ArticleScientific reports2024
Comprehensive serum glycopeptide spectra analysis to identify early-stage epithelial ovarian cancer.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Enhancing the positive predictive value of early-stage ovarian cancer detection using a two-step machine learning framework.Journal of translational medicine · 2026Article
- Machine learning for identifying liver and pancreas cancers through comprehensive serum glycopeptide spectra analysis: a case-control study.Molecular oncology · 2025Article
- Engrailed-2 (EN2) protein in cervical mucus: a novel biomarker for endometrial carcinoma.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Article
- Article
- Proteome-Wide Mendelian Randomization Analysis to Identify Potential Plasma Biomarkers and Therapeutic Targets for Epithelial Ovarian Cancer Subtypes.International journal of women's health · 2024Article
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21 authors.
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Abstract
Epithelial ovarian cancer (EOC) is widely recognized as the most lethal gynecological malignancy; however, its early-stage detection remains a considerable clinical challenge. To address this, we have introduced a new method, named Comprehensive Serum Glycopeptide Spectral Analysis (CSGSA), which detects early-stage cancer by combining glycan alterations in serum glycoproteins with tumor markers. We detected 1712 glycopeptides using liquid chromatography-mass spectrometry from the sera obtained from 564 patients with EOC and 1149 controls across 13 institutions. Furthermore, we used a convolutional neural network to analyze the expression patterns of the glycopeptides and tumor markers. Using this approach, we successfully differentiated early-stage EOC (Stage I) from non-EOC, with an area under the curve (AUC) of 0.924 in receiver operating characteristic (ROC) analysis. This method markedly outperforms conventional tumor markers, including cancer antigen 125 (CA125, 0.842) and human epididymis protein 4 (HE4, 0.717). Notably, our method exhibited remarkable efficacy in differentiating early-stage ovarian clear cell carcinoma from endometrioma, achieving a ROC-AUC of 0.808, outperforming CA125 (0.538) and HE4 (0.557). Our study presents a promising breakthrough in the early detection of EOC through the innovative CSGSA method. The integration of glycan alterations with cancer-related tumor markers has demonstrated exceptional diagnostic potential.
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