Comparative analysis of EGP expression in EOC and non-EOC groupsThe expression of 1712 EGPs (Supplementally Information) was analyzed in 1713 individuals using several statistical methods, including Principal Component Analysis (PCA), Uniform Manifold Approximation and Projection (UMAP), heatmap, volcano plot, and Orthogonal Partial Least Squares Discriminant Analysis (OPLSDA). The participants comprised 564 with EOC and 1149 without, including healthy women (n = 943) and those with gynecologic conditions other than ovarian cancer (n = 206). The latter group included 83 with uterine myoma (LE), 72 with endometrioma (EM), and 51 with ovarian cysts (OCY), as detailed in Table 1. PCA revealed a slight shift in the distribution of the EOC group compared with the non-EOC groups (Fig. 1A), suggesting that EOC serum glycosylation was altered by cancer development. OPLSDA, aiming to discriminate between the EOC and non-EOC groups, slightly improved the separation of these groups compared with using PCA; however, the extent of this improvement remained limited and did not attain a practical level for screening (Fig. 1B). The heatmap comprising 1712 EGPs and 1713 samples indicated that some serum glycosylation was overexpressed in the EOC group compared with the non-EOC group (Fig. 1C). Furthermore, the volcano plot analysis comparing advanced-stage EOC (EOC-A) versus non-EOCs and early-stage EOC (EOC-E) vs. non-EOCs revealed that glycan alterations increased as EOC progressed (Fig. 1D and E). Additionally, we utilized UMAP, a dimension reduction technique often employed to handle complex datasets, to visualize the glycopeptide profiles of EOC and non-EOC patients. The analysis revealed EOC-specific glycosylation changes, indicating disease-related alterations in glycoprotein structure. These findings highlight their potential utility as effective biomarkers for early detection (Fig. 1F).
Table 1 Demographic characteristics of the patients.Figure 1Comparison of EGP expressions between the EOC and non-EOC groups. The analyses included 1713 participants: 564 with EOC and 1149 without, comprising 943 healthy women (HE), 83 patients with uterine myoma (LE), 72 with endometrioma (EM), and 51 with ovarian cysts (OCY). (A) Score plot of the principal component analysis (PCA) of the EOC and non-EOC groups plotted based on the first and second components: Red: EOC, Blue: HE, Yellow: OCY, Pink: EM, and Green: LE. (B) Score plot of the OPLSDA model plotted using t1 and t01: Red: EOC, Blue: HE, Yellow: OCY, Pink: EM, and Green: LE. (C) Heatmap comprising 1712 EGPs and 1713 individuals: The EGPs were rearranged using cluster analysis. Red: relatively increased, Green: relatively decreased, and Black: not changed. (D) Volcano plot comparing the EOC-A (advanced-stage) and non-EOC groups, and (E) Volcano plot comparing the EOC-E (early-stage )and non-EOC groups: The vertical axis represents the − log10 (p-value) from the Student’s t-test, and the horizontal axis represents the log2 (mean fold change). The cut-off criteria were set at a − log10 (p-value) greater than 10 and a log2 (mean fold change) greater than 1 or less than − 1. (F) UMAP Analysis: The plots depict a UMAP analysis based on 1712 EGPs from five distinct groups. Each data point corresponds to an individual patient or healthy woman, and the contours illustrate the density of the distribution across all cases.Establishing CNN screening model for identifying EOC-E using 2D barcodesThe 2D barcodes that represent serum sugar chain expression profiles were generated using 1712 EGPs to allow the CNN to learn the features discriminating the EOC and non-EOC groups. Figure 2A displays the representative patterns of the 2D barcodes for the five groups. We established the CNN model using 70% of the randomly selected samples (training set) and evaluated its performance using the remaining 30% of the samples (test set). This process was repeated 10 times, and the sum of the test results was assessed using receiver operating characteristic (ROC) curve analysis (Fig. 2B). The area under the ROC curve (AUC) of the CNN model reached 0.924 (95% confidence interval [CI] 0.913–0.934) when patients with EOC-E (Stage I) were compared with those with non-EOC; this significantly surpassed the AUCs of the existing tumor markers, CA125 (0.842, 95% CI 0.812–0.871) and HE4 (0.717, 95% CI 0.674–0.759; Fig. 2C). The ROC-AUC was 0.982 (95% CI 0.978–0.987) when patients with EOC-A were compared with those with non-EOC; this value also exceeded that of CA125 (0.960, 95% CI 0.946–0.974) and HE4 (0.911, 95% CI 0.884–0.937). We further converted the CNN-predicted values, which range from 0 to 1, into CSGSA scores using the following equation and set two cutoffs (3 and 6) to classify the examinees into three (high, middle, and low) risk groups.$${\text{CSGSA}}\;{\text{score}} = – \log_{{{1}0}} \left( {{1}{-}{\text{CNN – predicted}}\;{\text{value}}} \right)$$Figure 2Establishing and evaluating CSGSA CNN model for identifying early-stage EOC. (A) Representative 2D barcode images illustrating EGP expression and tumor markers (CA125 and HE49) within each disease category. (B) CNN model establishment and assessment methodology. The model was developed using a training set (70%) and subsequently evaluated on a separate test set (30%). This process was iterated 10 times, and the ROC-AUC was calculated using the sum of 10 trials. (C) ROC-AUCs of CA125, HE4, and CSGSA between EOC-E and non-EOC , and between EOC-A and non-EOC; the values in the charts represent AUCs. (D) Histograms based on the CSGSA scores for each disease group; cutoffs were set at 3 and 6 and patients were divided into three groups; high, middle, and low-risk. Sensitivity, specificity, and PPV are shown in Table 2. (E) Correlation between CSGSA scores and tumor markers (CA125 and HE4): CA125 and HE4 values were logarithmically converted. (F) Gradient-weighted Class Activation Mapping (Grad-CAM) analysis: The portions that CNN used to discriminate EOCs are shown in red and yellow. (G) Histograms illustrating CSGSA scores for clear cell carcinoma (CCC), endometrioma (EN), mucinous (MU), and serous (SE) in the EOC-A and EOC-E groups: Scores are classified as high, middle, or low using cutoff values of 3 and 6. The number of patients is indicated in parentheses. (H) Correlations between CSGSA scores and the tumor markers CA125 and HE4 for CCC, EN, MU, and SE cases in the EOC-A and EOC-E groups. CA125 and HE4 values were logarithmically converted.Figure 2D shows the histograms arranged based on the CSGSA scores for each disease category. When the cutoff was set at “3,” the sensitivity of EOC-A and EOC-E was 86.3% and 56.2%, respectively. The specificities of EM, HE, uterine myoma (LE), and ovarian cyst (OCY) were 79.6%, 99.5%, 91.9%, and 94.0%, respectively (Table 2a). To estimate the positive predictive value (PPV), the number of cases was corrected for the prevalence of each disease. The prevalence rates of EOC, EM, LE, and OCY in over 40-year-old patients (possible examinees who will undergo the screening test) were assumed to be 30, 1000, 2000, and 1000 among 100,000 women, respectively3,40,41,42,43, and the ratio of EOC-E and EOC-A was assumed to be 1:1. Following the correction, the PPV was found to be 2.23% when the cutoff was set at “3” (Table 2b). The reason behind the low PPV, despite the high sensitivity and specificity, can be attributed to an increase in the number of false positives owing to the leverage effect. This effect is particularly observed when the prevalence rate of the target disease is low. When the cutoff was increased to “6,” the total EOC sensitivity decreased to 52.7%; however, the specificities were remarkably improved to 93.1% (EM), 100.0% (HE), 95.3% (LE), and 98.2% (OCY); the PPV reached 8.03% (Table 2c and 2d). When we assigned patients to three classes using the two cutoffs (3 and 6), 0.20% of the examinees were classified into the high-risk class (> 6), with a PPV of 8.03%; 0.76% were classified into the middle-risk class (3–6), with a PPV of 0.734%; and over 99% were classified into the low-risk class (< 3), with a PPV of 0.00872% (Table 2e). At the cutoff value of 6, 52.5% of the patients with total EOC were identified, and at the cutoff value of 3, 56.2% of patients with EOC-E (71.2% of total EOC) were identified. Although there was a slight positive correlation between the CSGSA scores and CA125 or HE4, it is evident that CSGSA accurately identified the patients with EOC, even when their CA125 and HE4 levels were low (Fig. 2E). In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) analysis was performed to identify the key areas in images in which the CNN recognizes EOC. The CNN recognized the peripheral area of the 2D barcode (Fig. 2F), which was not surprising because EGP expression was allocated in the order of PCA loadings; the EGPs with high variation were positioned at the peripheral area.
Table 2 Evaluation of the CSGSA screening model (CNN).When EOC was divided into four major histological types, CCC, endometrioid (EN), mucinous (MU), and serous (SE), the analysis revealed that the sensitivities (with a cutoff of “3”) of EOC-A remained relatively high regardless of the histological type (CCC: 78.2%, EN: 86.6%, MU: 79.5%, and SE: 89.4%). However, in the case of EOC-E, the sensitivities varied depending on the specific histological types (CCC: 50.0%, EN: 65.3%, MU: 39.7%, and SE: 73.7%; Fig. 2G). This variability in sensitivity may be attributed to the relatively lower levels of CA125 and HE4 in patients, particularly those with MU and CCC, compared with those with EN and SE (Fig. 2H).CSGSA selectivity: discriminating EOC from other cancersWe evaluated whether CSGSA could distinguish EOC from other cancers, assuming that multiple patients with various diseases would undergo this screening test. Borderline ovarian tumors (BOT), often referred to as low malignant potential tumors, constitute 10–20% of all EOC cases and tend to occur in younger individuals. While noninvasive, BOT has the potential to progress into cancer. To investigate how CSGSA classifies BOT, we recruited 61 patients with BOT, including those with SE (n = 16), MU (n = 39), EN (n = 4), and other histological types (n = 2). Based on the CSGSA scores, 15 of the 61 patients with BOT (24.6%) were considered high risk, 16 (26.2%) were middle risk, and the remaining 30 (49.2%) were low risk (Fig. 3A). Although CSGSA did not provide a clear distinction between EOC and BOT, it may indicate the potential for BOT malignancy because some BOT cases can progress into EOC.Figure 3CSGSA selectivity against other cancers. (A) Histogram of the CSGSA scores for borderline ovarian tumors (BOT). (B) Histograms of the CSGSA scores for ovarian cancers, except for EOC. (C) CSGSA scores for other general cancers. (D) UMAP analysis of BOT, other ovarian cancer (OVC), and other cancers. The number of patients is indicated in parentheses. (E) Correlation between CSGSA scores and the tumor markers CA125 and HE4 for other ovarian cancers, other general cancers, and BOT.To evaluate the ability of CSGSA to differentiate EOC from other types of ovarian cancer, we enrolled 50 patients diagnosed with ovarian cancers other than EOC. The ovarian cancer group comprised patients with mixed epithelial and mesenchymal tumors (MEMT, n = 6), sex cord–stromal tumors (SCST, n = 5), germ cell tumors (GST, n = 12), fallopian tube cancer (FTC, n = 9), peritoneal cancer (PC, n = 11), and metastatic cancer (ME, n = 7), which had metastasized to the ovary from primary tumors in other organs such as the stomach and large intestine. Our findings revealed that a high percentage of patients with MEMT (5 out of 6, 83.3%), PC (10 out of 11, 90.9%), and FTC (6 out of 9, 66.7%) were classified as middle-risk or higher (Fig. 3B). These outcomes were consistent with expectations because MEMT encompasses both epithelial and mesenchymal components, PC exhibits histological similarities to serous ovarian carcinoma, and the majority of FTC cases are classified as epithelial cancers. Furthermore, it was reasonable that all patients with SCST (5 out of 5) were classified as low-risk because SCST has distinct histological characteristics compared with EOC. The patients with GCT (8 of 12, 66.7%), and ME (4 of 7, 57.1%) were also classified as middle-risk or higher (Fig. 3B).Next, we conducted a study with 19 patients diagnosed with different types of cancer, including breast cancer (BC, n = 3), gastric cancer (GC, n = 4), colorectal cancer (CRC, n = 4), head and neck cancer (HNC, n = 2), liver cancer (HCC, n = 2), lung cancer (LC, n = 2), and pancreatic cancer (PC, n = 2). We observed that 6 of 19 (31.6%) patients, particularly those with CRC, LC, GC, and PC, were classified as middle-risk or higher (Fig. 3C). Considering that all these cancers were in advanced stages (BC, GC, CRC, NHC, HCC, LC: stage III, PC: stage II), these findings suggest that CSGSA has a certain level of selectivity in distinguishing EOC from other types of cancer. However, further improvements are required to enhance the discriminatory capacity, particularly when the prevalence rates of these cancers are much higher than those of EOC. When comparing the UMAP distributions of the patients with those of EOC, as illustrated in Fig. 1C, no significant differences were observed in these patterns (Fig. 3D). Furthermore, when we examined the correlation between CSGSA scores and CA125 or HE4 in these patients, we observed a strong correlation between CSGSA scores and CA125 in ovarian cancers such as GCT, ME, MEMT, and SCST; however, limited correlations were observed in the other cases (Fig. 3E).Identification of the glycopeptides contributing to EOC discriminationWe selected several EGPs located in the upper-right and upper-left positions of the volcano plot and compared their retention times, mass spectra, and MS/MS patterns with those of the glycopeptides obtained from purified human serum proteins. We identified four glycopeptides as follows: alpha-1-acid glycoprotein (AGP) with a fully sialylated tetraantennary glycan containing one fucose attached to the asparagine at position 103, haptoglobin with a fully sialylated triantennary glycan containing three fucoses attached to the asparagine at position 241, complement C9 with a fully sialylated biantennary glycan attached to the asparagine at position 415, and transferrin with a biantennary glycan containing one sialic acid attached to the asparagine at position 432 (Fig. 4). However, the detailed sugar chain structure, including sequence and linkage manner, remains unknown. The diagnostic performance of each glycopeptide, as well as the combined model of CA125, HE4, and the four glycopeptides, was evaluated using ROC curves and AUC values. The combination model was established through logistic regression. As illustrated in Supplementary Fig. S1, the performance of individual markers was slightly lower than that of CA125 alone. Although the combined model of CA125, HE4, and the four glycopeptides significantly improved performance, it did not exceed that of the CSGSA. These four glycopeptides were located in the periphery of the 2D barcodes, suggesting that they play a vital role in discriminating EOC, when considered in conjunction with the results of Grad-CAM analysis (Fig. 2F).Figure 4Identification of the glycopeptides contributing to EOC discrimination. Volcano plot displaying 1712 EGPs when comparing all EOC and non-EOC samples (center); the horizontal axis represents log2 (mean fold ratio) and the vertical axis represents log10 (Student’s t-test p-value). The identified glycopeptides are marked with red circles. Extracted ion chromatogram (EIC), single mass spectrum (MS), and MS/MS spectrum (MSMS) of the glycopeptides obtained from purified standard serum proteins (in black) and EOC serum (in red) are presented. Proposed proteins, peptide sequences, and sugar chain structures are illustrated in each corner. The positions of the identified glycopeptides on the 2D barcode, along with bar graphs illustrating their relative abundance in EOC and non-EOC groups, are presented.Discriminating CCC from EM using the OPLSDA modelBecause CCC can potentially arise from EM, distinguishing these two conditions is another important issue. We established a CSGSA CCC discrimination model using the OPLSDA approach instead of a CNN because the number of specimens (CCC: 180 and EM: 76) was too small to establish a robust CNN model. OPLSDA enables the identification of discriminating factors between two classes by iteratively reducing non-discriminable dimensions, ultimately uncovering an underlying factor that separates the two groups in a 1712-dimensional space. This single dimension can then be used to effectively discriminate between CCC and EM.First, we divided the samples into three groups and established an OPLSDA model using two groups as a training set. Then, we evaluated its performance using the remaining group as a test set (Fig. 5A). This process was repeated three times by changing the combinations. Finally, the test results were summed to calculate the ROC-AUC, sensitivity, and specificity. The discrimination patterns of the OPLADA model in the three trials were similar, and the distributions in the training sets were reproduced in the test sets (Fig. 5B). The ROC-AUC calculated using the OPLSDA t1 score (horizontal axis value) of the test sets reached 0.808 (95% CI 0.746–0.870), when CCC-E (early stage) was compared with EM, which significantly exceeded that of CA125 (0.538, 95% CI 0.454–0.622) and HE4 (0.557, 95% CI 0.476–0.638). When comparing CCC-A (advanced stage) with EM, the ROC-AUC reached 0.927 (95% CI 0.883–0.971), whereas that of CA125 and HE4 was 0.670 (95% CI 0.579–0.761) and 0.718 (95% CI 0.625–0.811), respectively (Fig. 5C). The sensitivities were 73.4% and 43.1% when CCC-A and CCC-E were compared with EM, respectively, and the specificity of EM was 95.8% when the cutoff was set at zero. When these numbers were corrected with the prevalence rates, the PPV and sensitivity were 11.2% and 52.2%, respectively.Figure 5Evaluating the CSGSA OPLSDA model for distinguishing CCC from EM. (A) Method to establish and assess the OPLSDA model: The samples were randomly divided into three groups; the OPLSDA model was established using two groups as a training set. The remaining group was used as a test set. (B) OPLSDA score plots of the training and test sets for three trials. Red: CCC and Green: EM. (C) Box plot and ROC-AUC values between CCC-E and EM, and between CCC-A and EM for CA125, HE4, and CSGSA; CA125 and HE4 values were logarithmically converted. The p-values of Student’s t-test for between CCC-E and EM and between CCC-A and EM were calculated. (D) Proposed scheme of CSGSA: 1712 EGPs, obtained through proteolysis, were subjected to liquid chromatography–mass spectrometry analysis. The resulting data, along with CA125 and HE4 values, were used to generate 2D barcodes. A pretrained CNN model was used to interpret the 2D barcode pattern to classify whether it corresponds to EOC. The OPLSDA model was used to differentiate between CCC and EM based on the expression patterns of these 1712 EGPs.Proposed scheme for EOC screening using CSGSAThe proposed scheme for EOC screening using CSGSA is illustrated in Fig. 5D. In total, 1712 EGPs derived from protein proteolysis were analyzed using liquid chromatography–quadrupole time-of-flight mass spectrometry (MS), and their intensities were transformed into 2D barcodes incorporating the CA125 and HE4 values. These barcodes were then input into a pretrained CNN model, which discriminates EOC from non-EOC. Additionally, the OPLSDA model was used to differentiate CCC from EM based on the expression patterns of the 1712 EGPs.Robustness and stability of CSGSATo assess the practicality of CSGSA as a screening test, we conducted several evaluations, including (i) reproducibility, (ii) inter-machine error, (iii) inter-operator error, (iv) effects of circadian rhythm and diet, (v) short-term stability in blood, and (vi) long-term storage stability in serum. For reproducibility assessment, we performed analysis on both HE and EOC samples, repeating the process 10 times for each sample. The results showed that the variations in the CSGSA scores were consistently within a range of ± 1 level (Supplementary Fig. S2A). To evaluate inter-machine and inter-operator error, two operators analyzed 10 HE samples and 10 EOC samples (comprising 5 EOC-E and 5 EOC-A) twice using different mass spectrometers. The results indicate that the level of the errors remained within an acceptable range (Supplementary Fig. S2B and S2C). In terms of circadian and dietary influences, there was not a significant change in the levels of glycopeptide expression between samples collected at 8 AM and 2 PM, as well as between 8 AM and 9 PM, for all four participants (R2 > 0.991, Supplementary Fig. S3A). The CSGSA scores remained stable, except for one spike observed in Woman A at 9 PM (Supplementary Fig. S3B). Additionally, the scattering of the OPLSDA spots was limited to each individual (Supplementary Fig. S3C). In the analysis of short-term stability of blood at room temperature (22–25 °C), glycopeptide expression did not show any noticeable changes within 6 h, 24 h (R2 > 0.937, Supplementary Fig. S4A, S4B and S4C), and remained stable even within a period of 3 days (Supplementary Fig. S4D). In the analysis for long-term storage stability in serum, the glycopeptides were stable both within 3 days and 1 month at − 20 °C and 4 °C (R2 > 0.946, Supplementary Fig. S5 and S6). We did not observe noticeable differences in CSGSA outcomes among different facilities (Supplementary Fig. S7), demonstrating that the CSGSA test is robust enough to yield consistent results regardless of the variations in blood collection methods or tubes used across different locations.
Comprehensive serum glycopeptide spectra analysis to identify early-stage epithelial ovarian cancer
