• Journal of Gastric Cancer
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• v.22 no.4
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• pp.273-305
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• 2022

Gastric cancer (GC) is one of the most common lethal malignant neoplasms worldwide, with limited treatment options for both locally advanced and/or metastatic conditions, resulting in a dismal prognosis. Although the widely used morphological classifications may be helpful for endoscopic or surgical treatment choices, they are still insufficient to guide precise and/or personalized therapy for individual patients. Recent advances in genomic technology and high-throughput analysis may improve the understanding of molecular pathways associated with GC pathogenesis and aid in the classification of GC at the molecular level. Advances in next-generation sequencing have enabled the identification of several genetic alterations through single experiments. Thus, understanding the driver alterations involved in gastric carcinogenesis has become increasingly important because it can aid in the discovery of potential biomarkers and therapeutic targets. In this article, we review the molecular classifications of GC, focusing on The Cancer Genome Atlas (TCGA) classification. We further describe the currently available biomarker-targeted therapies and potential biomarker-guided therapies. This review will help clinicians by providing an inclusive understanding of the molecular pathology of GC and may assist in selecting the best treatment approaches for patients with GC.

• BMB Reports
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• v.44 no.12
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• pp.765-771
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• 2011

Cancer treatment has been stratified by companion biomarker tests that serve to provide information on the genetic status of cancer patients and to identify patients who can be expected to respond to a given treatment. This stratification guarantees better efficiency and safety during treatment. Cancer patients, however, marginally benefit from the current companion biomarker-aided treatment regimens, presumably because companion biomarker tests are dependent solely on the mutation status of several genes status quo. In the true sense of the term, "personalized medicine", cancer patients are deemed to be identified individually by their molecular signatures, which are not necessarily confined to genetic mutations. Glycosylation is tremendously dynamic and shows alterations in cancer. Evidence is accumulating that aberrant glycosylation contributes to the development and progression of cancer, holding the promise for use of glycosylation status as a companion biomarker in cancer treatment. There are, however, several challenges derived from the lack of a reliable detection system for aberrant glycosylation, and a limited library of aberrant glycosylation. The challenges should be addressed if glycosylation status is to be used as a companion biomarker in cancer treatment and contribute to the fulfillment of personalized medicine.

• BMB Reports
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• v.51 no.12
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• pp.648-653
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• 2018

Serine protease inhibitor Kazal type 1 (SPINK1) plays a role in protecting the pancreas against premature activation of trypsinogen and is involved in cancer progression. SPINK1 promoted LAC cells growth, migration, and invasion. Mechanistically, we found that SPINK1 promoted LAC cells migration and invasion via up-regulating matrix metalloproteinase 12 (MMP12). We observed that SPINK1 expression was only up-regulated in lung adenocarcinoma (LAC) tissues, and was an independent prognostic factor for poor survival. Our results indicate that SPINK1 might be a potential biomarker for LAC that promotes progression by MMP12.

• Journal of Microbiology and Biotechnology
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• v.22 no.5
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• pp.654-658
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• 2012

Osteoarthritis (OA) is the most common rheumatic pathology. One of the major objectives of OA research is the development of early diagnostic strategies such as those using proteomic technology. Synovial fluid (SF) in OA patients is a potential source of biomarkers for OA. The efficient and reliable preparation of SF proteomes is a critical step towards biomarker discovery. In this study, we have optimized a pretreatment method for twodimensional gel electrophoresis (2DE) separation of the SF proteome, by enriching low-abundance proteins and simultaneously removing hyaluronic acid, albumin, and IgG. SF samples pretreated using this optimized method were then evaluated by 1DE and 2DE separation followed by immunodetection of cartilage oligomeric matrix protein (COMP), a known OA biomarker, and by the identification of 3 proteins (apolipoprotein, haptoglobin precursor, and fibrinogen D fragment) that are related to joint diseases.

• BMB Reports
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• v.53 no.1
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• pp.10-19
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• 2020

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by the accumulation of amyloid plaques and neurofibrillary tangles in the brain. The AD pathophysiology entails chronic inflammation involving innate immune cells including microglia, astrocytes, and other peripheral blood cells. Inflammatory mediators such as cytokines and complements are also linked to AD pathogenesis. Despite increasing evidence supporting the association between abnormal inflammation and AD, no well-established inflammatory biomarkers are currently available for AD. Since many reports have shown that abnormal inflammation precedes the outbreak of the disease, non-invasive and readily available peripheral inflammatory biomarkers should be considered as possible biomarkers for early diagnosis of AD. In this minireview, we introduce the peripheral biomarker candidates related to abnormal inflammation in AD and discuss their possible molecular mechanisms. Furthermore, we also summarize the current state of inflammatory biomarker research in clinical practice and molecular diagnostics. We believe this review will provide new insights into biomarker candidates for the early diagnosis of AD with systemic relevance to inflammation during AD pathogenesis.

• Journal of Preventive Medicine and Public Health
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• v.42 no.6
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• pp.343-348
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• 2009

The final decision of study design in molecular and genetic epidemiology is usually a compromise between the research study aims and a number of logistical and ethical barriers that may limit the feasibility of the study or the interpretation of results. Although biomarker measurements may improve exposure or disease assessments, it is necessary to address the possibility that biomarker measurement inserts additional sources of misclassification and confounding that may lead to inconsistencies across the research literature. Studies targeting multi-causal diseases and investigating gene-environment interactions must not only meet the needs of a traditional epidemiologic study but also the needs of the biomarker investigation. This paper is intended to highlight the major issues that need to be considered when developing an epidemiologic study utilizing biomarkers. These issues covers from molecular and genetic epidemiology (MGE) study designs including cross-sectional, cohort, case-control, clinical trials, nested case-control, and case-only studies to matching the study design to the MGE research goals. This review summarizes logistical barriers and the most common epidemiological study designs most relevant to MGE and describes the strengths and limitations of each approach in the context of common MGE research aims to meet specific MEG objectives.

• Progress in Medical Physics
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• v.30 no.2
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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.

• BMB Reports
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• v.57 no.3
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• pp.155-160
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• 2024

Lung cancer carries one of the highest mortality rates among all cancers. It is often diagnosed at more advanced stages with limited treatment options compared to other malignancies. This study focuses on calnexin as a potential biomarker for diagnosis and treatment of lung cancer. Calnexin, a molecular chaperone integral to N-linked glycoprotein synthesis, has shown some associations with cancer. However, targeted therapeutic or diagnostic methods using calnexin have been proposed. Through 1D-LCMSMS, we identified calnexin as a biomarker for lung cancer and substantiated its expression in human lung cancer cell membranes using Western blotting, flow cytometry, and immunocytochemistry. Anti-calnexin antibodies exhibited complement-dependent cytotoxicity to lung cancer cell lines, resulting in a notable reduction in tumor growth in a subcutaneous xenograft model. Additionally, we verified the feasibility of labeling tumors through in vivo imaging using antibodies against calnexin. Furthermore, exosomal detection of calnexin suggested the potential utility of liquid biopsy for diagnostic purposes. In conclusion, this study establishes calnexin as a promising target for antibody-based lung cancer diagnosis and therapy, unlocking novel avenues for early detection and treatment.

• BMB Reports
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• v.57 no.6
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• pp.287-292
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• 2024

Hepatocellular Carcinoma (HCC), the predominant primary hepatic malignancy, is the prime contributor to mortality. Despite the availability of multiple surgical interventions, patient outcomes remain suboptimal. Immunotherapies have emerged as effective strategies for HCC treatment with multiple clinical advantages. However, their curative efficacy is not always satisfactory, limited by the dysfunctional T cell status. Thus, there is a pressing need to discover novel potential biomarkers indicative of T cell exhaustion (Tex) for personalized immunotherapies. One promising target is Cyclin-dependent kinase inhibitor 2 (CDKN2) gene, a key cell cycle regulator with aberrant expression in HCC. However, its specific involvement remains unclear. Herein, we assessed the potential of CDKN2 expression as a promising biomarker for HCC progression, particularly for exhausted T cells. Our transcriptome analysis of CDKN2 in HCC revealed its significant role involving in HCC development. Remarkably, single-cell transcriptomic analysis revealed a notable correlation between CDKN2 expression, particularly CDKN2A, and Tex markers, which was further validated by a human cohort study using human HCC tissue microarray, highlighting CDKN2 expression as a potential biomarker for Tex within the intricate landscape of HCC progression. These findings provide novel perspectives that hold promise for addressing the unmet therapeutic need within HCC treatment.

• BMB Reports
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• v.45 no.6
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• pp.323-330
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• 2012

The glycome consists of all glycans (or carbohydrates) within a biological system, and modulates a wide range of important biological activities, from protein folding to cellular communications. The mining of the glycome for disease markers represents a new paradigm for biomarker discovery; however, this effort is severely complicated by the vast complexity and structural diversity of glycans. This review summarizes recent developments in analytical technology and methodology as applied to the fields of glycomics and glycoproteomics. Mass spectrometric strategies for glycan compositional profiling are described, as are potential refinements which allow structure-specific profiling. Analytical methods that can discern protein glycosylation at a specific site of modification are also discussed in detail. Biomarker discovery applications are shown at each level of analysis, highlighting the key role that glycoscience can play in helping scientists understand disease biology.