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Emerging proteomic technologies offer new insight in the study of malignant tumor to identify protein biomarkers for early detection, stratification, prediction and monitoring of treatment, as well as to detect target molecules for therapy. The tumor protein biomarker is responsible for the regulation of the cell cycle to promote cell proliferation and resistance to cell death. Important technologies include ELISA, immunohistochemistry, flow cytometry, western blot, mass spectrometry, protein microarray, and microfluidics for the study of screening, protein profiling, identification, qualitative and quantitative analysis of differential expressed oncoproteins relative to cancer tissues, counterparts at different stages of the disease from preneoplasia to neoplasia. It can also provide a detailed description of identifying tissue-specific protein biomarkers and to analysis the modification of protein activity in cancer conditions. In this chapter, we discuss current and emerging protein assays for improving cancer diagnosis, including trends toward advances in assay miniaturization, improve sensitivity and specificity, time and cost-effective, and accuracy in detection and measurement of protein activity. However, information from these protein diagnostic technologies should be integrated to obtain the optimal information required for the clinical management of a patient.
Postgraduate and Research Department of Zoology, Ayya Nadar Janaki Ammal College, India
Gopalan Rajagopal
Postgraduate and Research Department of Zoology, Ayya Nadar Janaki Ammal College, India
Balaji Sundaramahalingam
Postgraduate and Research Department of Zoology, Ayya Nadar Janaki Ammal College, India
Madasamy Sundar
Centre for Research and Postgraduate Studies in Botany, Ayya Nadar Janaki Ammal College, India
Ramar Thangam
Department of Materials Science and Engineering, Korea University, Republic of Korea
*Address all correspondence to: sundarrajbu09@gmail.com
1. Introduction
Cancer is the leading cause of death global population. As stated by the National Cancer Institute annual report revealed that there were 18.1 million new cases and 9.5 million cancer-related deaths globally in 2018. By 2040, the number of new cancer cases per year is anticipated to arise around 29.5 million and the number of cancer-related deaths to 16.4 million [1]. Cancer mortality can be reduced if cases are detected earlier and treated systematically and can result in a greater probability of survival rate and less morbidity [2]. Cancer diagnosis and prognosis have advanced dramatically during the last decades. Achieving this goal will necessitate not only improved therapies, but also enhanced methods for evaluating an individual’s risk of developing cancer, detecting cancers at an early stage when they can be treated more effectively, distinguishing aggressive from non-aggressive cancers, and monitoring recurrence and response to therapy.
Diagnostic imaging technologies can be used to detect people with cancer, these tests can be physically invasive, time-consuming and expensive to screen large groups of people who are asymptomatic and can cause unnecessary stress and worry. Furthermore, diagnostic imaging technologies frequently overlook minor lesions, resulting in the disease not being detected until it has progressed to the point when treatment intervention is less effective. However, insufficient diagnostics prohibit the detection of certain types of cancer until the advanced stage. For instance endoscopy with biopsy is the distinctive screening method for esophageal cancer and is generally performed after symptoms appear [3]. Other screens may be providing high levels of false positives or negatives. Hepatocellular carcinoma is generally detected by ultrasound, but this technique is subjected to operator mistake and often cannot distinguish between malignant and benign nodules [4]. Although mammography is the standard screening techniques for breast cancer, 20% of cases go undetected with this screening and specificity is 25%, leading to a large number of false positives [5].
Improving strategies for screening asymptomatic individuals for early-stage malignancies is a particularly difficult challenge. Overcome these challenges in the recent years, there has been a surge in interest in molecular markers as a cancer diagnosis, prognosis and therapeutic response [6]. The cancer antigen 15–3 (CA 15–3) act as a potential biomarker is used to screening and monitoring breast cancer [7]. The prostate-specific antigen (PSA) is a widely mentioned marker that is used to test male patients for prostate cancer [8]. The analysis of overexpression of human epidermal growth factor receptor type 2 (Her2) and estrogen receptor levels in breast cancer patients [9, 10].
Specifically, some modern molecule-oriented techniques used protein as a biomarker for monitoring of cancer progression and early tumor detection. Furthermore, tumor biomarker protein assays are suitable method for holding important clinical diagnostic tests in future because gene level studies may not correlation for the cancer alteration [11, 12]. Protein biomarkers played significant roles in accurate early diagnosis, therapy and prognosis in colorectal cancer [13].
Serum protein biomarkers are well developed tools for cancer diagnosis [14]. As proof, prostate-specific antigen (PSA), cancer antigen 125 (CA-125) and carcinoembryonic antigen (CEA) is extensively used for the diagnosis and management of various types of cancer, namely prostate, ovarian and gastrointestinal cancers [15]. The clinical sensitivity of a biomarker can simply be defined as the proportion of people with a confirmed disease who test positive for the biomarker assay whereas specificity refers to the proportion of healthy individuals who test negative for the biomarker assay [16]. Noninvasive assays, such as those using blood, stool, urine, or saliva are preferred because they cause less pain to the patient, have higher abidance rates and may be taken frequently for monitoring the treatment response [17]. Measurement of serum proteins ensures distinguishes between various types of malignancies from benign and thus leading imaging analysis, endoscopic examination and other diagnostic procedures and monitoring of the efficacy of the treatment.
The main objective of this chapter is to provide a new insight of emerging technologies based on protein detection for cancer diagnostic and prognostic. Proteins analytical techniques are especially suitable for the diagnosis of cancer are described here briefly along with their recent development. We also provide a brief description of techniques currently used to identify the protein activity in post-translational modification have been linked to cancer diagnosis and cancer progression.
The developments of proteomic patterns have emerged as effective method for diagnostics in the field of cancer proteomics in that it is represents new way for cancer detection and also clinically feasible. The enzyme-linked immunosorbent assay (ELISA), immunohistochemistry (IHC) and flow cytometry system represent the most reliable, sensitive and widely available protein-based testing platform in the clinic for the diagnosis, prognosis and treatment monitoring of cancer [18, 19]. Other protein analyses techniques such as mass spectrometry, Protein array and Microfluidics are currently at the laboratory level setup extensively used for cancer research purpose but these techniques are being developed for clinical applications (Figure 1 and Table 1).
Figure 1.
Schematic illustration of the different proteomic techniques for detection, identification, screening, protein profiling and modification of protein in various cancers.
2.1 Enzyme-linked immunosorbent assay
The enzyme-linked immunosorbent test (ELISA) has been widely utilised in regular clinical diagnostics and is still considered the gold standard for detecting cancer protein biomarkers in physiological samples [60, 61, 62]. Despite advances in developing cost-effective and label-free novel ELISA-based methods for future use in point-of-care cancer diagnostics, prognostics and therapy monitoring offers promise for improving the early detection of breast cancer (Figure 2). In conventional ELISA techniques, colorimetric or fluorescent readout signals are utilised to show FDA-approved protein biomarkers currently used in clinical practice (Table 2).
Figure 2.
Flowchart of nanocomposite used as a biosensing substrate and detection in breast cancer using nanoparticle coated arrays and ELISA based electrochemical immunosensor. First, a drop of blood from breast cancer (BC) patients is subjected to an nanoparticle fabricated array for a high-throughput screening of cancer biomarkers in breast cancer. Second, promising cancer biomarker candidates are selected from the array screening and validated in a large cohort of patients using ELISA, which can be used for early diagnosis, disease stratification, prediction of disease progression, or monitoring of drug responses. Finally, according to the function of nanoparticle coated array biomarker panel, biosensors could be designed and fabricated for clinical use in breast cancer.
Monitoring disease progression, response to therapy
Ovarian
Serum, plasma
Immunoassay
CA15–3
Monitoring disease response to therapy
Breast
Serum, plasma
Immunoassay
CA27.29
Monitoring disease response to therapy
Breast
Serum
Immunoassay
Free PSA
Discriminating cancer from benign Disease
Prostate
Serum
Immunoassay
Thyroglobulin
Aid in monitoring
Thyroid
Serum, plasma
Immunoassay
Nuclear Mitotic Apparatus protein
Diagnosis and monitoring of disease
Bladder
Urine
Lateral flow immunoassay
(NuMA, NMP22)
(professional and home use)
Alpha-fetoprotein (AFP)b
Management of cancer
Testicular
Serum, plasma amniotic fluidb
Immunoassay
Total PSA
Prostate cancer diagnosis and Monitoring
Prostate
Serum
Immunoassay
Carcino-embryonic antigen
Aid in management and prognosis
Not specified
Serum, plasma
Immunoassay
Human hemoglobin (fecal occult blood)
Detection of fecal occult blood (home use)
Colorectal
Feces
Lateral flow immunoassay
Table 2.
List of FDA-approved protein tumor markers currently used in clinical practice.
Stevens et al. generated a novel ELISA-based technique called plasmonic ELISA that uses gold nanoparticles as the probe to detect PSA in prostate cancer diagnosis [63, 64]. Shim et al. described a microfluidic droplet-based extremely flexible and sensitive diagnostic device for counting individual analyse molecules and identifying a biomarker for prostate cancer in buffer with a detection limit of 46 fM [65]. Zhang et al. described the estimation of PSA in a sandwich-type electrochemical ELISA with fabrication of PtNP-ferrocenedicarboxylic acid based infinite coordination polymer (ICP) in combination with polyamidoamine dendrimers modified sensor electrode [66]. Xu et al. described the simultaneous detection of triple cancer biomarkers, namely PSA, CEA and AFP using newly developed carbon and gold (CGN) nanocomposite-based immunoprobes [67].
We mainly focus on the recent advances made by a various group in improvement strategies for electrochemical ELISA-based immunosensors for improving access to diagnostics, increased detection sensitivity and specificity, magnification of the signal, ease of handling, potential for automation and combination with miniaturized analytical systems, low cost and comparative simplicity for mass production. The development of generation and characterization of double nanobody-based sandwich ELISA for the detection of FGL1 in cancer patient serum [41]. San Martin et al., developed the “gold standard” ELISA-based electrochemical immunosensor for the single determination of both proteins PD-L1 and HIF-1α in terms of assay time, compatibility making their use suitable for untrained users at the point of attention [40]. The fabrication of sandwich ELISA type electrochemical aptasensor is developed for the instantaneous determination of two important biomarkers arcinoembryonic antigen (CEA) and cancer antigen 15–3 (CA 15–3) in breast cancer. CEA and CA 15–3 aptamers linked to gold nanoparticles/redox probe/graphene nanocomposite were used as biosensing probes for signal amplification and to enhance the sensitivity of the immunoassay [33]. Poly(ε-caprolactone) electrospun scaffolds (ePCL) are used to arrange for a microstructured substrate with a high surface-to-volume ratio, capable of binding E7 oncoproteins when used for enzyme-linked immunosorbent assay (ELISA) tests [31]. Interestingly, the ultrasensitive detection of cancer biomarker matrix metalloproteinase-1 in urine, saliva, bovine serum, and cell culture media of oral and brain cancers using label-free electrochemical immunosensor based on gold nanoparticle/ polyethyleneimine/reduced graphene oxide nanocomposites [29]. Li et al., 2021 constructed a simple label-free electrochemical immunosensor based on worm-like platinum with a sandwich-like structure [21]. The fabricated electrochemical immunosensor showed a wide linear range, enhanced detection limit, good selectivity and stability for the determination of alpha-fetoprotein.
Applications of Enzyme-linked Immunosorbent Assay in cancer research
used to help diagnose certain diseases such as cancer
For determination and quantification of tumor markers
Detecting and measuring cell cycle checkpoint markers could be a promising pathway to better develop treatment strategies
2.2 Immunohistochemistry
Immunohistochemistry (IHC) is a fundamental method used for clinical decision making of diagnosis and prognosis of various cancers, such as breast [68], prostate [69], lung [70, 71]. It enables to find out the analysis of biomarker expression and tissue localization in cancer. Immunohistochemical techniques play critical roles a diagnosis and screening tools for distinguishing between malignant and benign with the help of biomarkers expression in lung cancer [72]. In recent years, the advancement of microfluidic-based immunohistochemistry represents clinically validated approaches to the standard chromogenic staining for rapid, accurate, and automated breast cancer diagnosis [73]. The automated chromogenic multiplexed immunohistochemistry assay approach provides an exclusive sample-sparing tool to characterize limited tissue samples in lung cancer and making it an emerging method in the clinical analysis for therapeutic decision making of advanced NSCLC, provided that validation in a larger population is performed. This implies limiting the number of tissue slides despite the existence of specific and sensitive biomarkers (ALK, ROS1, BRAF V600E, PD-L1) and the obligation to distinguish lung adenocarcinoma from squamous cell carcinoma [74].
Applications of Immunohistochemistry in cancer research
To predict the prognosis of tumors by identification of enzymes, tumor-specific antigens, oncogenes, tumor suppressor genes, and tumor cell proliferation markers.
To diagnosis of tumors of uncertain origin, primary as well as metastatic from unknown primary tumor.
To predict therapeutic response in two important tumors, i.e. carcinoma of breast and prostate.
Used to help tell the difference between different types of cancer
To determine the function of specific gene products in fundamental biological processes such as development and apoptosis.
2.3 Flow cytometry
Flow cytometry is a versatile technique with applications in a variety of fields, including immunology, virology, molecular biology, cancer biology, and infectious disease surveillance. Flow cytometry is a technique for swiftly analysing single cells that are suspended in a buffered salt solution and flow through one or more lasers. Each cell is subjected to a visible light scattering analysis as well as one or more fluorescence parameters. Visible light scatter is assessed in two directions: forward (Forward Scatter or FSC), which shows the cell’s relative size, and at 90° (Side Scatter or SSC), which reveals the cell’s internal complexity or granularity. Fluorescence measurements are performed on samples that have been transfected and expressed with fluorescent proteins (for example, Green Fluorescent Protein, GFP), stained with fluorescent dyes (for example, Propidium Iodide, DNA), or stained with fluorescently attached antibodies (e.g., CD3 FITC). It enables simultaneous characterization of mixed populations of cells from blood and bone marrow as well as dissociable solid tissues such as lymph nodes, spleen, mucosal tissues, and solid malignancies.
The availability of new reagents has resulted in an explosion in the number of parameters utilised in flow cytometry investigations during the last several years. The number of fluorochromes used to conjugate monoclonal antibodies has increased dramatically, including tandem dyes and polymer dyes. Additionally, the number of fluorescent proteins accessible for transfection beyond GFP has increased, including mCherry, mBanana, mOrange, and mNeptune. These advancements in fluorochromes and technology have enabled tests with over 30 parameters to be performed. Data analysis is the final step of a flow cytometry experiment. The two-parameter histogram (dot plot) gating and analysis method is still widely utilised. However, as the number of factors and complexity of experiments expand, newer cluster data analysis techniques such as PCA, SPADE, and tSNE are being used. These enhanced data mining techniques enable the extraction of relevant information from the high-dimensional data generated by flow cytometry.
Since Mack Fulwyler initially invented the present kind of flow cytometers in 1965 [75], flow cytometry has now been used in quite a broad range of clinical areas for assessing protein expression in cancer cells [76, 77]. It is commonly used to diagnose of acute lymphoblastic leukemia [78]. Flow cytometry is a rapid and sensitive diagnostic method that makes it possible to characterize more satisfactorily the heterogeneous group of acute lymphoblastic leukemias. Flow cytometry has been historically used to detect the expression of CD56 in the diagnosis of chronic myelomonocytic leukemia (CMML). CD56 is a cell surface marker that presents the surface of monocytes [79]. Following over a decade of extensive research, high-throughput image-based flow cytometry is now an accepted and widely used tool in scientific research, particularly in the field of cancer biology. Many researchers have replaced microscopy-based clinical tools with image-based flow cytometry. Erber’s team originally used image-based flow cytometry to identify the presence of promyelocytic leukemia (PML) bodies for the diagnosis and prognosis of acute myeloid leukemia (AML) [80].
Applications of Flow cytometry in cancer research
Emerging as a tool for diagnosis of cancer (abnormal DNA content)
Specific histopathological diagnosis (RNA for hematological cancers; surface markers for lymphoid and myeloid neoplasms)
Prognosis (adverse impact of aneuploidy and high S percentage)
Treatment (cytokinetically oriented, monoclonal antibodies, drug pharmacology)
2.4 Western blot
The Western blot (WB), also known as immunoblot, is an analytical and quantitative method for detecting particular proteins in various biological materials, including liquids and tissue/cellular homogenates [81]. Harry Towbin and colleagues developed the WB method in 1979. The WB approach provides clear and valuable information for assessing the phosphorylation state of a protein. We can evaluate the modified form of protein in the sample either qualitatively or quantitatively. Radenkovic et al., detected cyclin D1 expression in tumour and peritumoral tissue of breast cancer patients by Western blotting method to found that Cyclin D1 expression decreased significantly with each advanced clinical stage of disease and tumour size [25]. Kinase activity-tagged western blotting (KAT-WB) detected autophosphorylation of Tyr-kinase and site-specific phosphorylation by multiple kinases enables to interrogate multiple kinase signaling pathways without using radioactive substances [82]. Western blot analysis provides an opportunity to obtain more insight into cell cycle regulation factor in tumorigenesis, could spur the discovery of many more successful therapeutic targets [83].
Applications of Western blot in cancer research
To detect the presence of cancer proteins biomarkers
To determine the extent of post-translational modifications
To verify protein expression in cloning applications
To analyze protein and biomarker expression levels
In antibody epitope mapping
To test for markers of disease in clinical settings.
2.5 Mass spectrometry
The emphasis use of mass spectrometric analysis in clinical research has been on biomarker identification, which includes proteomics, lipidomics, and metabolomics [84, 85]. Metabolites, proteins and lipids have been shown to help distinguish between malignant and healthy tissue among the many compounds detectable with MS. To be identified as a qualifying biomarker, a molecule must be distinguishable from other molecules, ideally, the sample is simple, quick, and easy to collect, high sensitivity and specificity [86, 87], and is used to diagnose and prognosis for various cancer such as thyroid cancer [88], lung cancer [89, 90], bladder cancer [91], Pancreatic Cancer [92, 93], breast cancer [94], ovarian cancer [52], oral cancer [53], prostate cancer [95]. In general, various classes of molecules may function as biomarkers due to an imbalance of tumor-suppressing and promoting agents in cancer cells, regulating genetic changes, and changing the composition of lipids, metabolites, and proteins (Figure 3).
Figure 3.
Schematic representation showed that well established method immunohistochemistry was widely used in clinical diagnosis for cancer detection. In recent years, emerging proteomic technologies such as mass spectrometry and protein microarray has been developed for precise detection of cancer protein biomarkers.
The MALDI-TOF MS combined with magnetic bead used for detecting serum protein biomarkers and establishment of boosting decision tree model for diagnosis of colorectal cancer patients [96], breast cancer [97]. These differentially regulated proteins were considered as potential biomarkers for the patients with CRC in the serum. The emerging mass spectrometry methods of nanoLC–MS/MS, targeted LC–MS/MS, and stable isotope-labeled multiple reactions monitoring (MRM) MS coupled to test machine learning algorithms and logistic regression used to analyze plasma samples from colorectal cancer patients. The novel peptide biomarkers combination of PF4, ITIH4, and APOE achieved sensitivity 84.5%, specificity 97.5% and an AUC of 0.96 in CRC diagnosis [98]. Moran et al., developed an intact protein assay to analyze PSA by capillary electrophoresis-electrospray ionization-mass spectrometry after affinity purification from prostate cancer patients’ urine [99, 100].
The integrating mass spectrometry imaging and gold nanoparticle (AuNP)-based signal amplification was developed for quantitatively profiling protein biomarkers on the surface of exosomes in cancer diagnosis. Cancer protein biomarkers were modified with organic oligomers as mass tags and specific antibodies on AuNPs. Exosomes captured by the antibody-coated gold chip are recognized by the AuNPs probes, forming a sandwich immunoassay. Multiple protein biomarkers can be quantitatively detected from the exosomes with the mass tags by mass spectrometry imaging [101]. Park et al., 2019 developed a simple and robust cancer diagnostic method using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS)-based total serum protein fingerprinting to diagnose liver cancer [102]. The proteome profile of vimentin, tubulin beta 2C chain, tubulin alpha 1C chain, actin cytoplasmic 2, apolipoprotein A-I, and collagen alpha 2(VI) chain as a potential biomarker that exhibited differential expression in ovarian cancer using two-dimensional gel electrophoresis (2D-GE) and matrix-assisted laser desorption/ionization-time of flight mass spectrometric (MALDI-TOF MS) analysis [103]. The protein modifications in the N-glycosylation profile are usually associated with many cancers, like colorectal cancer. In turn, MALDI-TOF/MS and LC–MS methods are the most accurate technology in the quantification of N-glycans compositions in the serum and tissue of colorectal cancer patients [104]. The similar proteomic analysis was performed to identify potential lung cancer biomarkers such as CD5L, CLEC3B, ITIH4, SERFINF1, SAA4, SERFINC1, and C20ORF3 detected via a liquid biopsy-for the noninvasive diagnosis of lung cancer [105].
Applications of mass spectrometry in cancer research
Discovery-based proteomic experiments with increasing cohort size is imperative for this technology to transfer to the clinic
Application of tissue proteomics to cancer research is using it in a concerted effort to complement genomics.
Proteomics biomarker research in human body fluids, urine and alternative liquid biopsies
Mass spectrometry-based clinical proteomics in cancer biomarker research
Prospective versus retrospective profiling of clinical specimens
Human population heterogeneity & protein variability
2.6 Protein array
The analysis of protein biomarkers using these high throughput methods can provide robust and previously unachievable diagnostic and prognostic information for a variety of cancers [106, 107]. The analytical protein array is a useful developing technology that enables simultaneously analyzing >4000 protein samples in cell or tissues for biomarker discovery (Huang et al., 2017). The microarray is currently utilized to analyze biopsy samples in clinical research trials, essentially lead to the collection of information linked to posttranslational modifications of proteins reflecting the active status of signal pathways and networks [108]. This technique has the potential to enhance cancer detection, prognosis, and treatments. Protein microarray technology has been used effectively in fundamental and applied proteome research and affinity studies for protein identification, quantification, and functional analysis [109, 110]. A protein function array is made up of thousands of natural proteins that have been immobilised in a specific arrangement. When a functional protein array is used for serum protein profiling, autoantibodies are usually detected as biomarkers for diagnosis of cancer detection and for monitoring the cancer treatment due to their stability, specificity, and ease of detection, as compared with other serological components [111]. Protein microarrays have allowed researchers to examine functional protein dysregulation in various cancer namely colorectal cancers [112], pancreatic cancer [113]. Mirus et al., identified ERBB2, TNC and ESR1 in prediagnostic plasma from people that succumb to pancreatic ductal adenocarcinoma [114]. In addition to the understanding of the biological mechanisms, analytical protein arrays have also been applied to profile drug resistance [115].
In recent advancements in protein microarray is Reverse-phase protein array/microarray (RPPA/RPPM), which can precisely map functional proteomic profiling in individual cancer patient. The personalized therapy was prescribed by the identification of functional proteomics profilling. RPPA is an antibody-based highly quantitative proteomic technology, used for profiling the expression and modification of signaling proteins, mainly in low-abundance analytes cases. Clinical trials are using RPPA technology molecular-targeted therapeutics [116, 117]. Horton et al., 2021 found that the minimal effects on RPPA protein concentration distributions in peripheral blood and bone marrow, demonstrating that these preanalytical variables have been successfully managed in a multi-site clinical trial setting for leukemia [118]. A proteomic study was carried out for determining the levels of post-translational protein modifications and total protein expression in myeloproliferative neoplasms patients using RPPA [119]. These results highlight the robustness and the reproducibility of RPPA technology and its capacity to identify protein markers of cancer or response to therapy. Recent proteomics studies have focused on the expression of seven markers (CD5, CD10, BCL2, BCL6, MUM1, Ki-67, and C-MYC) is analyzed by RPPA using 37 diffuse large B-cell lymphomas (DLBCL) tissues [120]. These results suggest that RPPA could be applicable as a supportive tool for determining lymphoma prognosis. With all of these improvements, we believe that protein array technology will soon become a dominant tool for biomarker discovery in cancers.
Applications of protein array in cancer research
Analytical and functional protein array for cancer biomarker discovery
Personalized medicine in breast and ovarian cancers using protein microarray
Protein profiling of cancer cells using protein microarray
Drug discovery for target identification and validation
2.7 Microfluidics
Microfluidic technology, as new creativity has a great impact on automation and miniaturization via handling a small volume of materials and samples for cancer diagnosis [121]. This method has be considered as a primary screening tool for diagnosing breast cancer based on its robustness, high throughput, low energy requirements, excellent accuracy and accessibility to the general public [122]. A miniaturized instrument was developed for chemiluminescence detection and signal analysis with the advances in microfluidic technology. The system was validated by testing four biomarkers of colorectal cancer using plasma samples from patients [123]. Another design of the microfluidic device, magnetic nanoparticles (Fe3O4NPs) was successfully functionalized with an exosome-binding antibody (anti-CD9) to mediate the magnetic capture in a microdevice. The captured exosomes were then subjected to analysis of CA19–9, a protein often used to monitor pancreatic cancer patients [124]. In the line of discovery, the nuclear matrix protein 22 (NMP22) and bladder cancer antigen (BTA) from the urine samples was detected using the microfluidic paper-based analytical device (mu PAD) was developed by Jiang [125]. This method is feasible for home-based self-detection from urine samples within 10 min for the total process, which provides a new way for quick, economical, and convenient tumor diagnosis, prognosis evaluation, and drug response (Figure 4). The functions and recent development of microfluidic chip to provide great potential for advancing noninvasive cancer diagnosis [126, 127].
Figure 4.
Microfluidic technology, as new creativity has a great impact on automation and miniaturization via handling a small volume of materials and samples for cancer diagnosis. An effective management of cancer diagnosis screening by using body fluids and cancer protein biomarkers for diagnosis, prognosis, therapy and monitoring to treatment.
Microfluidic devices which used to mimic cancer metastasis process are usually applied to several cell types in order to culture two or more organoids. Different organoids are separated by some specific biomaterials, such as polydimethylsiloxane (PDMS), and connected with each other by channels and controllable fluids. Xu et al. designed and constructed a multi-organ microfluidic chip to mimic lung cancer metastasis to the brain, bone and liver. In this platform, organoids were divided into different chambers, including upstream lung organoid and three downstream organoids. Different types of cells were seeded in each chamber to culture different organoids and each organoid were linked by side channels. The culture medium flowed through microvascular channels to simulate blood circulation. At the same time, a circulating vacuum was applied to mimic the physiological breathing [128]. This system provided a physiologically relevant context to recapitulate the complex process of lung cancer metastasis and help us to effectively explore the underlying mechanism of lung cancer metastasis.
The integration between 3D bioprinting and microfluidic chip has given microfluidic chip greater potential to model cancers. Traditionally, in cancer modeling on chip, microfabrication such as micromachining, photolithography and injection molding, are used in the fabrication of microfluidic chips [129]. These methods have high resolution and accuracy, but their high cost, complex process and difficult reproducibility greatly limited the development of microfluidic chip [130]. The emerging of 3D printing technology greatly simplifies the fabrication process of microfluidic chips. The biggest characteristic of microfluidic chip is the customizability, which means microfluidic chip is a very flexible scientific tool that can accommodate with advanced technologies. To date, microfluidic chip shows tremendous promise in cancer diagnosis and treatment. Microfluidic chip can be applied in everything from anticancer drug development and screening to cancer modeling and diagnosis.
Applications of Microfluidics in cancer research
The development of cancer preclinical model; Animal models, 2D culture, 3D culture, as well as tumor organoid.
Detection of cancer biomarkers
Anti-cancer drug screening and nano-drug preparation
Exploring tumor heterogeneity on microfluidic chip
Over the past decade, protein profiling has emerged as a dynamic discipline, capable of generating a comprehensive perspective of protein patterns, modification of protein in various tissue-specific cancer types and mechanisms of cancer progression. Proteomics studies and analytical techniques are developed with modern materials for precise detection of tumor-specific alteration in proteins. Recent times, the proteomics technologies have fabricated with modern nanocomposites provide a new and more efficient methods for protein detection and identifying biomarkers for the early detection of cancer. The upgraded proteomics technology will modify the current pathological classification and grading methods of cancer during the next decade. Proteomic technologies will have an impact on the diagnosis and management of cancer.
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