Recent advances in label-free optical, electrochemical, and electronic biosensors for glioma biomarkers

Abstract

Gliomas are the most commonly occurring primary brain tumor with poor prognosis and high mortality rate. Currently, the diagnostic and monitoring options for glioma mainly revolve around imaging techniques, which often provide limited information and require supervisory expertise. Liquid biopsy is a great alternative or complementary monitoring protocol that can be implemented along with other standard diagnosis protocols. However, standard detection schemes for sampling and monitoring biomarkers in different biological fluids lack the necessary sensitivity and ability for real-time analysis. Lately, biosensor-based diagnostic and monitoring technology has attracted significant attention due to several advantageous features, including high sensitivity and specificity, high-throughput analysis, minimally invasive, and multiplexing ability. In this review article, we have focused our attention on glioma and presented a literature survey summarizing the diagnostic, prognostic, and predictive biomarkers associated with glioma. Further, we discussed different biosensory approaches reported to date for the detection of specific glioma biomarkers. Current biosensors demonstrate high sensitivity and specificity, which can be used for point-of-care devices or liquid biopsies. However, for real clinical applications, these biosensors lack high-throughput and multiplexed analysis, which can be achieved via integration with microfluidic systems. We shared our perspective on the current state-of-the-art different biosensor-based diagnostic and monitoring technologies reported and the future research scopes. To the best of our knowledge, this is the first review focusing on biosensors for glioma detection, and it is anticipated that the review will offer a new pathway for the development of such biosensors and related diagnostic platforms.

FIG. 1.

A schematic representation of the pathway of biomarker flow from a brain tumor through the blood–brain barrier into the blood circulation. Reproduced from Müller Bark et al., Br. J. Cancer 122, 295–305 (2020). Copyright 2020 Springer Nature.

FIG. 2.

Schematic illustration of (a) functionalization of TiN surface with anti-CD63 ABs for detection of U251 glioma-derived exosomes, (b) SPR response of the TiN-anti-CD63 biosensor and (c) its sensor calibration curve for detection of exosomal protein CD63. (d) Sensor calibration curve for detection of exosomal protein EGFRvIII. Reprinted with permission from Qiu et al., Adv. Funct. Mater. 29, 1806761 (2019). Copyright 2019 John Wiley and Sons, Inc.

FIG. 3.

Schematic illustration of (a) exosomes derived from the blood serum and CSF of a GBM mouse model and (b) its ultrasensitive detection using TiN nanohole LSPR biosensor. SPR phase response of the biosensor for exosomes derived from [(c)–(e)] blood serum and [(f)–(h)] CSF, [(c) and (f) for EGFRvIII, [(d) and (g)] for CD44, and [(e) and (h)] for CD163. Reprinted with permission from Thakur et al., Biosens. Bioelectron. 191, 113476 (2021). Copyright 2021 Elsevier.

FIG. 4.

Schematic representation of (a) TiO₂-CTFE-AuNI sensing chip and its (b) LSPR phase responses against BIGH3 protein from glioma-derived exosomes, and (c) sensor calibration curve. (d) Schematic illustration of the Ag@AuNI chip functionalized with anti-MCT4 antibodies along with their calibration curves for detection of MCT4 protein from (e) U87 derived exosomes and (f) blood serum derived exosomes. Schemes (a) and (c) are reprinted with permission from Xu et al., Chem. Eng. J. 415, 128948 (2021). Copyright 2021 Elsevier. Schemes (d)–(f) are reprinted with permission from Liu et al., Chem. Eng. J. 446, 137383 (2022). Copyright 2022 Elsevier.

FIG. 5.

(a) Illustration of the fabrication process of CoNi-MOF based biosensor for miR-126 detection. (b) EIS Nyquist plots for detection of miRNA-126 at different concentrations (1 fM–10 nM) and (c) calibration curve of the CoNi-MOF based biosensor with the inset as the linear fit with respect to logarithm of miR-126 concentration. Reprinted with permission from Hu et al., Appl. Surf. Sci. 542, 148586 (2021). Copyright 2021 Elsevier.

FIG. 6.

(a) Working principle of the SiNW-array FET biosensor. (b) Stepwise process of fabrication of SiNW-array FET biosensor. (c) Carboxyl groups activation by EDC/NHS. (d) The real-time response and (e) calibration curve of the SiNW-array FET biosensor for different concentrations of ctDNA. (f) The real-time response and (g) calibration curve of the SiNW-array FET biosensor for different concentrations of ctDNA in human serum. (g) Device architecture of the extended gate OFET biosensor for sensitive detection of GFAP. Schemes (a)–(g) are reprinted with permission from Li et al., Biosens. Bioelectron. 181, 113147 (2021). Copyright 2021 Elsevier. Scheme (h) is reprinted with permission from Song et al., Adv. Funct. Mater. 27, 1606506 (2017). Copyright 2017 John Wiley and Sons, Inc.