Fully-sialylated alpha-chain of complement 4-binding protein: Diagnostic utility for ovarian clear cell carcinoma

Mikami, Mikio; Tanabe, Kenichi; Matsuo, Koji; Miyazaki, Yuko; Miyazawa, Masaki; Hayashi, Meiso; Asai, Satoshi; Ikeda, Masae; Shida, Masako; Hirasawa, Takeshi; Kojima, Nozomi; Sho, Ryuichiro; Iijima, Sadayo

doi:10.1016/j.ygyno.2015.10.012

Cited by 19 publications

(17 citation statements)

References 18 publications

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“…Recent studies show that C4BPA is highly expressed in non-small cell lung cancer ( Liu et al , 2012 ). Furthermore, fully-sialylated C4BPA, A2160, is a newly identified serum biomarker for ovarian cancer, and interestingly, A2160 levels in patient sera are particularly useful for distinguishing early-stage clear cell carcinoma from endometrioma ( Mikami et al , 2015 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of a novel serum biomarker for pancreatic cancer, C4b-binding protein α-chain (C4BPA) by quantitative proteomic analysis using tandem mass tags

et al. 2016

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Background:Pancreatic ductal adenocarcinoma (PDAC) remains a devastating disease due to the lack of specific early diagnostic markers. To improve the outcomes, proteomic approaches are being developed for the discovery of novel biomarkers of PDAC.Methods:Using tandem mass tag labelling and LC-MS/MS, we performed comparative analyses of pre- and postoperative sera from PDAC patients to identify specific serum biomarkers for PDAC. In validation studies, we evaluated the discriminatory power of candidate proteins.Results:Among the 302 proteins analysed, 20 were identified as potential biomarkers, with C4b-binding protein α-chain (C4BPA) and polymeric immunoglobulin receptor (PIGR) being selected for further analysis. The sera levels of C4BPA and PIGR were significantly higher in the preoperative PDAC patients than in the postoperative ones (P<0.008, P<0.036, respectively). In addition, serum C4BPA levels, but not PIGR, in patients with PDAC were significantly higher than those in healthy controls as well as in patients with pancreatitis and other malignancies including biliary tract cancers (BTC) (P<0.001). The respective area under the receiver operator characteristics (ROC) curve (AUC) was 0.860 for C4BPA, 0.846 for CA19-9 and 0.930 for the combination of C4BPA and CA19-9 in PDAC vs non-cancer individuals. The respective AUC was 0.912 for C4BPA, 0.737 for CA19-9 in Stages I and II of PDAC, 0.854 for C4BPA and 0.264 for CA19-9 in PDAC vs BTC.Conclusions:We have demonstrated that C4BPA is a novel serum biomarker for detecting early stage PDAC, as well as for distinguishing PDAC from other gastroenterological cancers. Further analysis in large cohort studies will warrant C4BPA as a promising biomarker of PDAC in clinical use.

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Section: Discussionmentioning

confidence: 99%

Identification of a novel serum biomarker for pancreatic cancer, C4b-binding protein α-chain (C4BPA) by quantitative proteomic analysis using tandem mass tags

et al. 2016

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“…Sera were prepared by previously described methods [ 26 , 27 ]. Briefly, 400 μL of cold acetone containing 10% trichloroacetic acid (Wako Pure Chemical Industries, Ltd., Osaka, Japan), and 50 μg of fetal calf fetuin (Sigma, St. Louis, MO, USA) as an internal standard, were added to 100 μL of patient sera, and mixed at − 20 °C for 90 min to remove serum albumin.…”

Section: Methodsmentioning

confidence: 99%

Serum leucine-rich alpha-2-glycoprotein-1 with fucosylated triantennary N-glycan: a novel colorectal cancer marker

Shinozaki

Tanabe²,

Akiyoshi

et al. 2018

BMC Cancer

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BackgroundCarcinoembryonic antigen (CEA) and carbohydrate antigen (CA)19–9 are used in clinical practice as tumor markers to diagnose or monitor colorectal cancer (CRC) patients, However, their specificities and sensitivities are not ideal, and novel alternatives are needed. In this study, mass spectrometry was used to search for screening markers, focusing on glycan alterations of glycoproteins in the sera of CRC patients.MethodsGlycopeptides were prepared from serum glycoproteins separated from blood samples of 80 CRC patients and 50 healthy volunteers, and their levels were measured by liquid chromatography time-of flight mass spectrometry (LC–TOF–MS).ResultsLeucine-rich alpha-2-glycoprotein-1 with fucosylated triantennary N-glycan (LRG–FTG) was identified as CRC marker after evaluating 30,000 candidate glycopeptide peaks. The average LRG–FTG level in CRC patients (1.25 ± 0.973 U/mL) was much higher than that in healthy volunteers (0.496 ± 0.433 U/mL, P < 10− 10), and its sensitivity and specificity exceeded those of CA19–9. The combination of CEA and LRG–FTG showed a complementary effect and had better sensitivity (84%), specificity (90%), and AUC (0.91 by ROC analysis) than each marker alone or any other previously reported marker. LRG–FTG alone or combined with CEA also corresponded well with patient response to treatment.ConclusionsWe identified LRG–FTG as a new CRC marker, with a sensitivity and specificity exceeding CA19–9. The combination of LRG–FTG and CEA showed much higher sensitivity and specificity than each marker alone. Further validation beyond this initial exploratory cohort is warranted.Electronic supplementary materialThe online version of this article (10.1186/s12885-018-4252-6) contains supplementary material, which is available to authorized users.

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“…To establish a reliable and robust blood test for early-stage EOC detection, we developed a glycopeptide profiling technology using mass spectrometry, which detects aberrant serum sugar chains in patient sera [14]. In our previous studies, we have shown that C4-binding protein with fully sialylated sugar chains is specifically increased in the sera of EOC patients [15][16][17]. Based on this, we developed a comprehensive serum glycopeptide spectra analysis (CSGSA) technology by combining glycopeptide profiling with orthogonal partial least squares discriminant analysis (OPLS-DA) [18].…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer

Tanabe¹,

Ikeda

Hayashi

et al. 2020

Cancers

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Ovarian cancer is a leading cause of deaths among gynecological cancers, and a method to detect early-stage epithelial ovarian cancer (EOC) is urgently needed. We aimed to develop an artificial intelligence (AI)-based comprehensive serum glycopeptide spectra analysis (CSGSA-AI) method in combination with convolutional neural network (CNN) to detect aberrant glycans in serum samples of patients with EOC. We converted serum glycopeptide expression patterns into two-dimensional (2D) barcodes to let CNN learn and distinguish between EOC and non-EOC. CNN was trained using 60% samples and validated using 40% samples. We observed that principal component analysis-based alignment of glycopeptides to generate 2D barcodes significantly increased the diagnostic accuracy (88%) of the method. When CNN was trained with 2D barcodes colored on the basis of serum levels of CA125 and HE4, a diagnostic accuracy of 95% was achieved. We believe that this simple and low-cost method will increase the detection of EOC.

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Fully-sialylated alpha-chain of complement 4-binding protein: Diagnostic utility for ovarian clear cell carcinoma

Abstract: Our study suggested that A2160 may be a useful biomarker to distinguish early-stage OCCC from endometrioma. This new biomarker can be potentially applied for the monitoring of endometrioma patients, making possible the early diagnosis of OCCC.

Cited by 19 publications

References 18 publications

Identification of a novel serum biomarker for pancreatic cancer, C4b-binding protein α-chain (C4BPA) by quantitative proteomic analysis using tandem mass tags

Identification of a novel serum biomarker for pancreatic cancer, C4b-binding protein α-chain (C4BPA) by quantitative proteomic analysis using tandem mass tags

Serum leucine-rich alpha-2-glycoprotein-1 with fucosylated triantennary N-glycan: a novel colorectal cancer marker

Comprehensive Serum Glycopeptide Spectra Analysis Combined with Artificial Intelligence (CSGSA-AI) to Diagnose Early-Stage Ovarian Cancer

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