• Toxicological Research
• /
• v.32 no.1
• /
• pp.47-56
• /
• 2016

The identification of biomarkers for the early detection of acute kidney injury (AKI) is clinically important. Acute kidney injury (AKI) in critically ill patients is closely associated with increased morbidity and mortality. Conventional biomarkers, such as serum creatinine (SCr) and blood urea nitrogen (BUN), are frequently used to diagnose AKI. However, these biomarkers increase only after significant structural damage has occurred. Recent efforts have focused on identification and validation of new noninvasive biomarkers for the early detection of AKI, prior to extensive structural damage. Furthermore, AKI biomarkers can provide valuable insight into the molecular mechanisms of this complex and heterogeneous disease. Our previous study suggested that pyruvate kinase M2 (PKM2), which is excreted in the urine, is a sensitive biomarker for nephrotoxicity. To appropriately and optimally utilize PKM2 as a biomarker for AKI requires its complete characterization. This review highlights the major studies that have addressed the diagnostic and prognostic predictive power of biomarkers for AKI and assesses the potential usage of PKM2 as an early biomarker for AKI. We summarize the current state of knowledge regarding the role of biomarkers and the molecular and cellular mechanisms of AKI. This review will elucidate the biological basis of specific biomarkers that will contribute to improving the early detection and diagnosis of AKI.

• YAKHAK HOEJI
• /
• v.52 no.5
• /
• pp.331-344
• /
• 2008

Recently, the FDA (Food and Drug Administration) of the United States and many advanced countries remark biomarkers and surrogate endpoints as a critical path tool on model based drug development. Economic, technical and social profit on model based drug development like a reduction of the length of research and development have been achieved. Therefore we summarize previous studies about biomarkers and surrogate endpoints and suggest a development direction of therapeutic agents. In diabetes mellitus (DM) and osteoporosis, there are remarkable increases in number of patients and most of patients take medicine during their whole lifetime. For this reason, many patients with DM and osteoporosis have a tolerance on their medicine. We expect that research and development on biomarkers and surrogate endpoints will contribute to new drug development on DM and osteoporosis. Biomarkers for DM are blood levels of glucose, insulin, ${HbA}_{1c}$, CRP, alpha-glucosidase, adiponectin and DPP-4. Among these, validated surrogate endpoints for DM are blood levels of glucose, insulin and ${HbA}_{1c}$ Biomarkers for osteoporosis are BMD, BMC, trabecular volume, ICTP, DPD, osteocalcin, the activity of osteoclast and production of osteoblast. The validated surrogate endpoints for osteoporosis are BMD only. This review summarizes all suggested biomarkers and surrogate endpoints in DM and osteoporosis. The biomarkers are classified by drugs, and the method of validation for surrogate endpoints is suggested. This information would contribute to suggest a direction of DM and osteoporosis therapeutic agent development.

• Clinical and Experimental Reproductive Medicine
• /
• v.44 no.4
• /
• pp.159-170
• /
• 2017

The development of biomarkers of reproductive medicine is still in its infancy because many black boxes are still present in reproductive medicine. Novel approaches to human infertility diagnostics and treatment must be developed because reproductive medicine has lagged behind in the implementation of biomarkers in clinical medicine. Despite the dearth of the available literature, the current rapid pace of publications suggests that this gap will soon be filled therefore; this review is a $pr\acute{e}cis$ of the research that has been done so far and will provide a basis for the development of biomarkers in reproductive medicine.

• Biomolecules & Therapeutics
• /
• v.20 no.3
• /
• pp.268-272
• /
• 2012

Nephrotoxicity occurs when kidney-specific detoxification and excretion do not work properly due to the damage or destruction of kidney function by exogenous or endogenous toxicants. Exposure to drugs often results in toxicity in kidney which represents the major control system maintaining homeostasis of body and thus is especially susceptible to xenobiotics. Understanding the toxic mechanisms for nephrotoxicity provides useful information on the development of drugs with therapeutic benefits with reduced side effects. Mechanisms for drug-induced nephrotoxicity include changes in glomerular hemodynamics, tubular cell toxicity, inflammation, crystal nephropathy, rhabdomyolysis, and thrombotic microangiopathy. Biomarkers have been identified for the assessment of nephrotoxicity. The discovery and development of novel biomarkers that can diagnose kidney damage earlier and more accurately are needed for effective prevention of drug-induced nephrotoxicity. Although some of them fail to confer specificity and sensitivity, several promising candidates of biomarkers were recently proved for assessment of nephrotoxicity. In this review, we summarize mechanisms of drug-induced nephrotoxicity and present the list of drugs that cause nephrotoxicity and biomarkers that can be used for early assessment of nephrotoxicity.

• Clinical and Experimental Pediatrics
• /
• v.65 no.2
• /
• pp.56-64
• /
• 2022

Cerebrospinal fluid (CSF) is a dynamic metabolically active body fluid that has many important roles and is commonly analyzed in pediatric patients, mainly to diagnose central nervous system infection and inflammation disorders. CSF components have been extensively evaluated as biomarkers of neurological disorders in adult patients. Circulating microRNAs in CSF are a promising class of biomarkers for various neurological diseases. Due to the complexity of pediatric neurological disorders and difficulty in acquiring CSF samples from pediatric patients, there are challenges in developing CSF biomarkers of pediatric neurological disorders. This review aimed to provide an overview of novel CSF biomarkers of seizure disorders, infection, inflammation, tumor, traumatic brain injuries, intraventricular hemorrhage, and congenital hydrocephalus exclusively observed in pediatric patients.

• Journal of Genetic Medicine
• /
• v.19 no.1
• /
• pp.1-6
• /
• 2022

Radiation therapy (RT) is a very important treatment for cancer that irradiates a large amount of radiation to lead cancer cells and tissues to death. The progression of RT in the aspect of personalized medicine has greatly advanced over the past few decades in the field of technical precision responding anatomical characteristics of each patient. However, the consideration of biological heterogeneity that makes different effect in individual patients has not actually applied to clinical practice. There have been numerous discovery and validation of biomarkers that can be applied to improve the efficiency of radiotherapy, among which those related to genomic information are very promising developments. These genome-based biomarkers can be applied to identify patients who can benefit most from altering their therapeutic dose and to select the best chemotherapy improving sensitivity to radiotherapy. The genomics-based biomarkers in radiation oncology focus on mutational changes, particularly oncogenes and DNA damage response pathways. Although few have translated into clinically viable tools, there are many promising candidates in this field. In this review the prominent mutation-based biomarkers and their potential for clinical translation will be discussed.

• Asian Pacific Journal of Cancer Prevention
• /
• v.13 no.5
• /
• pp.1911-1915
• /
• 2012

Aim: New technologies for the early detection of pancreatic cancer (PC) are urgently needed. The aim of the present study was to screen for the potential protein biomarkers in serum using proteomic fingerprint technology. Methods: Magnetic beads combined with surface-enhanced laser desorption/ionization (SELDI) TOF MS were used to profile and compare the protein spectra of serum samples from 85 patients with pancreatic cancer, 50 patients with acute-on-chronic pancreatitis and 98 healthy blood donors. Proteomic patterns associated with pancreatic cancer were identified with Biomarker Patterns Software. Results: A total of 37 differential m/z peaks were identified that were related to PC (P < 0.01). A tree model of biomarkers was constructed with the software based on the three biomarkers (7762 Da, 8560 Da, 11654 Da), this showing excellent separation between pancreatic cancer and non-cancer., with a sensitivity of 93.3% and a specificity of 95.6%. Blind test data showed a sensitivity of 88% and a specificity of 91.4%. Conclusions: The results suggested that serum biomarkers for pancreatic cancer can be detected using SELDI-TOF-MS combined with magnetic beads. Application of combined biomarkers may provide a powerful and reliable diagnostic method for pancreatic cancer with a high sensitivity and specificity.

• Nutrition Research and Practice
• /
• v.15 no.1
• /
• pp.1-11
• /
• 2021

BACKGROUND/OBJECTIVES: It is difficult to consistently demonstrate the health effects of soy isoflavones owing to the multitude of factors contributing to their bioavailability. To accurately verify these health effects, dietary isoflavone intake should be measured using a biologically active dose rather than an intake dose. This concept has been expanded to the development of new exposure biomarkers in nutrition research. This review aims to provide an overview of the development of exposure biomarkers and suggest a novel research strategy for identifying the health effects of soy isoflavone intake. MATERIALS/METHODS: We cover recent studies on the health effects of soy isoflavones focusing on isoflavone metabolites as exposure biomarkers. RESULTS: Compared to non-fermented soy foods, fermented soy foods cause an increased concentration of isoflavones in the biofluid immediately following ingestion. The correlation between exposure biomarkers in blood and urine and the food frequency questionnaire was slightly lower than that of corresponding 24-h dietary recalls. Urinary and blood isoflavone levels did not show a consistent association with chronic disease and cancer risk. CONCLUSION: It is crucial to understand the variable bioavailabilities of soy isoflavones, which may affect evaluations of soy isoflavone intake in health and disease. Further studies on the development of valid exposure biomarkers are needed to thoroughly investigate the health effects of isoflavone.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
• /
• v.48 no.1
• /
• pp.3-12
• /
• 2022

Selection of potential disease-specific biomarkers from saliva or epithelial tissues through next generation sequencing (NGS)-based protein studies has recently become possible. The early diagnosis of oral squamous cell carcinoma (OSCC) has been difficult, if not impossible, until now due to the lack of an effective OSCC biomarker and efficient molecular validation method. The aim of this study was to summarize the advances in the application of NGS in cancer research and to propose potential proteomic and genomic saliva biomarkers for NGS-based study in OSCC screening and diagnosis programs. We have reviewed four categories including definitions and use of NGS, salivary biomarkers and OSCC, current biomarkers using the NGS-based technique, and potential salivary biomarker candidates in OSCC using NGS.

• Korean Journal of Biological Psychiatry
• /
• v.30 no.1
• /
• pp.24-30
• /
• 2023

Objectives Alzheimer's disease (AD) is the most common form of dementia in older adults, damaging the brain and resulting in impaired memory, thinking, and behavior. The identification of differentially expressed genes and related pathways among affected brain regions can provide more information on the mechanisms of AD. The aim of our study was to identify differentially expressed genes associated with AD and combined biomarkers among them to improve AD risk prediction accuracy. Methods Machine learning methods were used to compare the performance of the identified combined biomarkers. In this study, three publicly available gene expression datasets from the hippocampal brain region were used. Results We detected 31 significant common genes from two different microarray datasets using the limma package. Some of them belonged to 11 biological pathways. Combined biomarkers were identified in two microarray datasets and were evaluated in a different dataset. The performance of the predictive models using the combined biomarkers was superior to those of models using a single gene. When two genes were combined, the most predictive gene set in the evaluation dataset was ATR and PRKCB when linear discriminant analysis was applied. Conclusions Combined biomarkers showed good performance in predicting the risk of AD. The constructed predictive nomogram using combined biomarkers could easily be used by clinicians to identify high-risk individuals so that more efficient trials could be designed to reduce the incidence of AD.