Hairpin-Empowered Invasive Reaction Combined with Catalytic Hairpin Assembly Cascade Amplification for the Specific Detection of Single-Nucleotide Polymorphisms.

Single-nucleotide polymorphism (SNP) is widely used in the study of disease-related genes and in the genetic study of animal and plant strains. Therefore, SNP detection is crucial for biomedical diagnosis and treatment as well as for molecular design breeding of animals and plants. In this regard, this article describes a novel technique for detecting SNP using flap endonuclease 1 (FEN 1) as a specific recognition element and catalytic hairpin assembly (CHA) cascade reaction as a signal amplification strategy. The mutant target (MT) was hybridized with a biotin-modified upstream probe and hairpin-type downstream probe (DP) to form a specific three-base overlapping structure. Then, FEN 1 was employed for three-base overlapping structure-specific recognition, namely, the precise SNP site identification and the 5' flap of DP dissociation. After dissociation, the hybridized probes were magnetically separated by a streptavidin-biotin complex. Especially, the ability to establish such a hairpin-type DP provided a powerful tool that could be used to hide the cut sequence (CS) and avoid false-positive signals. The cleaved CS initiated the CHA reaction and allowed superior fluorescence signal generation. Owing to the high specificity of FEN 1 for single base recognition, only the MT could be distinguished from the wild-type target and mismatched DNA. Owing to the dual signal amplification, as low as 0.36 fM MT and 1% mutation abundance from the mixtures could be detected, respectively. Furthermore, it could accurately identify SNPs from human cancer cells, as well as soybean leaf genome extracts. This strategy paves the way for the development of more precise and sensitive tools for diagnosing early onset diseases as well as molecular design breeding tools.

Rationally Designed Dual Base Pair Mismatch Enables Toehold-Mediated Strand Displacement to Efficiently Recognize Single-Nucleotide Polymorphism without Enzymes.

The efficiency of the enzyme-free toehold-mediated strand displacement (TMSD) technique is often insufficient to detect single-nucleotide polymorphism (SNP) that possesses only single base pair mismatch discrimination. Here, we report a novel dual base pair mismatch strategy enabling TMSD biosensing for SNP detection under enzyme-free conditions when coupled with catalytic hairpin assembly (CHA) and fluorescence resonance energy transfer (FRET). The strategy is based on a competitive strand displacement reaction mechanism, affected by the thermodynamic stability originating from rationally designed dual base pair mismatch, for the specific recognition of mutant-type DNA. In particular, enzyme-free nucleic acid circuits, such as CHA, emerge as a powerful method for signal amplification. Eventually, the signal transduction of this proposed biosensor was determined by FRET between streptavidin-coated 605 nm emission quantum dots (605QDs, donor) and Cy5/biotin hybridization (acceptor, from CHA) when incubated with each other. The proposed biosensor displayed high sensitivity to the mutant target (MT) with a detection concentration down to 4.3 fM and led to high discrimination factors for all types of mismatches in multiple sequence contexts. As such, the application of this proposed biosensor to investigate mechanisms of the competitive strand displacement reaction further illustrates the versatility of our dual base pair mismatch strategy, which can be utilized for the creation of a new class of biosensors.

The Early Diagnostic Value of Procalcitonin in Pneumonia After Off-Pump Coronary Artery Bypass Grafting Surgery.

BACKGROUND The incidence of early postoperative pneumonia (EPOP) after off-pump coronary artery bypass grafting surgery (CABG) is relatively high, but its diagnosis by traditional methods remains difficult, which could be deleterious to the prognosis. Moreover, few data exist regarding procalcitonin (PCT) in early diagnosis of pneumonia after off-pump CABG. Thus, this study was performed to evaluate the value of PCT in diagnosing EPOP after off-pump CABG. MATERIAL AND METHODS A total of 402 consecutive patients undergoing off-pump CABG were retrospectively enrolled. Forty-four patients were diagnosed with EPOP and 112 patients were diagnosed with systemic inflammatory response syndrome (SIRS). Chest roentgenogram, serum PCT, white blood cells, neutral granulocyte ratio, and daily maximum body temperature were recorded. The ability of PCT to diagnose EPOP was evaluated by receiver operating characteristic (ROC) analyses in comparison with traditional methods. Clinical net benefits were estimated via decision curve analysis (DCA). RESULTS PCT presented satisfying accuracy in diagnosing EPOP with a cutoff value of 1.585 ng/mL (area under the curve [AUC] 0.808, 95% confidence interval [CI] 0.724-0.891, sensitivity 73%, specificity 86%). PCT performed better in diagnosing EPOP among SIRS patients (AUC 0.868, 95% CI 0.748-0.988, sensitivity 85%, specificity 89%). DCA showed valuable clinical net benefits of PCT in diagnosing EPOP after off-pump CABG regardless of threshold selected. CONCLUSIONS PCT could be a diagnostic marker for EPOP after off-pump CABG. The optimal cutoff value for diagnosing EPOP was 1.585 ng/mL. The application of PCT in diagnosing EPOP in SIRS patients was also satisfying with a cutoff value of 1.775 ng/mL.

Two-Dimensional Deep Learning Frameworks for Drug-Induced Cardiotoxicity Detection.

The identification of drug-induced cardiotoxicity remains a pressing challenge with far-reaching clinical and economic ramifications, often leading to patient harm and resource-intensive drug recalls. Current methodologies, including in vivo and in vitro models, have severe limitations in accurate identification of cardiotoxic substances. Pioneering a paradigm shift from these conventional techniques, our study presents two deep learning-based frameworks, STFT-CNN and SST-CNN, to assess cardiotoxicity with markedly improved accuracy and reliability. Leveraging the power of induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) as a more human-relevant cell model, we record mechanical beating signals through impedance measurements. These temporal signals were converted into enriched two-dimensional representations through advanced transformation techniques, specifically short-time Fourier transform (STFT) and synchro-squeezing transform (SST). These transformed data are fed into the proposed frameworks for comprehensive analysis, including drug type classification, concentration classification, cardiotoxicity classification, and new drug identification. Compared to traditional models like recurrent neural network (RNN) and 1-dimensional convolutional neural network (1D-CNN), SST-CNN delivered an impressive test accuracy of 98.55% in drug type classification and 99% in distinguishing cardiotoxic and noncardiotoxic drugs. Its feasibility is further highlighted with a stellar 98.5% average accuracy for classification of various concentrations, and the superiority of our proposed frameworks underscores their promise in revolutionizing drug safety assessments. With a potential for scalability, they represent a major leap in drug safety assessments, offering a pathway to more robust, efficient, and human-relevant cardiotoxicity evaluations.

A dual-mode, image-enhanced, miniaturized microscopy system for incubator-compatible monitoring of live cells.

Imaging live cells under stable culture conditions is essential to investigate cell physiological activities and proliferation. To achieve this goal, typically, a specialized incubation chamber that creates desired culture conditions needs to be incorporated into a microscopy system to perform cell monitoring. However, such imaging systems are generally large and costly, hampering their wide applications. Recent advances in the field of miniaturized microscopy systems have enabled incubator cell monitoring, providing a hospitable environment for live cells. Although these systems are more cost-effective, they are usually limited in imaging modalities and spatial temporal resolution. Here, we present a dual-mode, image-enhanced, miniaturized microscopy system (termed MiniCube) for direct monitoring of live cells inside incubators. MiniCube enables both bright field imaging and fluorescence imaging with single-cell spatial resolution and sub-second temporal resolution. Moreover, this system can also perform cell monitoring inside the incubator with tunable time scales ranging from a few seconds to days. Meanwhile, automatic cell segmentation and image enhancement are realized by the proposed data analysis pipeline of this system, and the signal-to-noise ratio (SNR) of acquired data is significantly improved using a deep learning based image denoising algorithm. Image data can be acquired with 5 times lower light exposure while maintaining comparable SNR. The versatility of this miniaturized microscopy system lends itself to various applications in biology studies, providing a practical platform and method for studying live cell dynamics within the incubator.

A novel biomimetic electrochemical taste-biosensor based on conformational changes of the taste receptor.

Taste sensor, a useful tool which could detect and identify thousands of different chemical substances in liquid environments, has attracted continuous concern from beverage and foodstuff industry and its consumers. Although many taste sensing methods have been extensively developed, the assessment of tastant content remains challenging due to the limitations of sensor selectivity and sensitivity. Here we present a novel biomimetic electrochemical taste-biosensor based on bioactive sensing elements and immune amplification with nanomaterials carrier to address above concerns, while taking sweet taste perception as a model. The proposed biosensor based on ligand binding domain (T1R2 VFT) of human sweet taste receptor protein showed human mimicking character and initiated the application of immune recognition in gustation biosensor, which can precisely and sensitively distinguish sweet substances against other related gustation substances with detection limit of 5.1 pM, far less than that of taste sensors without immune amplification whose detection limit was 0.48 nM. The performance test demonstrated the biosensor has the capacity of monitoring the response of sweet substances in real food environments, which is crucial in practical. This biomimetic electrochemical taste-biosensor can work as a new screening platform for newly developed tastants and disclose sweet perception mechanism.

In silico bioactivity prediction of proteins interacting with graphene-based nanomaterials guides rational design of biosensor.

Graphene-based nanomaterials have attracted significant attention for their potentials in biomedical and biotechnology applications in recent years, owing to the outstanding physical and chemical properties. However, the interaction mechanism and impact on biological activity of macro/micro biomolecules still require more concerns and further research in order to enhance their applicability in biosensors, etc. Herein, an integrated method has been developed to predict the protein bioactivity performance when interacting with nanomaterials for protein-based biosensor. Molecular dynamics simulation and molecular docking technique were consolidated to investigate several nanomaterials: C60 fullerene, single-walled carbon nanotube, pristine graphene and graphene oxide, and their effect when interacting with protein. The adsorption behavior, secondary structure changes and protein bioactivity changes were simulated, and the results of protein activity simulation were verified in combination with atomic force spectrum, circular dichroism spectrum fluorescence and electrochemical experiments. The best quantification alignment between bioactivity obtained by simulation and experiment measurements was further explored. The two proteins, RNase A and Exonuclease III, were regarded as analysis model for the proof of concept, and the prediction accuracy of protein bioactivity could reach up to 0.98. The study shows an easy-to-operate and systematic approach to predict the effects of graphene-based nanomaterials on protein bioactivity, which holds guiding significance for the design of protein-related biosensors. In addition, the proposed prediction model is not limited to carbon-based nanomaterials and can be extended to other types of nanomaterials. This facilitates the rapid, simple, and low-cost selection of efficient and biosafe nanomaterials candidates for protein-related applications in biosensing and biomedical systems.

G-quadruplex embedded in semi-CHA reaction combined with invasive reaction for label-free detection of single nucleotide polymorphisms.

G-quadruplex/thioflavin T (G4/THT) is one of the ideal label-free fluorescent light-emitting elements in the field of biosensors due to its good programmability and adaptability. However, the unsatisfactory luminous efficiency of single-molecule G4/THT limits its more practical applications. Here, we developed a G4 embedded semi-catalytic hairpin assembly (G4-SCHA) reaction by rationally modifying the traditional CHA reaction, and combined with the invasive reaction, supplemented by magnetic separation technology, for label-free sensitive detection of single nucleotide polymorphisms (SNPs). The invasive reaction enabled specific recognition of single-base mutations in DNA sequences as well as preliminary signal cycle amplification. Then, magnetic separation was used to shield the false positive signals. Finally, the G4-SCHA was created for secondary amplification and label-free output of the signal. This dual-signal amplified label-free biosensor has been shown to detect mutant targets as low as 78.54 fM. What's more, this biosensor could distinguish 0.01 % of the mutant targets from a mixed sample containing a large number of wild-type targets. In addition, the detection of real and complex biological samples also verified the practical application value of this biosensor in the field of molecular design breeding. Therefore, this study improves a label-free fluorescent light-emitting element, and then proposes a simple, efficient and universal label-free SNP biosensing strategy, which also provides an important reference for the development of other G4/THT based biosensors.

A bulged-type enzyme-free recognition strategy designed for single nucleotide polymorphisms integrating with label-free electrochemical biosensor.

Compared to conventional nucleic acid detection methods, label-free single nucleotide polymorphism (SNP) detection presents challenging due to the necessity of discerning single base mismatches, especially in the field of enzyme-free detection. In this study, we introduce a novel bulged-type DNA duplex probe designed to significantly amplify single-base differences. This probe is integrated with programmable DNA-based nanostructures to develop a sensitive, label-free biosensor for nonenzymatic SNP detection. The duplex probe with one bulge could selectively identify wild-typed DNA (WT) and mutant-type DNA (MT) based on a competitive strand displacement reaction mechanism. The hyperbranched HCR (HHCR) by incorporating of hairpin DNA into the DNA tetrahedron and surface-tethering on the portable screen printing electrode (SPCE) significantly favor the formation of negatively charged DNA nanostructure. We harnessed strong repulsion of DNA nanostructure towards the electroactive [Fe(CN)6]³â»/4⁻ in combination with electrochemical technique to create a label-free biosensor. This simple, enzyme-free and label-free biosensor could detect MT with a detection limit of 56 aM, even in multiple sequence backgrounds. The study served as the proof-of-concept for the integration of enzyme-free competitive mechanism and label-free strategy, which can be extended as a powerful tool to various fields.

Umami evaluation in taste epithelium on microelectrode array by extracellular electrophysiological recording.

Umami is one of the basic tastes along with sweet, bitter, sour and salty. It is often elicited by amino acids and can provide a palatable flavor for food. With taste epithelium as the sensing element, microelectrodes can be used to evaluate umami taste by biological responses of the tissue. The electrophysiological activities to umami stimuli are measured with a 60-channel microelectrode array (MEA). Local field potential (LFP) recorded by a MEA system showed different temporal characteristics respectively with l-glutamic acid (l-Glu), l-aspartic acid (l-Asp), l-monosodium glutamate (l-MSG) and l-monosodium aspartate (l-MSA), while remarkable differences were observed between amino acids and their sodium salts. We also found that a dose-dependent behavior in the increasing concentrations of umami stimulations and a synergistic enhancement between amino acids and purine nucleotides can be detected. The investigation of this evaluation for umami represents a promising approach for distinguishing and evaluating umami tastants.

An integrated label-free cell-based biosensor for simultaneously monitoring of cellular physiology multiparameter in vitro.

The study presented a novel integrated cell-based biosensor with light-addressable potentiometric sensor (LAPS) and electrical cell-substrate impedance sensor (ECIS). The integrated cell-based biosensor was fabricated in order to monitor the cellular metabolism and growth status by LAPS and ECIS. Moreover, the specific instrument was established for controlling the detection processes. Sensor test and cell experiments were carried out to determine the performance of integrated sensor. The result showed that integrated biosensor can monitor the change of cell electrical impedance and extracellular acidification simultaneously which can be used for drug evaluation by monitoring cell growth status (e.g. cell number, adhesion, and morphology) and cell energy metabolism status (e.g. extracellular acidification) in real time. With the development of sensor technology, the integrated cell-based biosensor will be a utility platform to study the mechanism of cellular metabolism and in vitro drug analysis.

A biosensing system employing nonlinear dynamic analysis-assisted neural network for drug-induced cardiotoxicity assessment.

Preclinical investigation of drug-induced cardiotoxicity is of importance for drug development. To evaluate such cardiotoxicity, in vitro high-throughput interdigitated electrode-based recording of cardiomyocytes mechanical beating is widely used. To automatically analyze the features from the beating signals for drug-induced cardiotoxicity assessment, artificial neural network analysis is conventionally employed and signals are segmented into cycles and feature points are located in the cycles. However, signal segmentation and location of feature points for different signal shapes require design of specific algorithms. Consequently, this may lower the efficiency of research and the applications of such algorithms in signals with different morphologies are limited. Here, we present a biosensing system that employs nonlinear dynamic analysis-assisted neural network (NDANN) to avoid the signal segmentation process and directly extract features from beating signal time series. By processing beating time series with fixed time duration to avoid the signal segmentation process, this NDANN-based biosensing system can identify drug-induced cardiotoxicity with accuracy over 0.99. The individual drugs were classified with high accuracies over 0.94 and drug-induced cardiotoxicity levels were accurately predicted. We also evaluated the generalization performance of the NDANN-based biosensing system in assessing drug-induced cardiotoxicity through an independent dataset. This system achieved accuracy of 0.85-0.95 for different drug concentrations in identification of drug-induced cardiotoxicity. This result demonstrates that our NDANN-based biosensing system has the capacity of screening newly developed drugs, which is crucial in practical applications. This NDANN-based biosensing system can work as a new screening platform for drug-induced cardiotoxicity and improve the efficiency of bio-signal processing.

A smart tablet-phone-based system using dynamic light modulation for highly sensitive colorimetric biosensing.

Facile, efficient, and inexpensive biosensing systems are in high demand for biomedical test. In recent years, numerous smartphone-based biosensing systems have been developed to match demand for biomedical test in source-limited environment. However, application of these smartphone-based biosensing systems was limited because of performance gap between the smartphone-based systems and commercial plate readers. In this study, we have developed a smart tablet-phone-based colorimetric plate reader (STPCPR) with intelligent and dynamic light modulation for broad-range colorimetric assays. The STPCPR allows controllable modulation of exciting light in three different color channels that is lack in conventional smartphone-based system. Using optimized exciting modulation, the STPCPR shows higher sensitivities, lower detection limits, and broader detection ranges in test of pigments, proteins, and cells when compared to conventional plate readers and smartphone-based system. Therefore, the developed STPCPR can serve as an ideal next-generation smartphone-based biosensing system for point-of-care colorimetric test in diverse biomedical applications in source-limited environment.

Olfactory epithelium biosensor: odor discrimination of receptor neurons from a bio-hybrid sensing system.

Bio-hybrid systems provide an opportunity for integrating a living bio-active unit and a proper biosensing system, to employ the unique properties of the bio-active unit. The biological olfactory system can sense and identify thousands of trace odors. The purpose of this study is to combine olfactory epithelium with microelectrode array (MEA) to establish an olfactory epithelium-MEA hybrid system to record the odor-induced electrophysiological activities of the tissue. In our experiments, extracellular potential of olfactory receptor neurons in intact epithelium were measured in the presence of ethyl ether, acetic acid, butanedione, and acetone, respectively. After the odor-induced response signals were analyzed in the time and frequency domain, the temporal characteristics of response signals were extracted. We found that olfactory epithelium-MEA hybrid system can reflect the in vitro odor information of different signal characteristics and firing modes in vitro. The bio-hybrid sensing system can represent a useful instrument to sense and detect the odorant molecules with well recognizing patterns. With the development of sensor technology, bio-hybrid systems will represent emerging and promising platforms for wide applications, ranging from health care to environmental monitoring.

Electromechanical integrated recording of single cardiomyocyte in situ by multimodal microelectrode biosensing system.

The development of new drugs is a lengthy process, while the observation of serious side effects, such as cardiotoxicity, can result in the drug to be withdrawn even after development, leading to heavy burden on human health and social economy. To assess the drug cardiotoxicity, the electrical and mechanical properties of cardiomyocytes are increasingly being used to investigate the mechanisms and potential toxicity of drugs. Conventional non-invasive and label-free recording strategies are not well suited to record the integrated electromechanical signals from the single cell in a high-throughput manner, whereas label-based recordings strategies suffer from phototoxicity and drug side effects, precluding their long-time detection. In this study, we established a new multimodal microelectrode biosensing system to achieve the simultaneous and dynamic interrogation of electromechanical signals from multisite single cardiomyocytes. This multimodal device can detect subtle changes in the electromechanical signals induced by ion channel drugs during the excitation-contraction coupling of cardiomyocytes. The use of electromechanical integrated single cell signals for drug assessment was compared with commercial drug assays, and our multimodal microelectrode biosensing system can afford record electromechanical integrated signals as well as efficiently identify the effects of ion channel-blocking drugs on the electrical and mechanical properties of cardiomyocytes. Our multimodal microelectrode biosensing system is a potential valuable platform in the fields of cardiology and pharmacology.

Electrochemistry Coupling Localized Surface Plasmon Resonance for Biochemical Detection.

Localized surface plasmon resonance (LSPR) associated with metal nanostructures has developed into highly useful sensor techniques. LSPR spectroscopy often shows absorption peaks which could be used for biomedical detection. Here we report nanoplasmonic sensors using LSPR on nanostructures such as nanoparticles, nanocups, and nanocones to recognize biomolecular. These sensors can be modified for quantitative detection of explosives and evaluation of enzymatic activity. Electrochemical LSPR sensors can also be designed by coupling electrochemistry and LSPR spectroscopy measurements for biochemical detection. Multiple sensing information can be obtained and electrochemical LSPR property can be investigated for biosensors. In some applications, the electrochemical LSPR biosensor can be used to quantify heavy metal ions, neurotransmitters, and sialic acid. The biosensors exhibit better performance than those of conventional optical LSPR measurements. With multitransducers, the nanoplasmonic biosensor can provide a promising approach for biochemical detection in environmental monitoring, healthcare diagnostics, and food quality control.

Smartphone-Based Electrochemical System for Biosensors and Biodetection.

With the advantages of high popularity, convenient operation, open-source operation systems, high resolution imaging, and excellent computing capabilities, smartphones have been widely used as the core of detection system for calculation, control, and real-time display. Hence, smartphones play an important role in electrochemical detection and optical detection. Smartphone-based electrochemical systems were combined with screen-printed electrode and interdigital electrodes for in situ detection. The electrodes were modified with biomaterials, chemical materials, and nanomaterials for biosensors and biodetection, such as 3-amino phenylboronic acid nanocomposites, graphene, gold nanoparticles, zinc oxide nanoparticles, carbon nanotubes, proteins, peptides, and antibodies. With the modified electrodes, the smartphone-based impedance system was used to detect acetone, bovine serum albumin, human serum albumin, and trinitrotoluene, while smartphone-based amperometric system was employed to monitor glucose, ascorbic acid, dopamine, uric acid, and levodopa. The smartphone-based electrochemical system for biosensors and biodetection has provided miniaturized and portable alternative for diagnosis, which is promising to find application in point-of-care testing (POCT).

Multi-labeled neural network model for automatically processing cardiomyocyte mechanical beating signals in drug assessment.

High-throughput cardiotoxicity assessment is important for large-scale preclinical screening in novel drug development. To improve the efficiency of drug development and avoid drug-induced cardiotoxicity, there is a huge demand to explore the automatic and intelligent drug assessment platforms for preclinical cardiotoxicity investigations. In this work, we proposed an automatic and intelligent strategy that combined automatic feature extraction and multi-labeled neural network (MLNN) to process cardiomyocytes mechanical beating signals detected by an interdigital electrode biosensor for the assessment of drug-induced cardiotoxicity. Taking advantages of artificial neural network, our work not only classified different drugs inducing different cardiotoxicities but also predicted drug concentrations representing severity of cardiotoxicity. This has not been achieved by conventional strategies like principal component analysis and visualized heatmap. MLNN analysis showed high accuracy (up to 96%) and large AUC (more than 98%) for classification of different drug-induced cardiotoxicities. There was a high correlation (over 0.90) between concentrations reported by MLNN and experimentally treated concentrations of various drugs, demonstrating great capacity of our intelligent strategy to predict the severity of drug-induced cardiotoxicity. This new intelligent bio-signal processing algorithm is a promising method for identification and classification of drug-induced cardiotoxicity in cardiological and pharmaceutical applications.

A Portable Smartphone-Based System for the Detection of Blood Calcium Using Ratiometric Fluorescent Probes.

Hypocalcemia is a disease that adversely affects the production and reproduction of dairy cows. A portable device for rapid bovine blood calcium sensing has been growing in demand. Herein, we report a smartphone-based ratiometric fluorescence probe (SRFP) platform as a new way to detect and quantify calcium ions (Ca2+) in blood serum. Specifically, we employed a cost-effective and portable smartphone-based platform coupled with customized software that evaluates the response of Ca2+ ions to ratiometric fluorescence probe in bovine serum. The platform consists of a three-dimensional (3D) printed housing and low-cost optical components that excite fluorescent probe and selectively transmit fluorescence emissions to smartphones. The customized software is equipped with a calibration model to quantify the acquired fluorescence images and quantify the concentration of Ca2+ ions. The ratio of the green channel to the red channel bears a highly reproducible relationship with Ca2+ ions concentration from 10 µM to 40 µM in bovine serum. Our detection system has a limit of detection (LOD) of 1.8 µM in bovine serum samples and the recoveries of real samples ranged from 92.8% to 110.1%, with relative standard deviation (RSD) ranging from 1.72% to 4.89%. The low-cost SRFP platform has the potential to enable campesino to rapidly detect Ca2+ ions content in bovine serum on-demand in any environmental setting.

Recent Advances of Organ-on-a-Chip in Cancer Modeling Research.

Although many studies have focused on oncology and therapeutics in cancer, cancer remains one of the leading causes of death worldwide. Due to the unclear molecular mechanism and complex in vivo microenvironment of tumors, it is challenging to reveal the nature of cancer and develop effective therapeutics. Therefore, the development of new methods to explore the role of heterogeneous TME in individual patients' cancer drug response is urgently needed and critical for the effective therapeutic management of cancer. The organ-on-chip (OoC) platform, which integrates the technology of 3D cell culture, tissue engineering, and microfluidics, is emerging as a new method to simulate the critical structures of the in vivo tumor microenvironment and functional characteristics. It overcomes the failure of traditional 2D/3D cell culture models and preclinical animal models to completely replicate the complex TME of human tumors. As a brand-new technology, OoC is of great significance for the realization of personalized treatment and the development of new drugs. This review discusses the recent advances of OoC in cancer biology studies. It focuses on the design principles of OoC devices and associated applications in cancer modeling. The challenges for the future development of this field are also summarized in this review. This review displays the broad applications of OoC technique and has reference value for oncology development.