Mather Quang, Priego Jonathon, Ward Kristi, Kundan Verma, Tran Dat, Dwivedi Alok, Bryan Brad A
Department of Biomedical Sciences, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX 79905, USA.
Division of Biostatistics and Epidemiology, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX 79905, USA.
Mol Clin Oncol. 2017 Sep;7(3):315-321. doi: 10.3892/mco.2017.1325. Epub 2017 Jul 13.
Benign lipomas and well-differentiated liposarcomas share many histological and molecular features. Due to their similarities, patients with these lipomatous tumors are misdiagnosed up to 40% of the time following radiological detection, up to 17% of the time following histological examination, and in as many as 15% of cases following fluorescent hybridization for chromosomal anomalies. Incorrect classification of these two tumor types leads to increased costs to the patient and delayed accurate diagnoses. In this study, we used genomics analysis to identify several genes whose mRNA expression patterns were significantly altered between lipomas and well-differentiated liposarcomas. We confirmed our findings at the protein level using a panel of 30 human lipomatous tumors, revealing that C4BPB, class II, major histocompatibility complex, CIITA, EPHB2, HOXB7, GLS2, RBBP5, and regulator of RGS2 protein levels were increased in well-differentiated liposarcomas compared to lipomas. We developed a multi-protein model of these markers to increase discriminatory ability, finding the combined expression model with CIITA and RGS2 provided a high ability (AUC=0.93) to differentiate between lipomas and well-differentiated liposarcomas with sensitivity at 83.3% and specificity at 90.9%.
良性脂肪瘤和高分化脂肪肉瘤具有许多组织学和分子特征。由于它们的相似性,这些脂肪瘤性肿瘤患者在放射学检测后误诊率高达40%,在组织学检查后误诊率高达17%,在荧光杂交检测染色体异常后误诊率高达15%。这两种肿瘤类型的错误分类会增加患者的费用,并导致准确诊断的延迟。在本研究中,我们使用基因组分析来鉴定几种基因,其mRNA表达模式在脂肪瘤和高分化脂肪肉瘤之间有显著改变。我们使用一组30例人类脂肪瘤性肿瘤在蛋白质水平上证实了我们的发现,结果显示,与脂肪瘤相比,高分化脂肪肉瘤中C4BPB、Ⅱ类主要组织相容性复合体、CIITA、EPHB2、HOXB7、GLS2、RBBP5和RGS2调节蛋白的水平升高。我们开发了这些标志物的多蛋白模型以提高鉴别能力,发现CIITA和RGS2的联合表达模型具有较高的区分脂肪瘤和高分化脂肪肉瘤的能力(AUC = 0.93),敏感性为83.3%,特异性为90.9%。