Kharrat Feras, Capaci Valeria, Conti Andrea, Golino Valentina, Campiglia Pietro, Balasan Nour, Aloisio Michelangelo, Licastro Danilo, Monasta Lorenzo, Caponneto Federica, Beltrami Antonio Paolo, Romano Federico, di Lorenzo Giovanni, Ricci Giuseppe, Ura Blendi
Institute for Maternal and Child Health, IRCCS Burlo Garofolo, 65/1 Via dell'Istria, 34137 Trieste, Italy.
Department of Pharmacy, University of Salerno, Via Giovanni Paolo II 132, 84084 Salerno, Italy.
Biomedicines. 2025 Jan 3;13(1):95. doi: 10.3390/biomedicines13010095.
: Endometrial cancer (EC) is the second most frequent gynecological malignant tumor in postmenopausal women. Pathogenic mechanisms related to the onset and development of the disease are still unknown. To identify dysregulated proteins associated with EC we exploited a combined in vitro/in silico approach analyzing the proteome of exosomes with advanced MS techniques and annotating their results by using Chymeris1 AI tools. : To this aim in this pilot study, we performed a deep proteomics analysis with high resolution MS (HRMS), advanced computational tools and western blotting for proteomics data validation. : That allowed us to identify 3628 proteins in serum albumin-depleted exosomes from 10 patients with EC compared to 10 healthy controls. This is the largest number of proteins identified in EC serum EVs. After quantification and statistical analysis, we identified 373 significantly ( < 0.05) dysregulated proteins involved in neutrophil and platelet degranulation pathways. A more detailed bioinformatics analysis revealed 61 dysregulated enzymes related to metabolic and catabolic pathways linked to tumor invasion. Through this analysis, we identified 49 metabolic and catabolic pathways related to tumor growth. : Altogether, data shed light on the metabolic pathways involved in tumors. This is very important for understanding the metabolism of EC and for the development of new therapies.
子宫内膜癌(EC)是绝经后女性中第二常见的妇科恶性肿瘤。该疾病发生和发展的致病机制仍不清楚。为了鉴定与EC相关的失调蛋白,我们采用了体外/计算机相结合的方法,用先进的质谱技术分析外泌体的蛋白质组,并使用Chymeris1人工智能工具注释其结果。在这项初步研究中,我们使用高分辨率质谱(HRMS)、先进的计算工具和蛋白质印迹法对蛋白质组学数据进行验证,进行了深入的蛋白质组学分析。这使我们能够在10名EC患者与10名健康对照者的去除血清白蛋白的外泌体中鉴定出3628种蛋白质。这是在EC血清细胞外囊泡中鉴定出的最大数量的蛋白质。经过定量和统计分析,我们鉴定出373种在中性粒细胞和血小板脱颗粒途径中显著(<0.05)失调的蛋白质。更详细的生物信息学分析揭示了61种与肿瘤侵袭相关的代谢和分解代谢途径失调的酶。通过这项分析,我们确定了49条与肿瘤生长相关的代谢和分解代谢途径。总之,这些数据揭示了肿瘤相关的代谢途径。这对于理解EC的代谢以及新疗法的开发非常重要。