July 2011
Volume 52, Issue 8
Free
Genetics  |   July 2011
Regional and Temporal Differences in Gene Expression of LHBETATAG Retinoblastoma Tumors
Author Affiliations & Notes
  • Samuel K. Houston
    From the Bascom Palmer Eye Institute,
  • Yolanda Pina
    From the Bascom Palmer Eye Institute,
  • Jennifer Clarke
    the Division of Biostatistics, Department of Epidemiology and Public Health, and
  • Tulay Koru-Sengul
    the Division of Biostatistics, Department of Epidemiology and Public Health, and
  • William K. Scott
    the Department of Molecular Genomics, University of Miami, Miami, Florida
  • Lubov Nathanson
    the Department of Molecular Genomics, University of Miami, Miami, Florida
  • Amy C. Schefler
    From the Bascom Palmer Eye Institute,
  • Timothy G. Murray
    From the Bascom Palmer Eye Institute,
  • Corresponding author: Timothy G. Murray, Bascom Palmer Eye Institute, P.O. Box 016880, Miami, FL 33101; tmurray@med.miami.edu
Investigative Ophthalmology & Visual Science July 2011, Vol.52, 5359-5368. doi:10.1167/iovs.10-6321
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      Samuel K. Houston, Yolanda Pina, Jennifer Clarke, Tulay Koru-Sengul, William K. Scott, Lubov Nathanson, Amy C. Schefler, Timothy G. Murray; Regional and Temporal Differences in Gene Expression of LHBETATAG Retinoblastoma Tumors. Invest. Ophthalmol. Vis. Sci. 2011;52(8):5359-5368. doi: 10.1167/iovs.10-6321.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose.: The purpose of this study was to evaluate by microarray the hypothesis that LHBETATAG retinoblastoma tumors exhibit regional and temporal variations in gene expression.

Methods.: LHBETATAG mice aged 12, 16, and 20 weeks were euthanatized (n = 9). Specimens were taken from five tumor areas (apex, anterior lateral, center, base, and posterior lateral). Samples were hybridized to gene microarrays. The data were preprocessed and analyzed, and genes with a P < 0.01, according to the ANOVA models, and a log2-fold change >2.5 were considered to be differentially expressed. Differentially expressed genes were analyzed for overlap with known networks by using pathway analysis tools.

Results.: There were significant temporal (P < 10−8) and regional differences in gene expression for LHBETATAG retinoblastoma tumors. At P < 0.01 and log2-fold change >2.5, there were significant changes in gene expression of 190 genes apically, 84 genes anterolaterally, 126 genes posteriorly, 56 genes centrally, and 134 genes at the base. Differentially expressed genes overlapped with known networks, with significant involvement in regulation of cellular proliferation and growth, response to oxygen levels and hypoxia, regulation of cellular processes, cellular signaling cascades, and angiogenesis.

Conclusions.: There are significant temporal and regional variations in the LHBETATAG retinoblastoma model. Differentially expressed genes overlap with key pathways that may play pivotal roles in murine retinoblastoma development. These findings suggest the mechanisms involved in tumor growth and progression in murine retinoblastoma tumors and identify pathways for analysis at a functional level, to determine significance in human retinoblastoma. Microarray analysis of LHBETATAG retinal tumors showed significant regional and temporal variations in gene expression, including dysregulation of genes involved in hypoxic responses and angiogenesis.

Retinoblastoma (RB) is the most common intraocular malignancy in children, affecting approximately 1 in 15,000, for an incidence of 250 to 300 new diagnoses a year in the United States. 1 4 As treatment has progressed from external beam radiation therapy (EBRT) to chemoreduction combined with focal therapies, survival rates have climbed to 99% with a large percentage of children maintaining vision. 5 Despite the significant advancements in treatment and survival, current chemotherapy regimens and focal therapies may result in complications. Children are subjected to toxic chemotherapeutic drugs for multiple cycles, resulting in considerable risk for systemic toxicities. 6 8 Focal consolidation therapies also contribute to morbidity, depending on the intraocular tumor size and location. 9 Finally, chemoreduction success varies depending on tumor classification, with more advanced eyes achieving tumor control in 47% to 83% of cases. 10,11 As a result, a greater understanding of tumorigenesis is necessary to develop adjuvant therapies to potentially treat tumors that are unresponsive to current treatment protocols and to minimize local and systemic complications of treatment. 
The genetics of RB development have been studied, beginning with Knudson's “two hit” hypothesis. 12 In RB development, mutations or epigenetic changes in both alleles of the RB1 gene lead to loss of retinoblastoma protein (pRB). pRB binds to E2F, which acts as a transcriptional regulator of the cell cycle. Loss of both RB1 alleles leads to susceptibility of retinal cells to formation of RB. It has been proposed that development of RB requires more than the two hits proposed by Knudson, with Corsen and Gallie 13 reviewing the literature for evidence of further genetic changes necessary for tumor development. 
The paradigm of cancer treatment and understanding has shifted from solely targeting hyperproliferative tumor cells and associated oncogenes/tumor suppressor genes to also targeting cancer stromal tissue, which consists of complex multicellular interactions, termed the tumor microenvironment. 14,15 This environment consists of a plethora of cell types, including endothelial cells, fibroblasts, and inflammatory cells, 16 that, along with numerous growth factors and signaling molecules, contribute to tumorigenesis. Hypoxia has been strongly correlated with tumor growth, progression, resistance to therapy, and metastasis. 17 It has been shown that through O2-sensitive pathways, hypoxia alters tumor cell behavior, resulting in an integrated response of tumor cells to the tumor microenvironment, leading to altered gene expression and tumor adaptation and survival. Alterations to hypoxia include signaling through the mammalian target of rapamycin (mTOR), hypoxia inducible factor (HIF), and the unfolded protein response (UPR). These responses lead to altered cellular metabolism, angiogenesis, and other cell survival mechanisms. 18 The genetic changes associated with a tumor's adaptation to the microenvironment are prospective avenues for more specific and targeted therapy. In addition, an understanding of the timing of gene expression is fundamental in the optimal use of novel, multimodal adjuvant treatments. 
Hypoxia and angiogenesis have been shown to be significantly associated with tumor proliferation and metastasis. Our previous studies with the LHBETATAG mouse model of RB have shown that 20% to 26% of tumoral areas are hypoxic and that there is a spatial difference, with hypoxia primarily in the central and basal regions of the tumor. 19 In addition, we have shown that there is a spatial distribution of blood vessel maturation, with mature blood vessels concentrated centrally, while immature neovessels radiate peripherally. As a result, we have proposed that RB tumors grow radially from the center, with the apex, anterior, and posterior margins serving as the leading edges. 20  
We hypothesize that there are regional and temporal differences in gene expression of murine RB tumors corresponding to areas of tumor heterogeneity and variations in tumor microenvironment. These variations may provide valuable information regarding specific genes and pathways that facilitate tumor growth, progression, and resistance to treatment. The purpose of our study was to evaluate these regional and temporal differences by using mRNA microarray analysis in a transgenic model of RB. 
Methods
LHBETATAG Mouse Model for RB
The study protocol was approved by the University of Miami Institutional Animal Care and Use Review Board Committee. The LHBETATAG transgenic mouse model used in this study has been characterized previously. 21 This animal model develops bilateral multifocal retinal tumors that are stable and grow at a predictable rate (i.e., tumor at 4 weeks is grossly undetectable, at 8 weeks is small, at 12 weeks is medium, and at 16 weeks is large), with histopathologic, immunopathologic, and ultrastructural features that resemble human RB tumors. The LHBETATAG mouse model has contributed to elucidating mechanisms in tumor development and progression and to providing a platform for the development of adjuvant therapies. 
Molecular Genomic Array Analysis in LHBETATAG Retinal Tumor Growth
Transgenic mice with documented intraocular tumors were killed and the eyes enucleated at 12, 16, and 20 weeks of age (n = 9; three at each time point). These time points were chosen to represent early to advanced tumors and to correspond to time points studied in this model regarding angiogenesis, hypoxia, gelatinase expression, and tumor response to therapy. Five 3.37-mm3 sections were obtained from each tumor (apex, anterior lateral, center, base, and posterior lateral). Samples were meticulously dissected under a microscope by an experienced handler. With the time points chosen (12, 16, and 20 weeks), tumors had already grown to a macroscopic size, allowing an experienced handler to dissect them without obtaining normal retina, as the tumor has already expanded into the globe with boundaries distinct from normal retina. Although there is always a possibility of contamination, dissection protocols were meticulously used in ascertaining the extraction of sufficient and appropriate samples. Samples were obtained from the five areas based on the proposed mechanism of tumor growth and progression, with radial growth from the center. Leading edges have been shown to consist of more immature vasculature, whereas, central areas consisted of mature vasculature as well as a higher percentage of hypoxia. 19,22 All specimens (n = 45) were placed in a lysis solution and stored at −20°C until analyzed. The samples were hybridized (16 hours) to a unique gene microarray chip that provides whole gene expression data (no 3′ bias) for over 28,000 genes (GeneChip Mouse Gene ST 1.0 arrays; Affymetrix, Santa Clara, CA). We used the Robust Multichip Average (RMA) Express method (http://rmaexpress.bmbolstad.com/ written by Ben Bolstad, University of California, Berkeley, and provided in the public domain) to measure differential gene and probe level expression measures (log2), with a false-discovery rate (FDR) set at 5%. Quality control plots and summary measures were generated with R/Bioconductor 2.9.10. 23,24 Gene level measures were analyzed by using analysis of variance (ANOVA) models for repeated measures, considering temporal or regional effects, using custom scripts (written for SAS ver. 9.2; SAS, Cary, NC). Genes with P < 0.01 from the ANOVA models and a log2-fold change >2.5 were considered to be differentially expressed. Differentially expressed genes were analyzed for overlap with known networks, by using pathway analysis tools (GeneGo; St. Joseph, MI). Network significance was evaluated on the basis of the size of the intersection between our list of significantly differentially expressed genes and the set of genes/proteins corresponding to a network module/pathway. Each network was associated with a z-score that ranked the networks according to saturation with the objects from the experimental gene list. The z-score ranked the networks of the analyzed network algorithm with regard to their saturation with genes from the experiment. A high z-score means that the network is highly saturated with genes from the particular experiment. Each network was also associated with a g-score, which modifies the z-score on the basis of the number of canonical pathways used to build the network. If a network has a high g-score, it is saturated with expressed genes (from the z-score), and it contains many canonical pathways. A P value was determined by comparing the observed amount of intersection with the amount expected under the null hypothesis that the amount of overlap follows a hypergeometric distribution. 
Results
A total of 28,000 genes were assessed over the three different time points in the five regions. Significant temporal differences in gene expression were found between 12-, 16-, and 20-week LHBETATAG RB tumors (P < 10−8, two-way analysis of variance, ANOVA). In addition, analysis identified genes with a greater than 2.5-fold difference in expression between the three time points that varied depending on region. There were significant differences in gene expression across time for 190 genes apically, 84 genes anterolaterally, 126 genes posteriorly, 56 genes centrally, and 134 genes at the base (Tables 1A, 1B). 
Table 1.
 
Differential Gene Expression by More Than 2.5-Fold in Five Tumor Regions with the Corresponding Ratio of Change
Table 1.
 
Differential Gene Expression by More Than 2.5-Fold in Five Tumor Regions with the Corresponding Ratio of Change
A. Upregulated Genes
Base Center Apex Anterior-lateral Posterior-lateral
Gene Ratio of Change Gene Ratio of Change Gene Ratio of Change Gene Ratio of Change Gene Ratio of Change
KRT15 5.1448 GM9912 2.6366 CENPK 4.0012 KRT16 3.802 No gene name 3.6121
KRT4 5.0582 2810417H13R1 3.8653 No gene name 3.1226 GEN1 3.3636
CYP4A12B 4.6897 CASC5 3.7474 SPINK5 3.0778 CCNB1 3.2991
KRT13 4.5248 BUB1 3.7178 XIST 3.0497 HMMR 3.2884
KRT14 4.4163 RRM2 3.6037 KRT14 2.9956 CCNB1 3.2238
ANXA8 4.3149 CDH9 3.5187 S100A9 2.9409 EG665955 3.2117
KRT5 4.301 MKI67 3.5062 No gene name 2.9264 CCNB1 3.1662
TACSTD2 4.182 PBK 3.4692 KRT5 2.8168 NCAPG2 3.0262
KRT6B 4.1757 KRT4 3.466 BC100530 2.7341 LCN2 3.0002
CEACAM1 4.1426 MCM6 3.4537 SNORD116 2.7022 LPHN2 2.951
9930032O22RI 4.1396 KRT14 3.4365 No gene name 2.702 C79407 2.9377
UPK1B 4.0946 NUDT10 3.4343 DSG3 2.7003 CRYBA1 2.9014
DSG3 3.956 GEN1 3.3905 SNORD116 2.6892 PTPN3 2.8936
ENPP3 3.933 CCNB1 3.388 MMP3 2.6707 CDH9 2.8539
KRT6A 3.9257 FAM111A 3.3757 SNORD116 2.6558 SHCBP1 2.852
SLC6A14 3.8995 CCNB1 3.3655 S100A8 2.6524 BUB1 2.804
CBR2 3.8739 KIF11 3.3435 CRYBA1 2.628 PBK 2.7594
TMPRSS11B 3.7424 CCNB1 3.3391 SNORD116 2.5994 6720489N17RI 2.7205
DSC3 3.7068 KRT6B 3.3143 GM9912 2.5633 GINS1 2.7187
TRIM29 3.7054 KRT15 3.3032 KRT6B 2.5615 CASC5 2.6874
SCEL 3.6572 TOP2A 3.2889 SCG3 2.5175 TTK 2.6598
TMPRSS11A 3.6417 CENPH 3.2249 No gene name 2.5092 SERPINA3N 2.6336
ADH7 3.6356 ANLN 3.2177 SNORD116 2.5034 TPX2 2.629
ALDH3A1 3.6319 HIST1H2AB 3.1439 LPHN2 2.6035
AKR1B7 3.5832 BC100530 3.1268 SGOL2 2.5912
BC100530 3.5756 C330027C09RI 3.0901 KIF4 2.585
DSG1A 3.5619 SGOL2 3.074 C330027C09R1 2.5798
DSC2 3.5213 KRT5 3.0701 PPIL5 2.5518
PGLYRP1 3.4777 CKS2 3.0693 KIF14 2.5517
FABP6 3.4499 MNS1 3.0536 CENPK 2.5336
PLAC8 3.4191 KRT13 3.0486 RRM2 2.5299
DSP 3.3436 ACTC1 3.0294 CHEK1 2.528
S100A14 3.2942 KIF4 3.0204 SNORD116 2.5238
ADH1 3.2901 NCAPG 3.0026 KIF2C 2.5138
MUC4 3.2862 SPC24 3.0014 CLSPN 2.5114
RBP2 3.2579 HELLS 2.9981 NUF2 2.5102
CALML3 3.2514 NUF2 2.9795
CAPG 3.2201 KRT16 2.965 KIF14 2.5517
KRT19 3.1758 DSCC1 2.9644 CENPK 2.5336
LUM 3.1756 ARHGAP11A 2.9578 RRM2 2.5299
CMAH 3.1643 SMC2 2.9405 CHEK1 2.528
SERPINB5 3.1593 C79407 2.9212 SNORD116 2.5238
EMP1 3.1549 PRR11 2.8937 KIF2C 2.5138
PPL 3.1142 NDC80 2.8826 CLSPN 2.5114
PYHIN1 3.09 KRT6A 2.8734 NUF2 2.5102
IL1F9 3.0649 BIRC5 2.8706 KIF2C 2.5138
DEFB1 3.0552 LPHN2 2.8672 CLSPN 2.5114
SAMD9L 3 GINS1 2.8565 NUF2 2.5102
FGFBP1 2.9933 DSG3 2.8543 CLSPN 2.5114
ANXA2 2.9889 2810417H13RI 2.8429 NUF2 2.5102
9930023K05RI 2.9783 SHCBP1 2.8422 CLSPN 2.5114
IFITM1 2.9492 CKS2 2.8379 NUF2 2.5102
GSTA3 2.9068 CDC2A 2.8331 CLSPN 2.5114
GPR110 2.9005 LPHN2 2.8233 NUF2 2.5102
DCN 2.847 MASTL 2.8215
SPINK5 2.823 TPX2 2.8144
EHF 2.7891 KIF23 2.8003
KRT7 2.7817 GM9912 2.8001
1600029D21RI 2.769 TTK 2.7778
MALL 2.736 CKS2 2.7633
S100A9 2.7304 FBXO5 2.7586
OCM 2.7168 EG665955 2.7554
CLDN7 2.6877 PEG10 2.7433
SPINK5 2.7412
GM9573 2.6769 NCAPH 2.7266
PERP 2.6515 KRT24 2.7084
ESRP1 2.6479 ECT2 2.695
GSTO1 2.6415 PPIL5 2.6805
GDA 2.6149 RBBP8 2.6706
PTGR1 2.6138 ANXA1 2.67
CYP2F2 2.6113 CENPF 2.6546
IFITM1 2.5923 CKAP2L 2.6376
C130090K23RI 2.5153 CYP4A12B 2.6161
GALNT3 2.5097 NCAPG2 2.5946
GM11428 2.5001 DLGAP5 2.5916
LYZ2 2.588
PRIM1 2.5849
DBF4 2.5832
LPHN2 2.5785
ATAD2 2.5765
TRIM59 2.5672
MCM3 2.5618
HMMR 2.5427
NFIA 2.5368
E2F8 2.534
EXO1 2.5174
2610039C10RI 2.5138
B. Downregulated Genes
Base Center Apex Anterior-lateral Posterior-lateral
Gene Ratio of Change Gene Ratio of Change Gene Ratio of Change Gene Ratio of Change Gene Ratio of Change
CNGA1 −4.4701 GUCY2F −4.1373 PDE6B −5.0352 CNGA1 −4.6794 OPN1SW −5.0585
RP1 −4.4608 PDE6B −4.0316 CNGA1 −5.0191 RCVRN −4.4971 GUCY2F −4.6778
RCVRN −4.3932 RCVRN −3.8163 RCVRN −4.8462 PDE6B −4.4931 RCVRN −4.5381
PDE6B −4.3062 RHO −3.7673 RPE65 −4.8052 RHO −4.0053 CNGA1 −4.4204
GUCY2F −3.9616 2610034M16R1 −3.7485 PDE6A −4.7065 RHO −3.9785 PDE6B −4.3056
PDE6A −3.9498 RP1 −3.5564 RP1 −4.6844 RP1 −3.9687 No gene name −4.1752
RS1 −3.8567 PDE6A −3.5066 RHO −4.6441 PDE6A −3.8735 RP1 −4.0818
RHO −3.79 CNGA1 −3.4836 GUCY2F −4.6367 GNAT1 −3.7975 2610034M16RI −4.0801
TULP1 −3.6546 REEP6 −3.4382 RDH12 −4.5129 RS1 −3.6846 PDE6A −4.0469
GRK1 −3.5238 RS1 −3.3294 GNAT1 −4.4516 SAG −3.6444 RHO −3.9724
RDH12 −3.4964 SLC24A1 −3.2645 VTN −4.3343 GUCY2F −3.638 GRK1 −3.8474
OPN1SW −3.4265 RPE65 −3.2339 GUCA1A −4.2548 VTN −3.5997 RHO −3.7115
GUCA1B −3.4165 ARR3 −3.2278 SAG −4.1852 SLC24A1 −3.449 CDS1 −3.6255
ADAMTS3 −3.4098 VTN −3.1777 RHO −4.1269 2610034M16RI −3.4238 RS1 −3.5685
2610034M16RI −3.3635 TULP1 −3.0543 TTR −4.0968 IMPGI −3.4029 RDH12 −3.5593
IMPG2 −3.3173 GUCA1B −3.041 TULP1 −4.0863 REEP6 −3.4 GUCA1B −3.4517
GM626 −3.3102 IMPG1 −3.0188 GUCA1B −4.013 GRK1 −3.1713 IMPG1 −3.4482
PRPH2 −3.2885 PDE6G −2.9703 PDC −4.0107 GUCA1B −3.1536 ARR3 −3.4129
SAG −3.2643 OPNIMW −2.9658 2610034M16RI −4.0039 GUCA1A −3.1167 FABP12 −3.3855
CALB1 −3.2542 CNGB1 −2.9629 SLC24AI −3.9651 PDC −3.0939 C030002C11RI −3.3297
IMPG1 −3.2524 GRK1 −2.9333 IMPG1 −3.9048 TULP1 −3.0184 SLC24A1 −3.3201
PDE6G −3.231 ME1 −2.9195 RGR −3.8508 PRPH2 −3.0106 REEP6 −3.3172
WDR17 −3.2248 CALB1 −2.8971 REEP6 −3.8455 CNGB1 −2.9766 VTN −3.3057
GNAT1 −3.2196 NRL −2.8711 GM626 −3.8261 OPN1MW −2.9231 SAG −3.2897
RHO −3.1948 SAG −2.8696 RS1 −3.7915 PDE6G −2.8083 ADAMTS3 −3.1062
SLC24A1 −3.188 ABCA4 −2.8646 OPN1SW −3.7847 RPE65 −2.7849 WDR17 −3.0986
VTN −3.1816 PRPH2 −2.8531 IMPG2 −3.7759 ABCA4 −2.7098 TULP1 −3.0714
FABP12 −3.1415 C030002C11RI −2.8371 GRK1 −3.7481 NR2E3 −2.6847 UPK1B −3.0451
ARR3 −3.0916 RDH12 −2.835 CDS1 −3.6674 FABP12 −2.6803 CNGB1 −3.0442
PDC −3.0772 GLBIL2 −2.8234 PRPH2 −3.6533 PROM1 −2.652 OPN1MW −3.0287
ROM1 −2.9229 PEX5L −2.8231 ROM1 −3.6231 IMPG2 −2.6508 FAM161A −3.0234
FAM161A −2.9159 WDR78 −2.8094 FABP12 −3.6175 GM626 −2.6288 GLB1L2 −3.0131
A930003A15RI −2.9133 NXNL1 −2.8054 PDE6G −3.4549 GM10664 −2.61 PDC −2.9972
WDR78 −2.9098 WDR17 −2.7996 ABCA4 −3.4477 NRL −2.6025 TAC1 −2.9925
GM10664 −2.8845 NR2E3 −2.7562 PROM1 −3.3796 WDR17 −2.5942 ABCA4 −2.988
SAMD7 −2.8688 RGR −2.7358 WDR78 −3.3315 GLB1L2 −2.5708 LRIT2 −2.9737
ADAMTS3 −2.8323 PROM1 −2.7204 CALB2 −3.2487 RDH12 −2.5568 PROM1 −2.9684
OPN1MW −2.8125 SPATA1 −2.7178 GLB1L2 −3.2272 C530030P08RI −2.5517 PDE6G −2.9669
REEP6 −2.7962 RHO −2.7129 NR2E3 −3.2055 WDR78 −2.5363 KRT12 −2.9552
LRIT2 −2.7883 PLA2R1 −2.7107 PPARGC1A −3.2005 No gene name −2.5165 PRPH2 −2.9497
GUCA1A −2.7699 GNAT1 −2.701 CNGB1 −3.1234 LRIT2 −2.5045 WDR78 −2.9474
ABCA4 −2.7201 CDS1 −2.6796 MPP4 −3.104 GNAT1 −2.9465
C030002C11RI −2.7019 FAM161A −2.6649 WDR17 −3.1008 PSCA −2.946
CNGB1 −2.6999 FABP12 −2.6637 NRN1 −3.0902 RPE65 −2.9378
LRRC2 −2.6992 LRIT2 −2.6099 NRL −3.0787 PLA2R1 −2.9029
SPATA1 −2.6869 ME1 −2.5699 CALB1 −3.077 CDR2 −2.9013
GM626 −2.665 A930003A15RI −2.5299 PEX5L −3.0711 NRL −2.9007
HIST2H3C2 −2.6462 3632451O06RI −2.5294 GM11744 −3.0574 MPP4 −2.8886
A330023F24RI −2.6434 BST1 −2.5082 LGI1 −2.993 GM626 −2.874
PEX5L −2.6154 GM11744 −2.5007 FAM161A −2.9842 GM11744 −2.8575
PDE6C −2.6118 GM10664 −2.9635 RABGEF1 −2.8558
TAC1 −2.5092 CHRNB3 −2.9436 TDRD7 −2.8429
SLC17A6 −2.9268 PEX5L −2.8263
OPN1MW −2.9101 UNC13C −2.7611
LRIT2 −2.9017 NR2E3 −2.7492
COX8B −2.8988 APBH −2.7348
ENPP2 −2.8935 HERC3 −2.7337
ARR3 −2.8684 PDE6C −2.7175
HERC3 −2.8628 GP2 −2.7166
C030002C11RI −2.8626 4833423E24RI −2.7154
GM626 −2.8602 DCN −2.7131
CD59A −2.8517 CENPH 2.7061
SPATA1 −2.809 RGR −2.7039
ALDOC −2.8089 CALML3 −2.6967
LDHB −2.7821 STFA3 −2.684
SAMD7 −2.7803 GUCA1A −2.6827
GM626 −2.7474 PPEF2 −2.6757
UNC13B −2.7459 NT5E −2.667
RLBP1 −2.726 NXNL1 −2.6568
RGS9 −2.7235 IMPG2 −2.6363
RP1L1 −2.7206 MLANA −2.6293
GNB1 −2.7097 PGLYRP1 −2.6228
PLA2G5 −2.7038 C130021120RI −2.619
PLA2R1 −2.674 GM626 −2.6093
ME1 −2.664 ATP8A2 −2.6083
KRT18 −2.6561 S100A14 −2.6036
TAC1 −2.6465 GM10664 −2.6028
CLIC6 −2.6461 CALB2 −2.6025
PPEF2 −2.644 CHRNB3 −2.5758
ATP8A2 −2.6388 TYRP1 −2.5606
HK2 −2.638 LRAT −2.5599
LRRC67 −2.6246 ADH1 −2.5317
GRIA3 −2.6234 ROM1 −2.5257
ZDHHC2 −2.5984 CSMD3 −2.5134
LYNX1 −2.5834 PVALB −2.5085
PVALB −2.5725
GM626 −2.561
NXNL1 −2.5568
AIPL1 −2.5277
LRAT −2.5118
ME1 −2.5094
CDR2 −2.5021
To identify functional activity of the unique genes, we performed pathway analysis (GeneGo software). Tables 2 to 6 show the key networks involved in cellular growth and proliferation, hypoxia, cell signaling, and angiogenesis for the five regions with key objects, processes, pathways, and statistical significance. 
The basal regions (Table 2) of the RB tumors had significant differences (P = 1.54 × 10−11 to 3.72 × 10−61) in regulation of Ras/G-protein signaling, regulation of cellular proliferation, mTOR/PKC signaling, and JAK/STAT signaling. The z-scores ranged from 13.56 to 48.21, and the g-scores ranged from 28.06 to 146.23. On analysis of networks, key elements mediating cellular proliferation included c-Myc, JAK/STAT, TGF-β, MDM2, RB protein, AMPK, let-7a microRNA, and p53. We also found cellular responses mediated by the PI3K/akt/mTOR pathway, as well as HIF, VEGF, NOTCH, and IGF-1 (Fig. 1). 
Table 2.
 
Key Networks with Associated Key Objects and Processes for Basal Tumor Regions
Table 2.
 
Key Networks with Associated Key Objects and Processes for Basal Tumor Regions
Top Networks Key Network Objects GO Processes Total Nodes Root Nodes Pathways P z-Score g-Score
3 STAT1, c-Raf-1, DNM1L (DRP1), NDPK B, 14-3-3 zeta/delta Regulation of cell proliferation, Positive regulation of cellular process, regulation of apoptosis 50 18 23 3.46 × 10−22 22.44 51.19
4 mTOR, PKC, Tuberin, DLL1, NEURL1 Enzyme linked receptor protein signaling pathway, insulin-like growth factor receptor signaling pathway, cellular response to insulin stimulus 50 11 30 1.54 × 10−11 13.56 51.06
Figure 1.
 
Network in basal aspects of tumors involved in hypoxic responses (network 4; P < 10−11, 30 pathways), with key elements including mammalian target of rapamycin (mTOR), phosphoinositide-3 kinase (PI3K), Akt or protein ki), hypoxia-inducible factor (HIF), vascular endothelial growth factor (VEGF), NOTCH, and insulin-like growth factor (IGF-1). Cyan lines: fragments of canonical pathways; red circles: upregulated genes; blue circles: downregulated genes.
Figure 1.
 
Network in basal aspects of tumors involved in hypoxic responses (network 4; P < 10−11, 30 pathways), with key elements including mammalian target of rapamycin (mTOR), phosphoinositide-3 kinase (PI3K), Akt or protein ki), hypoxia-inducible factor (HIF), vascular endothelial growth factor (VEGF), NOTCH, and insulin-like growth factor (IGF-1). Cyan lines: fragments of canonical pathways; red circles: upregulated genes; blue circles: downregulated genes.
The central regions (Table 3) of the tumors had significant differences (P = 4.44 × 10−7 to 3.22 × 10−61) in regulation of cellular proliferation, response to oxygen levels, and regulation of oxidoreductase activity, JAK/STAT signaling, and regulation of cellular metabolism. The z-scores ranged from 8.81 to 45.46, and the g-scores ranged from 33.60 to 133.81. Network analysis showed that cellular proliferation and regulation were mediated by TNF-α, ERK1/2, RB protein, MAPK, VEGF, ubiquitin, TGF-β, and MDM2. Tumor response to oxygen levels was mediated by MMP-2 and -9, TGF-β, E-cadherin, c-Myc, PAI, VEGF, and CDK2/CDK4. In addition, the cellular proliferation and signaling pathways were mediated by JAK/STAT, ERK1/2, cyclin D1, and 14-3-3. 
Table 3.
 
Key Networks with Associated Key Objects and Processes for Central Tumor Regions
Table 3.
 
Key Networks with Associated Key Objects and Processes for Central Tumor Regions
Top Networks Key Network Objects GO Processes Total Nodes Root Nodes Pathways P z-Score g-Score
2 c-Raf-1, NCOA3, (pCIP/SRC3), H-Ras, NCOA2 (GRIP1/TIF2), TFF1 Regulation of cell proliferation, positive regulation of cellular process 51 11 85 1.05 × 10−10 12.29 118.54
5 SMAD2, NF-κB, SMURF2, SerRS, RPS25 Anatomic structure morphogenesis, response to oxygen levels 50 14 35 5.58 × 10−15 16.04 59.79
9 FOXO3A, 14-3-3 zeta/delta, Cyclin D2, GPIAP1, BTF JAK-STAT cascade involved in growth hormone signaling pathway, regulation of cell proliferation 50 20 12 4.22 × 10−25 24.06 39.06
The apical regions (Table 4) of the tumors had significant differences (P = 1.91 × 10−7 to 1.29 × 10−83) in PKA/G-protein signaling, regulation of cell proliferation, oxygen transport, and regulation of stress responses. The z-scores ranged from 9.40 to 57.83, and the g-scores ranged from 32.46 to 184.4. Tumor anatomic and morphogenic responses were found to be mediated by key elements, including VEGF, MMP-2, MMP-9, and TGF-β (Fig. 2, left). Cellular proliferation was found to be mediated by JAK/STAT, ERK1/2, c-Myc, NF-κB, IGF-1, TNF-α, and caspase-8. Finally, cellular responses to stress were found to be mediated by HIF-1, NF-κB, and c-Myc (Fig. 2, right). 
Table 4.
 
Key Networks with Associated Key Objects and Processes for Apical Tumor Regions
Table 4.
 
Key Networks with Associated Key Objects and Processes for Apical Tumor Regions
Top Networks Key Network Objects GO Processes Total Nodes Root Nodes Pathways P z-Score g-Score
1 TGF-β1, RUNX3, TGF-β receptor type II, GAS1, FALZ Organ development, anatomic structure morphogenesis, system development 50 8 140 1.91 × 10−7 9.40 184.4
4 NF-κB, FCGRT, Cyclin D2, ISG20, SPT2 Positive regulation of cellular process, regulation of cell proliferation 50 14 27 1.21 × 10−15 17.05 50.80
9 NF-κB, COX17, Nectin-2, PSMD7, LOC283412 Regulation of oxidoreductase activity, positive regulation of defense response, positive regulation of oxidoreductase activity, regulation of response to stress 50 27 4 1.34 × 10−40 35.94 40.94
Figure 2.
 
(A) Network involved in angiogenesis (network 1), in apical leading edges of tumors (P < 10−7, 140 pathways). Key elements include vascular endothelial growth factor (VEGF), matrix metalloproteinase-9 (MMP-9), transforming growth factor-β (TGF-β). (B) Network involved in hypoxia (network 9) in the apical leading edges of tumors (P < 10−40, 4 pathways). Response to hypoxia mediated by hypoxia-inducible factor (HIF).
Figure 2.
 
(A) Network involved in angiogenesis (network 1), in apical leading edges of tumors (P < 10−7, 140 pathways). Key elements include vascular endothelial growth factor (VEGF), matrix metalloproteinase-9 (MMP-9), transforming growth factor-β (TGF-β). (B) Network involved in hypoxia (network 9) in the apical leading edges of tumors (P < 10−40, 4 pathways). Response to hypoxia mediated by hypoxia-inducible factor (HIF).
The anterior-lateral regions (Table 5) of the tumors had significant differences (P = 1.41 × 10−15 to 2.26 × 10−86) in cellular proliferation, cytokine-mediated signaling pathways, leukocyte migration, and glycosaminoglycan biosynthetic processes. The z-scores ranged from 16.94 to 58.76, and the g-scores ranged from 34.63 to 58.76. Tumor signaling cascades and growth regulation were found to be mediated by TGF-β, MMP-2, MMP-9, fibronectin, c-Myc, and NF-κB. Cytokine-mediated signaling was found to involve TNF-α, NF-κB, ICAM, E-selectin, and CCL-2/CCL-3. 
Table 5.
 
Key Networks with Associated Key Objects and Processes for Anterolateral Tumor Regions
Table 5.
 
Key Networks with Associated Key Objects and Processes for Anterolateral Tumor Regions
Top Networks Key Network Objects GO Processes Total Nodes Root Nodes Pathways P z-Score g-Score
5 SMURF2, CRELD2, eIF4A1, SARA, SAHH2 Response to organic cyclic substance, transforming growth factor beta receptor signaling pathway 50 14 24 1.41 × 10−15 16.94 46.94
8 CCL13, HMG1 (amphotercin), NDPK B, HRPT2, Symplekin Translational elongation, leukocyte migration, cytokine-mediated signaling pathway, response to mechanical stimulus, translation 50 21 9 9.13 × 10−28 26.67 37.92
Finally, the posterior-lateral regions (Table 6) of the RB tumors had significant differences (P = 1.07 × 10−8 to 1.27 × 10−72) in regulation of cellular proliferation, JAK/STAT signaling, and cytokine-mediated signaling. Network analysis showed anatomic morphogenesis and growth regulation to be mediated by TGF-β, MMP-2, MMP-9, ERK-1/2, MAPK, Akt, and VEGF. Cell proliferation and regulation of cellular processes were found to be mediated by TNF-α, ERK-1/2, VEGF, EGFR, MAPK, PKC, IGF-1, and COX-2. 
Table 6.
 
Key Networks with Associated Key Objects and Processes for Posterolateral Tumor Regions
Table 6.
 
Key Networks with Associated Key Objects and Processes for Posterolateral Tumor Regions
Top Networks Key Network Objects GO Processes Total Nodes Root Nodes Pathways P z-Score g-Score
1 SMAD1, SMAD2, TGFb 1, SCAP, RPS27A Anatomic structure morphogenesis, organ development, regulation of developmental process 50 11 67 2.42 × 10−11 13.26 97.01
3 SHP-2, Ubiquitin, IGF-2, NIX, GlyRS Regulation of cell proliferation, regulation of cellular process 50 9 38 1.07 × 10−8 10.70 58.20
Discussion
In the present study, we identified genes that are differentially expressed in the LHBETATAG murine model of RB in five tumor regions (base, center, apex, anterior-lateral margin, posterior-lateral margin), and at three time points in tumor development (12, 16, and 20 weeks). Overall, gene expression was shown to significantly differ temporally (P < 10−8), as well as regionally. Of the 28,000 gene probe sets analyzed, we found differential expression of 190 genes apically, 84 genes anterolaterally, 126 genes posteriorly, 56 genes centrally, and 134 genes at the base. Analysis showed that these dysregulated genes were associated with multiple networks and canonical pathways, including regulation of cellular proliferation, cellular signaling and stress responses, response to hypoxia, angiogenesis, as well as anatomic morphogenesis and growth regulation. 
As tumors grow, the proliferating cells experience an imbalance of oxygen metabolism, leading to a disorganized and irregular microvasculature network. As a result, there is reduced oxygen delivery, leading to a microenvironment with low oxygen partial pressure. 15,25 27 In the harsh tumor microenvironment depleted of oxygen and nutrients, cells undergo a hypoxic and/or angiogenic switch to support further growth. Growth of immature neovessels is stimulated, along with cell adaptation to hypoxia, including increase in glycolysis for energy metabolism. 26 These events represent a key transition in tumorigenesis as cells adapt, altering the gene expression necessary to drive further tumor proliferation. 
We have previously shown that the tumor vasculature is highly heterogenous, with higher concentrations of large, mature vessels toward the base/center and smaller neovessels radiating into the periphery. These observations in both human and the LHBETATAG model suggest that RB tumors proliferate radially from the center. 20,22 The present study is the first to show the regional variation in gene expression by microarray in the LHBETATAG RB model. Gene expression was found to be dysregulated in a regional distribution, with the apex and base showing the most variation. We hypothesized that the apical areas correspond with the leading edges of the proliferating tumor consisting of highly metabolic, hyperproliferating cells, and neovessels dependent on growth factor support. Concurrently, the basal regions correspond to areas of hypoxic stress necessitating cellular adaptation. The current observations support our previous model of RB progression and blood vessel development, identifying potential canonical pathways that may mediate these responses in the transgenic model of RB tumors. 27 29  
In the apical regions, more advanced tumors had a significantly different expression of genes involved in cellular proliferation, hypoxia, angiogenesis, and regulation of stress responses. TGF-β, NF-κB, PTEN, and cyclin D2, as well as signaling through the JAK/STAT pathway were shown to be key mediators. TNF-α and caspase-8, which were found to be dysregulated in RB cell lines 30 and human RBs, 31 respectively, were also found in the present study to be dysregulated in advanced LHBETATAG tumors. Hypoxic responses, including angiogenesis, were also identified in the apical regions, areas of intense growth composed of hyperproliferating cells competing for oxygen and nutrients. These adaptations were mediated through hypoxia-inducible factor (HIF), vascular endothelial growth factor (VEGF), and matrix metalloproteinases (MMP-2 and -9), factors known to be significantly associated with advanced tumors with high degrees of hypoxia. 
The urokinase plasminogen activator (uPA) and the receptor (uPAR) may play an integral role in tumor proliferation and metastasis. The uPA system consists of serine proteases that lead to activation of plasmin, which in turn activates matrix metalloproteinases (MMPs). MMPs have been linked to tumor growth and metastasis for their role in the degradation of the extracellular matrix. 32 Recent studies have identified hypoxic elements within the genes that regulate the uPA system of several other tumors. 33,34 The present study has identified dysregulated genes involved in pathways for the uPA system as well as angiogenic pathways that use MMPs. These findings support our prior work showing enhanced tumor control with reduced expression of MMPs using anecortave acetate. 35 MMPs appear to play a key role in the tumor microenvironment, and studies are needed to further elucidate the mechanisms and effects of tumor treatment. 
As neoplastic cells proliferate, they experience a highly anabolic state requiring altered function to provide sufficient energy and waste removal, thus preventing cell death signals. 36 38 In response to increasing metabolic demands, as well as altered microenvironments inside and outside the cells, neoplastic cells alter cellular metabolism, adopting a metabolic phenotype through differential gene expression for key enzymes and regulators of cellular metabolism. 39 Neoplastic cells preferentially use glycolysis, referred to as the Warburg effect, in normoxic and hypoxic conditions, rather than oxidative phosphorylation and its higher ATP yield. In basal regions, areas of RB tumors shown to consist of a high population of hypoxic cells, we found differential expression of genes involved in networks and pathways that are upstream regulators of tumor metabolism, including PI3K/Akt/mTOR, IGF-1 signaling, and AMPK. In addition, our study showed significant involvement of HIF and Akt, both of which have been shown to increase GLUT1, a glucose transport receptor. 37 Our prior studies in the LHBETATAG RB tumor model have demonstrated the efficacy of targeting cellular metabolism in enhancing tumor control. Using 2-DG, a glycolytic inhibitor, we have shown that tumor burden is significantly reduced when treated with both systemic and local subconjunctival delivery of 2-DG. We have also recently shown that local delivery of 2-DG, when combined with chemotherapy, further enhances tumor control over either treatment alone. 19  
In addition, similar to prior human RB microarray gene expression studies, the PI3K/Akt/mTOR pathway was found to be dysregulated, potentially implicating mTOR as a therapeutic target. 40 We found significant differential expression of this network in basal regions of advanced tumors, regions shown to experience significant hypoxia compared with other regions. The mammalian target of rapamycin (mTOR) is a serine-threonine kinase that is composed of two multiprotein complexes. Activation of these complexes leads to phosphorylation of downstream effectors, leading to regulation of protein translation, cell growth, proliferation, and metabolism. 41 Hypoxia has been shown to be a negative regulator of mTOR complexes, thus potentially acting as an inhibitor of growth and progression. However, it has been proposed that hypoxia drives the mutations necessary to deregulate mTOR signaling, termed hypoxic tolerance. As a result, it may be important for tumor cells to retain control of mTOR signaling for continued growth and proliferation. 18 mTOR inhibitors have been investigated in other tumors, and further studies are needed to define the role of mTOR and inhibitors of this pathway in RB tumor control. Notably, early studies with focal delivery of rapamycin in the LHBETATAG RB model show promise, as the mTOR inhibitor led to enhanced tumor control (Murray TG, et al. IOVS 2010;43:ARVO E-Abstract 2067). 
Our current and previous findings support an evolving and complex cellular metabolic phenotype that differs in early versus advanced tumors, as well as variations secondary to the local, heterogeneous tumor microenvironment. This study provides further evidence of dysregulation of pathways involved in cellular metabolism, including genes to assess at a functional level to determine the effect on tumor growth and development. 
In addition, the study is the first to show temporal variation in gene expression with advanced tumors compared to earlier tumors examined with microarray analysis. In transgenic RB tumors, differential gene expression and associated pathways suggest potential mechanisms of tumorigenesis. These specific genes and pathways require further functional analysis to determine whether direct targeting has an effect on tumor growth and development, or whether the changes seen are indirect effects of other tumorigenic processes and would not serve as useful targets for tumor control. As with other tumors, we propose that regions of RB tumors have differential gene expression and may respond differently to various treatments, depending on the tumor's age, tumor burden, location, hypoxia, vasculature, and cellular metabolism. Therefore, the present study provides evidence of regional and temporal tumor heterogeneity, emphasizing the potential importance of timing in gene expression in the development of optimally timed, multimodal treatments with agents that target tumor cells, as well as components of the tumor microenvironment, including angiogenesis, hypoxia, and cellular metabolism. We propose that future treatment of RB tumors must target not only proliferating tumor cells, but also the local tumor microenvironment, in a multimodal, optimally timed, local approach. 
As functional analyses are performed on specific genes and pathways, including studies on targeting key elements, it is important to identify alterations in gene expression after treatment as well as to identify key escape pathways that cells use during times of cellular stress. These escape pathways may prove to be important regarding resistance and adaptation of tumor cells to current and future targeted therapies. 
Limitations of the present study include the small sample size of three mice at each time point (12, 16, and 20-weeks), as well as for each different region at each of these time points. In addition, we investigated the effects of time and location in the LHBETATAG murine model of RB, which has been shown to share many similarities with human RB, but the correlation in gene expression between human and mouse tumors has not been fully determined. As a result, before the current findings can be related to human RB, further functional studies are needed of transgenic RB tumors and human RB cell lines. 
In conclusion, the findings in our study have shown that gene expression in the LHBETATAG model of RB has significant temporal variations. In addition, we have shown significant regional variations, with the apical and basal regions exhibiting dysregulation of genes in key pathways involved in the regulation of cellular proliferation and growth, response to oxygen levels and hypoxia, and regulation of cellular processes, cellular signaling cascades, and angiogenesis. Prior studies have defined the gene expression profiles of RB tumors 40,42 ; our study of the LHBETATAG murine model suggests that these gene expression profiles may be dynamic, varying in a temporal and regionally dependent fashion. We anticipate that future developments in ocular oncology will focus on locally delivered, optimally timed therapies that target tumor cells as well as the tumor microenvironment. 
Footnotes
 Supported by National Institutes of Health center Grants R01 EY013629, R01 EY12651, and P30 EY014801; the American Cancer Society, Sylvester Comprehensive Cancer Center; and an unrestricted grant to the University of Miami from Research to Prevent Blindness, Inc.
Footnotes
 Disclosure: S.K. Houston, None; Y. Pina, None; J. Clarke, None; T. Koru-Sengul, None; W.K. Scott, None; L. Nathanson, None; A.C. Schefler, None; T.G. Murray, None
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Figure 1.
 
Network in basal aspects of tumors involved in hypoxic responses (network 4; P < 10−11, 30 pathways), with key elements including mammalian target of rapamycin (mTOR), phosphoinositide-3 kinase (PI3K), Akt or protein ki), hypoxia-inducible factor (HIF), vascular endothelial growth factor (VEGF), NOTCH, and insulin-like growth factor (IGF-1). Cyan lines: fragments of canonical pathways; red circles: upregulated genes; blue circles: downregulated genes.
Figure 1.
 
Network in basal aspects of tumors involved in hypoxic responses (network 4; P < 10−11, 30 pathways), with key elements including mammalian target of rapamycin (mTOR), phosphoinositide-3 kinase (PI3K), Akt or protein ki), hypoxia-inducible factor (HIF), vascular endothelial growth factor (VEGF), NOTCH, and insulin-like growth factor (IGF-1). Cyan lines: fragments of canonical pathways; red circles: upregulated genes; blue circles: downregulated genes.
Figure 2.
 
(A) Network involved in angiogenesis (network 1), in apical leading edges of tumors (P < 10−7, 140 pathways). Key elements include vascular endothelial growth factor (VEGF), matrix metalloproteinase-9 (MMP-9), transforming growth factor-β (TGF-β). (B) Network involved in hypoxia (network 9) in the apical leading edges of tumors (P < 10−40, 4 pathways). Response to hypoxia mediated by hypoxia-inducible factor (HIF).
Figure 2.
 
(A) Network involved in angiogenesis (network 1), in apical leading edges of tumors (P < 10−7, 140 pathways). Key elements include vascular endothelial growth factor (VEGF), matrix metalloproteinase-9 (MMP-9), transforming growth factor-β (TGF-β). (B) Network involved in hypoxia (network 9) in the apical leading edges of tumors (P < 10−40, 4 pathways). Response to hypoxia mediated by hypoxia-inducible factor (HIF).
Table 1.
 
Differential Gene Expression by More Than 2.5-Fold in Five Tumor Regions with the Corresponding Ratio of Change
Table 1.
 
Differential Gene Expression by More Than 2.5-Fold in Five Tumor Regions with the Corresponding Ratio of Change
A. Upregulated Genes
Base Center Apex Anterior-lateral Posterior-lateral
Gene Ratio of Change Gene Ratio of Change Gene Ratio of Change Gene Ratio of Change Gene Ratio of Change
KRT15 5.1448 GM9912 2.6366 CENPK 4.0012 KRT16 3.802 No gene name 3.6121
KRT4 5.0582 2810417H13R1 3.8653 No gene name 3.1226 GEN1 3.3636
CYP4A12B 4.6897 CASC5 3.7474 SPINK5 3.0778 CCNB1 3.2991
KRT13 4.5248 BUB1 3.7178 XIST 3.0497 HMMR 3.2884
KRT14 4.4163 RRM2 3.6037 KRT14 2.9956 CCNB1 3.2238
ANXA8 4.3149 CDH9 3.5187 S100A9 2.9409 EG665955 3.2117
KRT5 4.301 MKI67 3.5062 No gene name 2.9264 CCNB1 3.1662
TACSTD2 4.182 PBK 3.4692 KRT5 2.8168 NCAPG2 3.0262
KRT6B 4.1757 KRT4 3.466 BC100530 2.7341 LCN2 3.0002
CEACAM1 4.1426 MCM6 3.4537 SNORD116 2.7022 LPHN2 2.951
9930032O22RI 4.1396 KRT14 3.4365 No gene name 2.702 C79407 2.9377
UPK1B 4.0946 NUDT10 3.4343 DSG3 2.7003 CRYBA1 2.9014
DSG3 3.956 GEN1 3.3905 SNORD116 2.6892 PTPN3 2.8936
ENPP3 3.933 CCNB1 3.388 MMP3 2.6707 CDH9 2.8539
KRT6A 3.9257 FAM111A 3.3757 SNORD116 2.6558 SHCBP1 2.852
SLC6A14 3.8995 CCNB1 3.3655 S100A8 2.6524 BUB1 2.804
CBR2 3.8739 KIF11 3.3435 CRYBA1 2.628 PBK 2.7594
TMPRSS11B 3.7424 CCNB1 3.3391 SNORD116 2.5994 6720489N17RI 2.7205
DSC3 3.7068 KRT6B 3.3143 GM9912 2.5633 GINS1 2.7187
TRIM29 3.7054 KRT15 3.3032 KRT6B 2.5615 CASC5 2.6874
SCEL 3.6572 TOP2A 3.2889 SCG3 2.5175 TTK 2.6598
TMPRSS11A 3.6417 CENPH 3.2249 No gene name 2.5092 SERPINA3N 2.6336
ADH7 3.6356 ANLN 3.2177 SNORD116 2.5034 TPX2 2.629
ALDH3A1 3.6319 HIST1H2AB 3.1439 LPHN2 2.6035
AKR1B7 3.5832 BC100530 3.1268 SGOL2 2.5912
BC100530 3.5756 C330027C09RI 3.0901 KIF4 2.585
DSG1A 3.5619 SGOL2 3.074 C330027C09R1 2.5798
DSC2 3.5213 KRT5 3.0701 PPIL5 2.5518
PGLYRP1 3.4777 CKS2 3.0693 KIF14 2.5517
FABP6 3.4499 MNS1 3.0536 CENPK 2.5336
PLAC8 3.4191 KRT13 3.0486 RRM2 2.5299
DSP 3.3436 ACTC1 3.0294 CHEK1 2.528
S100A14 3.2942 KIF4 3.0204 SNORD116 2.5238
ADH1 3.2901 NCAPG 3.0026 KIF2C 2.5138
MUC4 3.2862 SPC24 3.0014 CLSPN 2.5114
RBP2 3.2579 HELLS 2.9981 NUF2 2.5102
CALML3 3.2514 NUF2 2.9795
CAPG 3.2201 KRT16 2.965 KIF14 2.5517
KRT19 3.1758 DSCC1 2.9644 CENPK 2.5336
LUM 3.1756 ARHGAP11A 2.9578 RRM2 2.5299
CMAH 3.1643 SMC2 2.9405 CHEK1 2.528
SERPINB5 3.1593 C79407 2.9212 SNORD116 2.5238
EMP1 3.1549 PRR11 2.8937 KIF2C 2.5138
PPL 3.1142 NDC80 2.8826 CLSPN 2.5114
PYHIN1 3.09 KRT6A 2.8734 NUF2 2.5102
IL1F9 3.0649 BIRC5 2.8706 KIF2C 2.5138
DEFB1 3.0552 LPHN2 2.8672 CLSPN 2.5114
SAMD9L 3 GINS1 2.8565 NUF2 2.5102
FGFBP1 2.9933 DSG3 2.8543 CLSPN 2.5114
ANXA2 2.9889 2810417H13RI 2.8429 NUF2 2.5102
9930023K05RI 2.9783 SHCBP1 2.8422 CLSPN 2.5114
IFITM1 2.9492 CKS2 2.8379 NUF2 2.5102
GSTA3 2.9068 CDC2A 2.8331 CLSPN 2.5114
GPR110 2.9005 LPHN2 2.8233 NUF2 2.5102
DCN 2.847 MASTL 2.8215
SPINK5 2.823 TPX2 2.8144
EHF 2.7891 KIF23 2.8003
KRT7 2.7817 GM9912 2.8001
1600029D21RI 2.769 TTK 2.7778
MALL 2.736 CKS2 2.7633
S100A9 2.7304 FBXO5 2.7586
OCM 2.7168 EG665955 2.7554
CLDN7 2.6877 PEG10 2.7433
SPINK5 2.7412
GM9573 2.6769 NCAPH 2.7266
PERP 2.6515 KRT24 2.7084
ESRP1 2.6479 ECT2 2.695
GSTO1 2.6415 PPIL5 2.6805
GDA 2.6149 RBBP8 2.6706
PTGR1 2.6138 ANXA1 2.67
CYP2F2 2.6113 CENPF 2.6546
IFITM1 2.5923 CKAP2L 2.6376
C130090K23RI 2.5153 CYP4A12B 2.6161
GALNT3 2.5097 NCAPG2 2.5946
GM11428 2.5001 DLGAP5 2.5916
LYZ2 2.588
PRIM1 2.5849
DBF4 2.5832
LPHN2 2.5785
ATAD2 2.5765
TRIM59 2.5672
MCM3 2.5618
HMMR 2.5427
NFIA 2.5368
E2F8 2.534
EXO1 2.5174
2610039C10RI 2.5138
B. Downregulated Genes
Base Center Apex Anterior-lateral Posterior-lateral
Gene Ratio of Change Gene Ratio of Change Gene Ratio of Change Gene Ratio of Change Gene Ratio of Change
CNGA1 −4.4701 GUCY2F −4.1373 PDE6B −5.0352 CNGA1 −4.6794 OPN1SW −5.0585
RP1 −4.4608 PDE6B −4.0316 CNGA1 −5.0191 RCVRN −4.4971 GUCY2F −4.6778
RCVRN −4.3932 RCVRN −3.8163 RCVRN −4.8462 PDE6B −4.4931 RCVRN −4.5381
PDE6B −4.3062 RHO −3.7673 RPE65 −4.8052 RHO −4.0053 CNGA1 −4.4204
GUCY2F −3.9616 2610034M16R1 −3.7485 PDE6A −4.7065 RHO −3.9785 PDE6B −4.3056
PDE6A −3.9498 RP1 −3.5564 RP1 −4.6844 RP1 −3.9687 No gene name −4.1752
RS1 −3.8567 PDE6A −3.5066 RHO −4.6441 PDE6A −3.8735 RP1 −4.0818
RHO −3.79 CNGA1 −3.4836 GUCY2F −4.6367 GNAT1 −3.7975 2610034M16RI −4.0801
TULP1 −3.6546 REEP6 −3.4382 RDH12 −4.5129 RS1 −3.6846 PDE6A −4.0469
GRK1 −3.5238 RS1 −3.3294 GNAT1 −4.4516 SAG −3.6444 RHO −3.9724
RDH12 −3.4964 SLC24A1 −3.2645 VTN −4.3343 GUCY2F −3.638 GRK1 −3.8474
OPN1SW −3.4265 RPE65 −3.2339 GUCA1A −4.2548 VTN −3.5997 RHO −3.7115
GUCA1B −3.4165 ARR3 −3.2278 SAG −4.1852 SLC24A1 −3.449 CDS1 −3.6255
ADAMTS3 −3.4098 VTN −3.1777 RHO −4.1269 2610034M16RI −3.4238 RS1 −3.5685
2610034M16RI −3.3635 TULP1 −3.0543 TTR −4.0968 IMPGI −3.4029 RDH12 −3.5593
IMPG2 −3.3173 GUCA1B −3.041 TULP1 −4.0863 REEP6 −3.4 GUCA1B −3.4517
GM626 −3.3102 IMPG1 −3.0188 GUCA1B −4.013 GRK1 −3.1713 IMPG1 −3.4482
PRPH2 −3.2885 PDE6G −2.9703 PDC −4.0107 GUCA1B −3.1536 ARR3 −3.4129
SAG −3.2643 OPNIMW −2.9658 2610034M16RI −4.0039 GUCA1A −3.1167 FABP12 −3.3855
CALB1 −3.2542 CNGB1 −2.9629 SLC24AI −3.9651 PDC −3.0939 C030002C11RI −3.3297
IMPG1 −3.2524 GRK1 −2.9333 IMPG1 −3.9048 TULP1 −3.0184 SLC24A1 −3.3201
PDE6G −3.231 ME1 −2.9195 RGR −3.8508 PRPH2 −3.0106 REEP6 −3.3172
WDR17 −3.2248 CALB1 −2.8971 REEP6 −3.8455 CNGB1 −2.9766 VTN −3.3057
GNAT1 −3.2196 NRL −2.8711 GM626 −3.8261 OPN1MW −2.9231 SAG −3.2897
RHO −3.1948 SAG −2.8696 RS1 −3.7915 PDE6G −2.8083 ADAMTS3 −3.1062
SLC24A1 −3.188 ABCA4 −2.8646 OPN1SW −3.7847 RPE65 −2.7849 WDR17 −3.0986
VTN −3.1816 PRPH2 −2.8531 IMPG2 −3.7759 ABCA4 −2.7098 TULP1 −3.0714
FABP12 −3.1415 C030002C11RI −2.8371 GRK1 −3.7481 NR2E3 −2.6847 UPK1B −3.0451
ARR3 −3.0916 RDH12 −2.835 CDS1 −3.6674 FABP12 −2.6803 CNGB1 −3.0442
PDC −3.0772 GLBIL2 −2.8234 PRPH2 −3.6533 PROM1 −2.652 OPN1MW −3.0287
ROM1 −2.9229 PEX5L −2.8231 ROM1 −3.6231 IMPG2 −2.6508 FAM161A −3.0234
FAM161A −2.9159 WDR78 −2.8094 FABP12 −3.6175 GM626 −2.6288 GLB1L2 −3.0131
A930003A15RI −2.9133 NXNL1 −2.8054 PDE6G −3.4549 GM10664 −2.61 PDC −2.9972
WDR78 −2.9098 WDR17 −2.7996 ABCA4 −3.4477 NRL −2.6025 TAC1 −2.9925
GM10664 −2.8845 NR2E3 −2.7562 PROM1 −3.3796 WDR17 −2.5942 ABCA4 −2.988
SAMD7 −2.8688 RGR −2.7358 WDR78 −3.3315 GLB1L2 −2.5708 LRIT2 −2.9737
ADAMTS3 −2.8323 PROM1 −2.7204 CALB2 −3.2487 RDH12 −2.5568 PROM1 −2.9684
OPN1MW −2.8125 SPATA1 −2.7178 GLB1L2 −3.2272 C530030P08RI −2.5517 PDE6G −2.9669
REEP6 −2.7962 RHO −2.7129 NR2E3 −3.2055 WDR78 −2.5363 KRT12 −2.9552
LRIT2 −2.7883 PLA2R1 −2.7107 PPARGC1A −3.2005 No gene name −2.5165 PRPH2 −2.9497
GUCA1A −2.7699 GNAT1 −2.701 CNGB1 −3.1234 LRIT2 −2.5045 WDR78 −2.9474
ABCA4 −2.7201 CDS1 −2.6796 MPP4 −3.104 GNAT1 −2.9465
C030002C11RI −2.7019 FAM161A −2.6649 WDR17 −3.1008 PSCA −2.946
CNGB1 −2.6999 FABP12 −2.6637 NRN1 −3.0902 RPE65 −2.9378
LRRC2 −2.6992 LRIT2 −2.6099 NRL −3.0787 PLA2R1 −2.9029
SPATA1 −2.6869 ME1 −2.5699 CALB1 −3.077 CDR2 −2.9013
GM626 −2.665 A930003A15RI −2.5299 PEX5L −3.0711 NRL −2.9007
HIST2H3C2 −2.6462 3632451O06RI −2.5294 GM11744 −3.0574 MPP4 −2.8886
A330023F24RI −2.6434 BST1 −2.5082 LGI1 −2.993 GM626 −2.874
PEX5L −2.6154 GM11744 −2.5007 FAM161A −2.9842 GM11744 −2.8575
PDE6C −2.6118 GM10664 −2.9635 RABGEF1 −2.8558
TAC1 −2.5092 CHRNB3 −2.9436 TDRD7 −2.8429
SLC17A6 −2.9268 PEX5L −2.8263
OPN1MW −2.9101 UNC13C −2.7611
LRIT2 −2.9017 NR2E3 −2.7492
COX8B −2.8988 APBH −2.7348
ENPP2 −2.8935 HERC3 −2.7337
ARR3 −2.8684 PDE6C −2.7175
HERC3 −2.8628 GP2 −2.7166
C030002C11RI −2.8626 4833423E24RI −2.7154
GM626 −2.8602 DCN −2.7131
CD59A −2.8517 CENPH 2.7061
SPATA1 −2.809 RGR −2.7039
ALDOC −2.8089 CALML3 −2.6967
LDHB −2.7821 STFA3 −2.684
SAMD7 −2.7803 GUCA1A −2.6827
GM626 −2.7474 PPEF2 −2.6757
UNC13B −2.7459 NT5E −2.667
RLBP1 −2.726 NXNL1 −2.6568
RGS9 −2.7235 IMPG2 −2.6363
RP1L1 −2.7206 MLANA −2.6293
GNB1 −2.7097 PGLYRP1 −2.6228
PLA2G5 −2.7038 C130021120RI −2.619
PLA2R1 −2.674 GM626 −2.6093
ME1 −2.664 ATP8A2 −2.6083
KRT18 −2.6561 S100A14 −2.6036
TAC1 −2.6465 GM10664 −2.6028
CLIC6 −2.6461 CALB2 −2.6025
PPEF2 −2.644 CHRNB3 −2.5758
ATP8A2 −2.6388 TYRP1 −2.5606
HK2 −2.638 LRAT −2.5599
LRRC67 −2.6246 ADH1 −2.5317
GRIA3 −2.6234 ROM1 −2.5257
ZDHHC2 −2.5984 CSMD3 −2.5134
LYNX1 −2.5834 PVALB −2.5085
PVALB −2.5725
GM626 −2.561
NXNL1 −2.5568
AIPL1 −2.5277
LRAT −2.5118
ME1 −2.5094
CDR2 −2.5021
Table 2.
 
Key Networks with Associated Key Objects and Processes for Basal Tumor Regions
Table 2.
 
Key Networks with Associated Key Objects and Processes for Basal Tumor Regions
Top Networks Key Network Objects GO Processes Total Nodes Root Nodes Pathways P z-Score g-Score
3 STAT1, c-Raf-1, DNM1L (DRP1), NDPK B, 14-3-3 zeta/delta Regulation of cell proliferation, Positive regulation of cellular process, regulation of apoptosis 50 18 23 3.46 × 10−22 22.44 51.19
4 mTOR, PKC, Tuberin, DLL1, NEURL1 Enzyme linked receptor protein signaling pathway, insulin-like growth factor receptor signaling pathway, cellular response to insulin stimulus 50 11 30 1.54 × 10−11 13.56 51.06
Table 3.
 
Key Networks with Associated Key Objects and Processes for Central Tumor Regions
Table 3.
 
Key Networks with Associated Key Objects and Processes for Central Tumor Regions
Top Networks Key Network Objects GO Processes Total Nodes Root Nodes Pathways P z-Score g-Score
2 c-Raf-1, NCOA3, (pCIP/SRC3), H-Ras, NCOA2 (GRIP1/TIF2), TFF1 Regulation of cell proliferation, positive regulation of cellular process 51 11 85 1.05 × 10−10 12.29 118.54
5 SMAD2, NF-κB, SMURF2, SerRS, RPS25 Anatomic structure morphogenesis, response to oxygen levels 50 14 35 5.58 × 10−15 16.04 59.79
9 FOXO3A, 14-3-3 zeta/delta, Cyclin D2, GPIAP1, BTF JAK-STAT cascade involved in growth hormone signaling pathway, regulation of cell proliferation 50 20 12 4.22 × 10−25 24.06 39.06
Table 4.
 
Key Networks with Associated Key Objects and Processes for Apical Tumor Regions
Table 4.
 
Key Networks with Associated Key Objects and Processes for Apical Tumor Regions
Top Networks Key Network Objects GO Processes Total Nodes Root Nodes Pathways P z-Score g-Score
1 TGF-β1, RUNX3, TGF-β receptor type II, GAS1, FALZ Organ development, anatomic structure morphogenesis, system development 50 8 140 1.91 × 10−7 9.40 184.4
4 NF-κB, FCGRT, Cyclin D2, ISG20, SPT2 Positive regulation of cellular process, regulation of cell proliferation 50 14 27 1.21 × 10−15 17.05 50.80
9 NF-κB, COX17, Nectin-2, PSMD7, LOC283412 Regulation of oxidoreductase activity, positive regulation of defense response, positive regulation of oxidoreductase activity, regulation of response to stress 50 27 4 1.34 × 10−40 35.94 40.94
Table 5.
 
Key Networks with Associated Key Objects and Processes for Anterolateral Tumor Regions
Table 5.
 
Key Networks with Associated Key Objects and Processes for Anterolateral Tumor Regions
Top Networks Key Network Objects GO Processes Total Nodes Root Nodes Pathways P z-Score g-Score
5 SMURF2, CRELD2, eIF4A1, SARA, SAHH2 Response to organic cyclic substance, transforming growth factor beta receptor signaling pathway 50 14 24 1.41 × 10−15 16.94 46.94
8 CCL13, HMG1 (amphotercin), NDPK B, HRPT2, Symplekin Translational elongation, leukocyte migration, cytokine-mediated signaling pathway, response to mechanical stimulus, translation 50 21 9 9.13 × 10−28 26.67 37.92
Table 6.
 
Key Networks with Associated Key Objects and Processes for Posterolateral Tumor Regions
Table 6.
 
Key Networks with Associated Key Objects and Processes for Posterolateral Tumor Regions
Top Networks Key Network Objects GO Processes Total Nodes Root Nodes Pathways P z-Score g-Score
1 SMAD1, SMAD2, TGFb 1, SCAP, RPS27A Anatomic structure morphogenesis, organ development, regulation of developmental process 50 11 67 2.42 × 10−11 13.26 97.01
3 SHP-2, Ubiquitin, IGF-2, NIX, GlyRS Regulation of cell proliferation, regulation of cellular process 50 9 38 1.07 × 10−8 10.70 58.20
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