scholarly journals Electrochemical micro-aptasensors for exosome detection based on hybridization chain reaction amplification

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Wenfen Zhang ◽  
Zhenhua Tian ◽  
Shujie Yang ◽  
Joseph Rich ◽  
Shuaiguo Zhao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Enzyme Reaction ◽  
High Specificity ◽  
Hybridization Chain Reaction ◽  
Chain Reaction ◽  
Serum Samples ◽  
Liquid Biopsies ◽  
Early Cancer Diagnosis ◽  
Wide Range

AbstractExosomes are cell-derived nanovesicles that have recently gained popularity as potential biomarkers in liquid biopsies due to the large amounts of molecular cargo they carry, such as nucleic acids and proteins. However, most existing exosome-based analytical sensing methods struggle to achieve high sensitivity and high selectivity simultaneously. In this work, we present an electrochemical micro-aptasensor for the highly sensitive detection of exosomes by integrating a micropatterned electrochemical aptasensor and a hybridization chain reaction (HCR) signal amplification method. Specifically, exosomes are enriched on CD63 aptamer-functionalized electrodes and then recognized by HCR products with avidin-horseradish peroxidase (HRP) attached using EpCAM aptamers as bridges. Subsequently, the current signal that is generated through the enzyme reaction between the HRP enzyme and 3,3’,5,5’-tetramethylbenzidine (TMB)/H2O2 directly correlates to the amount of bound HRP on the HCR products and thus to the number of target exosomes. By introducing anti-EpCAM aptamers, micro-aptasensors can detect cancerous exosomes with high specificity. Due to the micropatterned electrodes and HCR dual-amplification strategy, the micro-aptasensors achieve a linear detection response for a wide range of exosome concentrations from 2.5×103 to 1×107 exosomes/mL, with a detection limit of 5×102 exosomes/mL. Moreover, our method successfully detects lung cancer exosomes in serum samples of early-stage and late-stage lung cancer patients, showcasing the great potential for early cancer diagnosis.

scholarly journals Metabolic Changes in Early-Stage Non–Small Cell Lung Cancer Patients after Surgical Resection

Cancers ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 3012
Author(s):  
Naseer Ahmed ◽  
Biniam Kidane ◽  
Le Wang ◽  
Zoann Nugent ◽  
Nataliya Moldovan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Surgical Resection ◽  
Cell Lung Cancer ◽  
Early Stage ◽  
Small Cell ◽  
Resonance Spectroscopy ◽  
Small Cell Lung ◽  
Serum Samples ◽  
Post Surgery

Metabolic alterations in malignant cells play a vital role in tumor initiation, proliferation, and metastasis. Biofluids from patients with non–small cell lung cancer (NSCLC) harbor metabolic biomarkers with potential clinical applications. In this study, we assessed the changes in the metabolic profile of patients with early-stage NSCLC using mass spectrometry and nuclear magnetic resonance spectroscopy before and after surgical resection. A single cohort of 35 patients provided a total of 29 and 32 pairs of urine and serum samples, respectively, pre-and post-surgery. We identified a profile of 48 metabolites that were significantly different pre- and post-surgery: 17 in urine and 31 in serum. A higher proportion of metabolites were upregulated than downregulated post-surgery (p < 0.01); however, the median fold change (FC) was higher for downregulated than upregulated metabolites (p < 0.05). Purines/pyrimidines and proteins had a larger dysregulation than other classes of metabolites (p < 0.05 for each class). Several of the dysregulated metabolites have been previously associated with cancer, including leucyl proline, asymmetric dimethylarginine, isopentenyladenine, fumaric acid (all downregulated post-surgery), as well as N6-methyladenosine and several deoxycholic acid moieties, which were upregulated post-surgery. This study establishes metabolomic analysis of biofluids as a path to non-invasive diagnostics, screening, and monitoring in NSCLC.

Residual ctDNA after treatment predicts early relapse in patients with early-stage NSCLC.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 8517-8517
Author(s):  
Davina Gale ◽  
Katrin Heider ◽  
Malcolm Perry ◽  
Giovanni Marsico ◽  
Andrea Ruiz-Valdepeñas ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Deep Sequencing ◽  
Early Stage ◽  
Post Treatment ◽  
Disease Stage ◽  
Analytical Sensitivity ◽  
Disease Relapse ◽  
Clinical Progression ◽  
Liquid Biopsies ◽  
Early Stage Disease

8517 Background: Liquid biopsies based on circulating tumor DNA (ctDNA) analysis are being investigated for detection of residual disease and recurrence. Conclusive evidence for utility of ctDNA in early-stage non-small cell lung cancer (NSCLC) is awaited. Due to low ctDNA levels in early-stage disease or post-treatment, effective methods require high analytical sensitivity to detect mutant allele fractions (MAF) below 0.01%. Methods: We analysed 363 plasma samples from 88 patients with NSCLC recruited to the LUng cancer CIrculating tumour DNA (LUCID) study, with disease stage I (49%), II (28%) and III (23%). 62% were adenocarcinomas. Plasma was collected before and after treatment, and at 3, 6 and 9 months after surgery (N = 69) or chemoradiotherapy (N = 19). Additional plasma was collected at disease relapse for 17 patients. Median follow-up was 3 years, and 40 patients progressed or died of any cause. We employed the RaDaR™ assay, a highly sensitive personalized assay using deep sequencing of up to 48 tumor-specific variants. Variants identified by tumor exome analysis were tested by deep sequencing of tumor tissue and buffy coat DNA to verify somatic mutations and exclude clonal hematopoiesis. The RaDaR assay demonstrated 90% sensitivity at 0.001% MAF in analytical validation studies. Results: ctDNA was detected in 26% of samples, at median MAF of 0.047% (range: 0.0007% to > 2%), and prior to treatment in 87%, 77% and 24% for disease stage III, II and I respectively. For 62 patients, plasma was collected at a landmark timepoint, between 2 weeks and 4 months after initial treatment. ctDNA detection at the landmark timepoint was strongly predictive of clinical disease relapse, with Hazard Ratio of 20.7 (CI: 7.7-55.5, p-value < 0.0001). All 11 cases with ctDNA detected at landmark had disease progression, a median of 121 days after detection, and these included all 8 patients that relapsed within 300 days of treatment. Across 27 patients whose disease progressed during the study, ctDNA was detected at any timepoint post-treatment in 17 cases, with a median lead time of 203 days, and up to 741 days prior to clinical progression. ctDNA was detected post-treatment, in 13 of the 15 patients that progressed and had ctDNA detected prior to treatment. Conclusions: Our results support an emerging paradigm shift, by demonstrating that liquid biopsies can reliably detect recurrence of NSCLC at a preclinical stage, many months before clinical progression, thereby offering the opportunity for earlier therapeutic intervention. Clinical trial information: NCT04153526.

Does multiplicity of synchronous multiple lung primaries (SMLPs) affect prognosis.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e20530-e20530
Author(s):  
RuoBing Xue ◽  
Satish Maharaj ◽  
Rohit Kumar ◽  
Goetz H. Kloecker
Keyword(s):  
Lung Cancer ◽  
Adjuvant Therapy ◽  
Early Stage ◽  
Primary Lung Cancer ◽  
Case Series ◽  
Current Recommendation ◽  
Lung Cancers ◽  
T Stage ◽  
Wide Range ◽  
Common Risk Factors

e20530 Background: SMLPs are detected more commonly due to advancements in screening technology. Their workup and classification; however, are still lacking a clear standard. T stage of the largest lesion has been used as the major prognostic marker. This; however, does not take the number of SMLPs and their genomic drivers into consideration. This study aims to identify and review common risk factors associated with SMLPs and determine whether the number of primaries influence the prognosis. Methods: A systematic review of the literature published between 2000 and 2021 was conducted through PubMed and Medline by using the combination of keywords, including: “synchronous multiple primary lung cancer”, “simultaneous multifocal lung cancer”, “synchronous solitary lung metastasis”, “risk factor” and “prognosis”. A total of fifty studies were identified, among them only sixteen retrospective research articles and two review articles were relevant to the study at hand. Results: Sixteen retrospective studies including a total of 1685 eligible patients were reviewed. Thirteen of these studies reported the main histology type to be adenocarcinoma with a ratio ranging from 35% to 96.8%. Eight studies have reported the numbers of synchronous primary lung cancers, including one study found 11 SMLPs. Among these, one study by van Rens found number of SMLPs impact prognosis adversely compared to a single lung cancer. However, three other studies demonstrated multiple SMPLs do not adversely affect survival (Finley et al, 2010; Kocaturk et al, 2011; Li et al, 2020). Four of the sixteen studies analysed the effect of multiple lobes involvement and distance between tumors, with varying conclusions; two studies reported no difference in prognosis while one study revealed worse survival with multiple lobe involvement and one study found favorable outcome. Most studies confirm the usual prognostic factors for SMLPs, including: gender, smoking, type of surgery, comorbidities and adjuvant therapy. The median 5 year OS reported for SMLPs is 66%, with a wide range from 19% to 95.8%.The 3 year OS is 75% in most studies. Conclusions: The data on how the number of SMLPs affects the prognosis is uncertain. The current recommendation to base the decision for adjuvant therapy on the highest T stage is not supported by prospective evidence or consistent among published case series. Considering the recent approval of targeted therapies in early stage lung cancers, a better prognostic scoring system for SMLPs is required.

scholarly journals HypermethylatedFAM5CandMYLKin Serum as Diagnosis and Pre-Warning Markers for Gastric Cancer

2012 ◽  
Vol 32 (3) ◽  
pp. 195-202 ◽  
Author(s):  
Lu Chen ◽  
Liping Su ◽  
Jianfang Li ◽  
Yanan Zheng ◽  
Beiqin Yu ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Candidate Genes ◽  
Early Stage ◽  
Precancerous Lesions ◽  
Single Gene ◽  
High Specificity ◽  
Serum Samples ◽  
Promoter Regions ◽  
Gastric Cancer Cell Lines ◽  
Combined Detection

Most cases of gastric cancer (GC) are not diagnosed at early stage which can be curable, so it is necessary to identify effective biomarkers for its diagnosis and pre-warning. We have used methylated DNA immunoprecipitation (MeDIP) to identify genes that are frequently methylated in gastric cancer cell lines. Promoter regions hypermethylation of candidate genes were tested by methylation-specific polymerase chain reaction (MSP) in serum samples, including GC (n= 58), gastric precancerous lesions (GPL,n= 46), and normal controls (NC,n= 30). Eighty two hypermethylated genes were acquired by array analysis and 5 genes (BCAS4, CHRM2, FAM5C, PRACandMYLK) were selected as the candidate genes. Three genes (CHRM2, FAM5CandMYLK) were further confirmed to show methylation rates increased with progression from NC to GPL, then to GC. There was obvious decrease in detection ofFAM5CandMYLKhypermethylation, but notCHRM2, from preoperative to postoperative evaluation (P< 0.001). Combined detection of FAM5C and MYLK hypermethylation had a higher sensitivity in GC diagnosis (77.6%,45/58) and pre-warning (30.4%,14/46) than one single gene detection and also had a high specificity of 90%. The combined hypermethylated status ofFAM5CandMYLKcorrelated with tumor size (P< 0.001), tumor invasion depth (P= 0.001) and tumor-node-metastasis (TNM) stage (P= 0.003). HypermethylatedFAM5CandMYLKcan be used as potential biomarkers for diagnosis and pre-warning of GC.

Molecular Detection of Leptospira Species Serotypes in Iranian Stray Dogs

2019 ◽  
Author(s):  
Saam Torkan ◽  
Hassan Momtaz
Keyword(s):  
Public Health ◽  
Polymerase Chain Reaction ◽  
Chain Reaction ◽  
Serum Samples ◽  
Blood Samples ◽  
Stray Dogs ◽  
Wide Range ◽  
Leptospira Spp ◽  
Polymerase Chain ◽  
Leptospira Species

Background and Aims: Leptospirosis is a spirochetal disease with public health importance globally. This disease affects a wide range of domestic and wild animals. Dogs are one of the species most sensitive to Leptospira canicola and Leptospira icterohaemorrhagiae. The present study was concluded to evaluate the prevalence rate of Leptospira species and L. canicola and L. icterohaemorrhagiae serovars in Iranian stray dogs. Materials and Methods: One-hundred and twenty blood samples were first taken from stray dogs. Then the samples were transferred to the laboratory. Sera were extracted from blood samples and genomic DNA was extracted. DNA samples were subjected to conventional polymerase chain reaction. Positive samples for Leptospira spp. were analyzed for presence of L. canicola and L. icterohaemorrhagiaeserovars using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP). Results: Nine samples out of 120 serum samples (7.5%) were positive for the flagella gene of the Leptospira spp. Prevalence of Leptospira spp. in serum samples of male and female dogs were 5.4% and 10.86%, respectively. Prevalence of L. canicola and L. icterohaemorrhagiae serovars were 55.55% and 33.33%, respectively. We found that 11.11% of samples were positive for both serovars. Two to three and 3-4 year old dogs had the highest prevalence of Leptospira spp. Conclusions: The considerable prevalence of leptospirta spp. and also their zoonotic serovars among Iranian stray dogs represented an important public health issue regarding the contact of healthy human with these dogs. Identification of infected dogs and their vaccination can inhibit the distribution of Leptospira spp.

Metabolomics signature in serum months to years before thoracic cancer: The HUNT study.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e23059-e23059
Author(s):  
Oluf D. Røe ◽  
Vincenzo Lagani ◽  
Hans Fredrik Kvitvang ◽  
Maria Markaki ◽  
Ioannis Tsamardinos ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Data Analysis ◽  
Cross Validation ◽  
Performance Estimation ◽  
Serum Samples ◽  
Analysis Pipeline ◽  
Early Cancer Diagnosis ◽  
Non Linear ◽  
Hunt Study ◽  
Data Analysis Pipeline

e23059 Background: The Cancer-Biomarkers in HUNTinitiative seeks to identify novel biomarkers for the early cancer diagnosis. For lung cancers and mesothelioma clinically useful early markers are not available. In the prospective HUNT study in Norway, pre-diagnostic samples ranging 0-20 years before diagnosis are available for research purposes. Here we present our first results on high-throughput metabolomics analysis in serum two months to 16 years before diagnosis. Methods: LC-MS untargeted (Amide-) metabolites (n = 1042) were profiled in serum samples from 48 future patients (12 each of adeno-, squamous cell carcinoma, small-cell lung cancer and mesothelioma) and from 48 controls that were cancer-free 5 years after blood sampling. All were active smokers. Metabolic features for (a) each cancer and (b) all cancers pooled together were analyzed with moderated t-test (R limma package). Multivariate analyses included (a) OPLS-DA and (b) signature identification through a data-analysis pipeline that includes feature selection (such as the algorithm in [1]), non-linear modelers (e.g., Random Forests) and Cross-Validation with bootstrapping [2] for optimizing algorithms and providing unbiased performance estimation. The pipeline is implemented in the Just Add Data software (Gnosis Data Analysis). Results: Univariate and OPLS-DA analyses did not identify any association between metabolites and cancer. The non-linear data analysis pipeline identified a signature containing five metabolites able to discriminate between cancer and non-cancer patients, statistically significantly better than random (AUC = 0.667, CI = [0.536, 0.784]). Conclusions: Our results indicate that metabolic profiling in serum may help in identifying subjects who are likely to be diagnosed with lung cancer/mesothelioma in a time period of several years before diagnosis. More data will be presented at the annual meeting. Further validation studies are planned for confirming the replicability of these findings. 1) Lagani V et al., 2016. arXiv:1611.03227 2) Greasidou L, 2017. Bias Correction of the Cross-Validation Performance Estimate and Speed Up of its Execution Time, MSc Thesis, University of Crete

scholarly journals Blood-Derived Extracellular Vesicle-Associated miR-3182 Detects Non-Small Cell Lung Cancer Patients

Cancers ◽  
2022 ◽  
Vol 14 (1) ◽  
pp. 257
Author(s):  
Kekoolani S. Visan ◽  
Richard J. Lobb ◽  
Shu Wen Wen ◽  
Justin Bedo ◽  
Luize G. Lima ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Early Stage ◽  
Survival Rates ◽  
Cancer Biomarkers ◽  
Extracellular Vesicle ◽  
Lung Cells ◽  
Sequencing Analysis ◽  
Sample Collection ◽  
Liquid Biopsies ◽  
Lung Cancer Patients

With five-year survival rates as low as 3%, lung cancer is the most common cause of cancer-related mortality worldwide. The severity of the disease at presentation is accredited to the lack of early detection capacities, resulting in the reliance on low-throughput diagnostic measures, such as tissue biopsy and imaging. Interest in the development and use of liquid biopsies has risen, due to non-invasive sample collection, and the depth of information it can provide on a disease. Small extracellular vesicles (sEVs) as viable liquid biopsies are of particular interest due to their potential as cancer biomarkers. To validate the use of sEVs as cancer biomarkers, we characterised cancer sEVs using miRNA sequencing analysis. We found that miRNA-3182 was highly enriched in sEVs derived from the blood of patients with invasive breast carcinoma and NSCLC. The enrichment of sEV miR-3182 was confirmed in oncogenic, transformed lung cells in comparison to isogenic, untransformed lung cells. Most importantly, miR-3182 can successfully distinguish early-stage NSCLC patients from those with benign lung conditions. Therefore, miR-3182 provides potential to be used for the detection of NSCLC in blood samples, which could result in earlier therapy and thus improved outcomes and survival for patients.

scholarly journals Prediction of Lung Cancer Risk using Random Forest Algorithm Based on Kaggle Data Set

2020 ◽  
Vol 8 (6) ◽  
pp. 1623-1630
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Random Forest ◽  
Naive Bayes ◽  
Early Stage ◽  
Naïve Bayes ◽  
Training Data ◽  
Random Forest Algorithm ◽  
Data Set ◽  
Wide Range

As huge amount of data accumulating currently, Challenges to draw out the required amount of data from available information is needed. Machine learning contributes to various fields. The fast-growing population caused the evolution of a wide range of diseases. This intern resulted in the need for the machine learning model that uses the patient's datasets. From different sources of datasets analysis, cancer is the most hazardous disease, it may cause the death of the forbearer. The outcome of the conducted surveys states cancer can be nearly cured in the initial stages and it may also cause the death of an affected person in later stages. One of the major types of cancer is lung cancer. It highly depends on the past data which requires detection in early stages. The recommended work is based on the machine learning algorithm for grouping the individual details into categories to predict whether they are going to expose to cancer in the early stage itself. Random forest algorithm is implemented, it results in more efficiency of 97% compare to KNN and Naive Bayes. Further, the KNN algorithm doesn't learn anything from training data but uses it for classification. Naive Bayes results in the inaccuracy of prediction. The proposed system is for predicting the chances of lung cancer by displaying three levels namely low, medium, and high. Thus, mortality rates can be reduced significantly.

scholarly journals Are There New Biomarkers in Tissue and Liquid Biopsies for the Early Detection of Non-Small Cell Lung Cancer?

2019 ◽  
Vol 8 (3) ◽  
pp. 414 ◽  
Author(s):  
Fiorella Calabrese ◽  
Francesca Lunardi ◽  
Federica Pezzuto ◽  
Francesco Fortarezza ◽  
Stefania Vuljan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
New Technologies ◽  
Early Stage ◽  
Liquid Biopsies ◽  
Early Stage Lung Cancer ◽  
Assay Sensitivity ◽  
New Approach ◽  
Liquid Based Cytology ◽  
New Biomarkers ◽  
Stage Lung Cancer

Lung cancer is one of the most lethal malignancies worldwide, mainly due to its late diagnoses. The detection of molecular markers on samples provided from routine bronchoscopy including several liquid-based cytology tests (e.g., bronchoaspirate, bronchoalveolar lavage) and/or on easily obtained specimens such as sputum could represent a new approach to improve the sensitivity in lung cancer diagnoses. Recently growing interest has been reported for “noninvasive” liquid biopsy as a valuable source for molecular profiling. Unfortunately, a biomarker and/or composition of biomarkers capable of detecting early-stage lung cancer has yet to be discovered even if in the last few years there has been, through the use of revolutionary new technologies, an explosion of lung cancer biomarkers. Assay sensitivity and specificity need to be improved particularly when new approaches and/or tools are used. We have focused on the most important markers detected in tissue, and on several cytological specimens and liquid biopsies overall.

scholarly journals Colorimetric Detection of Class A Soybean Saponins by G-Quadruplex-Based Hybridization Chain Reaction

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Congcong Yin ◽  
Qiaoling Zhao ◽  
Aiqin Yue ◽  
Weijun Du ◽  
Dingbin Liu ◽  
...  
Keyword(s):  
Visual Detection ◽  
Colorimetric Detection ◽  
High Specificity ◽  
Hybridization Chain Reaction ◽  
Chain Reaction ◽  
Class A ◽  
Key Enzyme ◽  
Hairpin Probes ◽  
Type Ab ◽  
Triton X

Soybean saponin is one of the important secondary metabolites in seeds, which has various beneficial physiological functions to human health. GmSg-1 gene is the key enzyme gene for synthesizing class A saponins. It is of great significance to realize the visual and rapid detection of class A saponins at the genetic level. The hybridization chain reaction (HCR) was employed to the visual detection of GmSg-1 gene, which was implemented by changing the length of the target fragment to 92 bp and using the hairpin probes we designed to detect the GmSg-1a and GmSg-1b genes. The best condition of HCR reaction is hemin (1.2 μM), Triton X-100 (0.002%), ABTS (3.8 μM), and H2O2 (1.5 mM). It was found that HCR has high specificity for GmSg-1 gene and could be applied to the visual detection of different soybean cultivars containing Aa type, Ab type, and Aa/Ab type saponins, which could provide technical reference and theoretical basis for molecular breeding of soybean and development of functional soybean products.

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