Precision diagnostics: moving towards protein biomarker signatures of clinical utility in cancer

Sub-zeptomole Detection of Biomarker Proteins Using a Microfluidic Immunoarray with Nanostructured Sensors

We report here a low-cost electrochemical immunoarray with unprecedented sensitivity in the sub-zeptomole range with up to 5 log-decades dynamic range for accurate, multiplexed protein determinations. The microfluidic array features eight carbon sensors coated with a dense layer of 5 nm gold-nanoparticles derivatized with primary antibodies. Analyte proteins are captured by secondary antibody-poly-HPR (horseradish peroxidase) bioconjugates containing 400 HRP enzyme labels, with amplified amperometric peaks developed using H2O2 activator and hydroquinone mediator. Prostate cancer biomarkers prostate specific antigen (PSA), vascular endothelial growth factor-D (VEGF-D), ETS-related gene protein (ERG), and insulin-like growth factor-1 (IGF-1) were measured simultaneously with sub-fg/mL LODs (0.08-0.22 zmol). These proteins were determined in serum of postprostatectomy cancer patients which had much lower levels than prostate cancer patients without surgery. This immunoassay protocol makes thousands of low-abundance proteins accessible to quantitative measurements down to zeptomole levels.

A novel luminescent Ir(iii) complex for dual mode imaging: synergistic response to hypoxia and acidity of the tumor microenvironment

The early detection of cancer shows great promise for the control and prevention of cancer. For early detection, one of the challenges that still exists is searching for methods that can illuminate tumors with high sensitivity. Here, acidity and hypoxia, two typical features that exist universally in a solid tumor microenvironment, were focused on to attain synergistic imaging with an enhanced signal-to-noise ratio. This was realized using an iridium(iii) based optical probe (Ir-1) that could sense acidity and hypoxia simultaneously and synergistically. Through the synergistic sensing of acidic pH and hypoxia, stronger emission signals or larger lifetime changes can be obtained than if a single factor (acidity or hypoxia) is used to induce variations. Furthermore, its potential for biological applications was confirmed by employing Ir-1 for phosphorescence synergistic intensity and lifetime imaging of acidity and hypoxia in live monolayer cells and 3D multicellular spheroids.

Differential Urinary Proteome Analysis for Predicting Prognosis in Type 2 Diabetes Patients with and without Renal Dysfunction

Renal dysfunction, a major complication of type 2 diabetes, can be predicted from estimated glomerular filtration rate (eGFR) and protein markers such as albumin concentration. Urinary protein biomarkers may be used to monitor or predict patient status. Urine samples were selected from patients enrolled in the retrospective diabetic kidney disease (DKD) study, including 35 with good and 19 with poor prognosis. After removal of albumin and immunoglobulin, the remaining proteins were reduced, alkylated, digested, and analyzed qualitatively and quantitatively with a nano LC-MS platform. Each protein was identified, and its concentration normalized to that of creatinine. A prognostic model of DKD was formulated based on the adjusted quantities of each protein in the two groups. Of 1296 proteins identified in the 54 urine samples, 66 were differentially abundant in the two groups (area under the curve (AUC): p-value < 0.05), but none showed significantly better performance than albumin. To improve the predictive power by multivariate analysis, five proteins (ACP2, CTSA, GM2A, MUC1, and SPARCL1) were selected as significant by an AUC-based random forest method. The application of two classifiers—support vector machine and random forest—showed that the multivariate model performed better than univariate analysis of mucin-1 (AUC: 0.935 vs. 0.791) and albumin (AUC: 1.0 vs. 0.722). The urinary proteome can reflect kidney function directly and can predict the prognosis of patients with chronic kidney dysfunction. Classification based on five urinary proteins may better predict the prognosis of DKD patients than urinary albumin concentration or eGFR.

The Possible Influence of Mediterranean Diet on Extracellular Vesicle miRNA Expression in Breast Cancer Survivors

The Mediterranean diet (MD) has been reported to have beneficial effects on breast cancer and cardiovascular diseases. Recently, microRNAs (miRNAs) have been suggested as biomarkers for the diagnosis and disease prognosis in cancer and cardiovascular diseases. We evaluated the influence of the MD on the plasma-derived extracellular vesicle miRNA signature of overweight breast cancer survivors. Sixteen participants instructed to adhere to the MD for eight weeks were included in this study. To curate differentially expressed miRNAs after MD intervention, we employed two methods: significance analysis of microarrays and DESeq2. The selected miRNAs were analyzed using ingenuity pathway analysis. After an eight-week intervention, body mass index, waist circumference, fasting glucose, fasting insulin, and homeostatic model assessment for insulin resistance were significantly improved. Expression levels of 798 miRNAs were comprehensively analyzed, and 42 extracellular vesicle miRNAs were significantly differentially regulated after the eight-week MD (36 were up and 6 were down-regulated). We also identified enriched pathways in genes regulated by differentially expressed 42 miRNAs, which include signaling associated with breast cancer, energy metabolism, glucose metabolism, and insulin. Our study indicates that extracellular vesicle miRNAs differentially expressed as a result of the MD might be involved in the mechanisms that relate to cardiometabolic risk factors in overweight breast cancer survivors.

Facile Surface Functionalization of Cyclic Olefin Copolymer Film with Anhydride Groups for Protein Microarray Fabrication

Immobilization of protein at high efficiency is a challenge for fabricating polymer-based protein chips. Here, a simple but effective approach was developed to fabricate a cyclic olefin copolymer (COC)-based protein microarray with a high immobilization density. In this strategy, poly(maleic anhydride-co-vinyl acetate) (poly(MAH-co-VAc)) brushes were facilely attached on the COC surface via UV-induced graft copolymerization. The introduction of poly(MAH-co-VAc) brushes resulted in an obvious increase in the surface roughness of COC. The functionalized COC showed little reduction in transparency compared with pristine COC, indicating that the photografting treatment did not alter its optical property. The graft density of the anhydride groups on the modified COC could be tuned from 0.46 to 3.2 μmol/cm². The immobilization efficiency of immunoglobulin G (IgG) on functionalized COC reached 88% due to the high reactivity between anhydride groups and amine groups of IgGs. An immunoassay experiment demonstrated that the microarray showed high sensitivity to the target analyte.

Evaluation of associations between genetically predicted circulating protein biomarkers and breast cancer risk

A small number of circulating proteins have been reported to be associated with breast cancer risk, with inconsistent results. Herein, we attempted to identify novel protein biomarkers for breast cancer via the integration of genomics and proteomics data. In the Breast Cancer Association Consortium (BCAC), with 122,977 cases and 105,974 controls of European descendants, we evaluated the associations of the genetically predicted concentrations of >1,400 circulating proteins with breast cancer risk. We used data from a large-scale protein quantitative trait loci (pQTL) analysis as our study instrument. Summary statistics for these pQTL variants related to breast cancer risk were obtained from the BCAC and used to estimate odds ratios (OR) for each protein using the inverse-variance weighted method. We identified 56 proteins significantly associated with breast cancer risk by instrumental analysis (false discovery rate <0.05). Of these, the concentrations of 32 were influenced by variants close to a breast cancer susceptibility locus (ABO, 9q34.2). Many of these proteins, such as insulin receptor, insulin-like growth factor receptor 1 and other membrane receptors (OR: 0.82-1.18, p values: 6.96 × 10-4 -3.28 × 10-8 ), are linked to insulin resistance and estrogen receptor signaling pathways. Proteins identified at other loci include those involved in biological processes such as alcohol and lipid metabolism, proteolysis, apoptosis, immune regulation and cell motility and proliferation. Consistent associations were observed for 22 proteins in the UK Biobank data (p < 0.05). The study identifies potential novel biomarkers for breast cancer, but further investigation is needed to replicate our findings.

Establishment of a universal and sensitive plasmonic biosensor platform based on the hybridization chain reaction (HCR) amplification induced by a triple-helix molecular switch

Herein, we established a universal and sensitive plasmonic sensing strategy for biomolecule assays by coupling the hybridization chain reaction (HCR) strategy and a triple-helix molecular switch. Upon the recognition of the target, a single-stranded DNA as a universal trigger (UT) was released from the triple-helix molecular switch (THMS). Thus, the HCR process can be triggered between two hairpins M1 and M2, resulting in the aggregation of gold nanoparticles (AuNPs) via the hybridization between the tail sequence on M1 (or M2) and a DNA-AuNP probe with a dramatic change in the absorbance at 521 nm. More specifically, the strategy, which was conducted by the introduction of target-specific recognition of THMS and universalized by virtue of altering the aptamer or DNA sequence without changing the triple-helix structure, enables simple design for multiple target detection. By taking advantage of THMS, this strategy could enable stable and sensitive detection of a variety of targets including nucleic acids, small molecules and proteins, which may possess great potential for practical applications.

Integrating Artificial Intelligence and Nanotechnology for Precision Cancer Medicine

Artificial intelligence (AI) and nanotechnology are two fields that are instrumental in realizing the goal of precision medicine-tailoring the best treatment for each cancer patient. Recent conversion between these two fields is enabling better patient data acquisition and improved design of nanomaterials for precision cancer medicine. Diagnostic nanomaterials are used to assemble a patient-specific disease profile, which is then leveraged, through a set of therapeutic nanotechnologies, to improve the treatment outcome. However, high intratumor and interpatient heterogeneities make the rational design of diagnostic and therapeutic platforms, and analysis of their output, extremely difficult. Integration of AI approaches can bridge this gap, using pattern analysis and classification algorithms for improved diagnostic and therapeutic accuracy. Nanomedicine design also benefits from the application of AI, by optimizing material properties according to predicted interactions with the target drug, biological fluids, immune system, vasculature, and cell membranes, all affecting therapeutic efficacy. Here, fundamental concepts in AI are described and the contributions and promise of nanotechnology coupled with AI to the future of precision cancer medicine are reviewed.

An Integrated Meta-Analysis of Secretome and Proteome Identify Potential Biomarkers of Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is extremely aggressive, has an unfavorable prognosis, and there are no biomarkers for early detection of the disease or identification of individuals at high risk for morbidity or mortality. The cellular and molecular complexity of PDAC leads to inconsistences in clinical validations of many proteins that have been evaluated as prognostic biomarkers of the disease. The tumor secretome, a potential source of biomarkers in PDAC, plays a crucial role in cell proliferation and metastasis, as well as in resistance to treatments, which together contribute to a worse clinical outcome. The massive amount of proteomic data from pancreatic cancer that has been generated from previous studies can be integrated and explored to uncover secreted proteins relevant to the diagnosis and prognosis of the disease. The present study aimed to perform an integrated meta-analysis of PDAC proteome and secretome public data to identify potential biomarkers of the disease. Our meta-analysis combined mass spectrometry data obtained from two systematic reviews of the pancreatic cancer literature, which independently selected 20 studies of the secretome and 35 of the proteome. Next, we predicted the secreted proteins using seven in silico tools or databases, which identified 39 secreted proteins shared between the secretome and proteome data. Notably, the expression of 31 genes of these secretome-related proteins was upregulated in PDAC samples from The Cancer Genome Atlas (TCGA) when compared to control samples from TCGA and The Genotype-Tissue Expression (GTEx). The prognostic value of these 39 secreted proteins in predicting survival outcome was confirmed using gene expression data from four PDAC datasets (validation set). The gene expression of these secreted proteins was able to distinguish high- and low-survival patients in nine additional tumor types from TCGA, demonstrating that deregulation of these secreted proteins may also contribute to the prognosis in multiple cancers types. Finally, we compared the prognostic value of the identified secreted proteins in PDAC biomarkers studies from the literature. This analysis revealed that our gene signature performed equally well or better than the signatures from these previous studies. In conclusion, our integrated meta-analysis of PDAC proteome and secretome identified 39 secreted proteins as potential biomarkers, and the tumor gene expression profile of these proteins in patients with PDAC is associated with worse overall survival.

Translating Cancer Molecular Variability into Personalized Information Using Bulk and Single Cell Approaches

Cancer research is striving toward new frontiers of assigning the correct personalized drug(s) to a given patient. However, extensive tumor heterogeneity poses a major obstacle. Tumors of the same type often respond differently to therapy, due to patient-specific molecular aberrations and/or untargeted tumor subpopulations. It is frequently not possible to determine a priori which patients will respond to a certain therapy or how an efficient patient-specific combined therapy should be designed. Large-scale datasets have been growing at an accelerated pace and various technologies and analytical tools for single cell and bulk level analyses are being developed to extract significant individualized signals from such heterogeneous data. However, personalized therapies that dramatically alter the course of the disease remain scarce, and most tumors still respond poorly to medical care. In this review, the basic concepts of bulk and single cell approaches are discussed, as well as their emerging role in individualized designs of drug therapies, including the advantages and limitations of their applications in personalized medicine.

The Hypoxic Response Expression as a Survival Biomarkers in Treatment-Naive Advanced Breast Cancer

OBJECTIVE

Hypoxia-associated biomarkers profiling may provide information for prognosis, staging, and subsequent therapy. We aim to evaluate whether the quantitative gene and protein expression of hypoxic response tumor markers - carbonic anhydrase IX (CAIX) and hypoxia- inducible factor 1 alpha (HIF1A) - may have a role in predicting survival in advanced breast cancer of Indonesian population.

METHODS

Tumor tissues and peripheral blood samples were collected from treatment - naïve locally advanced (LABC) or metastatic breast cancer patients (MBC) at Wahidin Sudirohusodo General Hospital (Makassar, South Sulawesi) and its referral network hospitals from July 2017 to March 2019. The level of mRNA (of blood and tumor tissue samples) and soluble protein (of blood samples) of CAIX and HIF1A were measured by RT-qPCR and ELISA methods, respectively, besides the standard histopathological grading and molecular subtype assessment. The CAIX and HIF1A expression, patients' age, tumor characteristics, surgery status, and neoadjuvant chemotherapy drug classes were further involved in survival analyses for overall survival (OS) and progression-free survival (PFS).

RESULTS

Forty (30 LABC, 10 MBC) eligible patients examined were 21 hormone-receptors positives (15 Luminal A, 6 Luminal B) and 19 hormone-receptors negatives (10 HER2-enriched, 9 triple-negative). The CAIX blood mRNA and CAIX soluble protein levels in hormone-receptors negative patients were higher than in hormone-receptor-positive patients (p < 0.05). In univariate analysis, both CAIX and HIF1A levels predict OS (except HIF1A protein) with CAIX tissue mRNA has the highest hazard ratio (HR 8.04, 95%CI:2.45-26.39), but not PFS. Cox proportional hazard model confirmed that CAIX tissue mRNA is the independent predictor of OS (HR 6.10, 95%CI: 1.16-32.13) along with surgical status and tumor advancement type (LABC or MBC).

CONCLUSIONS

CAIX mRNA expression of tumor tissue in treatment-naïve advanced breast cancer has a predictive value for OS.
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Label-free peptide quantification coupled with in silico mapping of proteases for identification of potential serum biomarkers in gastric adenocarcinoma patients

OBJECTIVES

We aimed to identify serum level variations in protein-derived peptides between patients diagnosed with gastric adenocarcinoma (GAC) and non-cancer persons (control) to detect the activity changes of proteases and explore the auxiliary diagnostic value in the context of GAC physiopathology.

METHODS

The label-free quantitative peptidome approach was applied to identify variants in serum levels of peptides that can differentiate GAC patients from the control group. Peptide sequences were submitted against Proteasix tool predicting proteases potentially involved in their generation. The activity change of proteases was subsequently estimated based on the peptides with significantly altered relative abundance. In turn, activity change prediction of proteases was correlated with relevant protease expression data from the literature.

RESULTS

A total of 191 peptide sequences generated by the cleavage of 36 precursor proteins were identified. Using the label-free quantification approach, 33 peptides were differentially quantified (adjusted fold change ≥ 1.5 and p-value < 0.05) in which 19 were up-regulated and 14 were down-regulated in GAC samples. Of these peptides, fibrinopeptide A was significantly decreased and its phosphorylated form ADpSGEGDFLAEGGGVR was upregulated in GAC samples. Activity change prediction yielded 10 proteases including 6 Matrix Metalloproteinases (MMPs), Thrombin, Plasmin, and kallikreins 4 and 14. Among predicted proteases in our analysis, MMP-7 was presented as a more promising biomarker associated with useful assays of clinical practice for GAC diagnosis.

CONCLUSION

Our experimental results demonstrate that the serum levels of peptides were significantly differentiated in GAC physiopathology. The hypotheses built on protease regulation could be used for further investigations to measure proteases and their activity levels that have been poorly studied for GAC diagnosis.

Nanoparticles modified by triple single chain antibodies for MRI examination and targeted therapy in pancreatic cancer

Precise diagnosis and effective treatment are crucial to the prognosis of pancreatic ductal adenocarcinoma (PDAC). Magnetic iron oxide nanoparticles (IONPs) are superior magnetic resonance imaging (MRI) contrast agents, while antibodies are significant immunotherapy reagents. Herein, we firstly generated a novel nanocomposite combining triple single chain antibodies (scAbs) and IONPs for the detection and treatment of PDAC.

METHODS

Triple scAbs (scAbMUC4, scAbCEACAM6, scFvCD44v6, MCC triple scAbs) were conjugated to the surface of polyethylene glycol modified IONPs (IONPs-PEG), forming the IONPs-PEG-MCC triple scAbs nanocomposite. Characterization of the nanocomposite was performed, and its cytotoxicity, specificity, and apoptosis induction were evaluated. In vivo MRI study and anti-pancreatic cancer effect assessment were performed in tumor-bearing nude mice.

RESULTS

The size of the IONPs-PEG-MCC triple scAbs nanocomposite was about 23.6 nm. The nanocomposite was non-toxic to normal pancreatic ductal epithelial cells, and could specifically bind to and be internalized by MUC4/CEACAM6/CD44v6-expressing PDAC cells. With an r2 relaxivity of 104.2 mM^-1 s^-1, the IONPs-PEG-MCC triple scAbs nanocomposite could significantly shorten the MRI T2-weighted signal intensity both in vitro and in vivo. The IONPs-PEG-MCC triple scAbs nanocomposite also showed a favorable anti-pancreatic cancer effect.

CONCLUSION

In the present study, the IONPs-PEG-MCC triple scAbs nanocomposite was firstly confirmed as a bi-functional nanocomposite in both MRI and treatment, providing its critical clinical transformation potential in PDAC detection and treatment.

Mass spectrometry based proteomics and metabolomics in personalized oncology

Precision medicine (PM) means the customization of healthcare with decisions and practices adjusted to the individual patient. It includes personalized diagnostics, patients' sub-classification, individual treatment selection and the monitoring of its effectiveness. Currently, in oncology, PM is based on the molecular and cellular features of a tumor, its microenvironment and the patient's genetics and lifestyle. Surprisingly, the available targeted therapies were found effective only in a subset of patients. An in-depth understanding of tumor biology is crucial to improve their effectiveness and develop new therapeutic targets. Completion of genetic information with proteomics and metabolomics can give broader knowledge about tumor biology which consequently provides novel biomarkers and indicates new therapeutic targets. Recently, metabolomics and proteomics have extensively been applied in the field of oncology. In the context of PM, human studies, with the use of mass spectrometry (MS) which allows the detection of thousands of molecules in a large number of samples, are the most valuable. Such studies, focused on cancer biomarkers discovery or patients' stratification, are presented in this review. Moreover, the technical aspects of MS-based clinical proteomics and metabolomics are described.

Polymeric nanoassemblies for enrichment and detection of peptides and proteins in human breast milk

Human breast milk is an understudied biological fluid that may be useful for early detection of breast cancer. Methods for enriching and detecting biomarkers in human breast milk, however, are not as well-developed as compared with other biological fluids. In this work, we demonstrate a new enrichment method based on polymeric nanoassemblies that is capable of enhancing the mass spectrometry-based detection of peptides and proteins in human breast milk. In this method, positively charged nanoassemblies are used to selectively deplete abundant proteins in milk based on electrostatic interactions, which simplifies the mixture and enhances detection of positively charged peptides and proteins. Negatively charged nanoassemblies are used in a subsequent enrichment step to further enhance the detection and quantification of trace-level peptides and proteins. Together the depletion and enrichment steps allow model biomarkers to be detected at low nM levels, which are close to instrumental limits of detection. This new method not only demonstrates the ability to detect proteins in human breast milk but also provides an alternative approach for targeted protein detection in complex biological matrices. Graphical abstract.

Clinically accurate diagnosis of Alzheimer's disease via multiplexed sensing of core biomarkers in human plasma

Alzheimer’s disease (AD) is the most prevalent neurodegenerative disorder, affecting one in ten people aged over 65 years. Despite the severity of the disease, early diagnosis of AD is still challenging due to the low accuracy or high cost of neuropsychological tests and neuroimaging. Here we report clinically accurate and ultrasensitive detection of multiple AD core biomarkers (t-tau, p-tau₁₈₁, Aβ₄₂, and Aβ₄₀) in human plasma using densely aligned carbon nanotubes (CNTs). The closely packed and unidirectionally aligned CNT sensor array exhibits high precision, sensitivity, and accuracy, evidenced by a low coefficient of variation (<6%), a femtomolar-level limit of detection, and a high degree of recovery (>93.0%). By measuring the levels of t-tau/Aβ₄₂, p-tau₁₈₁/Aβ₄₂, and Aβ₄₂/Aβ₄₀ in clinical blood samples, the sensor array successfully discriminates the clinically diagnosed AD patients from healthy controls with an average sensitivity of 90.0%, a selectivity of 90.0%, and an average accuracy of 88.6%.

Detection of Alzheimer’s disease (AD) biomarkers from patients’ blood is challenging because these are present in very low concentrations in the plasma. Here the authors develop a sensor array of densely aligned single-walled carbon nanotubes for clinically accurate detection of femtomolar AD biomarkers in human plasma samples.

Serum IGFBP-1 as a potential biomarker for diagnosis of early-stage upper gastrointestinal tumour

BACKGROUND

Early detection would improve upper gastrointestinal cancer prognosis. We aimed to identify serum protein biomarker for the detection of early-stage upper gastrointestinal cancer.

METHODS

We performed a three-tiered study including 2028 participants from three medical centres. First, we applied two different antibody arrays to screen candidate serum proteins that increased in 20 patients with oesophageal squamous cell carcinoma (ESCC) compared with 20 normal controls. We then evaluated the selected protein by enzyme-linked immunosorbent assay in 1064 participants including 731 upper gastrointestinal cancer patients (287 ESCCs, 237 oesophagogastric junction adenocarcinomas (EJAs), and 207 stomach cancers) and 333 normal controls. The diagnostic value of the selected protein was finally validated in two independent cohorts of ESCC patients and controls (n=472 and 452, respectively). The receiver operating characteristic was used to calculate diagnostic accuracy.

FINDINGS

Serum insulin-like growth factor binding protein-1 (IGFBP-1) identified in both antibody arrays showed significantly elevated levels in upper gastrointestinal cancers, compared with normal controls. Serum IGFBP-1 provided high diagnostic accuracy of early-stage ESCC, EJA, stomach and cancer (areas under the curve: 0·898, 0·936 and 0·864, respectively). This protein maintained diagnostic performance for early-stage ESCC in independent cohorts 1 and 2 (0·849 and 0·911, respectively). Additionally, serum levels of IGFBP-1 dropped significantly after surgical resection of primary tumours, compared with the corresponding pre-operative ESCC samples (p < 0·05).

INTERPRETATION

Serum IGFBP-1 represents a promising diagnostic biomarker to detect early-stage upper gastrointestinal cancer.

Label-free fluorescence DNA walker for protein analysis based on terminal protection and dual enzyme assisted cleavage induced G-quadruplex/berberine conformation

Development of a simple, fast, cost-efficient and sensitive approach for accurate protein analysis is of high significance due to its potential application in disease diagnosis and biomedicine research.

Nonstochastic Protein Counting Analysis for Precision Biomarker Detection: Suppressing Poisson Noise at Ultralow Concentration

Protein counting analysis obtains the quantitative results of specific protein through counting the number of target signals and displays a great value in disease diagnosis. Current protein counting techniques just stochastically count a small portion of the target signal, which causes a considerable information loss and limits the accuracy and precision of the protein assay at ultralow concentration. Here, we present a nonstochastic and ultrasensitive protein counting method through combining multiround evaporation-induced particle sedimentation, grid-assisted multiframe imaging, and microsphere-enhanced high-resolution signals. Using carcinoembryonic antigen (CEA) as the model, the dynamic range was from 5 × 10^-18 M (aM) to 5 × 10^-16 M, and the limit of detection was 4.9 aM. For CEA-spiked plasma detection, the relative standard deviation and the relative error of CEA concentrations were both lower than 8.0%, and the recoveries reached 92.5% and 98.8% for 20.0 aM and 40.0 aM CEA respectively. Two clinical plasma samples were measured by the standard addition method, and the results showed little deviation with the values provided by the hospital. The established approach suppresses Poisson noise of the stochastic counting, offers ultrahigh sensitivity, and features a remarkable potential in early disease screening.

Nanotechnology in cancer diagnosis: progress, challenges and opportunities

In the fight against cancer, early detection is a key factor for successful treatment. However, the detection of cancer in the early stage has been hindered by the intrinsic limits of conventional cancer diagnostic methods. Nanotechnology provides high sensitivity, specificity, and multiplexed measurement capacity and has therefore been investigated for the detection of extracellular cancer biomarkers and cancer cells, as well as for in vivo imaging. This review summarizes the latest developments in nanotechnology applications for cancer diagnosis. In addition, the challenges in the translation of nanotechnology-based diagnostic methods into clinical applications are discussed.

The Translational Status of Cancer Liquid Biopsies

Abstract

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. This can be achieved by leveraging omics information for accurate molecular characterization of tumors. Tumor tissue biopsies are currently the main source of information for molecular profiling. However, biopsies are invasive and limited in resolving spatiotemporal heterogeneity in tumor tissues. Alternative non-invasive liquid biopsies can exploit patient’s body fluids to access multiple layers of tumor-specific biological information (genomes, epigenomes, transcriptomes, proteomes, metabolomes, circulating tumor cells, and exosomes). Analysis and integration of these large and diverse datasets using statistical and machine learning approaches can yield important insights into tumor biology and lead to discovery of new diagnostic, predictive, and prognostic biomarkers. Translation of these new diagnostic tools into standard clinical practice could transform oncology, as demonstrated by a number of liquid biopsy assays already entering clinical use. In this review, we highlight successes and challenges facing the rapidly evolving field of cancer biomarker research.

Lay Summary

Precision oncology aims to tailor clinical decisions specifically to patients with the objective of improving treatment outcomes. The discovery of biomarkers for precision oncology has been accelerated by high-throughput experimental and computational methods, which can inform fine-grained characterization of tumors for clinical decision-making. Moreover, advances in the liquid biopsy field allow non-invasive sampling of patient’s body fluids with the aim of analyzing circulating biomarkers, obviating the need for invasive tumor tissue biopsies. In this review, we highlight successes and challenges facing the rapidly evolving field of liquid biopsy cancer biomarker research.

Biosensors for early diagnosis of pancreatic cancer: a review

Pancreatic cancer is characterized by extremely high mortality and poor prognosis and is projected to be the leading cause of cancer deaths by 2030. Due to the lack of early symptoms and appropriate methods to detect pancreatic carcinoma at an early stage as well as its aggressive progression, the disease is often quite advanced by the time a definite diagnosis is established. The 5-year relative survival rate for all stages is approximately 8%. Therefore, detection of pancreatic cancer at an early surgically resectable stage is the key to decrease mortality and to improve survival. The traditional methods for diagnosing pancreatic cancer involve an imaging test, such as ultrasound or magnetic resonance imaging, paired with a biopsy of the mass in question. These methods are often expensive, time consuming, and require trained professionals to use the instruments and analyze the imaging. To overcome these issues, biosensors have been proposed as a promising tool for the early diagnosis of pancreatic cancer. The present review critically discusses the latest developments in biosensors for the early diagnosis of pancreatic cancer. Protein and microRNA biomarkers of pancreatic cancer and corresponding biosensors for pancreatic cancer diagnosis have been reviewed, and all these cases demonstrate that the emerging biosensors are becoming an increasingly relevant alternative to traditional techniques. In addition, we discuss the existing problems in biosensors and future challenges.

A Toolbox for Functional Analysis and the Systematic Identification of Diagnostic and Prognostic Gene Expression Signatures Combining Meta-Analysis and Machine Learning

The identification of biomarker signatures is important for cancer diagnosis and prognosis. However, the detection of clinical reliable signatures is influenced by limited data availability, which may restrict statistical power. Moreover, methods for integration of large sample cohorts and signature identification are limited. We present a step-by-step computational protocol for functional gene expression analysis and the identification of diagnostic and prognostic signatures by combining meta-analysis with machine learning and survival analysis. The novelty of the toolbox lies in its all-in-one functionality, generic design, and modularity. It is exemplified for lung cancer, including a comprehensive evaluation using different validation strategies. However, the protocol is not restricted to specific disease types and can therefore be used by a broad community. The accompanying R package vignette runs in ~1 h and describes the workflow in detail for use by researchers with limited bioinformatics training.

A fluorescent probe for simultaneously sensing NTR and hNQO1 and distinguishing cancer cells

Identifying cancer at the cellular level during an early stage offers the hope of greatly improved outcomes for cancer patients. As potential cancer biomarkers, nitroreductase (NTR) and human quinine oxidoreductase 1 (hNQO1) are overexpressed in many type of cancer cells. Simultaneous detection of these two biomarkers would benefit diagnostic precision in related cancers without yielding false positive results. Herein, based on a dye generated in situ strategy, a dual-enzyme-responsive probe, CNN, was rationally designed and synthesized by installing p-nitrobenzene and trimethyl-locked quinone propionic acid groups, which are specific for NTR and hNQO1, respectively, into a single fluorophore. This probe is only activated in the presence of both NTR and hNQO1 and produces a large fluorescence response, enabling the detection of both endogenous NTR and hNQO1 activity in living cells. The imaging results indicate that the CNN probe differentiates cancer cells (HeLa, MDA-MB-231 and HepG2 cells) from normal liver HL-7702 cells owing to the existence of relatively high endogenous levels of both biomarkers in these cancer cells.

Optical Interrogation Techniques for Nanophotonic Biochemical Sensors

The manipulation of light via nanoengineered surfaces has excited the optical community in the past few decades. Among the many applications enabled by nanophotonic devices, sensing has stood out due to their capability of identifying miniscule refractive index changes. In particular, when free-space propagating light effectively couples into subwavelength volumes created by nanostructures, the strongly-localized near-fields can enhance light’s interaction with matter at the nanoscale. As a result, nanophotonic sensors can non-destructively detect chemical species in real-time without the need of exogenous labels. The impact of such nanophotonic devices on biochemical sensor development became evident as the ever-growing research efforts in the field started addressing many critical needs in biomedical sciences, such as low-cost analytical platforms, simple quantitative bioassays, time-resolved sensing, rapid and multiplexed detection, single-molecule analytics, among others. In this review, the optical transduction methods used to interrogate optical resonances of nanophotonic sensors will be highlighted. Specifically, the optical methodologies used thus far will be evaluated based on their capability of addressing key requirements of the future sensor technologies, including miniaturization, multiplexing, spatial and temporal resolution, cost and sensitivity.

Serum tRNA-derived fragments (tRFs) as potential candidates for diagnosis of nontriple negative breast cancer

Breast cancer has become the most common cancer in women, and nontriple negative breast cancer (non-TNBC) accounts for 80-90% of all invasive breast cancers. Early detection, diagnosis, and treatment are considered key to a successful cure. Conventionally, breast imaging and needle core biopsy are used for detection and monitoring. However, small variations in volume might be ignored in imaging, and traditional biopsies are spatially and temporally limited, leading to a significant delay in cancer detection and thus prompting renewed focus on early and accurate diagnosis. In this article, we investigated whether there is an accurate molecule in peripheral blood that can help diagnose breast cancer. Similar to microRNAs, tRNA-derived fragments (tRFs) have been reported to be involved in many pathological processes in breast cancer, but whether they can serve as candidate biomarkers for breast cancer remains unclear. Using high-throughput sequencing technology, we identified 4,021 differentially expressed tRFs in normal and breast cancer cell lines, and eight tRFs were selected to establish a signature as a predictive biomarker of non-TNBC. Furthermore, quantitative reverse-transcriptase polymerase chain reaction was performed to verify the expression of the signature and analyze the correlation between dysregulated tRFs and breast cancer. The results indicated that tDR-7816, tDR-5334, and tDR-4733 might be promising biomarkers. Through further bioinformatics analysis, we predicted that tDR-7816 influences the xenobiotic metabolic processes that support the oncogenesis of breast cancer. In summary, our results provide a rationale for using circulating tDR-7816 expression as a novel potential biomarker for the diagnosis of patients with early non-TNBC.

TPM2 as a potential predictive biomarker for atherosclerosis

Cardiac-cerebral vascular disease (CCVD), is primarily induced by atherosclerosis, and is a leading cause of mortality. Numerous studies have investigated and attempted to clarify the molecular mechanisms of atherosclerosis; however, its pathogenesis has yet to be completely elucidated. Two expression profiling datasets, GSE43292 and GSE57691, were obtained from the Gene Expression Omnibus (GEO) database. The present study then identified the differentially expressed genes (DEGs), and functional annotation of the DEGs was performed. Finally, an atherosclerosis animal model and neural network prediction model was constructed to verify the relationship between hub gene and atherosclerosis. The results identified a total of 234 DEGs between the normal and atherosclerosis samples. The DEGs were mainly enriched in actin filament, actin binding, smooth muscle cells, and cytokine-cytokine receptor interactions. A total of 13 genes were identified as hub genes. Following verification of animal model, the common DEG, Tropomyosin 2 (TPM2), was found, which were displayed at lower levels in the atherosclerosis models and samples. In summary, DEGs identified in the present study may assist clinicians in understanding the pathogenesis governing the occurrence and development of atherosclerosis, and TPM2 exhibits potential as a promising diagnostic and therapeutic biomarker for atherosclerosis.

Graphene-based biosensors for the detection of prostate cancer protein biomarkers: a review

Prostate cancer (PC) is the sixth most common cancer type in the world, which causes approximately 10% of total cancer fatalities. The detection of protein biomarkers in body fluids is the key topic for the diagnosis and prognosis of PC. Highly sensitive screening of PC is the most effective approach for reducing mortality. Thus, there are a growing number of literature that recognizes the importance of new technologies for early diagnosis of PC. Graphene is playing an important role in the biosensor field with remarkable physical, optical, electrochemical and magnetic properties. Many recent studies demonstrated the potential of graphene materials for sensitive detection of protein biomarkers. In this review, the graphene-based biosensors toward PC analysis are mainly discussed in two groups: Firstly, novel biosensor interfaces were constructed through the modification of graphene materials onto sensor surfaces. Secondly, ingenious signal amplification strategies were developed using graphene materials as catalysts or carriers. Graphene-based biosensors have exhibited remarkable performance with high sensitivities, wide detection ranges, and long-term stabilities.

A New Approach for the Diagnosis of Myelodysplastic Syndrome Subtypes Based on Protein Interaction Analysis

Myelodysplastic syndromes (MDS) are a heterogeneous group of hematological malignancies with a high risk of transformation to acute myeloid leukemia (AML). MDS are associated with posttranslational modifications of proteins and variations in the protein expression levels. In this work, we present a novel interactomic diagnostic method based on both protein array and surface plasmon resonance biosensor technology, which enables monitoring of protein-protein interactions in a label-free manner. In contrast to conventional methods based on the detection of individual biomarkers, our presented method relies on measuring interactions between arrays of selected proteins and patient plasma. We apply this method to plasma samples obtained from MDS and AML patients, as well as healthy donors, and demonstrate that even a small protein array comprising six selected proteins allows the method to discriminate among different MDS subtypes and healthy donors.

The functions of long non-coding RNAs in colorectal cancer

Colorectal cancer (CRC) is the third most prevalent malignant neoplasm worldwide. Recently, in terms of the mechanism of CRC, most studies have focused on protein-coding genes. However, studies have increasingly shown that long non-coding RNAs (lncRNAs) play crucial roles in the proliferation and metastasis of CRC. Investigating this molecular mechanism may provide potential diagnostic tools and therapeutic targets for CRC. This review closely examines the dysregulation of lncRNAs in CRC. On account of different mechanisms being involved in the occurrence and development of CRC, there are several categories of lncRNAs, including lncRNAs related to the Wnt/β-catenin pathway, epithelial mesenchymal transition, epigenetic regulation, angiopoiesis, and chemoresistance. This review summarizes lncRNAs related to the progression of CRC, which may provide insight into the mechanisms and potential markers for prognostic prediction and monitoring relapse of CRC.

Surface Plasmon Resonance for Biomarker Detection: Advances in Non-invasive Cancer Diagnosis

Biomarker-based cancer analysis has great potential to lead to a better understanding of disease at the molecular level and to improve early diagnosis and monitoring. Unlike conventional tissue biopsy, liquid biopsy allows the detection of a large variety of circulating biomarkers, such as microRNA (miRNA), exosomes, circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), and proteins, in an easily accessible and minimally invasive way. In this review, we describe and evaluate the relevance and applicability of surface plasmon resonance (SPR) and localized SPR (LSPR)-based platforms for the detection of different classes of cancer biomarkers in liquid biopsy samples. Firstly, we critically discuss unsolved problems and issues in capturing and analyzing biomarkers. Secondly, we highlight current challenges which need to be resolved in applying SPR biosensors into clinical practice. Then, we mainly focus on applications of SPR-based platforms that process a patient sample aiming to detect and quantify biomarkers as a minimally invasive liquid biopsy tool for cancer patients appearing over the last 5 years. Finally, we describe the analytical performances of selected SPR biosensor assays and their significant advantages in terms of high sensitivity and specificity as well as accuracy and workflow simplicity.

Barcoded DNA nanostructures for the multiplexed profiling of subcellular protein distribution

Massively parallel DNA sequencing is established, yet high-throughput protein profiling remains challenging. Here, we report a barcoding approach that leverages the combinatorial sequence content and the configurational programmability of DNA nanostructures for high-throughput multiplexed profiling of the subcellular expression and distribution of proteins in whole cells. The barcodes are formed by in situ hybridization of tetrahedral DNA nanostructures and short DNA sequences conjugated with protein-targeting antibodies, and by nanostructure-assisted ligation (either enzymatic or chemical) of the nanostructures and exogenous DNA sequences bound to nanoparticles of different sizes (which cause these localization sequences to differentially distribute across subcellular compartments). Compared with linear DNA barcoding, the nanostructured barcodes enhance the signal by more than 100-fold. By implementing the barcoding approach on a microfluidic device for the analysis of rare patient samples, we show that molecular subtypes of breast cancer can be accurately classified and that subcellular spatial markers of disease aggressiveness can be identified.

Prognostic value of aspartate transaminase to alanine transaminase (De Ritis) ratio in solid tumors: a pooled analysis of 9,400 patients

BACKGROUND

Numerous studies have reported the association between pretreatment serum aspartate transaminase to alanine transaminase (AST/ALT) ratio and prognosis in multiple cancers. However, the results remain controversial and no consensus has been reached. Thus, we conducted this meta-analysis to quantitatively assess the prognostic value of pretreatment AST/ALT ratio in solid tumors.

METHODS

A systematic literature search was conducted by using PubMed, EMBASE, Web of Science, Cochrane Library, and Wanfang databases, as well as several trial registry platforms, including ClinicalTrials.gov, WHO International Clinical Trials Registry Platform, and Chinese Clinical Trial Registry, up to April 5, 2019. HR and 95% CI for overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS) were calculated to estimate the effect size.

RESULTS

A total of 18 studies with 9,400 patients were included. Overall, a high level of pretreatment AST/ALT ratio was significantly associated with worse OS (pooled HR=1.70, 95% CI=1.38-2.09). The statistical significance was observed in all cancer types, including renal cell carcinoma (pooled HR=1.64, 95% CI=1.30-2.05), liver cancer (pooled HR=1.16, 95% CI=1.04-1.29), urinary tract urothelial carcinoma (pooled HR=1.96, 95% CI=1.53-2.51), bladder cancer (pooled HR =2.66, 95% CI=1.69-4.20), and other cancers (pooled HR=1.44, 95% CI=1.18-1.76). Moreover, an increased level of serum AST/ALT ratio predicted unfavorable CSS (pooled HR=2.07, 95% CI=1.74-2.46) and RFS (pooled HR=1.51, 95% CI=1.15-1.99).

CONCLUSION

Elevated level of serum AST/ALT ratio before treatment is significantly associated with poor clinical outcomes of OS, CSS, and RFS in patients with solid tumors. Pretreatment AST/ALT ratio can serve as a useful prognostic predictor for malignant patients.

Microflow UPLC and high-resolution MS as a sensitive and robust platform for quantitation of intact peptide hormones

Aim: Recent advances in microflow ultra performance liquid chromatography (UPLC) systems offer higher sensitivity with robustness to meet the routine bioanalytical demands. Modern high-resolution mass spectrometers (HRMS) enable the development of highly selective methods with broad dynamic range. Results: The quantitative performances of tandem quadrupole MS and HRMS were comprehensively compared using seven intact peptide hormones up to 9.4 kDa. Results show comparable performance between two platforms in sensitivity, accuracy and linearity. For some peptides, HRMS provided lower background interference. The benefit of increased sensitivity using microflow UPLC was also demonstrated. Conclusion: HRMS is a versatile platform capable of both basic characterization and reliable quantitation in complex matrices. Microflow UPLC provides lower LLOQs than conventional flow systems, even with less sample volume injected.

An integrated and automated electronic system for point-of-care protein testing

Protein testing in blood is important for clinical analysis. Traditional blood tests are performed in centralized laboratories and are slow to provide results. In contrast, point-of-care devices deliver rapid results in non-laboratory settings, allowing timely analysis and in turn reducing healthcare costs. Successful point-of-care platforms require seamless integration of chemical assays, fluid management and signal readout. In this regard, we present an integrated, compact and automated electronic system for point-of-care protein sensing. The system comprises of a microfluidics-based electrochemical biosensor, amperometry circuitry and automated microfluidic fluid handling circuitry. This platform utilizes magnetic microbeads to expedite an electronic enzyme-linked immunosorbent assay, and microfluidics to manage small volumes and automate assay operations. A commercial single-chip potentiostat is utilized for amperometry measurements and microfluidics control. Using this all-electrical system, we demonstrate an integrated and automated assay for human interleukin-6.

Oncoproteomics: Current status and future opportunities

Oncoproteomics is the systematic study of cancer samples using omics technologies to detect changes implicated in tumorigenesis. Recent progress in oncoproteomics is already opening new avenues for the identification of novel biomarkers for early clinical stage cancer detection, targeted molecular therapies, disease monitoring, and drug development. Such information will lead to new understandings of cancer biology and impact dramatically on the future care of cancer patients. In this review, we will summarize the advantages and limitations of the key technologies used in (onco)proteogenomics, (the Omics Pipeline), explain how they can assist us in understanding the biology behind the overarching "Hallmarks of Cancer", discuss how they can advance the development of precision/personalised medicine and the future directions in the field.

Surface-Enhanced Raman Spectroscopy in Cancer Diagnosis, Prognosis and Monitoring

As medicine continues to advance our understanding of and knowledge about the complex and multifactorial nature of cancer, new major technological challenges have emerged in the design of analytical methods capable of characterizing and assessing the dynamic heterogeneity of cancer for diagnosis, prognosis and monitoring, as required by precision medicine. With this aim, novel nanotechnological approaches have been pursued and developed for overcoming intrinsic and current limitations of conventional methods in terms of rapidity, sensitivity, multiplicity, non-invasive procedures and cost. Eminently, a special focus has been put on their implementation in liquid biopsy analysis. Among optical nanosensors, those based on surface-enhanced Raman scattering (SERS) have been attracting tremendous attention due to the combination of the intrinsic prerogatives of the technique (e.g., sensitivity and structural specificity) and the high degree of refinement in nano-manufacturing, which translate into reliable and robust real-life applications. In this review, we categorize the diverse strategic approaches of SERS biosensors for targeting different classes of tumor biomarkers (cells, nucleic acids and proteins) by illustrating key recent research works. We will also discuss the current limitations and future research challenges to be addressed to improve the competitiveness of SERS over other methodologies in cancer medicine.

Use of big data in drug development for precision medicine: an update

INTRODUCTION

Big-data-driven drug development resources and methodologies have been evolving with ever-expanding data from large-scale biological experiments, clinical trials, and medical records from participants in data collection initiatives. The enrichment of biological- and clinical-context-specific large-scale data has enabled computational inference more relevant to real-world biomedical research, particularly identification of therapeutic targets and drugs for specific diseases and clinical scenarios.

AREAS COVERED

Here we overview recent progresses made in the fields: new big-data-driven approach to therapeutic target discovery, candidate drug prioritization, inference of clinical toxicity, and machine-learning methods in drug discovery.

EXPERT OPINION

In the near future, much larger volumes and complex datasets for precision medicine will be generated, e.g., individual and longitudinal multi-omic, and direct-to-consumer datasets. Closer collaborations between experts with different backgrounds would also be required to better translate analytic results into prognosis and treatment in the clinical practice. Meanwhile, cloud computing with protected patient privacy would become more routine analytic practice to fill the gaps within data integration along with the advent of big-data. To conclude, integration of multitudes of data generated for each individual along with techniques tailored for big-data analytics may eventually enable us to achieve precision medicine.

The miRNA-kallikrein interaction: a mosaic of epigenetic regulation in cancer

The kallikrein-related peptidases (KLKs) constitute a family of 15 highly conserved serine proteases with trypsin- and chymotrypsin-like activities. Dysregulated expression and/or aberrant activation of KLKs has been linked to various pathophysiological processes, including cancer. Many KLKs have been identified as potential cancer biomarkers. microRNAs (miRNAs) are a class of small non-coding RNAs that regulate gene expression by pairing to the 3' untranslated region (UTR) of complimentary mRNA targets. miRNAs are dysregulated in many cancers, including prostate, kidney and ovarian cancers. Several studies have shown that miRNAs are involved in the post-transcriptional regulation of KLKs. However, recent evidence suggests that miRNAs can also act as downstream effectors of KLKs. In this review, we provide an update on the epigenetic regulation of KLKs by miRNAs. We also present recent experimental evidence that supports the regulatory role of KLKs on miRNA networks. The potential diagnostic and therapeutic applications of miRNA-kallikrein interactions are also discussed.

A new class of protein biomarkers based on subcellular distribution: application to a mouse liver cancer model

To-date, most proteomic studies aimed at discovering tissue-based cancer biomarkers have compared the quantity of selected proteins between case and control groups. However, proteins generally function in association with other proteins to form modules localized in particular subcellular compartments in specialized cell types and tissues. Sub-cellular mislocalization of proteins has in fact been detected as a key feature in a variety of cancer cells. Here, we describe a strategy for tissue-biomarker detection based on a mitochondrial fold enrichment (mtFE) score, which is sensitive to protein abundance changes as well as changes in subcellular distribution between mitochondria and cytosol. The mtFE score integrates protein abundance data from total cellular lysates and mitochondria-enriched fractions, and provides novel information for the classification of cancer samples that is not necessarily apparent from conventional abundance measurements alone. We apply this new strategy to a panel of wild-type and mutant mice with a liver-specific gene deletion of Liver receptor homolog 1 (Lrh-1^hep−/−), with both lines containing control individuals as well as individuals with liver cancer induced by diethylnitrosamine (DEN). Lrh-1 gene deletion attenuates cancer cell metabolism in hepatocytes through mitochondrial glutamine processing. We show that proteome changes based on mtFE scores outperform protein abundance measurements in discriminating DEN-induced liver cancer from healthy liver tissue, and are uniquely robust against genetic perturbation. We validate the capacity of selected proteins with informative mtFE scores to indicate hepatic malignant changes in two independent mouse models of hepatocellular carcinoma (HCC), thus demonstrating the robustness of this new approach to biomarker research. Overall, the method provides a novel, sensitive approach to cancer biomarker discovery that considers contextual information of tested proteins.

Identifying occult maternal malignancies from 1.93 million pregnant women undergoing noninvasive prenatal screening tests

PURPOSE

Multiple chromosomal aneuploidies may be associated with maternal malignancies and can cause failure of noninvasive prenatal screening (NIPS) tests. However, multiple chromosomal aneuploidies show poor specificity and selectivity for diagnosing maternal malignancies.

METHODS

This multicenter retrospective analysis evaluated 639 pregnant women who tested positive for multiple chromosomal aneuploidies on initial NIPS test between January 2016 and December 2017. Women were assessed using genome profiling of copy-number variations, which was translated to cancer risk using a novel bioinformatics algorithm called the cancer detection pipeline (CDP). Sensitivity, specificity, and positive predictive value (PPV) of diagnosing maternal malignancies were compared for multiple chromosomal aneuploidies, the CDP model, and the combination of CDP and plasma tumor markers.

RESULTS

Of the 639 subjects, 41 maternal malignant cancer cases were diagnosed. Multiple chromosomal aneuploidies predicted maternal malignancies with a PPV of 7.6%. Application of the CDP algorithm to women with multiple chromosomal aneuploidies allowed 34 of the 41 (83%) cancer cases to be identified, while excluding 422 of 501 (84.2%) of the false positive cases. Combining the CDP with plasma tumor marker testing gave PPV of 75.0%.

CONCLUSION

The CDP algorithm can diagnose occult maternal malignancies with a reasonable PPV in multiple chromosomal aneuploidies-positive pregnant women in NIPS tests. This performance can be further improved by incorporating findings for plasma tumor markers.

Applying Machine Learning of Erythrocytes Dynamic Antigens Store in Medicine

Erythrocytes Dynamic Antigens Store (EDAS) is a new discovery. EDAS consists of self-antigens and foreign (non-self) antigens. In patients with infectious diseases or malignancies, antigens of infection microorganism or malignant tumor exist in EDAS. Storing EDAS of normal individuals and patients in a database has, at least, two benefits. First, EDAS can be mined to determine biomarkers representing diseases which can enable researchers to develop a new line of laboratory diagnostic tests and vaccines. Second, EDAS can be queried, directly, to reach a precise diagnosis without the need to do many laboratory tests. The target is to find the minimum set of proteins that can be used as biomarkers for a particular disease. A hypothetical EDAS is created. Hundred-thousand records are randomly generated. The mathematical model of hypothetical EDAS together with the proposed techniques for biomarker discovery and direct diagnosis are described. The different possibilities that may occur in reality are experimented. Biomarkers' proteins are identified for pathogens and malignancies, which can be used to diagnose conditions that are difficult to diagnose. The presented tool can be used in clinical laboratories to diagnose disease disorders.

Dual-biomarker-triggered fluorescence probes for differentiating cancer cells and revealing synergistic antioxidant effects under oxidative stress

Hydrogen sulfide (H2S) and human NAD(P)H:quinine oxidoreductase 1 (hNQO1) are potential cancer biomarkers and also vital participants in cellular redox homeostasis. Simultaneous detection of these two biomarkers would benefit the diagnostic precision of related cancers and could also help to investigate their crosstalk in response to oxidative stress. Despite this importance, fluorescent probes that can be activated by the dual action of H2S detection and hNQO1 activity have not been investigated. To this end, dual-biomarker-triggered fluorescent probes 1 and 2 were rationally constructed by installing two chemoselective triggering groups into one fluorophore. Probe 1 provides a small turn-on fluorescence response toward H2S but a much larger response to both H2S and hNQO1 in tandem. By contrast, fluorescence probe 2 is activated only in the presence of both H2S and hNQO1. Probe 2 exhibits a large fluorescence turn-on (>400 fold), high sensitivity, excellent selectivity as well as good biocompatibility, enabling the detection of both endogenous H2S and hNQO1 activity in living cells. Bioimaging results indicated that probe 2 could differentiate HT29 and HepG2 cancer cells from HCT116, FHC and HeLa cells owing to the existence of relatively high endogenous levels of both biomarkers. Expanded investigations using 2 revealed that cells could generate more endogenous H2S and hNQO1 upon exposure to exogenous hydrogen peroxide (H2O2), implying the synergistic antioxidant effects under conditions of cellular oxidative stress.