Department of Gynecology, School of Medicine, Fujita Health University, Toyoake, Japan.
Department of Obstetrics and Gynecology, School of Medicine, Fujita Health University, Toyoake, Japan.
Cancer Sci. 2024 Nov;115(11):3672-3681. doi: 10.1111/cas.16323. Epub 2024 Aug 22.
Approximately 660,000 women are diagnosed with cervical cancer annually. Current screening options such as cytology or human papillomavirus testing have limitations, creating a need to identify more effective ancillary biomarkers for triage. Here, we evaluated whether metabolomic analysis of cervical mucus metabolism could be used to identify biomarkers of cervical intraepithelial neoplasia (CIN) and cervical cancer. The case-control group consisted of 181 CIN, 69 squamous cell carcinoma (SCC) patients, and 48 healthy controls in the primary cohort. We undertook metabolomic analyses using ultra-HPLC-tandem mass spectrometry. Univariate and multivariate analyses were carried out to profile metabolite characteristics, and receiver operating characteristic (ROC) analysis identified biomarker candidates. Five metabolites conferred the highest discriminatory power for SCC: oxidized glutathione (GSSG) (area under the ROC curve, 0.924; 95% confidence interval, 0.877-0.971), malic acid (0.914, 0.859-0.968), kynurenine (0.884, 0.823-0.945), GSSG/glutathione (GSH) (0.936, 0.892-0.979), and kynurenine/tryptophan (0.909, 0.856-0.961). Malic acid was the best marker for detection of CIN2 or worse (0.858, 0.793-0.922) and was a clinically useful metabolite. We confirmed the reproducibility of the results by validation cohort. Additionally, metabolomic analyses revealed eight pathways strongly associated with cervical neoplasia. Of these, only the tricarboxylic acid cycle was strongly associated with all CINs and cancer, indicating active energy production. Aberrant arginine metabolism by decreasing arginine and increasing citrulline might reduce tumor immunity. Changes in cysteine-methionine and GSH pathways might drive the initiation and progression of cervical cancer. These results suggest that metabolic analysis can identify ancillary biomarkers and could improve our understanding of the pathophysiological mechanisms underlying cervical neoplasia.
每年约有 66 万名女性被诊断患有宫颈癌。目前的筛查选择,如细胞学或人乳头瘤病毒检测,存在局限性,因此需要确定更有效的辅助生物标志物来进行分流。在这里,我们评估了宫颈粘液代谢的代谢组学分析是否可用于识别宫颈上皮内瘤变(CIN)和宫颈癌的生物标志物。该病例对照研究的原始队列包括 181 名 CIN 患者、69 名鳞状细胞癌(SCC)患者和 48 名健康对照者。我们使用超高效液相色谱-串联质谱进行代谢组学分析。进行了单变量和多变量分析以描绘代谢物特征,并进行了接收者操作特征(ROC)分析以确定生物标志物候选物。有 5 种代谢物对 SCC 具有最高的判别能力:氧化谷胱甘肽(GSSG)(ROC 曲线下面积,0.924;95%置信区间,0.877-0.971)、苹果酸(0.914,0.859-0.968)、犬尿氨酸(0.884,0.823-0.945)、GSSG/谷胱甘肽(GSH)(0.936,0.892-0.979)和犬尿氨酸/色氨酸(0.909,0.856-0.961)。苹果酸是检测 CIN2 或更严重病变的最佳标志物(0.858,0.793-0.922),是一种具有临床应用价值的代谢物。我们通过验证队列证实了结果的可重复性。此外,代谢组学分析揭示了 8 条与宫颈癌强烈相关的途径。其中,只有三羧酸循环与所有 CIN 和癌症强烈相关,表明活跃的能量产生。通过减少精氨酸和增加瓜氨酸,异常的精氨酸代谢可能会降低肿瘤免疫力。半胱氨酸-蛋氨酸和 GSH 途径的变化可能推动宫颈癌的发生和发展。这些结果表明,代谢分析可以识别辅助生物标志物,并有助于我们理解宫颈癌发生的病理生理机制。