Biomarker Insights最新文献

{"title":"Measuring Some Oxidative Stress Biomarkers in Autistic Syrian Children and Their Siblings: A Case-Control Study.","authors":"Oula Nasrallah,&nbsp;Samar Alzeer","doi":"10.1177/11772719221123913","DOIUrl":"https://doi.org/10.1177/11772719221123913","url":null,"abstract":"<p><strong>Objective: </strong>Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder whose cause remains unknown. Oxidative stress is one of the possible causes of many disorders, including neurological ones. This study aims to measure some oxidative stress biomarkers (Malondialdehyde \"MDA,\" Advanced Oxidation Protein Product \"AOPP,\" Glutathione \"GSH\") within Syrian children with ASD.</p><p><strong>Methods: </strong>MDA, AOPP & GSH were measured in the plasma of a total of 60 children. The ages of the children ranged from 1 to 13 years old. Thirty children had ASD and were compared with 30 controls that don't have ASD. Fifteen of the controls were siblings of an ASD child, while the remaining 15 had no relations with ASD.</p><p><strong>Results: </strong>MDA and AOPP plasma levels were higher in ASD children compared with non-related controls (<i>P</i> = .0001). However, there were no significant differences between MDA and AOPP plasma levels in ASD children in comparison with related controls (<i>P</i> > .05). Alternatively, GSH plasma levels were lower in ASD children compared with both related and non-related controls (<i>P</i> = .0001).</p><p><strong>Conclusion: </strong>Further studies are needed to investigate more regarding the diagnostic use of oxidative stress biomarkers, and the therapeutic use of antioxidants in children affected with the autism spectrum disorder.</p>","PeriodicalId":47060,"journal":{"name":"Biomarker Insights","volume":" ","pages":"11772719221123913"},"PeriodicalIF":3.8,"publicationDate":"2022-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/fe/5c/10.1177_11772719221123913.PMC9476242.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40368002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nephrotoxic Biomarkers with Specific Indications for Metallic Pollutants: Implications for Environmental Health.","authors":"István Pócsi,&nbsp;Mark E Dockrell,&nbsp;Robert G Price","doi":"10.1177/11772719221111882","DOIUrl":"https://doi.org/10.1177/11772719221111882","url":null,"abstract":"<p><p>Environmental and occupational exposure to heavy metals and metalloids is a major global health risk. The kidney is often a site of early damage. Nephrotoxicity is both a major consequence of heavy metal exposure and potentially an early warning of greater damage. A paradigm shift occurred at the beginning of the 21st century in the field of renal medicine. The medical model of kidney failure and treatment began to give way to a social model of risk factors and prevention with important implications for environmental health. This development threw into focus the need for better biomarkers: markers of exposure to known nephrotoxins; markers of early damage for diagnosis and prevention; markers of disease development for intervention and choice of therapy. Constituents of electronic waste, e-waste or e-pollution, such as cadmium (Cd), lead (Pb), mercury (HG), arsenic (As) and silica (SiO₂) are all potential nephrotoxins; they target the renal proximal tubules through distinct pathways. Different nephrotoxic biomarkers offer the possibility of identifying exposure to individual pollutants. In this review, a selection of prominent urinary markers of tubule damage is considered as potential tools for identifying environmental exposure to some key metallic pollutants.</p>","PeriodicalId":47060,"journal":{"name":"Biomarker Insights","volume":" ","pages":"11772719221111882"},"PeriodicalIF":3.8,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/1d/90/10.1177_11772719221111882.PMC9290154.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40621543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint Modeling of Repeated Measurements of Different Biomarkers Predicts Mortality in COVID-19 Patients in the Intensive Care Unit.","authors":"Kirby Tong-Minh,&nbsp;Yuri van der Does,&nbsp;Joost van Rosmalen,&nbsp;Christian Ramakers,&nbsp;Diederik Gommers,&nbsp;Eric van Gorp,&nbsp;Dimitris Rizopoulos,&nbsp;Henrik Endeman","doi":"10.1177/11772719221112370","DOIUrl":"https://doi.org/10.1177/11772719221112370","url":null,"abstract":"<p><strong>Introduction: </strong>Predicting disease severity is important for treatment decisions in patients with COVID-19 in the intensive care unit (ICU). Different biomarkers have been investigated in COVID-19 as predictor of mortality, including C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and soluble urokinase-type plasminogen activator receptor (suPAR). Using repeated measurements in a prediction model may result in a more accurate risk prediction than the use of single point measurements. The goal of this study is to investigate the predictive value of trends in repeated measurements of CRP, PCT, IL-6, and suPAR on mortality in patients admitted to the ICU with COVID-19.</p><p><strong>Methods: </strong>This was a retrospective single center cohort study. Patients were included if they tested positive for SARS-CoV-2 by PCR test and if IL-6, PCT, suPAR was measured during any of the ICU admission days. There were no exclusion criteria for this study. We used joint models to predict ICU-mortality. This analysis was done using the framework of joint models for longitudinal and survival data. The reported hazard ratios express the relative change in the risk of death resulting from a doubling or 20% increase of the biomarker's value in a day compared to no change in the same period.</p><p><strong>Results: </strong>A total of 107 patients were included, of which 26 died during ICU admission. Adjusted for sex and age, a doubling in the next day in either levels of PCT, IL-6, and suPAR were significantly predictive of in-hospital mortality with HRs of 1.523 (1.012-6.540), 75.25 (1.116-6247), and 24.45 (1.696-1057) respectively. With a 20% increase in biomarker value in a subsequent day, the HR of PCT, IL-6, and suPAR were 1.117 (1.03-1.639), 3.116 (1.029-9.963), and 2.319 (1.149-6.243) respectively.</p><p><strong>Conclusion: </strong>Joint models for the analysis of repeated measurements of PCT, suPAR, and IL-6 are a useful method for predicting mortality in COVID-19 patients in the ICU. Patients with an increasing trend of biomarker levels in consecutive days are at increased risk for mortality.</p>","PeriodicalId":47060,"journal":{"name":"Biomarker Insights","volume":" ","pages":"11772719221112370"},"PeriodicalIF":3.8,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9290097/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40621542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Role of Biomarkers in Hospitalized COVID-19 Patients With Systemic Manifestations.","authors":"Michael Schneider","doi":"10.1177/11772719221108909","DOIUrl":"https://doi.org/10.1177/11772719221108909","url":null,"abstract":"<p><p>The following article aims to review COVID-19 biomarkers used in hospital practice. It is apparent that COVID-19 is not simply a pulmonary disease but has systemic manifestations. For this reason, biomarkers must be used in the management of diagnosed patients to provide holistic care. Patients with COVID-19 have been shown to have pulmonary, hepatobiliary, cardiovascular, neurologic, and renal injury, along with coagulopathy and a distinct cytokine storm. Biomarkers can effectively inform clinicians of systemic organ injury due to COVID-19. Furthermore, biomarkers can be used in predictive models for severe COVID-19 in admitted patients. The utility of doing so is to allow for risk stratification and utilization of proper treatment protocols. In addition, COVID-19 biomarkers in the pediatric population are discussed, specifically in predicting Multisystem Inflammatory Syndrome. Ultimately, biomarkers can be used as predictive tools to allow clinicians to identify and adequately manage patients at increased risk for worse outcomes from COVID-19. Both literature review and anecdotal evidence has shown that severe COVID-19 is a systemic disease, and understanding associated biomarkers are crucial for hospitalized patients' proper clinical decision-making. For example, the cytokine storm releases inflammatory markers in different organ systems such as the pulmonary, hepatobiliary, hematological, cardiac, neurological, and renal systems. This review summarizes the latest research of COVID-19 that can help inform healthcare professionals how to better mitigate morbidity and mortality associated with this disease and provides information about certain systemic biomarkers that can be incorporated into hospital practice to provide more comprehensive care for hospitalized COIVD-19 patients.</p>","PeriodicalId":47060,"journal":{"name":"Biomarker Insights","volume":" ","pages":"11772719221108909"},"PeriodicalIF":3.8,"publicationDate":"2022-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/76/7a/10.1177_11772719221108909.PMC9243490.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40559409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glycerophosphoinositol is Elevated in Blood Samples From <i>CLN3</i> ^Δex7-8 pigs, <i>Cln3</i> ^Δex7-8 Mice, and CLN3-Affected Individuals.","authors":"Jon J Brudvig,&nbsp;Vicki J Swier,&nbsp;Tyler B Johnson,&nbsp;Jacob C Cain,&nbsp;Melissa Pratt,&nbsp;Mitch Rechtzigel,&nbsp;Hannah Leppert,&nbsp;An N Dang Do,&nbsp;Forbes D Porter,&nbsp;Jill M Weimer","doi":"10.1177/11772719221107765","DOIUrl":"https://doi.org/10.1177/11772719221107765","url":null,"abstract":"<p><strong>Introduction: </strong>CLN3 Batten disease is a rare pediatric neurodegenerative lysosomal disorder caused by biallelic disease-associated variants in <i>CLN3.</i> Despite decades of intense research, specific biofluid biomarkers of disease status have not been reported, hindering clinical development of therapies. Thus, we sought to determine whether individuals with CLN3 Batten disease have elevated levels of specific metabolites in blood.</p><p><strong>Methods: </strong>We performed an exhaustive metabolomic screen using serum samples from a novel minipig model of CLN3 Batten disease and validated findings in <i>CLN3</i> pig serum and CSF and <i>Cln3</i> mouse serum. We further validate biomarker candidates with a retrospective analysis of plasma and CSF samples collected from participants in a natural history study. Plasma samples were evaluated from 22 phenotyped individuals with CLN3 disease, 15 heterozygous carriers, and 6 non-affected non-carriers (NANC).</p><p><strong>Results: </strong>CLN3 pig serum samples from 4 ages exhibited large elevations in 4 glycerophosphodiester species: glycerophosphoinositol (GPI), glycerophosphoethanolamine (GPE), glycerophosphocholine (GPC), and glycerophosphoserine (GPS). GPI and GPE exhibited the largest elevations, with similar elevations found in <i>CLN3</i> pig CSF and <i>Cln3</i> mouse serum. In plasma samples from individuals with CLN3 disease, glycerophosphoethanolamine and glycerophosphoinositol were significantly elevated with glycerophosphoinositol exhibiting the clearest separation (mean 0.1338 vs 0.04401 nmol/mL for non-affected non-carriers). Glycerophosphoinositol demonstrated excellent sensitivity and specificity as a biomarker, with a receiver operating characteristic area under the curve of 0.9848 (<i>P</i> = .0003).</p><p><strong>Conclusions: </strong>GPE and GPI could have utility as biomarkers of CLN3 disease status. GPI, in particular, shows consistent elevations across a diverse cohort of individuals with CLN3. This raises the potential to use these biomarkers as a blood-based diagnostic test or as an efficacy measure for disease-modifying therapies.</p>","PeriodicalId":47060,"journal":{"name":"Biomarker Insights","volume":" ","pages":"11772719221107765"},"PeriodicalIF":3.8,"publicationDate":"2022-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/c2/f3/10.1177_11772719221107765.PMC9535353.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"33497288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Progress Toward a Multiomic Understanding of Traumatic Brain Injury: A Review.","authors":"Philip A Kocheril,&nbsp;Shepard C Moore,&nbsp;Kiersten D Lenz,&nbsp;Harshini Mukundan,&nbsp;Laura M Lilley","doi":"10.1177/11772719221105145","DOIUrl":"https://doi.org/10.1177/11772719221105145","url":null,"abstract":"<p><p>Traumatic brain injury (TBI) is not a single disease state but describes an array of conditions associated with insult or injury to the brain. While some individuals with TBI recover within a few days or months, others present with persistent symptoms that can cause disability, neuropsychological trauma, and even death. Understanding, diagnosing, and treating TBI is extremely complex for many reasons, including the variable biomechanics of head impact, differences in severity and location of injury, and individual patient characteristics. Because of these confounding factors, the development of reliable diagnostics and targeted treatments for brain injury remains elusive. We argue that the development of effective diagnostic and therapeutic strategies for TBI requires a deep understanding of human neurophysiology at the molecular level and that the framework of multiomics may provide some effective solutions for the diagnosis and treatment of this challenging condition. To this end, we present here a comprehensive review of TBI biomarker candidates from across the multiomic disciplines and compare them with known signatures associated with other neuropsychological conditions, including Alzheimer's disease and Parkinson's disease. We believe that this integrated view will facilitate a deeper understanding of the pathophysiology of TBI and its potential links to other neurological diseases.</p>","PeriodicalId":47060,"journal":{"name":"Biomarker Insights","volume":" ","pages":"11772719221105145"},"PeriodicalIF":3.8,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/83/be/10.1177_11772719221105145.PMC9201320.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40012586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Query About Validity of uVDBP as a Biomarker of Steroid-Resistant Nephrotic Syndrome.","authors":"Ahmed H Aoun","doi":"10.1177/11772719221078372","DOIUrl":"https://doi.org/10.1177/11772719221078372","url":null,"abstract":"","PeriodicalId":47060,"journal":{"name":"Biomarker Insights","volume":" ","pages":"11772719221078372"},"PeriodicalIF":3.8,"publicationDate":"2022-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/f8/3a/10.1177_11772719221078372.PMC8832616.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39914504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of a Prognostic Epigenetic Classification System in Chronic Lymphocytic Leukemia Patients.","authors":"Christina Grimm,&nbsp;Carmen Diana Herling,&nbsp;Anastasia Komnidi,&nbsp;Michelle Hussong,&nbsp;Karl-Anton Kreuzer,&nbsp;Michael Hallek,&nbsp;Michal R Schweiger","doi":"10.1177/11772719211067972","DOIUrl":"https://doi.org/10.1177/11772719211067972","url":null,"abstract":"<p><strong>Background: </strong>Methylation at 5 CpG sites was previously shown to classify chronic lymphocytic leukemia (CLL) into 3 prognostic subgroups. Here, we aimed to validate the marker set in an additional cohort and to evaluate its clinical utility for CLL patient stratification.</p><p><strong>Methods: </strong>We evaluated this epigenetic marker set in 79 German patients using bisulfite treatment followed by pyrosequencing and classification using a support vector machine-learning tool.</p><p><strong>Results: </strong>The n-CLL, i-CLL, and m-CLL classification was detected in 28 (35%), 10 (13%), and 41 (51%) patients, respectively. Epigenetic grouping was associated with <i>IGHV</i> mutational status (<i>P</i> = 2 × 10^-12), isolated <i>del13q</i> (<i>P</i> = 9 × 10^-6), <i>del17p</i> (<i>P</i> = .015), complex karyotype (<i>P</i> = .005), VH-usage, and clinical outcome as time to first treatment (<i>P</i> = 1.4 × 10^-12) and overall survival (<i>P</i> = .003). Multivariate Cox regression analysis identified n-CLL as a factor for earlier treatment hazard ratio (HR), 6.3 (95% confidence interval [CI] 2.4-16.4; <i>P</i> = .0002) compared to IGHV mutational status (HR 4.6, 95% CI 1.9-11.3, <i>P</i> = .0008). In addition, when comparing the prognostic value of the epigenetic classification system with the IGHV classification, epigenetic grouping performed better compared to IGHV mutational status using Kaplan-Meier estimation and allowed the identification of a third, intermediate (i-CLL) group. Thus, our study confirmed the prognostic value of the epigenetic marker set for patient stratification in routine clinical diagnostics.</p>","PeriodicalId":47060,"journal":{"name":"Biomarker Insights","volume":" ","pages":"11772719211067972"},"PeriodicalIF":3.8,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/26/63/10.1177_11772719211067972.PMC8793417.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39871864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Potential Urinary Metabolite Biomarkers of Pseudomonas aeruginosa Ventilator-Associated Pneumonia","authors":"B. Jongers, A. Hotterbeekx, Kenny Bielen, P. Vervliet, J. Boddaert, C. Lammens, E. Fransen, G. Baggerman, A. Covaci, H. Goossens, S. Malhotra-Kumar, P. Jorens, S. Kumar-Singh","doi":"10.1177/11772719221099131","DOIUrl":"https://doi.org/10.1177/11772719221099131","url":null,"abstract":"Introduction: Ventilator-associated pneumonia (VAP) caused by Pseudomonas aeruginosa is a major cause of morbidity and mortality in hospital intensive care units (ICU). Rapid identification of P. aeruginosa-derived markers in easily accessible patients’ samples can enable an early detection of P. aeruginosa VAP (VAP-PA), thereby stewarding antibiotic use and improving clinical outcomes. Methods: Metabolites were analysed using liquid chromatography-mass spectrometry (LC-MS) in prospectively collected urine samples from mechanically ventilated patients admitted to the Antwerp University Hospital ICU. Patients were followed from the start of mechanical ventilation (n = 100 patients) till the time of clinical diagnosis of VAP (n = 13). Patients (n = 8) in whom diagnosis of VAP was further confirmed by culturing respiratory samples and urine samples were studied for semi-quantitative metabolomics. Results: We first show that multivariate analyses highly discriminated VAP-PA from VAP–non-PA as well as from the pre-infection groups (R2 = .97 and .98, respectively). A further univariate analysis identified 58 metabolites that were significantly elevated or uniquely present in VAP-PA compared to the VAP–non-PA and pre-infection groups (P < .05). These comprised both a known metabolite of histidine as well as a novel nicotine metabolite. Most interestingly, we identified 3 metabolites that were not only highly upregulated for, but were also highly specific to, VAP-PA, as these metabolites were completely absent in all pre-infection timepoints and in VAP–non-PA group. Conclusions: Considerable differences exist between urine metabolites in VAP-PA compared to VAP due to other bacterial aetiologies as well to non-VAP (pre-infection) timepoints. The unique urinary metabolic biomarkers we describe here, if further validated, could serve as highly specific diagnostic biomarkers of VAP-PA.","PeriodicalId":47060,"journal":{"name":"Biomarker Insights","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46598480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advantages and Limitations of Monitoring Circulating Tumor DNA Levels to Predict the Prognosis of Patients Diagnosed With Gastric Cancer.","authors":"Wan He,&nbsp;Jingxin Yang,&nbsp;Xiao Sun,&nbsp;Shunda Jiang,&nbsp;Jinchan Jiang,&nbsp;Ming Liu,&nbsp;Tianhao Mu,&nbsp;Yingmei Li,&nbsp;Xiaoni Zhang,&nbsp;Jingxian Duan,&nbsp;Ruilian Xu","doi":"10.1177/11772719221141525","DOIUrl":"https://doi.org/10.1177/11772719221141525","url":null,"abstract":"<p><p>Next-generation sequencing-based genomic profiling facilitates biomarker detection by cell-free DNA (cfDNA) liquid biopsy. However, the efficiency of mutation calling and the prognostic value of cfDNA biomarkers are disputed. We investigated 24 patients with gastric cancer in this study, using a 605-gene sequencing panel to sequence their plasma cfDNA and tumor tissue DNA. The mutation concordance between plasma cfDNA and tumor tissue DNA was 70.6% in stage IV gastric cancer and 30.2% in stage III gastric cancer, indicating insufficient mutation detection rates in stage III and early-stage cancer. When compared with total cfDNA load and blood tumor mutation burden (bTMB), the variant allele frequencies (VAF) of commonly mutated genes are highly accurate in representing disease burden. Further, VAF are a better prognostic indicator compared with serum biomarkers including carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), cancer antigen 125 (CA125), and alpha-fetoprotein (AFP). The use of cfDNA in molecular profiling of patients allows prediction of patient survival and clinical response, as well as the development of personalized therapy regimens.</p>","PeriodicalId":47060,"journal":{"name":"Biomarker Insights","volume":"17 ","pages":"11772719221141525"},"PeriodicalIF":3.8,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/33/80/10.1177_11772719221141525.PMC9751168.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10406501","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}