Screening and Biosensor-Based Approaches for Lung Cancer Detection

Ultrasensitive detection of NSE employing a novel electrochemical immunosensor based on a conjugated copolymer

In the current study a simple and highly specific label-free impedimetric neuron specific enolase (NSE) immunosensor based on a copolymer matrix-coated disposable electrode was designed and tested. The copolymer matrix was prepared using a very conductive EDOT monomer and semi-conductive thiophene-bearing epoxy groups (ThEp), and the combination of the two monomers enhanced the conductivity and protein loading capacity of the electrode surface. The P(ThEp-co-EDOT) copolymer matrix was prepared via a drop-casting process and anti-NSE recognition biomolecules were immobilized directly on the epoxy groups of the copolymer. After the coupling of NSE molecules on the P(ThEp-co-EDOT) copolymer matrix-coated electrode surface, the charge transfer resistance (Rct) of the biosensor changed dramatically. These changes in Rct were proportional to the NSE molecule amounts captured by anti-NSE molecules. Under optimized experimental conditions, the increment in the Rct value was proportional to the NSE concentration over a range of 0.01 to 25 pg mL-1 with a detection limit (LOD) of 2.98 × 10-3 pg mL-1. This copolymer-coated electrode provided a lower LOD than the other biosensors. In addition, the suggested electrochemical immuno-platform showed good selectivity, superior reproducibility, long-term stability, and high recovery of NSE in real serum (95.64-102.20%) and saliva (95.28-105.35%) samples. These results showed that the present system had great potential for electrochemical biosensing applications.

Biosensing of DNA through difference in interaction between microparticle and glass plate based on particle dissociation in a coupled acoustic-gravitational field

A novel approach for detecting DNA without labeling the target DNA was developed based on the particle dissociation behavior in a combined acoustic-gravitational field. The particles, which are tethered on a glass plate via intermolecular interactions (Fbind), are dissociated by the resultant force of the acoustic radiation force (Fac), which is a function of the applied voltage (V), and the sedimentation force. In this system, V required for particle dissociation is dependent on Fbind. The differences in Fbind were exploited for detecting the target DNA. A glass plate and polystyrene (PS) particles were respectively modified with anchor and capture DNAs. The target DNA induces immobilization of the PS particles on the glass plate through sandwich hybridization, with a large accompanying Fbind. In the absence of the target DNA, the anchor DNA on the glass plate interacted weakly with the capture DNA on the PS particles via direct binding (small Fbind). The particle dissociation behavior varies based on the concentration of the target DNA due to changes in the ratio of the PS particles tethered through direct binding and sandwich hybridization. Target DNA with a length exceeding 12 base pairs (bps) can be detected on the picomolar scale at concentrations of 10-12 to 10-5 M. This detection scheme was applied to a specific sequence of HIV-2 with 20 bps, achieving a picomolar detection limit.

MicroRNA biosensors in lung cancer

Lung cancer has been one of the leading causes of death over the past century. Unfortunately, the reliance on conventional methods to diagnose the phenotypic properties of tumors hinders early-stage cancer diagnosis. However, recent advancements in identifying disease-specific nucleotide biomarkers, particularly microRNAs, have brought us closer to early-stage detection. The roles of miR-155, miR-197, and miR-182 have been established in stage I lung cancer. Recent progress in synthesizing nanomaterials with higher conductivity has enhanced the diagnostic sensitivity of electrochemical biosensors, which can detect low concentrations of targeted biomarkers. Therefore, this review article focuses on exploring electrochemical biosensors based on microRNA in lung cancer.

Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis

Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.

Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging

One of the leading causes of cancer deaths is esophageal cancer (EC) because identifying it in early stage is challenging. Computer-aided diagnosis (CAD) could detect the early stages of EC have been developed in recent years. Therefore, in this study, complete meta-analysis of selected studies that only uses hyperspectral imaging to detect EC is evaluated in terms of their diagnostic test accuracy (DTA). Eight studies are chosen based on the Quadas-2 tool results for systematic DTA analysis, and each of the methods developed in these studies is classified based on the nationality of the data, artificial intelligence, the type of image, the type of cancer detected, and the year of publishing. Deeks' funnel plot, forest plot, and accuracy charts were made. The methods studied in these articles show the automatic diagnosis of EC has a high accuracy, but external validation, which is a prerequisite for real-time clinical applications, is lacking.

Development of a QCM-based biosensor for the detection of non-small cell lung cancer biomarkers in liquid biopsies

Lung cancer is the main malignant cancer reported worldwide, with one of the lowest survival rates. Deletions in the Epidermal Growth Factor Receptor (EGFR) gene are often associated with non-small cell lung cancer (NSCLC), a common subtype of lung cancer. The detection of such mutations provides key information for the diagnosis and treatment of the disease; therefore, the early screening of such biomarkers is of vital importance. The need for fast, reliable, and early detection means applied to NSCLC has led to the development of highly sensitive devices that can detect cancer-associated mutations. Such devices, known as biosensors, are a promising alternative to more conventional detection methods and can potentially alter the way cancer is diagnosed and treated. In this study, we report the development of a DNA-based biosensor, namely a quartz crystal microbalance (QCM), applied to the detection of NSCLC, from liquid biopsies samples. The detection, as is the case of most DNA biosensors, is based on the hybridization between the NSCLC-specific probe and the sample DNA (containing specific mutations associated with NSCLC). The surface functionalization was performed with a blocking agent (dithiothreitol) and thiolated-ssDNA strands. The biosensor was able to detect specific DNA sequences in both synthetic and real samples. Aspects such as reutilization and regeneration of the QCM electrode were also studied.

Diagnostic test accuracy of artificial intelligence-based imaging for lung cancer screening: A systematic review and meta-analysis

BACKGROUND

Lung cancer is the principal cause of cancer-related deaths worldwide. Early detection of lung cancer with screening is indispensable to reduce the high morbidity and mortality rates. Artificial intelligence (AI) is widely utilised in healthcare, including in the assessment of medical images. A growing number of reviews studied the application of AI in lung cancer screening, but no overarching meta-analysis has examined the diagnostic test accuracy (DTA) of AI-based imaging for lung cancer screening.

OBJECTIVE

To systematically review the DTA of AI-based imaging for lung cancer screening.

METHODS

PubMed, EMBASE, Cochrane Library, CINAHL, IEEE Xplore, Web of Science, ACM Digital Library, Scopus, PsycINFO, and ProQuest Dissertations and Theses were searched from inception to date. Studies that were published in English and that evaluated the performance of AI-based imaging for lung cancer screening were included. Two independent reviewers screened titles and abstracts and used the Quality Assessment of Diagnostic Accuracy Studies-2 tool to appraise the quality of selected studies. Grading of Recommendations Assessment, Development, and Evaluation to diagnostic tests was used to assess the certainty of evidence.

RESULTS

Twenty-six studies with 150,721 imaging data were included. Hierarchical summary receiver-operating characteristic model used for meta-analysis demonstrated that the pooled sensitivity for AI-based imaging for lung cancer screening was 94.6 % (95 % CI: 91.4 % to 96.7 %) and specificity was 93.6 % (95 % CI: 88.5 % to 96.6 %). Subgroup analyses revealed that similar results were found among different types of AI, region, data source, and year of publication, but the overall quality of evidence was very low.

CONCLUSION

AI-based imaging could effectively detect lung cancer and be incorporated into lung cancer screening programs. Further high-quality DTA studies on large lung cancer screening populations are required to validate AI's role in early lung cancer detection.

Sensitive Electrochemical Biosensor for Rapid Screening of Tumor Biomarker TP53 Gene Mutation Hotspot

Rapid and sensitive detection of cancer biomarkers is crucial for cancer screening, early detection, and improving patient survival rate. The present study proposes an electrochemical gene-sensor capable of detecting tumor related TP53 gene mutation hotspots by self-assembly of sulfhydryl ended hairpin DNA probes tagged with methylene blue (MB) onto a gold electrode. By performing a hybridization reaction with the target DNA sequence, the gene-sensor can rearrange the probe's structure, resulting in significant electrochemical signal differences by differential pulse voltammetry. When the DNA biosensor is hybridized with 1 μM target DNA, the peak current response signal can decrease more than 60%, displaying high sensitivity and specificity for the TP53 gene. The biosensor achieved rapid and sensitive detection of the TP53 gene with a detection limit of 10 nmol L-1, and showed good specific recognition ability for single nucleotide polymorphism (SNP) and base sequence mismatches in the TP53 gene affecting residue 248 of the P53 protein. Moreover, the biosensor demonstrated good reproducibility, repeatability, operational stability, and anti-interference ability for target DNA molecule in the complex system of 50% fetal bovine serum. The proposed biosensor provides a powerful tool for the sensitive and specific detection of TP53 gene mutation hotspot sequences and could be used in clinical samples for early diagnosis and detection of cancer.

Application of Particle Swarm Optimization with Simulated Annealing in MIT Regularization Image Reconstruction

Background and Objectives: Due to the soft-field effect of the electromagnetic field and the limit of detection, image reconstruction of magnetic induction tomography has to recover more complex electrical characteristics from very few signals. These cause a problem which have underdetermination, nonlinearity, and ill-posed characteristics, and therefore lead to many difficulties in finding its solution. Although many regularization image reconstruction methods exist, they are not suitable for MIT applications due to regularization parameter selection. The purpose of this paper is to study the principle of particle swarm optimization with simulated annealing, and to propose a regularization method for reconstruction, which will provide a new way to solve the MIT image problems. Methods and Models: Firstly, the regularization principle of image reconstruction of MIT will be analyzed. Then the hybrid regularization algorithm, including Tikhonov and NOSER regularization, will be developed, using the dimension of the Hessian matrix as a penalty term respecting the prior knowledge. PSO-SA algorithm will be applied to obtain an optimal solution for regularization parameters. Finally, six typical numerical models and approximately symmetrical cerebral hemorrhage models by COMSOL will be carried out, and the voltage signals obtained from the simulation will be used to verify the proposed reconstruction method. Results: Through the simulation results, the proposed imaging method has the average CC values of 0.9932, 0.8286 and the average RE values of 0.4982, 0.8320 for simple and complex models, respectively. Moreover, when the SNR changes from 55dB to 35dB, the CC value of the cerebral hemorrhage model reduced by 0.1034. The results demonstrate the effectiveness and high theoretical feasibility of the proposed method in MIT image reconstruction. Conclusions: This study indicates the potential application of PSO-SA algorithm in regularization imaging problem. Compared with traditional regularization imaging methods, the proposed method has the advantages of better accuracy, robustness and noise resistance, showing the certain application value in other similar ill-ness imaging problems.

Chemical binding of molecular-imprinted polymer to biotinilated antibody: Utilization of molecular imprinting polymer as intelligent synthetic biomaterials toward recognition of carcinoma embryonic antigen in human plasma sample

In this study, a novel biosensor based on molecular imprinting polymer (MIP) methodology was fabricated toward recognition of carcinoembryonic antigen (CEA). For this purpose, poly (toluidine blue) (PTB) was electropolymerized on the surface of gold electrode in the absence and presence of CEA. So, the target molecules were entrapped into the imprinted specific cavities of MIP. Obtained results show that, the binding affinity of the MIP system was significantly higher than that of revealed for the nonimprinted polymer (NIP) system, MIP-based biosensor revealed linear response from (0.005 to 75 μg/L) and low limit of quantification of (0.005 μg/L) by using chronoamperometry technique, leading to CEA monitoring in real and clinical samples. Thus, a novel technique for rapid, simple, sensitive and affordable monitoring of CEA (LLOQ = 0.005 μg/L) has provided through developed biosensor. From a future perspective, moreover, this method can be considered as an applicable candidate in biomedical and clinical analysis for point-of-care usages.

Improved EGFR mutation detection sensitivity after enrichment by Cas9/sgRNA digestion and PCR amplification

The detection of circulating tumor DNA is important in cancer research and clinical practice. In the present study, we aimed to improve the sensitivity of downstream mutation detection of next-generation sequencing using the clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) system to selectively target wild-type fragments but with low or no cleavage activity to mutant fragments, followed by amplification using polymerase chain reaction. We selected different mutant sites of epidermal growth factor receptor gene (EGFR)-exon19 deletions in patients with lung cancer and constructed mixed templates of mutant and wild-type DNA comprising ratios of 10% to 0.01% to test the effectiveness of the enrichment method. The results showed that after CRISPR/Cas9 enrichment, a low concentration of mutant DNA fragments (0.01%) could be detected by Sanger sequencing, which represented a 1000-fold increase compared with the untreated samples. We further verified the feasibility of the introduced method and obtained similar results in clinical samples from patients with non-small cell lung cancer, indicating that this method has the potential to detect low copy number mutations at the early stage.

Aptamer-based electrochemical biosensing strategy toward human non-small cell lung cancer using polyacrylonitrile/polypyrrole nanofibers

In the present study, a sensitive electrochemical aptamer-based biosensing strategy for human non-small cell lung cancer (NSCLC) detection was proposed using nanofiber-modified disposable pencil graphite electrodes (PGEs). The composite nanofiber was comprised of polyacrylonitrile (PAN) and polypyrrole (PPy) polymers, and fabrication of the nanofibers was accomplished using electrospinning process onto PGEs. Development of the nanofibers was confirmed using scanning electron microscopy (SEM). The high-affinity 5'-aminohexyl-linked aptamer was immobilized onto a PAN/PPy composite nanofiber-modified sensor surface via covalent bonding strategy. After incubation with NSCLC living cells (A549 cell line) at 37.5 °C, the recognition between aptamer and target cells was monitored by electrochemical impedance spectroscopy (EIS). The selectivity of the aptasensor was evaluated using nonspecific human cervical cancer cells (HeLa) and a nonspecific aptamer sequence. The proposed electrochemical aptasensor showed high sensitivity toward A549 cells with a detection limit of 1.2 × 10³ cells/mL. The results indicate that our label-free electrochemical aptasensor has great potential in the design of aptasensors for the diagnostics of other types of cancer cells with broad detection capability in clinical analysis. Graphical abstract.

Emerging role of phytochemicals in targeting predictive, prognostic, and diagnostic biomarkers of lung cancer

Lung-cancer is the foremost cause of cancer in humans worldwide, of which 80-85% cases are composed of non-small cell lung carcinoma. All treatment decisions depend on the pattern of biomarkers selection to enhance the response to the targeted therapies. Although advanced treatments are available for lung-cancer, the disease treatment remains not adequate. There are several synthetic chemotherapeutic agents available for the treatment of lung cancer. However, due to their toxic effect, survival rate is still 15-18%. Besides, medicinal plants are a huge reservoir of natural products that provide protective effects against lung cancer. Likewise, successful studies of potential phytochemicals in targeting lung-cancer biomarkers have created a novel paradigm for the discovery of potent drugs against lung-cancer. Hence, to defeat severe toxicity and resistance towards the synthetic drugs, detailed studies are required regarding the available phytochemicals and targets responsible for the treatment of lung-cancer. The present review provides a comprehensive information about the lung-cancer biomarkers under the classification of predictive, prognostic, and diagnostic type. Moreover, it discusses and enlists the phytochemicals with mode of action against different biomarkers, effective doses in in vitro, in vivo, and clinical studies, the limitations associated with usage of phytochemicals as a drug to prevent/cure lung-cancer and the latest techniques employed to overcome such issues.

Multi-Dimensionally Extended Functionalization Innovates to an Entropy-Driven Detection of Multi-miRNAs for One-Step Cancer Screening and Diagnosis in Living Cells

Compared with tedious multi-step detections, multi-functional nanoprobes are effective for one-step screening and diagnosis of cancers by multi-detection of microRNAs (miRNAs). However, limited probe density, spatial mutual interference, and low target-triggered hybridization efficiency of nanoprobes will hinder intracellular applications. Here, for obtaining high loading density but low spatial mutual interference between functional biomolecules on nanoprobes, an extended biofunctionalization in three dimensions (the two-dimensional surface and a special "height" direction) is designed. Therefore, a multi-functional probe is constructed for one-step detection of multi-miRNAs for cancer screening and diagnosis. With linker-bridged multiple single-stranded DNAs swung out rigidly, multi-dimensionally extended upconversion nanorods (ME-UCNRs) covered by chitosan are constructed to load and deliver multiple biomolecules into living cells. Escaping from endolysosomes, ME-UCNRs maintain good biological activities of functionalized DNAs for effective detection of multi-miRNAs in living cells. Thereby, with multiple targets of miRNAs, toehold-mediated entropy-driven strand displacements are employed to give respectively changed fluorescent signals via fluorescence resonance energy transfer. Thus, a universal cancer biomarker of miR-21 and two specific liver-cancer biomarkers (miR-199a and miR-224) are efficiently detected through a one-step detection. By discriminating cancer cells from normal ones and determining liver-cancer cells simultaneously, this work innovates an efficient and definite one-step strategy for fast screening and early cancer diagnosis.

Perfluorosulfonic acid polymer based eATRP for ultrasensitive detection of CYFRA21-1 DNA

The sensitive detection of biomarker cytokeratin fragment antigen 21-1 (CYFRA21-1) is crucial for early diagnosis and screening of non-small cell lung cancer (NSCLC). In this work, an electrochemical biosensor based on Nafion-initiated eATRP has been built for ultrasensitive detection of CYFRA21-1 DNA for the first time. Specifically, peptide nucleic acid (PNA) probes are immobilized onto a gold electrode surface and then hybridized with target DNA to form PNA/DNA heteroduplexes for the subsequent attachment of Nafion by the identified carboxyl-Zr4+-phosphoric acid chemistry. Finally, polymer chains are obtained by linking the monomer of ferrocenylmethyl methacrylate to the PNA/MCH/DNA/Zr4+/Nafion probes via eATRP. Under optimized steady-state conditions, the sensor offers a wide current response for CYFRA21-1 DNA from 10-11 to 10-16 M with a detection limit of 6.42 × 10-17 M. The proposed method of using Nafion as the eATRP initiator exhibits high sensitivity, reproducibility and stability and is a promising strategy for early diagnosis of NSCLC.

Non-invasive Biodiversified Sensors: A Modernized Screening Technology for Cancer

Background:

Biological sensors revolutionize the method of diagnoses of diseases from early to final stages using the biomarkers present in the body. Biosensors are advantageous due to the involvement of minimal sample collection with improved specificity and sensitivity for the detection of biomarkers.

Methods:

Conventional biopsies restrict problems like patient non-compliance, cross-infection and high cost and to overcome these issues biological samples like saliva, sweat, urine, tears and sputum progress into clinical and diagnostic research for the development of non-invasive biosensors. This article covers various non-invasive measurements of biological samples, optical-based, mass-based, wearable and smartphone-based biosensors for the detection of cancer.

Results:

The demand for non-invasive, rapid and economic analysis techniques escalated due to the modernization of the introduction of self-diagnostics and miniature forms of devices. Biosensors have high sensitivity and specificity for whole cells, microorganisms, enzymes, antibodies, and genetic materials.

Conclusion:

Biosensors provide a reliable early diagnosis of cancer, which results in faster therapeutic outcomes with in-depth fundamental understanding of the disease progression.

Correction: Screening and Biosensor-Based Approaches for Lung Cancer Detection. Sensors 2017, 17, 2420

The authors wish to make the following corrections to this paper [...].

F-containing initiatior for ultrasensitive fluorescent detection of lung cancer DNA via atom transfer radical polymerization

An ultrasensitive fluorescence method for early diagnosis of lung cancer via Nafion-initiated atom transfer radical polymerization (ATRP) is reported, in this paper. In the proposed method, thiolated peptide nucleic acid (PNA) is modified to amino magnetic beads (MBs) via a cross-linking agent to specifically capture target DNA (tDNA), and the initiator (Nafion) of ATRP is attached to PNA/DNA heteroduplexes based on the phosphate groups of the tDNA and sulfonate groups of Nafion via phosphate-Zr⁴⁺-sulfonate chemistry. Nafion as a macroinitiator of ATRP possesses multiple C-F active sites to initiate polymerization, and numerous polymeric chains that significantly amplify the fluorescent signal are formed. Under optimal conditions, a good linear relationship is obtained in the range of 0.1 nM-0.1 fM with correlation coefficients of 0.9975, and the detection limit is as low as 35.5 aM (∼214 molecules). The proposed strategy has several advantages of simplicity, cost-effectiveness, selectivity and sensitivity. More importantly, the anti-interference results demonstrate that the proposed Nafion-initiated ATRP strategy has great potential in bioanalytical applications.

DNA origami-based aptasensors

Traditional analytical techniques face many limitations such as time-consuming process, complicated sample preparation, high consumption of reagents and need for expensive equipment. So, it is important that simple, rapid and sensitive detection methods are introduced. Nucleic acids-based assays, particularly aptamers, have a great impact on modern life sciences for biological analysis and target detection. Aptamer-based biosensors with unique recognition properties including high specificity and affinity, rapid response and simple fabrication have attracted much attention. It is believed that two- and three-dimensional structures, sometimes referred to as DNA origami, using DNA aptamers can show more selective binding affinity and better stability over other nucleic acids forms. In this review, we will focus on recent advances in the development and uses of electrochemical and optical DNA origami-based aptasensors to supply readers with a comprehensive understanding of their improvements. Also, the challenges and awards of these approaches are discussed.

Identification of tumor-educated platelet biomarkers of non-small-cell lung cancer

BACKGROUND

Lung cancer is a severe cancer with a high death rate. The 5-year survival rate for stage III lung cancer is much lower than stage I. Early detection and intervention of lung cancer patients can significantly increase their survival time. However, conventional lung cancer-screening methods, such as chest X-rays, sputum cytology, positron-emission tomography (PET), low-dose computed tomography (CT), magnetic resonance imaging, and gene-mutation, -methylation, and -expression biomarkers of lung tissue, are invasive, radiational, or expensive. Liquid biopsy is non-invasive and does little harm to the body. It can reflect early-stage dysfunctions of tumorigenesis and enable early detection and intervention.

METHODS

In this study, we analyzed RNA-sequencing data of tumor-educated platelets (TEPs) in 402 non-small-cell lung cancer (NSCLC) patients and 231 healthy controls. A total of 48 biomarker genes were selected with advanced minimal-redundancy, maximal-relevance, and incremental feature-selection (IFS) methods.

RESULTS

A support vector-machine (SVM) classifier based on the 48 biomarker genes accurately predicted NSCLC with leave-one-out cross-validation (LOOCV) sensitivity, specificity, accuracy, and Matthews correlation coefficients of 0.925, 0.827, 0.889, and 0.760, respectively. Network analysis of the 48 genes revealed that the WASF1 actin cytoskeleton module, PRKAB2 kinase module, RSRC1 ribosomal protein module, PDHB carbohydrate-metabolism module, and three intermodule hubs (TPM2, MYL9, and PPP1R12C) may play important roles in NSCLC tumorigenesis and progression.

CONCLUSION

The 48-gene TEP liquid-biopsy biomarkers will facilitate early screening of NSCLC and prolong the survival of cancer patients.

Early detection of lung cancer biomarkers through biosensor technology: A review

Lung cancer is undoubtedly one of the most serious health issues of the 21 st century. It is the second leading cause of cancer-related deaths in both men and women　worldwide, accounting for about 1.5 million deaths annually. Despite advances in the treatment of lung cancer with new pharmaceutical products and technological improvements, morbidity and mortality rates remains a significant challenge for the cancer biologists and oncologists. The vast majority of lung cancer patients present with advanced-stage of pathological process that ultimately leads to poor prognosis and a five-year survival rate less than 20%. Early and accurate screening and analysis using cost-effective means are urgently needed to effectively diagnose the disease, improve the survival rate or to reduce mortality and morbidity associated with lung cancer patients. Thus, the only hope for early recognition of risk factors and timely diagnosis and treatment of lung cancer is biosensors technology. Novel biosensing based diagnostics approaches for predicting metastatic risks are likely to have significant therapeutic and clinical impact in the near future. This article systematically provides a brief overview of various biosensing platforms for identification of lung cancer disease biomarkers, with a specific focus on recent advancements in electrochemical and optical biosensors, analytical performances of different biosensors, challenges and further research opportunities for routine clinical analysis.