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综合血清糖肽谱分析鉴定早期上皮性卵巢癌。

Comprehensive serum glycopeptide spectra analysis to identify early-stage epithelial ovarian cancer.

机构信息

Department of Obstetrics and Gynecology, Tokai University School of Medicine, Isehara, Kanagawa, Japan.

Medical Solution Promotion Department, Medical Solution Segment, LSI Medience Corporation, Itabashi-ku, Tokyo, Japan.

出版信息

Sci Rep. 2024 Aug 28;14(1):20000. doi: 10.1038/s41598-024-70228-6.

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.

摘要

上皮性卵巢癌(EOC)被广泛认为是最致命的妇科恶性肿瘤;然而,其早期检测仍然是一个相当大的临床挑战。为了解决这个问题,我们引入了一种新的方法,称为综合血清糖肽谱分析(CSGSA),它通过结合血清糖蛋白中的聚糖改变和肿瘤标志物来检测早期癌症。我们使用液相色谱-质谱法从 13 家机构的 564 名 EOC 患者和 1149 名对照者的血清中检测到 1712 种糖肽。此外,我们使用卷积神经网络来分析糖肽和肿瘤标志物的表达模式。通过这种方法,我们成功地区分了早期 EOC(I 期)和非 EOC,在接受者操作特征(ROC)分析中曲线下面积(AUC)为 0.924。这种方法明显优于传统的肿瘤标志物,包括癌抗原 125(CA125,0.842)和人附睾蛋白 4(HE4,0.717)。值得注意的是,我们的方法在区分早期卵巢透明细胞癌和子宫内膜瘤方面表现出显著的疗效,ROC-AUC 为 0.808,优于 CA125(0.538)和 HE4(0.557)。我们的研究通过创新的 CSGSA 方法在 EOC 的早期检测方面取得了有希望的突破。糖链改变与癌症相关的肿瘤标志物的结合显示出了出色的诊断潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bbe8/11358426/72758570913c/41598_2024_70228_Fig1_HTML.jpg

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