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Glycoproteins have many important biological functions. In particular, aberrant glycosylation has been observed in various cancers, such as liver cancer. A well-known glycoprotein biomarker is α-fetoprotein (AFP), a surveillance biomarker for hepatocellular carcinoma (HCC) that contains a glycosylation site at asparagine 251. The low diagnostic sensitivity of AFP led researchers to focus on AFP-L3, which has the same sequence as conventional AFP but contains a fucosylated glycan. AFP-L3 has high affinity for

Supplementary Tables 1-8.

Supplementary Figures 1-5.

Purpose:To develop and validate a protein-based, multi-marker panel that provides superior pancreatic ductal adenocarcinoma (PDAC) detection abilities with sufficient diagnostic performance.Experimental Design:A total of 959 plasma samples from patients at multiple medical centers were used. To construct an optimal, diagnostic, multi-marker panel, we applied data preprocessing procedure to biomarker candidates. The multi-marker panel was developed using a training set comprised of 261 PDAC cases and 290 controls. Subsequent evaluations were performed in a validation set comprised of 65 PDAC cases and 72 controls. Further validation was performed in an independent set comprised of 75 PDAC cases and 47 controls.Results:A multi-marker panel containing 14 proteins was developed. The multi-marker panel achieved AUCs of 0.977 and 0.953 for the training set and validation set, respectively. In an independent validation set, the multi-marker panel yielded an AUC of 0.928. The diagnostic performance of the multi-marker panel showed significant improvements compared with carbohydrate antigen (CA) 19-9 alone (training set AUC = 0.977 vs. 0.872, P < 0.001; validation set AUC = 0.953 vs. 0.832, P < 0.01; independent validation set AUC = 0.928 vs. 0.771, P < 0.001). When the multi-marker panel and CA 19-9 were combined, the diagnostic performance of the combined panel was improved for all sets.Conclusions:This multi-marker panel and the combined panel showed statistically significant improvements in diagnostic performance compared with CA 19-9 alone and has the potential to complement CA 19-9 as a diagnostic marker in clinical practice.

Supplementary Materials

Because major depressive disorder (MDD) and bipolar disorder (BD) manifest with similar symptoms, misdiagnosis is a persistent issue, necessitating their differentiation through objective methods. This study was aimed to differentiate between these disorders using a targeted proteomic approach. Multiple reaction monitoring-mass spectrometry (MRM-MS) analysis was performed to quantify protein targets regarding the two disorders in plasma samples of 270 individuals (90 MDD, 90 BD, and 90 healthy controls (HCs)). In the training set (72 MDD and 72 BD), a generalizable model comprising nine proteins was developed. The model was evaluated in the test set (18 MDD and 18 BD). The model demonstrated a good performance (area under the curve (AUC) >0.8) in discriminating MDD from BD in the training (AUC = 0.84) and test sets (AUC = 0.81) and in distinguishing MDD from BD without current hypomanic/manic/mixed symptoms (90 MDD and 75 BD) (AUC = 0.83). Subsequently, the model demonstrated excellent performance for drug-free MDD versus BD (11 MDD and 10 BD) (AUC = 0.96) and good performance for MDD versus HC (AUC = 0.87) and BD versus HC (AUC = 0.86). Furthermore, the nine proteins were associated with neuro, oxidative/nitrosative stress, and immunity/inflammation-related biological functions. This proof-of-concept study introduces a potential model for distinguishing between the two disorders.

Purpose: To develop and validate a protein-based, multi-marker panel that provides superior pancreatic ductal adenocarcinoma (PDAC) detection abilities with sufficient diagnostic performance. Experimental Design: A total of 959 plasma samples from patients at multiple medical centers were used. To construct an optimal, diagnostic, multi-marker panel, we applied data preprocessing procedure to biomarker candidates. The multi-marker panel was developed using a training set comprised of 261 PDAC cases and 290 controls. Subsequent evaluations were performed in a validation set comprised of 65 PDAC cases and 72 controls. Further validation was performed in an independent set comprised of 75 PDAC cases and 47 controls. Results: A multi-marker panel containing 14 proteins was developed. The multi-marker panel achieved AUCs of 0.977 and 0.953 for the training set and validation set, respectively. In an independent validation set, the multi-marker panel yielded an AUC of 0.928. The diagnostic performance of the multi-marker panel showed significant improvements compared with carbohydrate antigen (CA) 19-9 alone (training set AUC = 0.977 vs. 0.872, P < 0.001; validation set AUC = 0.953 vs. 0.832, P < 0.01; independent validation set AUC = 0.928 vs. 0.771, P < 0.001). When the multi-marker panel and CA 19-9 were combined, the diagnostic performance of the combined panel was improved for all sets. Conclusions: This multi-marker panel and the combined panel showed statistically significant improvements in diagnostic performance compared with CA 19-9 alone and has the potential to complement CA 19-9 as a diagnostic marker in clinical practice.

Conventional methods for the surveillance of hepatocellular carcinoma (HCC) by imaging, with and without serum tumor markers, are suboptimal with regard to accuracy. We aimed to develop and validate a reliable serum biomarker panel for the early detection of HCC using a proteomic technique. This multicenter case-control study comprised 727 patients with HCC and patients with risk factors but no HCC. We developed a multiple reaction monitoring-mass spectrometry (MRM-MS) multimarker panel using 17 proteins from the sera of 398 patients. Area under the receiver operating characteristics curve (AUROC) values of this MRM-MS panel with and without α-fetoprotein (AFP) and protein induced by vitamin K absence or antagonist-II (PIVKA-II) were compared. The combination and standalone MRM-MS panels had higher AUROC values than AFP in the training (0.940 and 0.929 vs 0.775, both

Suicide is a leading cause of death worldwide, presenting a serious public health problem. We aimed to investigate the biological basis of suicide completion using proteomics on postmortem brain tissue. Thirty-six postmortem brain samples (23 suicide completers and 13 controls) were collected. We evaluated the proteomic profile in the prefrontal cortex (Broadmann area 9, 10) using tandem mass tag-based quantification with liquid chromatography–tandem mass spectrometry. Bioinformatics tools were used to elucidate the biological mechanisms related to suicide. Subgroup analysis was conducted to identify common differentially expressed proteins among clinically different groups. Of 9801 proteins identified, 295 were differentially expressed between groups. Suicide completion samples were mostly enriched in the endocannabinoid and apoptotic pathways (CAPNS1, CSNK2B, PTP4A2). Among the differentially expressed proteins, GSTT1 was identified as a potential biomarker among suicide completers with psychiatric disorders. Our findings suggest that the previously under-recognized endocannabinoid system and apoptotic processes are highly involved in suicide.

Quantifying multiple protein biomarkers in a blood sample at one time has many advantages for diagnosing human diseases. In this study, 34 multiplex assays by multiple reaction monitoring-mass spectrometry (MRM-MS) for serum biomarkers were characterized according to Clinical Proteomic Tumor Analysis Consortium (CPTAC) guidelines. The assays revealed that the median lower limit of quantitation (LLOQ) was 0.37 fmol/μL (16.0 ng/mL) and that the median total coefficient of variation (CV) was 18.2%, 12.2%, and 10.6% in the low-,medium-, and high-quality control (QC) samples. With regard to selectivity, the median mean differences in slope and concentration were 2.1% and 4.3%, respectively. The median values for all CVs and %difference from the nominal concentration for stability were 9.5% and 2.7% in low-QC and 3.8% and 3.1% in medium-QC. The median total CV was 9.8% in the reproducibility. Finally, 17 protein-based biomarker assays were reliable and transferrable for preclinical purposes per CPTAC guidelines.

Multiple reaction monitoring-mass spectrometry became a mainstream method for quantitative proteomics, which made the validation of a method and the analyzed data important. In this portal for validation of the MRM-MS assay, we developed a website that automatically evaluates uploaded MRM-MS data, based on biomarker assay guidelines from the European Medicines Agency, the US Food & Drug Administration, and the Korea Food & Drug Administration. The portal reads a Skyline output file and produces the following results—calibration curve, specificity, sensitivity, carryover, precision, recovery, matrix effect, recovery, dilution integrity, stability, and QC—according to the standards of each independent agency. The final tables and figures that pertain to the 11 evaluation categories are displayed in an individual page. Spring boot was used as a framework for development of the webpage, which follows MVC Pattern. JSP, HTML, XML, and Java Script were used to develop the webpage. A server was composed of Apache Tomcat, MySQL. Input files were skyline-derived output files (csv file), and each files were organized by specific columns in order. SQL, JAVA were interworked to evaluate all the categories and show the results. Method Validation Portal can be accessed via any kind of explorer from https://pnbvalid.snu.ac.kr.

Background Human epidermal growth factor receptor 2 (HER2) is often overexpressed in breast cancer and correlates with a worse prognosis. Thus, the accurate detection of HER2 is crucial for providing the appropriate measures for patients. However, the current techniques used to detect HER2 status, immunohistochemistry and fluorescence in situ hybridization (FISH), have limitations. Specifically, FISH, which is mandatory for arbitrating 2+ cases, is time-consuming and costly. To address this shortcoming, we established a multiple reaction monitoring-mass spectrometry (MRM-MS) assay that improves on existing methods for differentiating HER2 status. Methods We quantified HER2 expression levels in 210 breast cancer formalin-fixed paraffin-embedded (FFPE) tissue samples by MRM-MS. We aimed to improve the accuracy and precision of HER2 quantification by simplifying the sample preparation through predicting the number of FFPE slides required to ensure an adequate amount of protein and using the expression levels of an epithelial cell-specific protein as a normalization factor when measuring HER2 expression levels. Results To assess the correlation between MRM-MS and IHC/FISH data, HER2 quantitative data from MRM-MS were divided by the expression levels of junctional adhesion molecule A, an epithelial cell-specific protein, prior to statistical analysis. The normalized HER2 amounts distinguished between HER2 2+/FISH-negative and 2+/FISH-positive groups (AUROC = 0.908), which could not be differentiated by IHC. In addition, all HER2 status were discriminated by MRM-MS. Conclusions This MRM-MS assay yields more accurate HER2 expression levels relative to immunohistochemistry and should help to guide clinicians toward the proper treatment for breast cancer patients, based on their HER2 expression.

BACKGROUND Lens culinaris agglutinin-reactive fraction of α-fetoprotein (AFP-L3) is a serum biomarker for hepatocellular carcinoma (HCC). AFP-L3 is typically measured by liquid-phase binding assay (LiBA). However, LiBA does not always reflect AFP-L3 concentrations because of its low analytical sensitivity. Thus, we aimed to develop an analytically sensitive multiple reaction monitoring–mass spectrometry (MRM-MS) assay to quantify AFP-L3 in serum. METHODS The assay entailed the addition of a stable isotope-labeled internal standard protein analog, the enrichment of AFP using a monoclonal antibody, the fractionation of AFP-L3 using L. culinaris agglutinin lectin, deglycosylation, trypsin digestion, online desalting, and MRM-MS analysis. The performance of the MRM-MS assay was compared with that of LiBA in 400 human serum samples (100 chronic hepatitis, 100 liver cirrhosis, and 200 HCC). Integrated multinational guidelines were followed to validate the assay for clinical implementation. RESULTS The lower limit of quantification of the MRM-MS assay (0.051 ng/mL) for AFP-L3 was less than that of LiBA (0.300 ng/mL). Thus, AFP-L3, which was not observed by LiBA in HCC samples (n = 39), was detected by the MRM-MS assay, improving the clinical value of AFP-L3 as a biomarker by switching to a more analytical sensitive platform. The method was validated, meeting all the criteria in integrated multinational guidelines. CONCLUSIONS Because of the lower incidence of false-negative findings, the MRM-MS assay is more suitable than LiBA for early detection of HCC.

†‡ This study was performed to develop analytical methods to better understand the properties and reactivity of petroleum, which is a highly complex organic mixture, using high-resolution mass spectrometry and statistical analysis. Ten crude oil samples were analyzed using negative-mode electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry (ESI FT-ICR MS). Clustering methods, including principle component analysis (PCA), hierarchical clustering analysis (HCA), and k-means clustering, were used to comparatively interpret the spectra. All the methods were consistent and showed that oxygen and sulfur-containing heteroatom species played important roles in clustering samples or peaks. The oxygen-containing samples had higher acidity than the other samples, and the clustering results were linked to properties of the crude oils. This study demonstrated that clustering methods provide a simple and effective way to interpret complex petroleomic data.

In this study, the peaks observed using Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS) were correlated with properties of crude oils. The correlations were statistically an...

Complex petroleum mass spectra obtained by Fourier-transform ion cyclotron resonance mass spectrometry (FTICR MS) were successfully interpreted at the molecular level by applying principle component analysis (PCA) and hierarchical clustering analysis (HCA). A total of 40 mass spectra were obtained from 20 crude oil samples using both positive and negative atmospheric pressure photoionization (APPI). Approximately 400,000 peaks were identified at the molecular level. Conventional data analyses would have been impractical with so much data. However, PCA grouped samples into score plots based on their molecular composition. In this way, the overall compositional difference between samples could be easily displayed and identified by comparing score and loading plots. HCA was also performed to group and compare samples based on selected peaks that had been grouped by PCA. Subsequent heat map analyses revealed detailed compositional differences among grouped samples. This study demonstrates a promising new approach for studying multiple, complex petroleum samples at the molecular level.

Biofuels, including biodiesel, are an attractive replacement for petroleum diesel because they are renewable and compatible with existing diesel engines. Biodiesel can be made from oils extracted from a variety of materials ranging from soybeans to algae. Algae in particular has gained considerable interest in recent years because it yields more oil per hectare than any other crop. However, one of the major bottlenecks in the large-scale production of algae biodiesel is low conversion efficiency. To improve the efficiency with which algae can be processed into biodiesel, one must fully understand the chemical composition of the raw materials. The alkane and alkene compounds in algae are particularly interesting because these classes of compounds comprise the major constituents in gasoline and jet fuels. Previous studies have reported the presence of various hydrocarbons in algae, but these findings have been controversial. In addition, while several studies have indicated the presence of hydrocarbons in prokaryotic algae (cyanobacterium), relatively few studies have examined eukaryotic algae. Although many studies have investigated the chemical composition of algae biodiesels in general, few reports have characterized biodiesels made from domestic Korean algae. Domestic algae harvesting and biodiesel production are important in terms of both cultivation and conversion efficiencies. The current study characterized the chemical compositions of biodiesels made from two prokaryotic and one eukaryotic freshwater algae. The algae used in this study are commonly found in the wild in Korea. The hydrocarbon content of the samples depended greatly on the alga species.