Emerging molecular biomarkers--blood-based strategies to detect and monitor cancer. Nat Rev Clin Oncol. 2011;8:142-150. [PMID: 21364687 DOI: 10.1038/nrclinonc.2010.220]

Nucleic acid-based wearable and implantable electrochemical sensors

The rapid advancements in nucleic acid-based electrochemical sensors for implantable and wearable applications have marked a significant leap forward in the domain of personal healthcare over the last decade. This technology promises to revolutionize personalized healthcare by facilitating the early diagnosis of diseases, monitoring of disease progression, and tailoring of individual treatment plans. This review navigates through the latest developments in this field, focusing on the strategies for nucleic acid sensing that enable real-time and continuous biomarker analysis directly in various biofluids, such as blood, interstitial fluid, sweat, and saliva. The review delves into various nucleic acid sensing strategies, emphasizing the innovative designs of biorecognition elements and signal transduction mechanisms that enable implantable and wearable applications. Special perspective is given to enhance nucleic acid-based sensor selectivity and sensitivity, which are crucial for the accurate detection of low-level biomarkers. The integration of such sensors into implantable and wearable platforms, including microneedle arrays and flexible electronic systems, actualizes their use in on-body devices for health monitoring. We also tackle the technical challenges encountered in the development of these sensors, such as ensuring long-term stability, managing the complexity of biofluid dynamics, and fulfilling the need for real-time, continuous, and reagentless detection. In conclusion, the review highlights the importance of these sensors in the future of medical engineering, offering insights into design considerations and future research directions to overcome existing limitations and fully realize the potential of nucleic acid-based electrochemical sensors for healthcare applications.

Comprehensive serum proteomics profiles and potential protein biomarkers for the early detection of advanced adenoma and colorectal cancer

BACKGROUND

The majority of colorectal cancer (CRC) cases develop from precursor advanced adenoma (AA). With the development of proteomics technologies, blood protein biomarkers have potential applications in the early screening of AA and CRC in the general population.

AIM

To identify serum protein biomarkers for the early screening of AA and CRC.

METHODS

We collected 43 serum samples from 8 normal controls (NCs), 19 AA patients and 16 CRC patients at China-Japan Friendship Hospital. Quantitative proteomic analysis was performed using liquid chromatography-mass spectrometry/mass spectrometry and data independent acquisition, and differentially expressed proteins (DEPs) with P-values < 0.05 and absolute fold changes > 1.5 were screened out, followed by bioinformatics analysis. Prognosis was further analyzed based on public databases, and proteins expression in tissues were validated by immunohistochemistry.

RESULTS

A total of 2132 proteins and 17365 peptides were identified in the serum samples. There were 459 upregulated proteins and 118 downregulated proteins in the NC vs AA group, 289 and 180 in the NC vs CRC group, and 52 and 248 in the AA vs CRC group, respectively. Bioinformatic analysis revealed that these DEPs had different functions and participated in extensive signaling pathways. We also identified DIAPH1, VASP, RAB11B, LBP, SAR1A, TUBGCP5, and DOK3 as important proteins for the progression of AA and CRC. Furthermore, VASP (P < 0.01), LBP (P = 0.01), TUBGCP5 (P < 0.01), and DOK3 (P < 0.01) were associated with a poor prognosis. In addition, we propose that LBP and VASP may be more promising protein biomarkers for the early screening of colorectal tumors.

CONCLUSION

Our study elucidated the serum proteomic profiles of AA and CRC patients, and the identified proteins, such as LBP and VASP, may contribute to the early detection of AA and CRC.

Surface-enhanced Raman spectroscopy of the filtrate portions of the blood serum samples of breast cancer patients obtained by using 30 kDa filtration device

Raman spectroscopy is reliable tool for analyzing and exploring early disease diagnosis related to body fluids, such as blood serum, which contain low molecular weight fraction (LMWF) and high molecular weight fraction (HMWF) proteins. The disease biomarkers consist of LMWF which are dominated by HMWF hence their analysis is difficult. In this study, in order to overcome this issue, centrifugal filter devices of 30 kDa were used to obtain filtrate and residue portions obtained from whole blood serum samples of control and breast cancer diagnosed patients. The filtrate portions obtained in this way are expected to contain the marker proteins of breast cancer of the size below this filter size. These may include prolactin, Microphage migration inhabitation factor (MIF), γ-Synuclein, BCSG1, Leptin, MUC1, RS/DJ-1 present in the centrifuged blood serum (filtrate portions) which are then analyzed by the SERS technique to recognize the SERS spectral characteristics associated with the progression of breast cancer in the samples of different stages as compared to the healthy ones. The key intention of this study is to achieve early-stage breast cancer diagnosis through the utilization of Surface Enhanced Raman Spectroscopy (SERS) after the centrifugation of healthy and breast cancer serum samples with Amicon ultra-filter devices of 30 kDa. The silver nanoparticles with high plasmon resonance are used as a substrate for SERS analysis. Principal Component Analysis (PCA) and Partial Least Discriminant Analysis (PLS-DA) models are utilized as spectral classification tools to assess and predict rapid, reliable, and non-destructive SERS-based analysis. Notably, they were particularly effective in distinguishing between different SERS spectral groups of the cancerous and non-cancerous samples. By comparing all these spectral data sets to each other PLSDA shows the 79 % accuracy, 76 % specificity, and 81 % sensitivity in samples with AUC value of AUC = 0.774 SERS has proven to be a valuable technique for the rapid identification of the SERS spectral features of blood serum and its filtrate fractions from both healthy individuals and those with breast cancer, aiding in disease diagnosis.

Mechanistic insight into glioma through spatially multidimensional proteomics

Characterizing the tumor microenvironment at the molecular level is essential for understanding the mechanisms of tumorigenesis and evolution. However, the specificity of the blood proteome in localized region of the tumor and its linkages with other systems is difficult to investigate. Here, we propose a spatially multidimensional comparative proteomics strategy using glioma as an example. The blood proteome signature of tumor microenvironment was specifically identified by in situ collection of arterial and venous blood from the glioma region of the brain for comparison with peripheral blood. Also, by integrating with different dimensions of tissue and peripheral blood proteomics, the information on the genesis, migration, and exchange of glioma-associated proteins was revealed, which provided a powerful method for tumor mechanism research and biomarker discovery. The study recruited multidimensional clinical cohorts, allowing the proteomic results to corroborate each other, reliably revealing biological processes specific to gliomas, and identifying highly accurate biomarkers.

Bimetallic Nanozyme: A Credible Tag for In Situ-Catalyzed Reporter Deposition in the Lateral Flow Immunoassay for Ultrasensitive Cancer Diagnosis

The lateral flow immunoassay (LFIA) is a sought-after point-of-care testing platform, yet the insufficient sensitivity of the LFIA limits its application in the detection of tumor biomarkers. Here, a colorimetric signal amplification method, bimetallic nanozyme-mediated in situ-catalyzed reporter deposition (BN-ISCRD), was designed for ultrasensitive cancer diagnosis. The bimetallic nanozyme used, palladium@iridium core-shell nanoparticles (Pd@Ir NPs), had ultrahigh enzyme-like activity, which was further explained by the electron transfer of Pd@Ir NPs and the change in the Gibbs free energy during catalysis through density functional theory calculations. With gastric cancer biomarkers pepsinogen I and pepsinogen II as model targets, this assay could achieve a cutoff value of 10 pg/mL, which was 200-fold lower than that without signal enhancement. The assay was applied to correctly identify 8 positive and 28 negative clinical samples. Overall, this BN-ISCRD-based LFIA showed great merits and potential in the application of ultrasensitive disease diagnosis.

Novel PBMC LncRNA signatures as diagnostic biomarkers for colorectal cancer

The expression of long non-coding RNAs (LncRNAs) in peripheral blood mononuclear cell (PBMC) and its clinical relevance in colorectal cancer (CRC) remains largely uncharacterized. To address these gaps, we investigated the expression profiles of lncRNAs in PBMC from CRC and healthy controls (HC) by RNA sequencing. The expression level of differentially expressed lncRNAs (DElncRNAs) were evaluated by quantitative PCR in PBMC samples from CRC patients and HC. A total of 447 DElncRNAs were identified, with 178 elevated lncRNAs and 269 decreased lncRNAs in PBMC from CRC patients as compared with that from HC. RT-PCR results supported a significant elevation of NEAT1:11, lnc-PDZD8-1:5 and LINC00910:16 in 98 CRC patients and 82 HC. The clinical implication of NEAT1:11, lnc-PDZD8-1:5 and LINC00910:16 as CRC diagnostic biomarker were determined by receiver operating characteristic (ROC) curve, showing sensitivity 74.5% and specificity 84.5% for joint detection the three lncRNAs. Notably, NEAT1:11 was closely related with the size and extent of primary tumor, with higher relative expression of NEAT1:11 in higher T stage (P = 0.0047). Moreover, NEAT1:11 was related with grade (P = 0.012). Collectively, PBMC from patients with CRC show significantly variable expression profiles of lncRNAs, and detection of these differential expression lncRNAs may provide useful information for basic and clinical research.

Development of antibody-aptamer sandwich-like immunosensor based on RCA and Nicked-PAM CRISPR/Cas12a system for the ultra-sensitive detection of a biomarker

Biomarkers are the most sensitive reactants and early indicators of many kinds of diseases. The development of highly sensitive and simple techniques to quantify them is challenging. In this study, based on rolling cycle amplification (RCA) and the Nicked PAM/CRISPR-Cas12a system (RNPC) as a signal reporter, a sandwich-type method was developed using antibody@magnetic beads and aptamer for the high-sensitive detection of the C-reactive protein (CRP). The antibody-antigen (target)-aptamer sandwich-like reaction was coupled to RCA, which can produce hundreds of similar binding sites and are discriminated by CRISPR/Cas12a for signal amplification. The ultrasensitivity is achieved based on the dual-signal enhancing strategy, which involves the special recognition of aptamers, RCA, and trans-cleavage of CRISPR/Cas12a. By incorporating the CRISPR/Cas12a system with cleaved PAM, the nonspecific amplification of the RCA reaction alone was greatly reduced, and the dual signal output of RCA and Cas12a improved the detection sensitivity. Our assay can be performed only in two steps. The first step takes only 20 min of target capture, followed by a one-pot reaction, where the target concentration can be obtained by fluorescence values as long as there are 37 °C reaction conditions. Under optimal conditions, this system detected CRP with high sensitivity. The fabricated biosensor showed detection limits of 0.40 pg/mL in phosphate-buffered saline and 0.73 pg/mL in diluted human serum and a broad linear dynamic range of 1.28 pg/mL to 100 ng/mL within a total readout time of 90 min. The method could be used to perform multi-step signal amplification, which can help in the ultrasensitive detection of other proteins. Overall, the proposed biosensor might be used as an immunosensor biosensor platform.

Tumor-Associated and Systemic Autoimmunity in Pre-Clinical Breast Cancer among Post-Menopausal Women

Autoantibodies to tumor-associated antigens (anti-TAA) are potential biomarkers for breast cancer, but their relationship systemic autoimmunity as ascertained though antinuclear antibodies (ANA) is unknown and warrants consideration given the common occurrence of autoimmunity and autoimmune diseases among women. The relationship between anti-TAAs and ANA among women who were later diagnosed with breast cancer and others who remained cancer free in the Women's Health Initiative cohort. The study sample included 145 post-menopausal women with baseline ANA data. A total of 37 ANA-positive women who developed breast cancer (i.e., cases; mean time to diagnosis 6.8 years [SE 3.9]) were matched to a random sample of 36 ANA-negative cases by age and time to diagnosis. An age-matched control sample was selected including 35 ANA-positive and 37 ANA-negative women who did not develop breast cancer (i.e., controls; follow-up time ~13 years [SE 3]). Baseline sera were assessed for Immunoglobulin G (IgG) antibodies, measured by custom microarray for 171 breast and other cancer-associated TAA. We used linear regression to estimate cross-sectional associations of ANA with log-transformed anti-TAA among cases and controls. Most anti-TAA did not vary by ANA status. Two anti-TAA were elevated in ANA-positive compared to ANA-negative cases: anti-PGM3 (p = 0.004) and anti-TTN (p = 0.005, especially in cases up to 7 years before diagnosis, p = 0.002). Anti-TAA antibodies were not generally related to ANA, a common marker of systemic autoimmunity. Associations of ANA with particular antigens inducing autoimmunity prior to breast cancer warrant further investigation.

Mass spectrometry-based proteomics as an emerging tool in clinical laboratories

Mass spectrometry (MS)-based proteomics have been increasingly implemented in various disciplines of laboratory medicine to identify and quantify biomolecules in a variety of biological specimens. MS-based proteomics is continuously expanding and widely applied in biomarker discovery for early detection, prognosis and markers for treatment response prediction and monitoring. Furthermore, making these advanced tests more accessible and affordable will have the greatest healthcare benefit.This review article highlights the new paradigms MS-based clinical proteomics has created in microbiology laboratories, cancer research and diagnosis of metabolic disorders. The technique is preferred over conventional methods in disease detection and therapy monitoring for its combined advantages in multiplexing capacity, remarkable analytical specificity and sensitivity and low turnaround time.Despite the achievements in the development and adoption of a number of MS-based clinical proteomics practices, more are expected to undergo transition from bench to bedside in the near future. The review provides insights from early trials and recent progresses (mainly covering literature from the NCBI database) in the application of proteomics in clinical laboratories.

Metabolomics Approaches for the Diagnosis, Treatment, and Better Disease Management of Viral Infections

Metabolomics is an analytical approach that involves profiling and comparing the metabolites present in biological samples. This scoping review article offers an overview of current metabolomics approaches and their utilization in evaluating metabolic changes in biological fluids that occur in response to viral infections. Here, we provide an overview of metabolomics methods including high-throughput analytical chemistry and multivariate data analysis to identify the specific metabolites associated with viral infections. This review also focuses on data interpretation and applications designed to improve our understanding of the pathogenesis of these viral diseases.

Blood Transcriptomic Analyses Reveal Functional Pathways Associated with Thermotolerance in Pregnant Ewes Exposed to Environmental Heat Stress

Environmental heat stress triggers a series of compensatory mechanisms in sheep that are dependent on their genetic regulation of thermotolerance. Our objective was to identify genes and regulatory pathways associated with thermotolerance in ewes exposed to heat stress. We performed next-generation RNA sequencing on blood collected from 16 pregnant ewes, which were grouped as tolerant and non-tolerant to heat stress according to a physiological indicator. Additional samples were collected to measure complete blood count. A total of 358 differentially expressed genes were identified after applying selection criteria. Gene expression analysis detected 46 GO terms and 52 KEGG functional pathways. The top-three signaling pathways were p53, RIG-I-like receptor and FoxO, which suggested gene participation in biological processes such as apoptosis, cell signaling and immune response to external stressors. Network analysis revealed ATM, ISG15, IRF7, MDM4, DHX58 and TGFβR1 as over-expressed genes with high regulatory potential. A co-expression network involving the immune-related genes ISG15, IRF7 and DXH58 was detected in lymphocytes and monocytes, which was consistent with hematological findings. In conclusion, transcriptomic analysis revealed a non-viral immune mechanism involving apoptosis, which is induced by external stressors and appears to play an important role in the molecular regulation of heat stress tolerance in ewes.

Identification and evaluation of novel serum autoantibody biomarkers for early diagnosis of gastric cancer and precancerous lesion

PURPOSE

Early diagnosis is crucial for optimal prognosis of gastric cancer (GC). Hereby, we aimed to identify novel serum autoantibody-based biomarkers for precancerous lesion (PL) and early GC.

METHODS

We performed serological proteome analysis (SERPA) combined with nanoliter-liquid chromatography combined with quadrupole time of flight tandem mass spectrometry (Nano-LC-Q-TOF-MS/MS) to screen for GC-associated autoantibodies. The identified autoantibodies were analyzed for potential detection value for PL and GC by enzyme-linked immunosorbent assay (ELISA). Receiver operating characteristic (ROC) curves analysis was conducted to evaluate the accuracy of the biomarkers.

RESULTS

We identified seven candidates, such as mRNA export factor (RAE1), Nucleophosmin 1 (NPM1), phosphoglycerate kinase 1 (PGK1), and ADP-ribosylation factor 4 (ARF4). Antibodies against all seven proteins were present at higher levels in sera from 242 patients (51 PL, 78 early GC, 113 advanced GC) compared with sera from 122 healthy individuals. RAE1-specific autoantibody discriminated best between patients at different GC stages, with area under the curve (AUC) values of 0.710, 0.745, and 0.804 for PL, early GC, and advanced GC, respectively. Two predictive models composed of gender, RAE1, PGK1, NPM1, and ARF4 autoantibodies (Model 2 for PL) and of age, gender, RAE1, PGK1, and NPM1 autoantibodies (Model 3 for early GC) had improved diagnostic efficiencies, with AUCs of 0.803 and 0.857, sensitivities of 66.7% and 75.6%, and specificities of 78.7% and 87.7%, respectively.

CONCLUSION

The identified serum tumor-associated autoantibodies (TAAbs) may have good potential for early detection of GC and PL.

Increasing the Capture Rate of Circulating Tumor DNA in Unaltered Plasma Using Passive Microfluidic Mixer Flow Cells

A limiting factor in using blood-based liquid biopsies for cancer detection is the volume of extracted blood required to capture a measurable number of circulating tumor DNA (ctDNA). To overcome this limitation, we developed a technology named the dCas9 capture system to capture ctDNA from unaltered flowing plasma, removing the need to extract the plasma from the body. This technology has provided the first opportunity to investigate whether microfluidic flow cell design can affect the capture of ctDNA in unaltered plasma. With inspiration from microfluidic mixer flow cells designed to capture circulating tumor cells and exosomes, we constructed four microfluidic mixer flow cells. Next, we investigated the effects of these flow cell designs and the flow rate on the rate of captured spiked-in BRAF T1799A (BRAF^Mut) ctDNA in unaltered flowing plasma using surface-immobilized dCas9. Once the optimal mass transfer rate of ctDNA, identified by the optimal ctDNA capture rate, was determined, we investigated whether the design of the microfluidic device, flow rate, flow time, and the number of spiked-in mutant DNA copies affected the rate of capture by the dCas9 capture system. We found that size modifications to the flow channel had no effect on the flow rate required to achieve the optimal capture rate of ctDNA. However, decreasing the size of the capture chamber decreased the flow rate required to achieve the optimal capture rate. Finally, we showed that, at the optimal capture rate, different microfluidic designs using different flow rates could capture DNA copies at a similar rate over time. In this study, the optimal capture rate of ctDNA in unaltered plasma was identified by adjusting the flow rate in each of the passive microfluidic mixer flow cells. However, further validation and optimization of the dCas9 capture system are required before it is ready to be used clinically.

Carcinoembryonic antigen kinetics predict response to first-line treatment in metastatic colorectal cancer: Analysis from PRODIGE 9 trial

BACKGROUND

To examine the relationship between carcinoembryonic antigen (CEA) kinetics and prognosis in metastatic colorectal cancer (mCRC) patients receiving first-line chemotherapy in the PRODIGE9 trial.

METHODS

Associations between monthly CEA measurements within 6 months since baseline and progression-free survival (PFS) were evaluated using a joint-latent class-mixed model. A validation set was used to test our prognosis model. Correlations between CEA trajectories (classes) and baseline characteristics were also investigated.

RESULTS

Three classes were identified. Class 1 had low baseline CEA with small variations. Class 2 had high baseline CEA with a rapid decrease reaching the same CEA level at 6 months as in class 1. Class 3 had high baseline CEA with a transient decrease followed by an increase to reach, at 6 months, the same CEA level as at baseline. Six-month PFS was significantly lower in class 3 than in classes 1 and 2 (57% vs. 91% and 93% respectively; p<0.01). Class 3 was significantly associated with ECOG 2 status, a high LDH level and non-resected primary tumor.

DISCUSSION

Variations in CEA kinetics correlate with prognosis in patients receiving first-line chemotherapy for mCRC. We propose here a user-friendly application to classify CEA trajectory.

Type XXII Collagen Complements Fibrillar Collagens in the Serological Assessment of Tumor Fibrosis and the Outcome in Pancreatic Cancer

Circulating fragments of type III collagen, measured by PRO-C3, has shown promising results as a tumor fibrosis biomarker. However, the fibrotic tumor microenvironment consists of many other collagens with diverse functions and unexplored biomarker potential. One example hereof is type XXII collagen (COL22). In this study, we investigated the biomarker potential of COL22 by measuring this in serum. An ELISA, named PRO-C22, was developed and measured in two serum cohorts consisting of patients with various solid tumors (n = 220) and healthy subjects (n = 33) (Cohort 1), and patients with pancreatic ductal adenocarcinoma (PDAC) (n = 34), and healthy subjects (n = 20) (Cohort 2). In Cohort 1, PRO-C22 was elevated in the serum from patients with solid tumors, compared to healthy subjects (p < 0.01 to p < 0.0001), and the diagnostic accuracy (AUROC) ranged from 0.87 to 0.98, p < 0.0001. In Cohort 2, the high levels of PRO-C22, in patients with PDAC, were predictive of a worse overall survival (HR = 4.52, 95% CI 1.90−10.7, p = 0.0006) and this remained significant after adjusting for PRO-C3 (HR = 4.27, 95% CI 1.24−10.4, p = 0.0013). In conclusion, PRO-C22 has diagnostic biomarker potential in various solid tumor types and prognostic biomarker potential in PDAC. Furthermore, PRO-C22 complemented PRO-C3 in predicting mortality, suggesting an additive prognostic value when quantifying different collagens.

Immunoarray Measurements of Parathyroid Hormone-Related Peptides Combined with Other Biomarkers to Diagnose Aggressive Prostate Cancer

Parathyroid hormone-related peptide (PTHrP) is related to bone metastasis and hypercalcemia in prostate and breast cancers and should be an excellent biomarker for aggressive forms of these cancers. Current clinical detection protocols for PTHrP are immunoradiometric assay and radioimmunoassay but are not sensitive enough to detect PTHrPs at early stages. We recently evaluated a prostate cancer biomarker panel, including serum monocyte differentiation antigen (CD-14), ETS-related gene protein, pigment epithelial-derived factor, and insulin-like growth factor-1, with promise for identifying aggressive prostate cancers. This panel predicted the need for patient biopsy better than PSA alone. In the present paper, we report an ultrasensitive microfluidic assay for PTHrPs and evaluate their diagnostic value and the value of including them with our prior biomarker panel to diagnose aggressive forms of prostate cancer. The immunoarray features screen-printed carbon sensor electrodes coated with 5 nm glutathione gold nanoparticles with capture antibodies attached. PTHrPs are bound to a secondary antibody attached to a polyhorseradish peroxidase label and delivered to the sensors to provide high sensitivity when activated by H₂O₂ and a mediator. We obtained an unprecedented 0.3 fg mL^-1 limit of detection for PTHrP bioactive moieties PTHrP 1-173 and PTHrP 1-86. We also report the first study of PTHrPs in a large serum pool to identify aggressive malignancies. In assays of 130 human patient serum samples, PTHrP levels distinguished between aggressive and indolent prostate cancers with 83-91% clinical sensitivity and 78-96% specificity. Logistic regression identified the best predictive model as a combination of PTHrP 1-86 and vascular endothelial growth factor-D. PTHrP 1-173 alone also showed a high ability to differentiate aggressive and indolent cancers.

An in-Depth Analysis of Ovarian Cancer: Pathogenesis and Clinical Manifestation

Ovarian cancer is characterized by the establishment of tolerance, the recurrence of disease, as well as a poor prognosis. Gene signatures in ovarian cancer cells enable cancer medicine research, therapy, prevention, & management problematic. Notwithstanding advances in tumor puncture surgery, novel combinations regimens, and abdominal radiation, which can provide outstanding reaction times, the bulk of gynecological tumor patients suffer from side effects & relapse. As a consequence, more therapy alternatives for individuals with ovarian cancer must always be studied to minimize side effects and improve progression-free and total response rates. The development of cancer medications is presently undergoing a renaissance in the quest for descriptive and prognostic ovarian cancer biomarkers. Nevertheless, abnormalities in the BRCA2 or BRCA1 genes, a variety of hereditary predispositions, unexplained onset and progression, molecular tumor diversity, and illness staging can all compromise the responsiveness and accuracy of such indicators. As a result, current ovarian cancer treatments must be supplemented with broad-spectrum & customized targeted therapeutic approaches. The objective of this review is to highlight recent contributions to the knowledge of the interrelations between selected ovarian tumor markers, various perception signs, and biochemical and molecular signaling processes, as well as one's interpretation of much more targeted and effective treatment interventions.

Microfluidic flow cytometry for blood-based biomarker analysis

Flow cytometry has proven its capability for rapid and quantitative analysis of individual cells and the separation of targeted biological samples from others. The emerging microfluidics technology makes it possible to develop portable microfluidic diagnostic devices for point-of-care testing (POCT) applications. Microfluidic flow cytometry (MFCM), where flow cytometry and microfluidics are combined to achieve similar or even superior functionalities on microfluidic chips, provides a powerful single-cell characterisation and sorting tool for various biological samples. In recent years, researchers have made great progress in the development of the MFCM including focusing, detecting, and sorting subsystems, and its unique capabilities have been demonstrated in various biological applications. Moreover, liquid biopsy using blood can provide various physiological and pathological information. Thus, biomarkers from blood are regarded as meaningful circulating transporters of signal molecules or particles and have great potential to be used as non (or minimally)-invasive diagnostic tools. In this review, we summarise the recent progress of the key subsystems for MFCM and its achievements in blood-based biomarker analysis. Finally, foresight is offered to highlight the research challenges faced by MFCM in expanding into blood-based POCT applications, potentially yielding commercialisation opportunities.

Capturing ctDNA from Unaltered Stationary and Flowing Plasma with dCas9

Many studies have established that blood-based liquid biopsies can be used to detect cancer in its early stages. However, the limiting factor for early cancer detection is the volume of blood required to capture the small amount of circulating tumor DNA (ctDNA). An apheresis machine is a device that can draw whole blood, separate the blood components, and infuse the blood components back into the individual. This device provides the opportunity to screen large volumes of plasma without extracting it from the body. However, current DNA capture technologies require the plasma to be altered before the ctDNA can be captured. Our goal was to develop the first technology that can capture ctDNA from flowing unaltered plasma. To simulate cancer patient plasma, we spiked BRAF T1799A (BRAF^Mut) DNA into plasma from healthy individuals. We used catalytically dead Cas9 (dCas9), guide RNA, and allele-specific quantitative polymerase chain reaction (qPCR) to capture and measure the number of captured BRAF^Mut DNA copies. We found that dCas9 captured BRAF^Mut alleles with equal efficiency at room temperature (25 °C) and body temperature (37 °C). Next, we showed that, in stationary unaltered plasma, dCas9 was as efficient in capturing BRAF^Mut as a commercial cell-free DNA (cfDNA) capture kit. However, in contrast to the cfDNA capture kit, dCas9 enriched BRAF^Mut by 1.8-3.3-fold. We then characterized the dCas9 capture system in laminar and turbulent flowing plasma. We showed that the capture rate using turbulent flow was greater than that in laminar flow and stationary plasma. With turbulent flow, the number of captured BRAF^Mut copies doubles with time (slope = -1.035 Ct) and is highly linear (R² = 0.874). While we showed that the dCas9 capture system can capture ctDNA from unaltered flowing plasma, further optimization and validation of this technology is required before its clinical utility can be determined.

Serum Metabolic Disturbances in Lung Cancer Investigated through an Elaborative NMR-Based Serum Metabolomics Approach

Detection of metabolic disturbances in lung cancer (LC) has the potential to aid early diagnosis/prognosis and hence improve disease management strategies through reliable grading, staging, and determination of neoadjuvant status in LC. However, a majority of previous metabolomics studies compare the normalized spectral features which not only provide ambiguous information but further limit the clinical translation of this information. Various such issues can be resolved by performing the concentration profiling of various metabolites with respect to formate as an internal reference using commercial software Chenomx. Continuing our efforts in this direction, the serum metabolic profiles were measured on 39 LC patients and 42 normal controls (NCs, comparable in age/sex) using high-field 800 MHz NMR spectroscopy and compared using multivariate statistical analysis tools to identify metabolic disturbances and metabolites of diagnostic potential. Partial least-squares discriminant analysis (PLS-DA) model revealed a distinct separation between LC and NC groups and resulted in excellent discriminatory ability with the area under the receiver-operating characteristic (AUROC) = 0.97 [95% CI = 0.89-1.00]. The metabolic features contributing to the differentiation of LC from NC samples were identified first using variable importance in projection (VIP) score analysis and then checked for their statistical significance (with p-value < 0.05) and diagnostic potential using the ROC curve analysis. The analysis revealed relevant metabolic disturbances associated with LC. Among various circulatory metabolites, six metabolites, including histidine, glutamine, glycine, threonine, alanine, and valine, were found to be of apposite diagnostic potential for clinical implications. These metabolic alterations indicated altered glucose metabolism, aberrant fatty acid synthesis, and augmented utilization of various amino acids including active glutaminolysis in LC.

Emerging Biomarkers for Diagnosis, Prevention and Treatment of Brain Metastases-From Biology to Clinical Utility

Primary malignancies of the lung, skin (melanoma), and breast have higher propensity for metastatic spread to the brain. Advances in molecular tumour profiling have aided the development of targeted therapies, stereotactic radiotherapy, and immunotherapy, which have led to some improvement in patient outcomes; however, the overall prognosis remains poor. Continued research to identify new prognostic and predictive biomarkers is necessary to further impact patient outcomes, as this will enable better risk stratification at the point of primary cancer diagnosis, earlier detection of metastatic deposits (for example, through surveillance), and more effective systemic treatments. Brain metastases exhibit considerable inter- and intratumoural heterogeneity-apart from distinct histology, treatment history and other clinical factors, the metastatic brain tumour microenvironment is incredibly variable both in terms of subclonal diversity and cellular composition. This review discusses emerging biomarkers; specifically, the biological context and potential clinical utility of tumour tissue biomarkers, circulating tumour cells, extracellular vesicles, and circulating tumour DNA.

A novel panel of blood-based microRNAs capable of discrimination between benign breast disease and breast cancer at early stages

Breast cancer (BC) as a leading cause of cancer death among women, exhibits a wide range of genetic heterogeneity in affected individuals. Satisfactory management of BC depends on early diagnosis and proper monitoring of patients' response to therapy. In this study, we aimed to assess the relation between the expression patterns of blood-based microRNAs (miRNAs) with demographic characteristics of the patients with BC in an attempt to find novel diagnostic markers for BC with acceptable precision in clinical applications. To this end, we performed comprehensive statistical analysis of the data of the Cancer Genome Atlas (TCGA) database and the blood miRNome dataset (GSE31309). As a result, 21 miRNAs were selected for experimental verification by quantitative RT-PCR on blood samples of 70 BC patients and 60 normal individuals (without any lesions or benign breast diseases). Statistical one-way ANOVA revealed no significant difference in the blood levels of the selected miRNAs in BC patients compared to any lesions or benign breast diseases. However, the multi-marker panel consisting of hsa-miR-106b-5p, -126-3p, -140-3p, -193a-5p, and -10b-5p could detect early-stages of BC with 0.79 sensitivity, 0.86 specificity and 0.82 accuracy. Furthermore, this multi-marker panel showed the potential of detecting benign breast diseases from BC patients with 0.67 sensitivity, 0.80 specificity, and 0.74 accuracy. In conclusion, these data indicate that the present panel might be considered an asset in detecting benign breast disease and BC.

In vivo dual fluorescence imaging of mucin 1 and its glycoform in tumor cells

The most efficient approach for cancer identification and monitoring is the detection of cancer-associated protein biomarkers but an accurate diagnosis requires multiple analyses. Glycosylation profiling can provide important biological information since different glycoforms are involved in malignant transformation. Here, a near-infrared (NIR) light activated fluorescence resonance energy transfer (FRET) strategy for the efficient and reliable simultaneous dual imaging of the mucin 1 (MUC1) protein backbone and MUC1-specific sialic acid (Sia) is reported. MUC1, an important tumor biomarker, is overexpressed and under-glycosylated in most tumor cells. Two aptamer-functionalized nanoprobes, Cy5-labeled Sia aptamer-functionalized gold nanostars (Sia-GNSs) and MUC1 aptamer-functionalized quantum dots (MUC1-QDs), were successfully constructed with high specificity and biocompatibility. Upon excitation with NIR light, Sia-GNSs endothermically released the Cy5-labeled Sia aptamer that specifically binds to Sia. The Cy5 fluorescence can be observed due to the FRET effect when the Cy5-labeled Sia aptamer and MUC1-QDs bind to the same MUC1 molecule. Dual imaging and relative quantification of MUC1 and its sialylation were achieved in vitro, in vivo and in clinical tissue samples. This efficient platform allows for the simultaneous detection of protein biomarkers and their glycosylation pattern, with significant potential for clinical cancer diagnostics.

CEA, CA19-9, circulating DNA and circulating tumour cell kinetics in patients treated for metastatic colorectal cancer (mCRC)

BACKGROUND

We previously reported that CEA kinetics are a marker of progressive disease (PD) in metastatic colorectal cancer (mCRC). This study was specifically designed to confirm CEA kinetics for predicting PD and to evaluate CA19-9, cell-free DNA (cfDNA), circulating tumour DNA (ctDNA) and circulating tumour cell (CTC) kinetics.

METHODS

Patients starting a chemotherapy (CT) with pre-treatment CEA > 5 ng/mL and/or CA19.9 > 30 UI/mL were prospectively included. Samples were collected from baseline to cycle 4 for CEA and CA19-9 and at baseline and the sixth week for other markers. CEA kinetics were calculated from the first to the third or fourth CT cycle.

RESULTS

A total of 192 mCRC patients were included. CEA kinetics based on the previously identified >0.05 threshold was significantly associated with PD (p < 0.0001). By dichotomising by the median value, cfDNA, ctDNA and CA19-9 were associated with PD, PFS and OS in multivariate analysis. A circulating scoring system (CSS) combining CEA kinetics and baseline CA19-9 and cfDNA values classified patients based on high (n = 58) and low risk (n = 113) of PD and was independently associated with PD (ORa = 4.6, p < 0.0001), PFS (HRa = 2.07, p < 0.0001) and OS (HRa = 2.55, p < 0.0001).

CONCLUSIONS

CEA kinetics alone or combined with baseline CA19-9 and cfDNA are clinically relevant for predicting outcomes in mCRC.

TRIAL REGISTRATION NUMBER

NCT01212510.

A systematic review of extracellular vesicles as non-invasive biomarkers in glioma diagnosis, prognosis, and treatment response monitoring

The present systematic review was done to investigate the possible application of Extracellular vesicles (EVs) in the diagnosis, prognosis, and treatment response monitoring of gliomas using available literature to wrap up the final applicable conclusion in this regard. we searched PubMed/MEDLINE, Scopus, and ISI Web of Science databases. Authors evaluated the quality of the included studies by the QUADAS-2 tool. In total, 2037 published datasets were retrieved through systematic search. Upon screening for eligibility, 35 datasets were determined as eligible. Exosome was the EV-subtype described in the majority of studies, and most datasets used serum as the primary EVs isolation source. EVs isolation was primarily conducted by ultracentrifugation. 31 datasets reported that EVs hold considerable potential for being used in diagnostics, with the majority reporting different types of miRNAs as biomarkers. Besides, 8 datasets reported that EVs could be a potential source of prognostic biomarkers. And finally, 3 datasets reported that EVs might be a reliable strategy for monitoring therapy response in glioma patients. According to the findings of the current systematic review, it seems that miR-301, miR-21, and HOTAIR had the highest diagnostic accuracy. However, heterogeneous and limited evidence regarding prognosis and treatment response monitoring precludes us from drawing a practical conclusion regarding EVs.

Diagnostic value of oncofetal miRNAs in cancers: A comprehensive analysis of circulating miRNAs in pan-cancers and UCB

BACKGROUND

Circulating miRNAs are promising biomarkers for detection of various cancers. As a "developmental" disorder, cancer showed great similarities with embryos.

OBJECTIVE

A comprehensive analysis of circulating miRNAs in umbilical cord blood (UCB) and pan-cancers was conducted to identify circulating miRNAs with potential for cancer detection.

METHODS

A total of 3831 cancer samples (2050 serum samples from 15 types of cancers and 1781 plasma samples from 13 types of cancers) and 248 UCB samples (120 serum and 128 plasma samples) with corresponding NCs from Chinese populations were analyzed via consistent experiment workflow with Exiqon panel followed by multiple-stage validation with qRT-PCR.

RESULTS

Thirty-four serum and 32 plasma miRNAs were dysregulated in at least one type of cancer. Eighteen serum and 16 plasma miRNAs were related with embryos. Among them, 9 serum and 8 plasma miRNAs with consistent expression patterns between pan-cancers and UCB were identified as circulating oncofetal miRNAs. Retrospective analysis confirmed the diagnostic ability of circulating oncofetal miRNAs for specific cancers. And the oncofetal miRNAs were mainly up-regulated in tissues of pan-cancers.

CONCLUSIONS

Our study might serve as bases for the potential application of the non-invasive biomarkers in the future clinical.

Transcriptomic analysis of cardiac gene expression across the life course in male and female mice

Risk for heart disease increases with advanced age and differs between sexes, with females generally protected from heart disease until menopause. Despite these epidemiological observations, the molecular mechanisms that underlie sex‐specific differences in cardiac function have not been fully described. We used high throughput transcriptomics in juvenile (5 weeks), adult (4–6 months), and aged (18 months) male and female mice to understand how cardiac gene expression changes across the life course and by sex. While male gene expression profiles differed between juvenile‐adult and juvenile‐aged (254 and 518 genes, respectively), we found no significant differences in adult‐aged gene expression. Females had distinct gene expression changes across the life course with 1835 genes in juvenile‐adult and 1328 in adult‐aged. Analysis of differentially expressed genes (DEGs) suggests that juvenile to adulthood genes were clustered in cell cycle and development‐related pathways in contrast to adulthood‐aged which were characterized by immune‐and inflammation‐related pathways. Analysis of sex differences within each age suggests that juvenile and aged cardiac transcriptomes are different between males and females, with significantly fewer DEGs identified in adult males and females. Interestingly, the male–female differences in early age were distinct from those in advanced age. These findings are in contrast to expected sex differences historically attributed to estrogen and could not be explained by estrogen‐direct mechanisms alone as evidenced by juvenile sexual immaturity and reproductive incompetence in the aged mice. Together, distinct trajectories in cardiac transcriptomic profiles highlight fundamental sex differences across the life course and demonstrate the need for the consideration of age and sex as biological variables in heart disease.

Infrared Spectroscopy of Blood

The magnitude of infectious diseases in the twenty-first century created an urgent need for point-of-care diagnostics. Critical shortages in reagents and testing kits have had a large impact on the ability to test patients with a suspected parasitic, bacteria, fungal, and viral infections. New point-of-care tests need to be highly sensitive, specific, and easy to use and provide results in rapid time. Infrared spectroscopy, coupled to multivariate and machine learning algorithms, has the potential to meet this unmet demand requiring minimal sample preparation to detect both pathogenic infectious agents and chronic disease markers in blood. This focal point article will highlight the application of Fourier transform infrared spectroscopy to detect disease markers in blood focusing principally on parasites, bacteria, viruses, cancer markers, and important analytes indicative of disease. Methodologies and state-of-the-art approaches will be reported and potential confounding variables in blood analysis identified. The article provides an up to date review of the literature on blood diagnosis using infrared spectroscopy highlighting the recent advances in this burgeoning field.

Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance

Resistance to ionizing radiation, a first-line therapy for many cancers, is a major clinical challenge. Personalized prediction of tumor radiosensitivity is not currently implemented clinically due to insufficient accuracy of existing machine learning classifiers. Despite the acknowledged role of tumor metabolism in radiation response, metabolomics data is rarely collected in large multi-omics initiatives such as The Cancer Genome Atlas (TCGA) and consequently omitted from algorithm development. In this study, we circumvent the paucity of personalized metabolomics information by characterizing 915 TCGA patient tumors with genome-scale metabolic Flux Balance Analysis models generated from transcriptomic and genomic datasets. Metabolic biomarkers differentiating radiation-sensitive and -resistant tumors are predicted and experimentally validated, enabling integration of metabolic features with other multi-omics datasets into ensemble-based machine learning classifiers for radiation response. These multi-omics classifiers show improved classification accuracy, identify clinical patient subgroups, and demonstrate the utility of personalized blood-based metabolic biomarkers for radiation sensitivity. The integration of machine learning with genome-scale metabolic modeling represents a significant methodological advancement for identifying prognostic metabolite biomarkers and predicting radiosensitivity for individual patients.

Quantitative Mass Spectrometry-Based Proteomics for Biomarker Development in Ovarian Cancer

Ovarian cancer is the most lethal gynecologic malignancy among women. Approximately 70–80% of patients with advanced ovarian cancer experience relapse within five years and develop platinum-resistance. The short life expectancy of patients with platinum-resistant or platinum-refractory disease underscores the need to develop new and more effective treatment strategies. Early detection is a critical step in mitigating the risk of disease progression from early to an advanced stage disease, and protein biomarkers have an integral role in this process. The best biological diagnostic tool for ovarian cancer will likely be a combination of biomarkers. Targeted proteomics methods, including mass spectrometry-based approaches, have emerged as robust methods that can address the chasm between initial biomarker discovery and the successful verification and validation of these biomarkers enabling their clinical translation due to the robust sensitivity, specificity, and reproducibility of these versatile methods. In this review, we provide background information on the fundamental principles of biomarkers and the need for improved treatment strategies in ovarian cancer. We also provide insight into the ways in which mass spectrometry-based targeted proteomics approaches can provide greatly needed solutions to many of the challenges related to ovarian cancer biomarker development.

Carnitine palmitoyl transferase 1A is a novel diagnostic and predictive biomarker for breast cancer

BACKGROUND

Carnitine palmitoyl transferase 1A (CPT1A), the key regulator of fatty acid oxidation, contributes to tumor metastasis and therapeutic resistance. We aimed to identify its clinical significance as a biomarker for the diagnosis and prediction of breast cancer.

METHODS

Western blot, ELISA and in silico analysis were used to confirm CPT1A levels in breast cancer cell lines, cell culture medium and breast cancer tissues. Four hundred thirty breast cancer patients, 200 patients with benign breast disease, and 400 healthy controls were enrolled and randomly divided into a training set and a test set with a 7:3 ratio. Training set was used to build diagnostic models and 10-fold cross validation was used to demonstrate the performance of the models. Then test set was aimed to validate the effectiveness of the diagnostic models. ELISA was conducted to detect individual serum CPT1A levels. Receiver operating characteristic (ROC) curves were generated, and binary logistic regression analyses were performed to evaluate the effectiveness of CPT1A as a biomarker in breast cancer diagnosis. CPT1A levels between post-operative and pre-operative samples were also compared.

RESULTS

CPT1A was overexpressed in breast cancer tissues, cell lines and cell culture medium. Serum CPT1A levels were higher in breast cancer patients than in controls and were significantly associated with metastasis, TNM stage, histological grading and molecular subtype. CPT1A levels were decreased in post-operative samples compared with paired pre-operative samples. Moreover, CPT1A exhibited a higher efficacy in differentiating breast cancer patients from healthy controls (training set: area under the curve, AUC, 0.892, 95% CI, 0.872-0.920; test set, AUC, 0.904, 95% CI, 0.869-0.939) than did CA15-3, CEA, or CA125.

CONCLUSION

CPT1A is overexpressed in breast cancer and can be secreted out of breast cancer cell. Serum CPT1A is positively associated with breast cancer progression and could serve as an indicator for disease monitoring. Serum CPT1A displayed a remarkably high diagnostic efficiency for breast cancer and could be a novel biomarker for the diagnosis of breast cancer.

Highly Sensitive and Cost-Effective Portable Sensor for Early Gastric Carcinoma Diagnosis

Facile and efficient early detection of cancer is a major challenge in healthcare. Herein we developed a novel sensor made from a polycarbonate (PC) membrane with nanopores, followed by sequence-specific Oligo RNA modification for early gastric carcinoma diagnosis. In this design, the gastric cancer antigen CA72-4 is specifically conjugated to the Oligo RNA, thereby inhibiting the electrical current through the PC membrane in a concentration-dependent manner. The device can determine the concentration of cancer antigen CA72-4 in the range from 4 to 14 U/mL, possessing a sensitivity of 7.029 µAU^-1mLcm^-2 with a linear regression (R²) of 0.965 and a lower detection limit of 4 U/mL. This device has integrated advantages including high specificity and sensitivity and being simple, portable, and cost effective, which collectively enables a giant leap for cancer screening technologies towards clinical use. This is the first report to use RNA aptamers to detect CA72-4 for gastric carcinoma diagnosis.

Stability of interleukin-1β, -4, -6, -8, -10, -13, interferon-γ and tumor necrosis factor-α in human sera after repetitive freeze-thaw cycles and long storage

INTRODUCTION

Cytokines (CKs) are one of the key components of the molecular network modulating multiple immunological interactions. Within such biological systems, CKs functions are associated with several processes, thus quantification of these analytes in serum samples, as well as a faithful determination of its concentration, are crucial for the translational aspect of many studies.

AIM

This study is focused on the evaluation of the effects of storage duration and multiple freeze-thaw cycles on CKs stability.

MATERIALS AND METHODS

Serum samples were obtained from 24 healthy participants. Samples were prospectively stored at 4 °C for 1-7 and 30 days, and also underwent multiple freeze-thaw cycles. Afterwards, CK levels were determined by enzyme-linked immunosorbent assay.

RESULTS

Among the 8 examined CKs all of them showed significant degradation (determined with the two-way ANOVA and post-hoc test) after 4 days of sample storage at 4 °C. Serum were affected by freezing at -20 °C and thawing, and 2 of CKs (IL-1β and IL-8) showed significant concentration decrease after following 2 freeze-thaw cycles. It has been also determineded that CKs in serum samples after multiple freeze-thaw cycles had better stability, when samples were stored at -80 °C (compared to storage at -20 °C).

CONCLUSIONS

This study demonstrates that long storage at 4 °C, as well as multiple freeze-thaw cycles of serum samples, must be avoided and CK concentrations should be measured immediately after sample collection.

A Panel of Tumor-associated Autoantibodies for the Detection of Early-stage Breast Cancer

We previously found a panel of autoantibodies against multiple tumor-associated antigens (BMI-1, HSP70, MMP-7, NY-ESO-1, p53 and PRDX6) that might facilitate early detection of esophagogastric junction adenocarcinoma and esophageal squamous cell carcinoma. Here we aimed at assessing the diagnostic performance of these autoantibodies in breast cancer patients. Enzyme-linked immunosorbent assay was applied to detect sera autoantibodies in 123 breast cancer patients and 123 age-matched normal controls. We adopted logistic regression analysis to identify optimized autoantibody biomarkers for diagnosis and receiver-operating characteristics to analyze diagnostic efficiency. Five of six autoantibodies, BMI-1, HSP70, NY-ESO-1, p53 and PRDX6 demonstrated significantly elevated serum levels in breast cancer compared to normal controls. An optimized panel composed of autoantibodies to BMI-1, HSP70, NY-ESO-1 and p53 showed an area under the curve (AUC) of 0.819 (95% CI 0.766-0.873), 63.4% sensitivity and 90.2% specificity for diagnosing breast cancer. Moreover, this autoantibody panel could differentiate patients with early stage breast cancer from normal controls, with AUC of 0.805 (95% CI 0.743-0.886), 59.6% sensitivity and 90.2% specificity. Our findings indicated that the panel of autoantibodies to BMI-1, HSP70, NY-ESO-1 and p53 as serum biomarkers have the potential to help detect early stage breast cancer.

Prostate Cancer Diagnosis in the Clinic Using an 8-Protein Biomarker Panel

The inability to distinguish aggressive from indolent prostate cancer is a longstanding clinical problem. Prostate specific antigen (PSA) tests and digital rectal exams cannot differentiate these forms. Because only ∼10% of diagnosed prostate cancer cases are aggressive, existing practice often results in overtreatment including unnecessary surgeries that degrade patients' quality of life. Here, we describe a fast microfluidic immunoarray optimized to determine 8-proteins simultaneously in 5 μL of blood serum for prostate cancer diagnostics. Using polymeric horseradish peroxidase (poly-HRP, 400 HRPs) labels to provide large signal amplification and limits of detection in the sub-fg mL^-1 range, a protocol was devised for the optimization of the fast, accurate assays of 100-fold diluted serum samples. Analysis of 130 prostate cancer patient serum samples revealed that some members of the protein panel can distinguish aggressive from indolent cancers. Logistic regression was used to identify a subset of the panel, combining biomarker proteins ETS-related gene protein (ERG), insulin-like growth factor-1 (IGF-1), pigment epithelial-derived factor (PEDF), and serum monocyte differentiation antigen (CD-14) to predict whether a given patient should be referred for biopsy, which gave a much better predictive accuracy than PSA alone. This represents the first prostate cancer blood test that can predict which patients will have a high biopsy Gleason score, a standard pathology score used to grade tumors.

Diagnostic accuracy of cerebrospinal fluid and serum-isolated extracellular vesicles for glioblastoma: a systematic review and meta-analysis

BACKGROUND

Glioblastoma (GBM) is the most malignant brain cancer because there are no available biopsy-free methods for the diagnosis or the preoperative early detection. In this regard, the development of a non- or minimally invasive methods for early detection could increase the survival rate of GBM patients.

METHODS

The present study aimed to assess the diagnostic accuracy of extracellular vesicles (EVs) derived RNAs, isolated from patients' CSF or serum for GBM diagnosis. For this purpose, we searched all literature databases and performed a backward and forward reference checking procedure to retrieve appropriate studies. We conducted a meta-analysis on EVs derived biomarkers as well as sensitivity analysis and meta-regression.

RESULTS

We identified EVs-derived 24 RNAs, which can diagnose GBM. The analyzed pooled data showed 76% sensitivity, 80% specificity, and 0.85 AUC, for 16 biomarkers. Besides, the pooled PLR, NLR, and DOR were 3.7, 0.30, and 12, respectively. Subgroup analysis did not show a significant difference between serum and CSF.

CONCLUSIONS

According to the pooled sensitivity, specificity, and AUC for EVs derived biomarkers, we suggest that EVs-derived biomarkers might serve as a high potential and noninvasive diagnostic tool for GBM detection using serum and CSF samples.

The Human Blood Transcriptome in a Large Population Cohort and Its Relation to Aging and Health

Background: The blood transcriptome is expected to provide a detailed picture of an organism's physiological state with potential outcomes for applications in medical diagnostics and molecular and epidemiological research. We here present the analysis of blood specimens of 3,388 adult individuals, together with phenotype characteristics such as disease history, medication status, lifestyle factors, and body mass index (BMI). The size and heterogeneity of this data challenges analytics in terms of dimension reduction, knowledge mining, feature extraction, and data integration.

Methods: Self-organizing maps (SOM)-machine learning was applied to study transcriptional states on a population-wide scale. This method permits a detailed description and visualization of the molecular heterogeneity of transcriptomes and of their association with different phenotypic features.

Results: The diversity of transcriptomes is described by personalized SOM-portraits, which specify the samples in terms of modules of co-expressed genes of different functional context. We identified two major blood transcriptome types where type 1 was found more in men, the elderly, and overweight people and it upregulated genes associated with inflammation and increased heme metabolism, while type 2 was predominantly found in women, younger, and normal weight participants and it was associated with activated immune responses, transcriptional, ribosomal, mitochondrial, and telomere-maintenance cell-functions. We find a striking overlap of signatures shared by multiple diseases, aging, and obesity driven by an underlying common pattern, which was associated with the immune response and the increase of inflammatory processes.

Conclusions: Machine learning applications for large and heterogeneous omics data provide a holistic view on the diversity of the human blood transcriptome. It provides a tool for comparative analyses of transcriptional signatures and of associated phenotypes in population studies and medical applications.

SERS Immunosensor of Array Units Surrounded by Particles: A Platform for Auxiliary Diagnosis of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the diseases with high mortality worldwide, so its early diagnosis and treatment have attracted much attention. Due to the advantages of the high sensitivity of surface-enhanced Raman scattering (SERS) detection, SERS has excellent application value in the diagnosis of HCC. In this paper, silver nanoparticles (AgNPs) are modified by magnetron sputtering on the surface of polystyrene (PS) templates with spheres of two different diameters. The array of units surrounded by particles is successfully prepared and the SERS performance is characterized. The effect of the gap between AgNPs on plasmon coupling and hot spot distribution is discussed. Finite-difference time domain (FDTD) simulation is used to verify the electric fields and hot spot distribution of the array. The differences in the concentrations of HCC markers are analyzed by using the change of SERS signal intensity of the array. The whole process proves that the preparation of structures with a strong local electric field to provide highly sensitive SERS signals is a key link in the detection of HCC markers, which is conducive to the diagnosis of HCC and has potential application value in clinical diagnosis.

Non-Coding RNAs, a Novel Paradigm for the Management of Gastrointestinal Stromal Tumors

Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal malignancies found in the gastrointestinal tract. At a molecular level, most GISTs are characterized by gain-of-function mutations in V-Kit Hardy-Zuckerman 4 Feline Sarcoma Viral Oncogene Homolog (KIT) and Platelet Derived Growth Factor Receptor Alpha (PDGFRA), leading to constitutive activated signaling through these receptor tyrosine kinases, which drive GIST pathogenesis. In addition to surgery, treatment with the tyrosine kinase inhibitor imatinib forms the mainstay of GIST treatment, particularly in the advanced setting. Nevertheless, the majority of GISTs develop imatinib resistance. Biomarkers that indicate metastasis, drug resistance and disease progression early on could be of great clinical value. Likewise, novel treatment strategies that overcome resistance mechanisms are equally needed. Non-coding RNAs, particularly microRNAs, can be employed as diagnostic, prognostic or predictive biomarkers and have therapeutic potential. Here we review which non-coding RNAs are deregulated in GISTs, whether they can be linked to specific clinicopathological features and discuss how they can be used to improve the clinical management of GISTs.

Diagnostic and Prognostic Values of Serum EpCAM, TGM2, and HE4 Levels in Endometrial Cancer

Objectives: This study aims to investigate the diagnostic and prognostic values of EpCAM, TGM2, and HE4 in endometrial cancer (EC).

Methods: In this study, 42 patients diagnosed with EC (EC group), 41 patients diagnosed with myoma (benign group), and 43 healthy women (healthy group), who applied to Affiliated Hospital of Xuzhou Medical University between March 2018 - September 2019 were recruited. Serum EpCAM, TGM2, and IL-33 levels were measured by ELISA, while serum HE4 and CA-125 levels were measured by ECLIA. The serum markers listed above were also measured in 12 paired pre- and post-operative EC patients. The diagnostic and prognostic values of serum markers were analyzed.

Results: The serum EpCAM, TGM2, HE4, CA-125, and IL-33 levels were significantly higher in the EC group. The sensitivity and specificity of combined detection of EpCAM and HE4 was 92.86 and 69.05%, which were significantly higher than using a single marker or other combinations. Among these markers, serum HE4 levels were significantly higher in patients with myometrial invasion, metastasis, and lymphovascular invasion (p = 0.006, p = 0.0004, p = 0.0004, respectively). And serum TGM2 levels were significantly decreased in post-operative than that of pre-operative EC patients (p < 0.001).

Conclusions: The combination of EpCAM and HE4 showed the highest specificity and sensitivity in the diagnosis of EC. HE4 was successful in the detection of high-risk individuals preoperatively. Additionally, TGM2 might be a prognostic factor for EC.

Hypermethylation of SCAND3 and Myo1g Gene Are Potential Diagnostic Biomarkers for Hepatocellular Carcinoma

Presently, there is a lack of effective blood-based biomarkers facilitating the diagnosis of hepatocellular carcinoma (HCC). Thus, we aimed to investigate novel methylation markers for HCC diagnosis, and explore relationships between biomarker methylation and clinicopathology of HCC. The methylation status of the SCAN domain containing three (SCAND3) and myosin 1g (Myo1g) genes in HCC cell lines and tissues were detected by digital droplet PCR. The serum SCAND3 and Myo1g methylation levels were analyzed in HCC-afflicted patients and unafflicted controls. The results indicated SCAND3 and Myo1g methylation were abnormally high in the HCC cell lines and tissues. The values of serum SCAND3, Myo1g, and SCAND3 + Myo1g methylation with respect to facilitating the detection, and early detection of HCC were better than for alpha-fetoprotein (AFP) alone. Furthermore, when we combined SCAND3 + Myo1g with AFP, a high sensitivity and specificity resulted. Notably, in the AFP-negative HCC group, the methylation of SCAND3 and Myo1g also showed an excellent diagnostic performance. Besides this, a high serum SCAND3 methylation level was an independent risk factor for predicting portal vein tumor thrombus (PVTT) in HCC patients (OR = 4.746, p = 0.013). Finally, SCAND3 and Myo1g enhanced the HCC diagnostics as noninvasive serum methylation biomarkers, and the SCAND3 methylation status effectively indicated HCC accompanied by PVTT.

A combination of four serum miRNAs for screening of lung adenocarcinoma

Serum microRNAs (miRNAs), with their noticeable stability and unique expression pattern in patients with various diseases, are robust novel non-invasive biomarkers for cancer detection. The objective of this study was to identify specific serum miRNAs as potential biomarkers for screening lung adenocarcinoma. The study was divided into a screening phase, training phase, and validation phase. The expression of 46 serum miRNAs from lung adenocarcinoma (LUAD) patients and healthy controls (HCs) were examined in the screening phase. The expression of the most dysregulated miRNAs was further verified in training (30 LUAD vs. 30 HCs) and validation (82 LUAD vs. 90 HCs) phases. Seven serum miRNAs (miR-142-5p, miR-203a-5p, miR-409-3p, miR-223-3p, miR-150-5p, miR-486-5p and miR-146a-5p) in LUAD patients were significantly dysregulated compared to those in HCs. Their ability to diagnose lung cancer was also significant, with miR-142-5p (AUC = 0.743), miR-409-3p (AUC = 0.755), miR-223-3p (AUC = 0.828) and miR-146a-5p (AUC = 0.745) being more prominent. The combined use of these four could enhance diagnostic value (AUC = 0.933). Our findings define a distinct miRNA expression profile in the serum of LUAD patients. The four-miRNA panel (miR-142-5p, miR-409-3p, miR-223-3p and 146a-5p) may be considered as a novel, non-invasive biomarker for LUAD early detection.

What will radiation oncology look like in 2050? A look at a changing professional landscape in Europe and beyond

The number of newly diagnosed cancers per year is predicted to almost double in the next two decades worldwide, and it remains unclear if and when this alarming trend will level off or even reverse. As such, cancer is very likely to continue to pose a major threat to human health. Radiation oncology is an indispensable pillar of cancer treatment and a well‐developed discipline. Nevertheless, key trends in cancer research and care, including improved primary prevention, early detection, integrated multidisciplinary approaches, personalized strategies at all levels of care, value‐based assessments of healthcare systems, and global health perspectives, will all shape the future of radiation oncology. Broader scientific advances, such as rapid progress in digitization, automation, and in our biological understanding of cancer, as well as the wider societal view of healthcare systems will also influence radiation oncology and how it is practiced. To stimulate a proactive discussion on how to adapt and reshape our discipline, this review provides some predictions on what the role and practice of radiation oncology might look like in 30 years’ time.