Buraschi Simone, Pascal Gabriel, Liberatore Federico, Iozzo Renato V
Department of Pathology and Genomic Medicine, and the Translational Cellular Oncology Program, Sidney Kimmel Cancer Center, Sidney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA 19107, USA.
School of Computer Science and Informatics, Cardiff University, Cardiff CF24 4AG, UK.
Proteoglycan Res. 2025 Jan-Mar;3(1). doi: 10.1002/pgr2.70014. Epub 2025 Jan 8.
Solid tumors present a formidable challenge in oncology, necessitating innovative approaches to improve therapeutic outcomes. Proteoglycans, multifaceted molecules within the tumor microenvironment, have garnered attention due to their diverse roles in cancer progression. Their unique ability to interact with specific membrane receptors, growth factors, and cytokines provides a promising avenue for the development of recombinant proteoglycan-based therapies that could enhance the precision and efficacy of cancer treatment. In this study, we performed a comprehensive analysis of the proteoglycan gene landscape in human breast carcinomas. Leveraging the available wealth of genomic and clinical data regarding gene expression in breast carcinoma and using a machine learning model, we identified a unique gene expression signature composed of five proteoglycans differentially modulated in the tumor tissue: Syndecan-1 and asporin (upregulated) and decorin, PRELP and podocan (downregulated). Additional query of the breast carcinoma data revealed that serglycin, previously shown to be increased in breast carcinoma patients and mouse models and to correlate with a poor prognosis, was indeed decreased in the vast majority of breast cancer patients and its levels inversely correlated with tumor progression and invasion. This proteoglycan gene signature could provide novel diagnostic capabilities in breast cancer biology and highlights the need for further utilization of publicly available datasets for the clinical validation of preclinical experimental results.
实体瘤在肿瘤学领域构成了巨大挑战,因此需要创新方法来改善治疗效果。蛋白聚糖是肿瘤微环境中的多面分子,因其在癌症进展中的多种作用而受到关注。它们与特定膜受体、生长因子和细胞因子相互作用的独特能力,为开发基于重组蛋白聚糖的疗法提供了一条有前景的途径,这种疗法可以提高癌症治疗的精准性和疗效。在本研究中,我们对人类乳腺癌中的蛋白聚糖基因格局进行了全面分析。利用现有的关于乳腺癌基因表达的丰富基因组和临床数据,并使用机器学习模型,我们确定了一个独特的基因表达特征,该特征由肿瘤组织中差异调节的五种蛋白聚糖组成:Syndecan-1和阿泊脂蛋白(上调)以及核心蛋白聚糖、脯氨酸丰富的细胞外基质蛋白和足突蛋白(下调)。对乳腺癌数据的进一步查询显示,丝甘蛋白聚糖此前在乳腺癌患者和小鼠模型中显示增加且与预后不良相关,而在绝大多数乳腺癌患者中实际上是减少的,其水平与肿瘤进展和侵袭呈负相关。这种蛋白聚糖基因特征可为乳腺癌生物学提供新的诊断能力,并突出了进一步利用公开可用数据集对临床前实验结果进行临床验证的必要性。