Nikas Jason B
Genomix, Inc. Minneapolis, MN 55364, USA.
Am J Cancer Res. 2014 Nov 19;4(6):862-73. eCollection 2014.
Gliomas, the most common primary brain tumors in adults, constitute clinically, histologically, and molecularly a most heterogeneous type of cancer. Owing to this, accurate clinical prognosis for short-term vs. long-term survival for patients with grade II or III glioma is currently nonexistent. A rigorous, multi-method bioinformatic approach was used to identify the top most differentially expressed genes as captured by mRNA sequencing of tumor tissue. Mathematical modeling was employed to develop the model, and three different and independent methods of validation were used to assess its performance. I present here a mathematical model that can identify with a high accuracy (sensitivity=92.9%, specificity=96.0%) those patients with glioma (grade II or III) who will experience short-term survival (≤ 1 year), as well as those with long-term survival (≥ 3 years), at the time of diagnosis and prior to surgery and adjuvant chemotherapy. The 5 gene input variables to the model are: FAM120AOS, PDLIM4, OCIAD2, PCDH15, and MXI1. MXI1, a transcriptional repressor, represents the top biomarker of survival and the most promising target for the development of a pharmacological treatment.
神经胶质瘤是成人中最常见的原发性脑肿瘤，在临床、组织学和分子层面上都是一种高度异质性的癌症类型。正因如此，目前不存在针对II级或III级神经胶质瘤患者短期与长期生存的准确临床预后评估。本研究采用了一种严谨的多方法生物信息学方法，以识别肿瘤组织mRNA测序所捕获的差异表达最显著的基因。运用数学建模来开发该模型，并使用三种不同且独立的验证方法来评估其性能。在此，我展示一种数学模型，该模型能够在诊断时、手术和辅助化疗之前，以高精度（敏感性=92.9%，特异性=96.0%）识别出患有神经胶质瘤（II级或III级）且将经历短期生存（≤1年）以及长期生存（≥3年）的患者。该模型的5个基因输入变量为：FAM120AOS、PDLIM4、OCIAD2、PCDH15和MXI1。MXI1作为一种转录抑制因子，是生存的首要生物标志物，也是药物治疗开发最具潜力的靶点。
