Carcinoembryonic 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. Glycopeptides 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). Leucine-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. We 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.

Background: One of the current problems in the field of metabolomics is the difficulty in integrating data collected using different equipment at different facilities, because many metabolomic methods have been developed independently and are unique to each laboratory. Methods: In this study, we examined whether different analytical methods among 12 different laboratories provided comparable relative quantification data for certain metabolites. Identical samples extracted from two cell lines (HT-29 and AsPc-1) were distributed to each facility, and hydrophilic and hydrophobic metabolite analyses were performed using the daily routine protocols of each laboratory. Results: The results indicate that there was no difference in the relative quantitative data (HT-29/AsPc-1) for about half of the measured metabolites among the laboratories and assay methods. Data review also revealed that errors in relative quantification were derived from issues such as erroneous peak identification, insufficient peak separation, a difference in detection sensitivity, derivatization reactions, and extraction solvent interference. Conclusion: The results indicated that relative quantification data obtained at different facilities and at different times would be integrated and compared by using a reference materials shared for data normalization.

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.

• “Multiblock metabolomics” elucidates the global metabolic network in a whole body. • “Multiblock metabolomics” combines LC/MS-based metabolomics with multiblock PCA. • “Multiblock metabolomics” highlights and elicits organ-specific metabolism. • TGs with less unsaturated fatty acids were highly accumulated in the diabetic liver.

Objectives: To conduct a comprehensive glycopeptide spectra analysis of serum between cancer and non-cancer patients to identify early biomarkers of epithelial ovarian cancer (EOC). Methods: Approximately 30,000 glycopeptide peaks were detected from the digested serum glycoproteins of 39 EOC patients (23 early-stage, 16 advanced-stage) and 45 non-cancer patients (27 leiomyoma and ovarian cyst cases, 18 endometrioma cases) by liquid chromatography mass spectrometry (LC–MS). The differential glycopeptide peak spectra were analyzed to distinguish between cancer and non-cancer groups by employing multivariate analysis including principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and heat maps. Results: Examined spectral peaks were filtered down to 2281 serum quantitative glycopeptide signatures for differentiation between ovarian cancer and controls using multivariate analysis. The OPLS-DA model using cross-validation parameters R2 and Q2 and score plots of the serum samples significantly differentiated the EOC group from the non-cancer control group. In addition, women with early-stage clear cell carcinoma and endometriomas were clearly distinguished from each other by OPLS-DA as well as by PCA and heat maps. Conclusions: Our study demonstrates the potential of comprehensive serum glycoprotein analysis as a useful tool for ovarian cancer detection.