• Mass Spectrometry Letters
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• 제1권1호
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• pp.21-24
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• 2010

The development of clinical biomarkers involves discovery, verification, and validation. Recently, multiple reaction monitoring (MRM) coupled with stable isotope dilution mass spectrometry (IDMS) has shown considerable promise for the direct quantification of proteins in clinical samples. In particular, multiple biomarkers have been tracked in a single experiment using MRM-based MS approaches combined with liquid chromatography. We report here a highly reproducible, quantitative, and dynamic MRM system for validating multi-biomarker proteins using Nanoflow HPLC-Microfluidics Chip/Triple-Quadrupole MS. In this system, transitions were acquired only during the retention window of each eluting peptide. Transitions with the highest MRM-MS intensities for the five target peptides from colon cancer biomarker candidates were automatically selected using Optimizer software. Relative to the corresponding non-dynamic system, the dynamic MRM provided significantly improved coefficients of variation in experiments with large numbers of transitions. Linear responses were obtained with concentrations ranging from fmol to pmol for five target peptides.

• Asian-Australasian Journal of Animal Sciences
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• 제31권7호
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• pp.1062-1071
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• 2018

The misuse of anabolic hormones or illegal drugs is a ubiquitous problem in animal husbandry and in food safety. The ban on growth promotants in food producing animals in the European Union is well controlled. However, application regimens that are difficult to detect persist, including newly designed anabolic drugs and complex hormone cocktails. Therefore identification of molecular endogenous biomarkers which are based on the physiological response after the illicit treatment has become a focus of detection methods. The analysis of the 'transcriptome' has been shown to have promise to discover the misuse of anabolic drugs, by indirect detection of their pharmacological action in organs or selected tissues. Various studies have measured gene expression changes after illegal drug or hormone application. So-called transcriptomic biomarkers were quantified at the mRNA and/or microRNA level by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) technology or by more modern 'omics' and high throughput technologies including RNA-sequencing (RNA-Seq). With the addition of advanced bioinformatical approaches such as hierarchical clustering analysis or dynamic principal components analysis, a valid 'biomarker signature' can be established to discriminate between treated and untreated individuals. It has been shown in numerous animal and cell culture studies, that identification of treated animals is possible via our transcriptional biomarker approach. The high throughput sequencing approach is also capable of discovering new biomarker candidates and, in combination with quantitative RT-qPCR, validation and confirmation of biomarkers has been possible. These results from animal production and food safety studies demonstrate that analysis of the transcriptome has high potential as a new screening method using transcriptional 'biomarker signatures' based on the physiological response triggered by illegal substances.

• Toxicological Research
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• 제32권1호
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• pp.47-56
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• 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.

• Genomics & Informatics
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• 제13권3호
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• pp.70-75
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• 2015

MicroRNAs (miRNAs) have been demonstrated to play an important role in carcinogenesis. Previous studies revealed that miRNAs are present in human plasma in a remarkably stable form that is protected from endogenous RNase activity. In this study, we measured the plasma expression levels of three miRNAs (miR-21, miR-27a, and miR-155) to investigate the usefulness of miRNAs for gastric cancer detection. We initially examined plasma miRNA expression levels in a screening cohort consisting of 15 patients with gastric cancer and 15 healthy controls from Korean population, using TaqMan quantitative real-time polymerase chain reaction. We observed that the expression level of miR-27a was significantly higher in patients with gastric cancer than in healthy controls, whereas the miR-21 and miR-155a expression levels were not significantly higher in the patients with gastric cancer. Therefore, we further validated the miR-27a expression level in 73 paired gastric cancer tissues and in a validation plasma cohort from 35 patients with gastric cancer and 35 healthy controls. In both the gastric cancer tissues and the validation plasma cohort, the miR-27a expression levels were significantly higher in patients with gastric cancer. Receiver-operator characteristic (ROC) analysis of the validation cohort, revealed an area under the ROC curve value of 0.70 with 75% sensitivity and 56% specificity in discriminating gastric cancer. Thus, the miR-27a expression level in plasma could be a useful biomarker for the diagnosis and/or prognosis of gastric cancer.

• Genomics & Informatics
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• 제6권3호
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• pp.136-141
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• 2008

A large number of studies have been performed to identify biomarkers that will allow efficient detection and determination of the precise status of a patient’s disease. The use of microarrays to assess biomarker status is expected to improve prediction accuracies, because a whole-genome approach is used. Despite their potential, however, patient samples can differ with respect to biomarker status when analyzed on different platforms, making it more difficult to make accurate predictions, because bias may exist between any two different experimental conditions. Because of this difficulty in experimental standardization of microarray data, it is currently difficult to utilize microarray-based gene sets in the clinic. To address this problem, we propose a method that predicts disease status using gene expression data that are transformed by their ranks, a concept that is easily applied to two datasets that are obtained using different experimental platforms. NCI and colon cancer datasets, which were assessed using both Affymetrix and cDNA microarray platforms, were used for method validation. Our results demonstrate that the proposed method is able to achieve good predictive performance for datasets that are obtained under different experimental conditions.

• 한국의학물리학회지:의학물리
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• 제30권2호
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• pp.39-48
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• 2019

Deep learning has been applied to various medical data. In particular, current deep learning models exhibit remarkable performance at specific tasks, sometimes offering higher accuracy than that of experts for discriminating specific diseases from medical images. The current status of deep learning applications to molecular imaging can be divided into a few subtypes in terms of their purposes: differential diagnostic classification, enhancement of image acquisition, and image-based quantification. As functional and pathophysiologic information is key to molecular imaging, this review will emphasize the need for accurate biomarker acquisition by deep learning in molecular imaging. Furthermore, this review addresses practical issues that include clinical validation, data distribution, labeling issues, and harmonization to achieve clinically feasible deep learning models. Eventually, deep learning will enhance the role of theranostics, which aims at precision targeting of pathophysiology by maximizing molecular imaging functional information.

• 대한두경부종양학회지
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• 제33권1호
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• pp.21-29
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• 2017

Methylation of CpG islands in the promoter region of genes acts as a significant mechanism of epigenetic gene silencing in head and neck squamous cell carcinoma (HNSCC). DNA methylation markers are particularly advantageous because DNA methylation is an early event in tumorigenesis, and the epigenetic modification, 5-methylcytosine, is a stable mark. In the present study, we assessed the genome-wide preliminary screening and were to identify novel methylation biomarker candidate in HNSCC. Genome-wide methylation analysis was performed on 10 HNSCC tumors using the Methylated DNA Isolation Assay (MeDIA) CpG island microarray. Validation was done using immunohistochemistry using tissue microarray of 135 independent HNSCC tumors. In addition, in vitro proliferation, migration/invasion assays, RT-PCR and immunoblotting were performed to elucidate molecular regulating mechanisms. Our preliminary validation using CpG microarray data set, immunohisto-chemistry for HNSCC tumor tissues and in vitro functional assays revealed that methylation of the Homeobox B5 (HOXB5) and H6 Family Homeobox 2 (HMX2) could be possible novel methylation biomarkers in HNSCC.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제3권10호
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• pp.421-428
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• 2014

파킨슨병은 뇌의 흑질 영역에서 도파민계 신경이 파괴되는 질병으로 알츠하이머병과 함께 대표적인 퇴행성 뇌 질환이다. 현재까지 병을 완치시킬 수 있는 치료법은 없지만 병의 진행을 완화시킬 수 있는 치료법이 존재하기 때문에 병의 진단이 굉장히 중요하다. 파킨슨병을 진단하기 위한 과거의 연구는 대부분 단일 바이오마커를 이용한 것으로 이러한 방법은 파킨슨병 환자를 높은 정확도로 진단할 수 있지만 정상인에 대한 진단은 상대적으로 낮은 성능의 한계성이 존재한다. 따라서 본 연구에서는 생화학적 바이오마커인 뇌척수액 내의 ${\alpha}$-synuclein 단백질 수치와 영상학적 바이오마커인 확산 텐서 영상의 여러 모수들을 결합하여 특징으로 사용하는 파킨슨병 진단 모델을 개발하고 성능을 평가하였다. 진단을 위해 개발된 모든 모델은 10-fold cross validation 성능평가에서 정확도가 최고 91.3%의 높은 성능을 보였으며, test 성능평가에서는 확산 텐서 영상의 모수들 중 FA와 ${\alpha}$-synuclein 단백질 수치가 결합된 모델, MO와 ${\alpha}$-synuclein 단백질 수치가 결합된 두 모델에서 최고 72%의 정확도 성능을 보여 파킨슨병의 진단에 유용하게 사용될 수 있는 가능성을 제시하였다. 파킨슨병의 진단을 위해 개발된 모델의 영상학적 특징 벡터를 통하여 파킨슨병 환자와 정상인의 신경섬유 경로의 특징을 분석하였다.

• Nuclear Medicine and Molecular Imaging
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• 제41권4호
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• pp.299-308
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• 2007

본 연구의 목적은 의사결정트리를 생성하는 생물정보학 프로그램을 개발하고, 이를 갑상선유두암 혈청의 질량분석자료로 시험해 보는 것이다. 대상 및 방법: C4.5를 커스터마이징하여 의사결정트리 분석을 수행할 수 있는 'Protein analysis'라는 프로그램을 개발하였다 61개의 혈청시료(갑상선유두암 27, 자가면역성 갑상선염 17, 대조군 17)를 일정 기간 동안 순차적으로 냉동한 후 실온에서 일시에 해동하여 분석에 사용하였다. 모든 시료는 탈지질화 과정을 거쳐 준비한 후, 2종류의 단백질칩(CM10, IMAC3)에 각각 60개, 50개 시료를 적용하였다. 갑상선유두암의 특징적인 단백질 패턴을 찾기 위해 질량분석기를 이용하여 단백질칩을 분석했다. 'Protein analysis' 프로그램을 이용하여 단백질분포 자료로부터 의사결정트리를 작성하고, 생체표지자 후보물질을 검출하였다. CM10칩에서 발견된 생체표지자 후보물질을 무작위 표본추출 방법을 이용하여 검증하였다. 결과: 단백질분포 자료의 훈련과 검증이 가능한 의사결정트리 프로그램이 개발되었으며, 이 프로그램은 트리 구조와 노드 정보, 트리 구성 과정을 표시하는 3개의 창으로 구성되었다. CM10칩을 이용한 분석에서 총 113개의 단백질 피크 중 23개가 3그룹 간에 유의한 차이가 있었으며, IMAC3는 41개의 단백질 피크 중 8개가 3그룹 간에 유의한 차이가 있었다. 3그룹 분석에서 의사결정트리는 CM10칩과 IMAE3의 단백질분포 자료로부터 각각 60개와 50개의 시료를 높은 정확도로 분류하였으며(오차율 = 각각 3.3%, 2.0%), 각각 4개와 7개의 생체표지자 후보물질을 검출하였다. 암시료와 비암시료를 구분하는 2그룹 분석 에서, 의사결정트리는 모든 암시료를 정확히 구분하였으며(모두 오차율 = 0%), CM10칩을 이용한 분석에서는 단일 노드를 사용하고, IMAC3칩을 이용한 분석에서는 여러 개의 노드를 사용하였다. CM10칩의 단백질 분포자료를 5번의 무작위 추출에 의해 시행한 검증에서 암시료와 비암시료를 구분하는데 높은 정확도를 보였으나(정확도 = 98%, 54/55), 3그룹을 구분할 때는 중등도의 정확도를 보였다(정확도 = 65%, 36/55). 결론: 우리가 개발한 프로그램은 질량분석 자료로부터 성공적으로 의사결정트리를 생성하고, 생체표지자 후보물질을 검출할 수 있었다. 따라서 이 프로그램은 혈청 시료를 이용한 생체표지자 발굴 및 갑상선유두암의 추적관찰에 유용하게 사용될 수 있을 것이다.

• BMB Reports
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• 제53권12호
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• pp.664-669
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• 2020

Breast cancer is one of the most frequently diagnosed cancers. Although biomarkers are continuously being discovered, few specific markers, rather than classification markers, representing the aggressiveness and invasiveness of breast cancer are known. In this study, we used samples from canine mammary tumors in a comparative approach. We subjected 36 fractions of both canine normal and mammary tumor plasmas to high-performance quantitative proteomics analysis. Among the identified proteins, LCAT was selectively expressed in mixed tumor samples. With further MRM and Western blot validation, we discovered that the LCAT protein is an indicator of aggressive mammary tumors, an advanced stage of cancer, possibly highly metastatic. Interestingly, we also found that LCAT is overexpressed in high-grade and lymph-node-positive breast cancer in silico data. We also demonstrated that LCAT is highly expressed in the sera of advanced-stage human breast cancers within the same classification. In conclusion, we identified a possible common plasma protein biomarker, LCAT, that is highly expressed in aggressive human breast cancer and canine mammary tumor.