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卵巢癌:多组学数据整合

Ovarian Cancer: Multi-Omics Data Integration.

作者信息

Kliuchnikova Anna, Gordeeva Arina, Abdurakhimov Aziz, Materova Tatiana, Tarbeeva Svetlana, Sarygina Elizaveta, Kozlova Anna, Kiseleva Olga, Ponomarenko Elena, Ilgisonis Ekaterina

机构信息

Institute of Biomedical Chemistry, 119121 Moscow, Russia.

出版信息

Int J Mol Sci. 2025 Jun 21;26(13):5961. doi: 10.3390/ijms26135961.

Abstract

This study focuses on the systematization and integration of ovarian cancer multi-omics data, revealing patterns in the application of different omics-based approaches and assessing factors that affect the identification of potential biomarkers. An integrative analysis of 51 publications revealed 1649 potential biomarkers. The findings emphasized the molecular diversity of ovarian cancer. They demonstrated the importance of performing the comprehensive integration of molecular and clinical data to search for diagnostic alternatives and molecular patterns underlying ovarian cancer. The heterogeneity of data sources, differences in data acquisition and analysis protocols, and the lack of uniform standards affect the reproducibility of the results of genomic and post-genomic profiling. Multi-omics studies are more promising than mono-omics-based ones. Despite technological advances, researchers continue to focus on results obtained over a decade ago, which may hinder the scientific community from exploring new horizons in ovarian cancer research.

摘要

本研究聚焦于卵巢癌多组学数据的系统化与整合,揭示不同基于组学方法的应用模式,并评估影响潜在生物标志物识别的因素。对51篇出版物的综合分析揭示了1649个潜在生物标志物。研究结果强调了卵巢癌的分子多样性。它们证明了对分子和临床数据进行全面整合以寻找卵巢癌诊断替代方法和分子模式的重要性。数据源的异质性、数据采集和分析方案的差异以及缺乏统一标准影响了基因组和后基因组分析结果的可重复性。多组学研究比基于单一组学的研究更具前景。尽管有技术进步,但研究人员仍继续关注十多年前获得的结果,这可能会阻碍科学界在卵巢癌研究中探索新领域。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6a66/12249510/f7dbbc348b3f/ijms-26-05961-g001.jpg

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