TY - JOUR
T1 - Comprehensive serum glycopeptide spectra analysis to identify early-stage epithelial ovarian cancer
AU - Mikami, Mikio
AU - Tanabe, Kazuhiro
AU - Imanishi, Tadashi
AU - Ikeda, Masae
AU - Hirasawa, Takeshi
AU - Yasaka, Miwa
AU - Machida, Hiroko
AU - Yoshida, Hiroshi
AU - Hasegawa, Masanori
AU - Shimada, Muneaki
AU - Kato, Tomoyasu
AU - Kitamura, Shoichi
AU - Kato, Hisamori
AU - Fujii, Takuma
AU - Kobayashi, Yoichi
AU - Suzuki, Nao
AU - Tanaka, Kyoko
AU - Murakami, Isao
AU - Katahira, Tomoko
AU - Hayashi, Chihiro
AU - Matsuo, Koji
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
KW - Clear cell carcinoma
KW - Convolutional neural network
KW - Glycomics
KW - Glycopeptide
KW - Mass spectrometry
KW - Ovarian cancer
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U2 - 10.1038/s41598-024-70228-6
DO - 10.1038/s41598-024-70228-6
M3 - Article
C2 - 39198565
AN - SCOPUS:85202677069
SN - 2045-2322
VL - 14
JO - Scientific reports
JF - Scientific reports
IS - 1
M1 - 20000
ER -