Emerging Biosensing Methods to Monitor Lung Cancer Biomarkers in Biological Samples: A Comprehensive Review

Abstract

Lung cancer is the most commonly diagnosed of all cancers and one of the leading causes of cancer deaths among men and women worldwide, causing 1.5 million deaths every year. Despite developments in cancer treatment technologies and new pharmaceutical products, high mortality and morbidity remain major challenges for researchers. More than 75% of lung cancer patients are diagnosed in advanced stages, leading to poor prognosis. Lung cancer is a multistep process associated with genetic and epigenetic abnormalities. Rapid, accurate, precise, and reliable detection of lung cancer biomarkers in biological fluids is essential for risk assessment for a given individual and mortality reduction. Traditional diagnostic tools are not sensitive enough to detect and diagnose lung cancer in the early stages. Therefore, the development of novel bioanalytical methods for early-stage screening and diagnosis is extremely important. Recently, biosensors have gained tremendous attention as an alternative to conventional methods because of their robustness, high sensitivity, inexpensiveness, and easy handling and deployment in point-of-care testing. This review provides an overview of the conventional methods currently used for lung cancer screening, classification, diagnosis, and prognosis, providing updates on research and developments in biosensor technology for the detection of lung cancer biomarkers in biological samples. Finally, it comments on recent advances and potential future challenges in the field of biosensors in the context of lung cancer diagnosis and point-of-care applications.

Keywords: DNA methylation; aptasensors; biosensors; lung cancer; lung cancer biomarkers; microRNA; point of care testing; self-health monitoring.

Figure 3

(A) Determination of methylated levels of cancer-related genes from the CCP-fluorescein FRET pair. The chemical structures of the donor CCP polymer and the acceptor fluorescein are represented. (B) The FRET efficiency in the presence of different levels of methylated DNA: (i) low level of methylated DNA, (ii) moderate level of methylated DNA, and (iii) high level of methylated DNA. Fluorescence emission spectra were recorded from a Hitachi F-4500 fluorometer exciting the samples at 380 nm. (C) The level of methylation in 35 RASSF1A promoter cancer samples and 11 healthy samples were analyzed from the CCP-based FRET technique. Adapted from ref. [58], with copyright permission under the terms of the CC-BY-NC-ND 3.0 license. (D) The methylation levels of FAM-oligo-Au DNA duplex are indicated from the fluorescence spectra of (1 mg/mL) upon enzymatic reaction of a series of concentrations of M.SssI MTase enzyme (a–h: 0, 0.5, 1, 2, 4, 6, 8, 10 U mL). The insert represents the linear relationship of fluorescence intensity against M.SssI MTase enzyme concentration. (E) Change in the fluorescence signal of (1 mg/mL) FAM-oligo-Au ssDNA probe duplexed with variable concentrations of methylated DNA targets (a–g: 0 pM, 5 pM, 10 pM, 20 pM, 40 pM, 70 pM, 100 pM). The insert represents the linear relationship of fluorescence intensity against methylated DNA concentration. Adapted from [55], with copyright permission under the terms of the CC-BY-NC-ND 3.0 license.

Scheme 1

Schematic representation of lung cancer induced by various tumor-causing agents and the detection of cancer by biosensing methodologies. (A) Variable cancer-causing agents such as smoke, pollution, exposure to radiation, and genetic effect lead to liver cancer; as a result, many biomarkers are produced at the usual levels and released in the body fluids. (B) Liver-cancer-associated biomarkers including methylated DNA, microRNAs, and genes are listed. Biosensors are developed based on the recognition of biomarkers by the bioreceptors, which are integrated with the transducer and the signal amplification systems. (C) Different types of bioreceptors can recognize the biomarkers and transfer the signal to a (D) signal amplifier that amplifies the signal to a readable format (generated from biorenders.com, accessed on 12 June 2023).

Figure 1

(A) Schematic representation of methylated DNA detection by multiple signal amplification system for biosensing application. (B) Verification of signal amplification from the fabricated biosensor. (C) Change in the chronoamperometric signals for control (dot), unmethylated target (dash), and methylated target (solid); (D) variation in the chronoamperometric signals in the presence of methylated DNA in the range of 0 aM to 100 M (top to bottom). (E) The dose-dependent variation in the signal with different target concentrations. A linear relationship between the chronoamperometric signals and the logarithm of target concentrations in the range of 1 aM to 1 pM is shown in the insert. R represents the correlation coefficient and the error bars were obtained from more than one replicate. Adapted from [47], with copyright permission under the terms of the CC-BY-NC-ND 3.0 license.

Figure 2

(A) A pictorial representation of methylated adenomatous polyposis coli (APC) sequence detection mechanism: (i) in the first step, methylated APC target sequence was captured by immobilized MMPs; (ii) a partial complementary to the target probe was introduced to form partial dsDNA duplex; (iii) the captured probe dissociated from the hybrid-conjugated microspheres by heat denaturation; (iv) AuNp was used as a probe to detect the released target sequences. There is no significant change in the solution (red) after NaCl was added. (v) Since there are no methylated APC target sequences, and the functionalized MMPs did not capture the unmethylated APC and there was no duplex formation, therefore, the color of the solution changed from red to purple rapidly. (B) Colorimetric detection of methylated APC sequence and the change in the absorption spectra of AuNps with different concentrations of APC in the range of 80 fM to 80 pM. (C) Calibration plot of absorbance ratio against the target. The error bar indicates the standard deviation of the results from four different experiments. Adopted from [51], with copyright permission under the terms of the CC-BY-NC-ND 3.0 license.

Figure 4

(A). The schematic representation of LRET between the donor and the acceptor for adenosine sensor consists of terbium complex conjugated with DNA aptamer; (B) steady-state emission spectra of adenosine aptasensor in the presence of adenosine in 100% serum; (C) change in the time-resolved (a) and steady-state (b) emission intensity of the aptasensor at 545 nm versus different concentration of adenosine in 100% serum with the delay time of 50 μs. The insert represents the linear relationship of luminescence response against adenosine concentration in the low concentration range. (D) The cross-reactivity of aptasenor in the presence of 5 mM nucleosides in serum based on time-resolved (a) and steady-state (b) emission measurement. Adapted from ref. [99], with copyright permission under the terms of the CC-BY-NC-ND 3.0 license.

Figure 5

(A) Water-dispersible bulk MoS+; (B) Li intercalated MoS₂ (LixMoS₂); (C) MoS₂ in a single layer; (D) single-layered carboxyl-MoS₂ nanocomposites; (E) the carboxylated-MoS₂ SPR chip; (F) SPR sensing mechanism for the detection of CYFRA21-1 from carboxylated-MoS₂ SPR chip. SPR sensorgram of CYFRA21-1 spiked in human serum measured from carboxylated MoS₂ SPR chip; (G) Dose-dependent sensorgram of SPR for different serum concentrations. (H) The SPR signal for variable serum ratios. (I) Quantitative detection of CYFRA21-1 protein spike in S5 buffer with different concentrations from SPR signal changes; (J) The specificity test of the SPR sensor with non-specific biomolecules. Adapted from [114].

Figure 6

(A) Schematic representation of bead-based array detection method for the detection of three different lung cancer biomarkers using QDs. (A) Illustration of bead-based sandwich assay; (B) three different colored spots after sandwich complex on the chip. (C) The chip pattern; (D) SEM image of the microarray on a silicon wafer. (height = 9 μm); (E) SEM image of micro holes on PDMS with a diameter of 10 μm and spaced 10 μm apart from each other; (F) fluorescence microscopic image of the three different lung cancer biomarkers for simultaneous detection. The concentration range of antigen was 0.46–1000 ng/mL with an exposure time of 0.4 s; (G) the plot of fluorescence intensity against the logarithmic concentrations of CYRFA 21-1, NSE, and CEA in the multiplexed assays; (H) the standard calibration plot for the three cancer biomarkers. Adapted from [115], with copyright permission under the terms of the CC-BY-NC-ND 3.0 license.

Figure 7

(A) Principle and operation mechanism of SERS paper-based lateral flow strip (PLFS). (i) Top view and side view of PLFS; (ii) before and after detection of target protein; (iii) pictures of PLFS cassettes with positive and negative results; (B) pictures of colorimetric PLFS and calibration plots for detection of IgG (0~50 µg/mL (i) in PBS buffer and (ii) PBS and blood plasma ratio of 90:10; (iii) calibration curve in PBS (black) and a mixture of PBS and blood plasma (red).; (C) (i) SERS spectra (ii) SERS-PLFS with MBA as Raman reporter for detection. Insert: calibration plot obtained from the Raman intensity against NSE concentration. Adapted from [208], with copyright permission under the terms of the CC-BY-NC-ND 3.0 license.

Figure 8

(A) Illustration of the fluorescent biosensor working principle for small-cell lung cancer biomarker detection; (B) dose-dependent fluorescence response of anti-NSE/amine-N-GQDs@AuNps nanoprobe as a function of NSE concentration in the range of 0.1 pg mL⁻¹ to 1000 ng/mL from bottom to top); (C) the calibration plot obtained by plotting log concentration of NSE and PL intensity of the nanoprobe indicating the linear relationship; (D) control experiment of amine-N-GQDs@AuNps performed without anti-NSE antibody; (E) cross-reactivity test of anti-NSE/amine-N-GQDs@AuNps nanoprobe with other potential interfering biomolecules; (F) change in the fluorescence response of anti-NSE/amine-N-GQDs@AuNps nanoprobe with the NSE spiked serum samples (7, 10, 20, 30, 50, and 70 ng/mL); (G) the calibration plot of spiked serum samples shows the linear relationship of log concentration of NSE against PL intensity of the nanoprobe. Adapted from [214], with copyright permission.