Sojoudi Kiana, Solaimani Maryam, Azizi Hossein
Department of Biology, Faculty of Sciences, University of Guilan, Rasht, Iran.
Faculty of Biotechnology, Amol University of Special Modern Technologies, Amol, 49767, Iran.
J Ovarian Res. 2025 Jan 31;18(1):20. doi: 10.1186/s13048-025-01597-3.
Ovarian cancer is a deadly disease, often diagnosed at advanced stages due to a lack of reliable biomarkers. Exosomes, which carry a variety of molecules such as proteins, lipids, DNA, and non-coding RNAs, have recently emerged as promising tools for early cancer detection. While exosomes have been studied in various cancer types, comprehensive network analyses of exosome proteins in ovarian cancer remain limited. In this study, we used a protein-protein interaction (PPI) network. Using the Clustermaker2 app and the MCODE algorithm, we identified six significant clusters within the network, highlighting regions involved in functional pathways. A four-fold algorithmic approach, including MCC, DMNC, Degree, and EPC, identified 12 common hub genes. STRING analysis and visualization techniques provided a detailed understanding of the biological processes associated with these hub genes. Notably, 91.7% of the identified hub genes were involved in translational processes, showing an important role in protein synthesis regulation in ovarian cancer. In addition, we identified the miRNAs and LncRNAs carried by ovarian cancer exosomes. These findings highlight potential biomarkers for early detection and therapeutic targets.
卵巢癌是一种致命疾病,由于缺乏可靠的生物标志物,往往在晚期才被诊断出来。外泌体携带蛋白质、脂质、DNA和非编码RNA等多种分子,最近已成为早期癌症检测的有前景的工具。虽然外泌体已在多种癌症类型中得到研究,但对卵巢癌中外泌体蛋白质的全面网络分析仍然有限。在本研究中,我们使用了蛋白质-蛋白质相互作用(PPI)网络。利用Clustermaker2应用程序和MCODE算法,我们在网络中确定了六个显著的簇,突出了参与功能途径的区域。一种包括MCC、DMNC、Degree和EPC的四重算法方法确定了12个共同的枢纽基因。STRING分析和可视化技术提供了对与这些枢纽基因相关的生物学过程的详细理解。值得注意的是,91.7%的已确定枢纽基因参与翻译过程,在卵巢癌蛋白质合成调控中发挥重要作用。此外,我们还鉴定了卵巢癌外泌体携带的miRNA和LncRNA。这些发现突出了早期检测的潜在生物标志物和治疗靶点。