signal-off”. Due to the conformational change in aptamers in the signal-on mechanism, redox-active molecules are brought close to the electrode surface, and removed from the electrode surface (
Figure 1a–c)
[30].
Figure 1. Label-based electrochemical aptasensor. (a) The redox-active label Aptamer–Aptamer Duo strategy. (b,c) The redox-active label Aptamer–Antibody strategies. (d,e) The redox-active label Aptamer-switching and replacement strategies. (f,g) Enzyme-based label electrochemical aptasensor strategies. Ab: antibody, RCA: rolling circle amplification.
Recently, a signal-on electrochemical differential pulse voltammetry (DPV) aptasensor that detects mucin 1 (MUC1) was reported
[31]. The approach combines a dual signal amplification strategy of poly (o-phenylene diamine)–gold nanoparticles (PoPD–AuNPs) hybrid film as a carrier, along with gold nanoparticles-functionalized silica/multi-walled carbon nanotubes core-shell nanocomposites (AuNPs/SiO2@MWCNTs) as a tracing tag.
The PoPD–AuNPs film provides an appropriate substrate to stabilize the primary aptamer, and the AuNPs/SiO2@MWCNT improves the surface area to immobilize the secondary aptamer, as well as to load large amounts of redox-active probe thionine. When MUC1 is introduced to the assay, the sandwich-type recognition reacts on the aptasensors’ surface, and the Thi-AuNPs/SiO2@MWCNTs nanoprobes are captured onto the electrode surface. AuNPs and MWCNTs accelerate the electron transfer from Thi to the electrode, thus amplifying the detection response. This proposed method has detected MUC1 at rates as low as 1 pM. This aptasensor also showed great reproducibility, with a value of 2.8% RSD at 40 nM of MUC1 with long-time stability at 4 °C. In another study, Zhu et al.
[32] reported an electrochemical stripping voltammetry biosensor for the detection of both human epidermal growth factor receptor 2 (HER2) protein and SK-BR-3 breast cancer cells, which takes advantage of hydrazine and aptamer-conjugated gold nanoparticles. A sensor recognition element was immobilized onto a nanocomposite layer, which was prepared from self-assembled 2,5-bis (2-thienyl)-1H-pyrrole-1-(p-benzoic acid) (DPB) on gold nanoparticles. A hydrazine/AuNP/aptamer bioconjugate was utilized to reduce silver ions for signal amplification. Here, hydrazine reduces silver ion to silver metal, and is bound to AuNPs to provide a bioconjugate of hydrazine/AuNP/aptamer (Hyd/AuNP/Apt), where the aptamer specifically binds to breast cancer cell biomarkers. In the presence of biomarkers or cancer cells, a sandwich structure was formed on the surface of the electrode. Finally, by introducing silver ions, hydrazine reduced the silver ions to silver metal. After that, silver metal deposits onto the Hyd/AuNP/Apt bioconjugate and reacts with biomarkers or cancer cells. The deposited silver is then quantified via stripping voltammetry. The method showed a detection limit of 26 cells/mL for the detection of breast cancer cells in human serum. The reproducibility was reported with a standard deviation of less than 5% for the detection of HER2.
An electrochemical DPV aptasensor based on a signal-off strategy was reported by Qu et al.
[33] for the detection of circulating tumor cells in blood cells. In their work, two cell-specific aptamers, TLS1c and TLS11a, which recognize BNL 1ME A.7R.1 liver cancer cells (MEAR), were simultaneously conjugated to the surface of a glassy carbon electrode. These aptamers were coupled to the electrode surface using controlled linkers: TLS1c through a single-stranded DNA linker and TLS11a through a double-stranded DNA linker. The ss-TLS1c/ds-TLS11a design showed improved sensitivity for the effective recognition of cancer cells in comparison to other designs. with electrodes modified by a single type of aptamer or by dual-type aptamers. The specificity and sensitivity of the designed aptasensors were investigated using a DPV technique with [Fe(CN)6]
3−/4− as the redox indicator. The aptasensor detected cancer cells of as low as a single MEAR cell within 1 × 10
9 whole-blood cells (WBC). While this approach is suitable for the highly sensitive detection of tumor cells, their long-term use and efficiency cannot be ascertained without reproducibility and stability information being reported. Liu and coworkers developed a square wave voltammetry (SWV) aptasensor for the detection of TNF-α in complex media, which mimicked the human blood
[34]. The principle of the biosensor operation is based on conformational changes in the aptamer. When the target binds to the aptamer, the distance between the redox reporter and electrode changes so that a detectable electrochemical signal is produced. The aptasensor detected TNF-α with high sensitivity in spiked whole blood. The aptasensor delivered a detection limit of 10 ng/mL and a linear range of 100 ng/mL for TNF-α in whole blood. Overall, redox-active molecules provide stability to aptamers and enhance the surface area for immobilization, which allows for high-reliability nonvolatile application in electrochemical-based detections. In the above studies, both the signal-on and signal-off strategies provide good specificity, sensitivity, and acceptable reproducibility, demonstrating that redox-active molecules can be used as an electrochemical signaling strategy for cancer diagnostics.
3. Enzyme-Based Aptasensors
Although the application of redox-active molecules is a simple method to generate an electrochemical signal, electrochemical aptasensors suffer from low sensitivity
[28]. Therefore, the development of signal-amplification strategies to enhance sensitivity is critical.
To date, a wide variety of amplification strategies have been designed. Among them, enzymes (biocatalysts) show the advantage of enhancing through enzymatic electrochemical processes (
Figure 1f,g)
[35][36][37]. For example, Ravalli et al.
[35] described an enzyme-amplified electrochemical DPV aptasensor for the detection of vascular endothelial growth factor (VEGF), a well-known biomarker associated with the diagnosis of different types of cancer. The aptasensor was fabricated based on a gold-nanostructured, graphite, screen-printed electrode using alkaline phosphatase as an enzyme label. Two different DNA aptamers were utilized to complete a sandwich format. First, the primary thiolated aptamer was self-assembled onto the electrode, followed by the incubation of the VEGF protein on the aptasensor. After this, an enzyme detection strategy based on the coupling of a streptavidin–alkaline phosphatase conjugate and the secondary aptamer was applied, and, finally, an electro-inactive substrate was introduced to the aptasensor. The enzyme-catalyzed transformation of the substrate led to a product that is electroactive and can be detected using the DPV technique. The aptasensor detected VEGF at rates as low as 30 nmol/ L with a dynamic range of 0 and 250 nmol/L. The average coefficient of variation was around 6% and the aptasensor signal was unaffected in the presence of other interfering proteins, providing good reproducibility and selectivity, respectively. In another recently reported piece of research, an electrochemical DPV aptasensor based on hybrid enzyme and nanomaterials was developed for the detection of human liver hepatocellular carcinoma (HepG2) cells
[36]. For this purpose, an aptamer/cell/nanoprobe sandwich format was fabricated onto the AuNPs modified glassy carbon electrode surface using a whole-cell aptamer as a recognition element and electrochemical nanoprobe. A thiolated TLS11a aptamer was attached to the electrode surface via a gold-thiol bond to capture HepG2 cells. Electrochemical nanoprobes are constructed using the G-quadruplex/hemin/aptamer complexes and horseradish peroxidase (HRP) immobilized on the surface of Au@Pd core-shell nanoparticle-modified magnetic Fe3O4/MnO2 beads (Fe3O4/MnO2/Au@Pd). The hybrid Fe3O4/MnO2/Au@Pd nano-electrocatalysts, G-quadruplex/heminHRP-mimicking DNAzymes, and HRP enzyme efficiently enhanced the electrochemical signals. The detection limit of this electrochemical aptasensor was 15 cells/ mL. It also demonstrated an acceptable reproducibility, in addition to being regenerated two more times without significant loss of sensitivity. The enzyme-based strategy provides more rapid and enhanced signaling compared to redox-active molecules due to the high and efficient electron transfer by enzymes. However, enzymes could have limitations, with instability during usage in sensor devices, a low temperature being required for storage, and the nonspecific oxidation (or reduction) of redox-active interferences on the electrode.
4. Nanomaterials-Based Aptasensors
Owing to the unique characteristics of nanomaterials, such as their small size, increased surface-to-volume ratio, biocompatibility, and chemical stability, along with the excellent selectivity of aptamers as recognition elements, the combination of nanomaterials and aptamers can promote new innovations for the detection of cancer cells
[38]. Different strategies have been described to conjugate aptamers with nanomaterials
[39] (
Figure 2 and
Table 1). Nanomaterials can be utilized as either supporting substrates for immobilizing ligands or as labeling probes for signal amplification. Importantly, aptamer-conjugated-nanoparticles (Apt-NP) can be detected using electrochemical techniques, depending on their physical and/or chemical properties
[40]. Recently, an amplified electrochemical DPV biosensor was reported, based on an aptamer/antibody (Apt/Ab) sandwich format, for the detection of epidermal growth factor receptor (EGFR), a cancer biomarker
[41]. Here, a capture probe was designed by immobilizing a biotinylated anti-human EGFR Apt onto streptavidin-coated magnetic beads. On the other side, an anti-human EGFR antibody was conjugated to gold nanoparticles to be utilized as a signaling probe. When a sample containing EGFR was introduced to the magnetic bead-Apt system, EGFR was captured in the Apt–EGFR–Ab sandwich. Subsequently, a DPV of gold nanoparticles was used for the detection of EGFR. The detection limit and dynamic concentration range of the sensor were 50 pg/mL and 1–40 ng/mL, respectively. In addition, less than 4.2% RSD was reported as the reproducibility value.
Figure 2. Schematic presentation of nanomaterials-based Aptasensors. (a) Nanomaterial–Aptamer Duo sandwich-type aptasensors. (b) Encapsulated nanomaterials “Bio-gate” aptasensors. (c,d) Graphene oxide/nanotubes–electroactive aptasensors.
Table 1. Various Electrochemical Aptasensors for cancer detection applications.
Cancer Type
|
Target
|
Technique
|
Sample
|
Assay Time
|
LOD
|
Linear Range
|
Reference
|
Breast Cancer
|
EGFR
|
DPV
|
Serum
|
30 min
|
50 pg/mL
|
1–40 ng/mL
|
[41]
|
ER
|
DPV
|
Buffer
|
10 min
|
0.001 ng/mL
|
0.001–1000 pg/µL
|
[42]
|
Exosomes
|
CV
|
buffer
|
1 h
|
96 particles/μL.
|
1.12 × 102–1.12 × 108 particles/μL
|
[43]
|
Exosomes (MCF-7 cells)
|
ECL
|
Blood serum sample
|
120 min
|
7.41 × 104 particle/mL
|
3.4 × 105 –1.7 × 108 particle/mL
|
[44]
|
HER2
|
stripping voltammetry
|
Human serum
|
20 min
|
26 cells/mL
|
50 to 20,000 cells/mL
|
[32]
|
HER2
|
EIS
|
Buffer
|
-
|
0.047 pg/mL
|
0.01 to 5 ng/mL
|
[45]
|
HER2
|
CV, EIS
|
Serum
|
2 h
|
1 pM
|
1 pM–100 nM
|
[46]
|
HER2
|
EIS
|
Serum sample
|
40 min
|
50 fg/mL
|
0.1 pg/mL–1 ng/mL
|
[43]
|
HER2
|
CV, DPV, EIS
|
PBS buffer
|
5–10 min
|
0.001 ng/mL
|
0.001–100 ng/mL
|
[47]
|
MCF-7
|
CC, CV, EIS
|
Serum
|
25 min
|
47 cells/mL
|
0–500 cells/mL
|
[48]
|
MCF-7
|
SWV, CV
|
Human plasma
|
2 h
|
328 cells/mL
|
328–593 cells/mL
|
[49]
|
MCF-7
|
CV, DPV
|
Human serum
|
60 min
|
20 cells/mL
|
50–106 cells/mL
|
[50]
|
MCF-7 Exosomes
|
PEC
|
Buffer
|
110 min (total)
|
1.38 × 103 particles/μL
|
5.00 × 103 to 1.00 × 106 particles/mL
|
[51]
|
MDA-MB-231
|
DPV
|
Blood Serum
|
30 min
|
5 cell/ mL
|
10–1 × 103 cell/mL
|
[52]
|
MUC1
|
DPV
|
Serum sample
|
25 min
|
0.79 fM
|
1 fM–100 nM
|
[53]
|
MUC1
|
SWV, CV
|
Buffer
|
1 h
|
0.33 pM
|
1.0 pM–10 µM
|
[54]
|
MUC-1
|
EIS
|
PBS buffer
|
2 h
|
38 cells/mL
|
100 to 5.0 × 107 cells/mL
|
[27]
|
Nucleolin
|
DPV
|
Buffer
|
1 h
|
8 ± 2 cells ml/mL
|
10–106 cells/mL
|
[55]
|
Nucleolin
|
ECL
|
Buffer
|
10 min
|
10 cells
|
10–100 cells
|
[56]
|
Nucleolin
|
EIS
|
Buffer
|
-
|
40 cells/mL
|
103–107 cells/mL
|
[57]
|
Nucleolin
|
CV, EIS
|
Phosphate buffer
|
30 min
|
4 cells/mL
|
1 × 101–1 × 106 cells/mL
|
[58]
|
OPN
|
CV, SWV
|
Synthetic human plasma
|
60 min
|
1.3 ± 0.1 nM
|
CV: 25 to 100 nM
SWV: 12 to 100 nM
|
[59]
|
OPN
|
CV
|
PBS buffer
|
60 min
|
3.7 ± 0.6 nM
|
25–200 nM
|
[60]
|
PDGF-BB,
MCF-7 cells
|
CV, SWV
|
PBS buffer
|
-
|
PDGF-BB: 0.52 nM
MCF-7: 328 cells/mL
|
PDGF: 0.52–1.52 nM
MCF-7: 328 to 593 cells/mL
|
[49]
|
Lung Cancer
|
CEA, NSE
|
CV, DPV
|
Serum
|
1 h
|
CEA: 2 pg/mL
NSE: 10 pg/mL
|
CEA: 0.01–500 ng/mL
NSE: 0.05–500 ng/mL
|
[61]
|
CEA
|
DPV, EIS
|
Human serum
|
85 min (total)
|
1.5 pg/mL
|
5 pg/mL to 50 ng/mL
|
[62]
|
CEA
|
EIS
|
Buffer, serum
|
-
|
Buffer: 0.45 ng/mL
Serum: 1.06 ng/mL
|
0.77–14 ng/mL
|
[63]
|
Lung tumor
|
EIS
|
Blood plasma
|
~25 min
|
-
|
-
|
[64]
|
Lung cancer tissues (proteins)
|
SWV
|
Blood plasma
|
1 h
|
0.023 ng/mL
|
230 ng/mL to 0.023 ng/mL
|
[65]
|
VEGF165
|
CV, EIS
|
Lung cancer Serum samples
|
40 min
|
1.0 pg/mL
|
10.0–300.0 pg/mL
|
[66]
|
Lung cancer tumor
|
CV, DPV, SWV, EIS
|
Human blood
|
-
|
-
|
-
|
[14]
|
Lung/Breast/ others cancer
|
VEGF
|
DPV
|
Buffer
|
45 min
|
30 nmol/L
|
0–250 nmol/L
|
[35]
|
CEA
|
DPV
|
Spiked Serum
|
50 min
|
0.9 pg/mL
|
3 pg/mL to 40 ng/mL
|
[29]
|
CEA
|
DPV, EIS, CV
|
Human serum
|
1 h
|
0.34 fg/mL
|
0.5 fg/mL to 0.5 ng/mL
|
[38]
|
CEA
|
DPV, CV, EIS
|
Serum
|
1 h
|
0.31 pg/mL
|
1 pg/mL–80 ng/mL
|
[67]
|
CEA
|
EIS
|
Buffer/Blood sample
|
1 h 30 min
|
0.5 pg/mL
|
1 pg/mL–10 ng/mL
|
[68]
|
CEA
|
DPV
|
Buffer
|
1 h
|
40 fg/mL
|
0.0001–10 ng/mL
|
[69]
|
CEA
|
PES
|
Serum
|
60 min
|
0.39 pg/mL
|
0.001–2.5 ng/mL
|
[70]
|
VEGF165
|
CV
|
Buffer
|
1 h
|
30 fM
|
100 fM to 10 nM
|
[71]
|
MUC 1
|
CV, SWV, EIS
|
Buffer
|
120 min
|
4 pM
|
10 pM to 1 μM
|
[72]
|
CEA
|
CV, EIS
|
Buffer
|
1 h
|
3.4 ng/mL
|
5 ng/mL–40 ng/mL
|
[73]
|
CEA
|
CV
|
PBS/spiked human serum
|
40 min
|
6.3 pg/mL
|
50 pg/mL to 1.0 μg/mL
|
[11]
|
CEA
|
DPV
|
Buffer/spiked human serum
|
45 min
|
0.84 pg/mL
|
10 pg/mLto 100 ng/mL
|
[74]
|
CEA and CA153
|
PEC
|
Serum samples
|
20 min
|
CEA: 2.85 pg/mL
CA153: 0.0275 U/mL
|
CEA: 0.005–10 ng mL, CA153: 0.05–100 U/mL
|
[75]
|
Prostate Cancer
|
PSA
|
EIS
|
Buffer
|
2 h
|
0.5 pg/mL
|
0.05 ng/mL to 50 ng/mL
|
[5]
|
PSA
|
EIS
|
Buffer
|
2 h (total)
|
1 pg/mL
|
1 × 102 pg/mL–1 × 102 ng/mL
|
[76]
|
PSA
|
DPV
|
Serum samples
|
40 min
|
0.25 ng/ mL
|
0.25 to 200 ng/mL
|
[77]
|
PSA
|
SWV, EIS
|
Spiked human serum
|
-
|
EIS: 10 pg/mL
|
EIS: 10 pg/mL to 10 ng/mL
|
[78]
|
PSA
|
DPV
|
Blood serum
|
30 min
|
50 pg/mL
|
0.125 to 128 ng/mL
|
[79]
|
PSA
|
PEC
|
Human serum
|
-
|
0.34 pg/mL
|
0.001 to 80 ng/mL
|
[80]
|
PSA
|
DPV
|
Human serum
|
30 min
|
0.064 pg/mL
|
1 pg/mL to 100 ng/mL
|
[81]
|
PSA
|
DPV, EIS
|
Serum
sample
|
40 min
|
1.0 pg/ mL
|
DPV: 0.005–20 ng/mL
EIS: 0.005–100 ng/mL
|
[82]
|
PSA
|
EIS
|
Human serum
|
2 h 30 min
|
0.33 pg/mL
|
5 to 2 × 104 pg/mL
|
[83]
|
PSA
|
CV, SWV, EIS
|
Buffer
|
30 min
|
0.028 * and 0.007 ** ng/mL
|
0.5–7 ng/mL
|
[84]
|
PSA
|
PEC
|
PBS buffer/ spiked Serum
|
40 min
|
4.300 fg/mL
|
1.000 × 10−5 to 500.0 ng/mL,
|
[85]
|
PSA
|
SWV, EIS
|
Serum sample
|
4 h (total)
|
2.3 fg/mL
|
10 fg/mL–100 ng/mL
|
[86]
|
PSA
|
PEC
|
Human serum
|
90 min
|
0.52 pg/mL
|
1.0
pg/mL to 8.0 ng/mL
|
[87]
|
PSA
|
ECL
|
Human serum
|
60 min
|
0.17 pg/mL
|
0.5 pg/mL to 5.0 ng/mL
|
[88]
|
PSA
|
DPV
|
Spiked Urine Blood serum
|
60 min
|
280 pg/mL
|
1 to 300 ng/mL
|
[89]
|
PSA
|
DPV
|
Human serum
|
30 min
|
6.2 pg/mL
|
0.01–100 ng/mL
|
[90]
|
PSA, SAC
|
SWV
|
50%
Human serum
|
PSA: 2 h
SAC: 1 h
|
PSA: 2.5 fg/mL, SAC: 14.4 fg/mL
|
PSA: 1 fg/mL to 500 ng/mL
SAC: 1 fg/mL to 1 μg/mL
|
[91]
|
Blood cell cancer
|
Ramos cell
|
LSV
|
Human serum
|
3 h
|
10 cells/mL
|
1 × 101–1 × 106 cell/mL
|
[37]
|
Breast/ Liver cancer
|
HeLa, MCF-7, HepG2.
|
PEC
|
Buffer
|
4 h 20 min (total)
|
19 cell/mL (HeLa)
|
50–5 × 105 cell/mL (HeLa)
|
[92]
|
Breast/ Prostate cancer
|
CTC
HER2, PSMA, and MUC1
|
LSW
|
Spiked in Blood
|
1 h
|
2 cells/sensor
|
2–200
cells/sensor
|
[93]
|
PDGF-BB
|
DPV
|
PBS buffer
|
40 min
|
0.65 pM
|
0.0007–20 nM
|
[94]
|
PDGF-BB
|
CV, EIS
|
ID water, 5% trehalose
|
40 min
|
CV: 7 pM
EIS: 1.9 pM
|
CV: 0.01–50 nM
EIS: 0.005–50 nM
|
[95]
|
PDGF-BB
|
DPV
|
PBS buffer
|
2 h
|
0.034 pg/ mL
|
0.0001 to 60 ng/mL
|
[96]
|
PDGF-BB
|
EIS
|
PBS buffer
|
2 h
|
0.82 pg/ mL
|
1 pg/mL to 0.05 ng/mL
|
[97]
|
CAT
|
HER2
|
EIS, CV
|
Diluted human serum
|
2 h 20 min
(total)
|
15 fM
|
0.1 pM to 20 nM
|
[98]
|
Cervical cancer
|
HeLa
|
EIS
|
Buffer
|
2 h
|
90 cells/mL
|
2.4 × 102–2.4 × 105 cells/mL
|
[99]
|
Colon cancer
|
MUC-1
|
EIS, CV
|
Buffer
|
120 min
|
40 cells/mL
|
1.25 × 102–1.25 × 106 cells/mL
|
[100]
|
CEA
|
PES
|
Human serum
|
1 h
|
1.9 pg/mL
|
0.01 ng/mL to 2.5 ng
|
[101]
|
CEA
|
PEC
|
Serum
|
90 min
|
4.8 pg/ mL
|
10.0 pg/mL–5.0 ng/mL
|
[102]
|
inflammation-associated carcinogenesis
|
TNF-α
|
SWV
|
Human blood
|
4 h
|
10 ng/mL
|
10–100 ng/mL
|
[34]
|
Leukemia, blood cancer
|
CCRF-CEM
|
SWV
|
Buffer
|
40 min
|
10 cells/mL
|
1.0 × 102–1.0 × 106 Cells/mL
|
[103]
|
K562 cells
|
EIS
|
Buffer
|
40 min
|
30 cells/mL
|
1 × 102–1 × 107 cells/mL
|
[104]
|
Liver cancer
|
HepG2
|
EIS
|
Buffer
|
2 h
|
2 cells/mL
|
1 × 102–1 × 106 cells/mL
|
[22]
|
HepG2
|
DPV, CV, EIS
|
PBS buffer
|
60 min
|
15 cells/mL
|
1 × 102–1 × 107 cell/mL
|
[36]
|
MEAR
|
DPV, CV, EIS
|
Diluted human blood
|
60 min (Total)
|
1 cell/mL
|
1−14 Cells/mL
|
[33]
|
HepG2
|
CV
|
buffer
|
2 h
|
2 cells/mL
|
1 × 102–1 × 106 cells/mL
|
[22]
|
AFP
|
EIS
|
PBS/ diluted human serum
|
30 min
|
0.3 fg/mL
|
1 fg/mL to 100 ng/mL
|
[105]
|
Abbreviations: SWV: square wave voltammetry, PEC: photoelectrochemical, AFP: alpha-fetoprotein, CEA: carcinoembryonic antigen, HepG2: human liver hepatocellular carcinoma, PSA: prostate-specific antigen, MUC 1: Mucin1, HER2: human epidermal growth factor receptor 2, EGFR: epidermal growth factor receptor, MCF-7: breast cancer cell, OPN: osteopontin, VEGF165: vascular endothelial growth factor, MDA-MB-231: breast cancer cell, PSMA: prostate-specific membrane antigen cell line, ER: estrogen receptor, CCRF-CEM: human T lymphoblasts, SAC: sarcosine, MEAR: BNL 1ME A.7R.1 liver cancer cell line, PDGF-BB: platelet-derived growth factor-BB, CAT: cancer-associated thrombosis. * Total PSA, ** Free PSA.
In another report, taking advantage of the target-binding-induced, structure-switching aptamer and magnetic separation technology, Zhang et al.
[103] developed an electrochemical voltammetric aptasensor for the sensitive detection of acute leukemia cells. The aptasensor utilized the competitive binding of whole-cell aptamers to Human T lymphoblasts (CCRF-CEM) cells with the voltammetric quantification of silver ions. A synergistic strategy was applied through dual-signal amplification using magnetic nanoparticles with a high loading of gold nanoparticles and a AuNP-catalyzed silver deposition. The described aptasensor (cytosensor) showed a detection limit of as low as 10 cells and a reproducibility value of 3.8% with acceptable stability for 30 days when stored at 4 °C. A research study described a nanoparticle-based, multi-marker strategy using a linear sweep voltammetry (LSV) technique for the identification of circulating tumor cells (CTC)
[93]. In this approach, an electrochemical chip with multiple sensors was designed to capture cancer cells based on an epithelial marker. Subsequently, Cu, Ag, and Pd nanoparticles were introduced as marker-specific reporters that were modified with antibodies or aptamers via electrostatic binding and a thiol/metal bond, respectively, for the detection of cancer cell biomarkers to provide electrochemical detection. The electrochemical assay enabled the measurement of the oxidation signal of the metal nanoparticles for the simultaneous detection of different cancer cells. The electrochemical biochip detected cancerous biomarkers of as low as two cells per sensor and simultaneously measured three different cancer cells. Another nanomaterial strategy was to apply an electrochemical aptasensor using DPV and EIS techniques for the detection of carcinoembryonic antigen (CEA) using dendritic Pt@Au nanowires (Pt@AuNWs)
[67]. Dendritic Pt@AuNWs were utilized as nanocarriers to immobilize thiol-labeled CEA aptamer2 and a redox tag toluidine blue (Tb), to form the AuNWs-CEAapt2-Tb bioconjugate. In the presence of CEA, the bioconjugate was captured onto the surface of the electrode via a sandwich strategy. The electrochemical signal was achieved through the catalysis capacity of dendritic Pt@AuNWs towards the decomposition of H2O2, which was added to the electrolytic cell. This aptasensor showed a linear dynamic range of from 0.001 to 80 ng/mL and a detection limit of 0.31 pg/mL. Additionally, the aptasensor had an acceptable reproducibility value of 5.6% RSD and retained its sensitivity capacity after 10 days of storage at 4 °C. In conclusion, nanomaterials are excellent signaling transducers that provide a high surface area, electrical and electro-chemical properties, allowing aptamers to recognize targets with great selectivity and sensitivity. Most of the nanomaterial-based biosensors were reproducible and had good stability, but presented challenges regarding their fabrication, conjugation, and cost.