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多组学方法在早期卵巢癌诊断中的生物标志物发现。

Multi-omics approaches for biomarker discovery in early ovarian cancer diagnosis.

机构信息

Center for Reproductive Medicine, Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 10091, China; National Clinical Research Center for Obstetrics and Gynecology, Peking University Third Hospital, Beijing 10091, China; Key Laboratory of Assisted Reproduction, Ministry of Education, Peking University, Beijing 100191, China; Beijing Key Laboratory of Reproductive Endocrinology and Assisted Reproductive Technology, Peking University Third Hospital, Beijing 10091, China.

National Clinical Research Center for Obstetrics and Gynecology, Peking University Third Hospital, Beijing 10091, China.

出版信息

EBioMedicine. 2022 May;79:104001. doi: 10.1016/j.ebiom.2022.104001. Epub 2022 Apr 16.

Abstract

Ovarian cancer (OC) is a heterogeneous disease with the highest mortality rate and the poorest prognosis among gynecological malignancies. Because of the absence of specific early symptoms, most OC patients are often diagnosed at late stages. Thus, improved biomarkers of OC for use in research and clinical practice are urgently needed. The last decade has seen increasingly rapid advances in sequencing and biotechnological methodologies. Consequently, multiple omics technologies, including genomic/transcriptomic sequencings and proteomic/metabolomic mass spectra, have been widely applied to analyze tissue- and liquid-derived samples from OC patients. The integration of multi-omics data has increased our knowledge of the disease and identified valuable OC biomarkers. In this review, we summarize the recent advances and perspectives in the use of multi-omics technologies in OC research and highlight potential applications of multi-omics for identifying novel biomarkers and improving clinical assessments.

摘要

卵巢癌 (OC) 是一种异质性疾病,在妇科恶性肿瘤中死亡率最高,预后最差。由于缺乏特定的早期症状,大多数 OC 患者通常在晚期才被诊断出来。因此,迫切需要改进 OC 的生物标志物,用于研究和临床实践。过去十年中,测序和生物技术方法取得了越来越迅速的进展。因此,多种组学技术,包括基因组/转录组测序和蛋白质组/代谢组质谱,已被广泛应用于分析来自 OC 患者的组织和液体衍生样本。多组学数据的整合增加了我们对该疾病的认识,并确定了有价值的 OC 生物标志物。在这篇综述中,我们总结了多组学技术在 OC 研究中的最新进展和观点,并强调了多组学在识别新的生物标志物和改善临床评估方面的潜在应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6f72/9035645/e6e327e2d2d2/gr1.jpg

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