Abstract 3940: Transformation by ENO1 highlights the positive relationship between HIF1A's and VEGFA's RNA expression levels, putatively by counteracting heterogeneity in glioblastomas
Authors: Marie E. Beckner, Ian F. Pollack, Ronald L. Hamilton
Journal: Cancer Research (2017)

Abstract Analysis of metabolic gene expression is compromised by tumor heterogeneity. Therefore, we investigated the use of RNA expression levels from ENO1, which encodes enolase 1, to adjust for glycolytic heterogeneity within glioblastomas attributed to irregular vascularity, necrosis, surgical removal, etc. Recently, this approach revealed relationships between carbonic anhydrases and amplified oncogenes (Beckner, et al. BBA Clinical 5 (2016):1-15). Here in frozen tissue samples from 22 glioblastomas, expressions of the metabolic gene encoding hypoxia inducible factor - 1A (HIF1A) and its target, vascular endothelial growth factor A (encoded by VEGFA), were contrasted with two non-metabolic genes, i.e. those encoding platelet derived growth factor A (PDGFA) and epidermal growth factor (EGF) using RT-qPCR analysis. Genes of interest (GOI) were initially normalized with delta-delta crossing threshold methodology using housekeeping genes, ACTB and GAPDH. Then, concurrent expressions of ENO1 (ave 0.83 +/- 0.18 CI (95%), range of 0.22 - 1.97 times normal) were used to mathematically transform expressions of GOI to multiples of ENO1 to putatively correct for glycolytic variation. Expressions of PDGFA (ave 1.90 +/- 0.69 CI (95%), 0.17 - 4.01 times normal) and EGF (ave 1.25 +/- 0.57 CI (95%), 0.07 - 5.14 times normal), had correlations, r = 0.65 and 0.66, unranked (Pearson's) and ranked (Spearman's) data, respectively, among the 22 tumors. After ENO1 transformation, r = 0.68 for their unranked data &amp; the difference in their ranges rose to 1.31-fold. Prior to ENO1 transformation, expressions of HIF1A (ave 1.33 +/- 0.28 CI (95%), 0.25 - 2.55 times normal) and VEGFA (ave 2.89 +/- 1.36 CI (95%), 0.17 - 9.94 times normal) had negative correlations, r = - 0.15 and - 0.09, unranked and ranked data, respectively. However, after transforming HIF1A and VEGFA expressions to multiples of concurrent ENO1 expression, their correlation became positive in both unranked and ranked data, with r = 0.30 for the ranked (Spearman) data. The difference in the ranges of the two metabolic genes expanded to 6.76-fold. Whereas the Wilcoxon Rank Sum of VEGFA's untransformed values, with versus without 2.02-fold elevations of HIF1A expression, was insignificant, p = 0.704, using ENO1 transformed values indicated a significant relationship, p = 0.042. Therefore, ENO1 transformation revealed the anticipated relationship between HIF1A and its target, VEGFA, at the RNA expression level that was not initially apparent in this small group of tumors. Transformation via expression levels of ENO1 compensates for glycolytic heterogeneity to reveal and highlight relationships among metabolic genes when analyzing resected tumors. Support from The Pittsburgh Foundation's Walter L. Copeland Fund for Cranial Research (D2006-0379) and the Molecular Lab, Dept. of Pathology, Univ. of Pittsburgh Medical Center. Citation Format: Marie E. Beckner, Ian F. Pollack, Ronald L. Hamilton. Transformation by ENO1 highlights the positive relationship between HIF1A's and VEGFA's RNA expression levels, putatively by counteracting heterogeneity in glioblastomas [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3940. doi:10.1158/1538-7445.AM2017-3940