• BMB Reports
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• v.53 no.12
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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.

• Journal of Gastric Cancer
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• v.23 no.3
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• pp.410-427
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• 2023

Recent advances in artificial intelligence (AI) have provided novel tools for rapid and precise pathologic diagnosis. The introduction of digital pathology has enabled the acquisition of scanned slide images that are essential for the application of AI. The application of AI for improved pathologic diagnosis includes the error-free detection of potentially negligible lesions, such as a minute focus of metastatic tumor cells in lymph nodes, the accurate diagnosis of potentially controversial histologic findings, such as very well-differentiated carcinomas mimicking normal epithelial tissues, and the pathological subtyping of the cancers. Additionally, the utilization of AI algorithms enables the precise decision of the score of immunohistochemical markers for targeted therapies, such as human epidermal growth factor receptor 2 and programmed death-ligand 1. Studies have revealed that AI assistance can reduce the discordance of interpretation between pathologists and more accurately predict clinical outcomes. Several approaches have been employed to develop novel biomarkers from histologic images using AI. Moreover, AI-assisted analysis of the cancer microenvironment showed that the distribution of tumor-infiltrating lymphocytes was related to the response to the immune checkpoint inhibitor therapy, emphasizing its value as a biomarker. As numerous studies have demonstrated the significance of AI-assisted interpretation and biomarker development, the AI-based approach will advance diagnostic pathology.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.18
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• pp.8059-8065
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• 2016

Nasopharyngeal carcinoma (NPC) is a common tumor in southern China and south-eastern Asia. Effective strategies for the prevention or screening of NPC are limited. Exploring effective biomarkers for the early diagnosis and prognosis of NPC continues to be a rigorous challenge. Evidence is accumulating that DNA methylation alterations are involved in the initiation and progression of NPC. Over the past few decades, aberrant DNA methylation in single or multiple tumor suppressor genes (TSGs) in various biologic samples have been described in NPC, which potentially represents useful biomarkers. Recently, large-scale DNA methylation analysis by genome-wide methylation platform provides a new way to identify candidate DNA methylated markers of NPC. This review summarizes the published research on the diagnostic and prognostic potential biomarkers of DNA methylation for NPC and discusses the current knowledge on DNA methylation as a biomarker for the early detection and monitoring of progression of NPC.

• Mass Spectrometry Letters
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• v.10 no.3
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• pp.71-78
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• 2019

For several decades, cancer has been the primary cause of mortality worldwide. New diagnosis and regimens have been developed to improve the chemotherapeutic efficacy and the quality of life of the patients. However, cancer tissues are complex and difficult to assess. Understanding the various properties of the tumor and its environment is crucial for cancer and pharmaceutical research. Several analytical techniques have been providing new insights into cancer research. Recently, matrix-assisted laser desorption ionization (MALDI)-mass spectrometry imaging (MSI), an advanced analytical technique, has been applied to translational research. Proteomic and lipidomic profiling obtained by MALDI-MSI has been critical for biomarker discovery and for monitoring heterogenous tumor tissues. In this review, we discuss technical approaches, benefits and recent applications of MALDI-MSI as a valuable tool in cancer research, namely for diagnosis, therapy, prognosis.

• Journal of Digestive Cancer Research
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• v.11 no.1
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• pp.21-29
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• 2023

Despite recent advancements in the diagnosis and treatment of pancreatic cancer, clinical results remain dismal. Furthermore, there are no reliable biomarkers or alternatives beyond carbohydrate antigen 19-9. Circulating tumor cells (CTCs) may be a potential biomarker, but their therapeutic application is constrained by their rarity in peripheral venous blood. Theoretically, the portal vein can be a more appropriate location for the detection of CTCs, because the first venous drainage of pancreatic cancer is portal circulation. According to several studies, the number and detection rate of CTCs may be higher in the portal blood than in the peripheral blood. CTC counts in the portal blood are strongly correlated with several prognostic parameters such as hepatic metastasis, recurrence after surgery, and survival. The phenotypic and genotypic properties analyzed in the captured portal CTCs can assist us to comprehend tumor heterogeneity and predicting the prognosis of pancreatic cancer. The investigations to date are limited by small sample sizes and varied CTC detection techniques. Therefore, a large number of prospective studies are required to confirm portal CTCs as a valid biomarker in pancreatic cancer.

• BMB Reports
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• v.57 no.4
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• pp.194-199
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• 2024

Overexpression of mitofusin-2 (MFN2), a mitochondrial fusion protein, is frequently associated with poor prognosis in cervical cancer patients. Here, I aimed to investigate the involvement of MFN2 in cervical cancer progression and determine the effect of MFN2 on prognosis in cervical cancer patients. After generating MFN2-knockdown SiHa cells derived from squamous cell carcinoma, I investigated the effect of MFN2 on SiHa cell proliferation using the Cell Counting Kit-8 assay and determined the mRNA levels of proliferation markers. Colony-forming ability and tumorigenesis were evaluated using a colony-formation assay and tumor xenograft mouse models. The migratory and invasive abilities associated with MFN2 were measured using wound-healing and invasion assays. Wnt/β-catenin-mediated epithelial-mesenchymal transition (EMT) markers related to MFN2 were assessed through quantitative RT-PCR. MFN2-knockdown SiHa cells exhibited reduced proliferation, colony formation, migration, invasion, and tumor formation in vivo. The motility of SiHa cells with MFN2 knockdown was reduced through Wnt/β-catenin-mediated EMT inhibition. MFN2 promoted cancer progression and tumorigenesis in SiHa cells. Overall, MFN2 could serve as a therapeutic target and a novel biomarker for cervical cancer.

• Journal of Radiopharmaceuticals and Molecular Probes
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• v.6 no.2
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• pp.139-145
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• 2020

Polo-like kinase 1 (Plk1) is a key protein in mitosis and has been validated as a target for tumor therapy. It is well known to highly overexpress in many kinds of tumor, which has been implicated as a potential biomarker for tumor treatment and diagnosis. Plk1 consists of two domains, the N-terminus kinase domain and the C-terminus polo-box domain (PBD). The inhibitors have been developed for PBD of Plk1, which were shown a high level of affinity and selectivity for Plk1 that led to mitotic arrest and apoptotic cell death. This review discusses the inhibitors for PBD of Plk1 that are suitable for in vivo tumor treatment. They can be further extended for developing in vivo imaging probes for early diagnosis of tumor.

• 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.

• Current Optics and Photonics
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• v.3 no.1
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• pp.54-65
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• 2019

To develop a biomarker predicting tumor treatment efficacy is helpful to reduce time, medical expenditure, and efforts in oncology therapy. In clinics, microvessel density using immunohistochemistry has been proposed as an indicator that correlates with both tumor size and metastasis of cancer. In the preclinical study, we hypothesized that vascular morphometrics using optical coherence tomography angiography (OCTA) could be potential indicators to estimate the treatment efficacy of breast cancer. To verify this hypothesis, a 13762-MAT-B-III rat breast tumor was grown in a dorsal skinfold window chamber which was applied to a nude mouse, and the change in vascular morphology was longitudinally monitored during tumor growth and metronomic cyclophosphamide treatment. Based on the daily OCTA maximum intensity projection map, multiple vessel parameters (vessel skeleton density, vessel diameter index, fractal dimension, and lacunarity) were compared with the tumor size in no tumor, treated tumor, and untreated tumor cases. Although each case has only one animal, we found that the vessel skeleton density (VSD), vessel diameter index and fractal dimension (FD) tended to be positively correlated with tumor size while lacunarity showed a partially negative correlation. Moreover, we observed that the changes in the VSD and FD are prior to the morphological change of the tumor. This feasibility study would be helpful in evaluating the tumor vascular response to treatment in preclinical settings.

• Proceedings of the Korean Society for Bioinformatics Conference
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• 2004.11a
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• pp.272-278
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• 2004

Recently, Pepe et al. (2003) employed the receiver operating characteristic (ROC) approach to rank candidate genes from a microarray experiment that can be used for the biomarker development with the ultimate purpose of the population screening of a cancer, In the cancer microarray experiment based on n patients the researcher often wants to compare the tumor tissue with the normal tissue within the same individual using a common reference RNA. This design is referred to as a reference design or an indirect design. Ideally, this experiment produces n pairs of microarray data, where each pair consists of two sets of microarray data resulting from reference versus normal tissue and reference versus tumor tissue hybridizations. However, for certain individuals either normal tissue or tumor tissue is not large enough for the experimenter to extract enough RNA for conducting the microarray experiment, hence there are missing values either in the normal or tumor tissue data. Practically, we have $n_1$ pairs of complete observations, $n_2$ 'normal only' and $n_3$ 'tumor only' data for the microarray experiment with n patients, where n=$n_1$+$n_2$+$n_3$. We refer to this data set as a mixed data set, as it contains a mix of fully observed and partially observed pair data. This mixed data set was actually observed in the microarray experiment based on human tissues, where human tissues were obtained during the surgical operations of cancer patients. Pepe et al. (2003) provide the rationale of using ROC approach based on two independent samples for ranking candidate gene instead of using t or Mann -Whitney statistics. We first modify ROC approach of ranking genes to a paired data set and further extend it to a mixed data set by taking a weighted average of two ROC values obtained by the paired data set and two independent data sets.