• Users Online: 148
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2019  |  Volume : 10  |  Issue : 2  |  Page : 85-95

Gene expression analysis of retinoblastoma tissues with clinico-histopathologic correlation


1 Ophthalmic Pathology Laboratory, L V Prasad Eye Institute Hyderabad; Radiation Signaling and Cancer Biology Section, RB&HSD, BSG, Bhabha Atomic Research Center, Mumbai, Maharashtra, India
2 School of Medical Sciences, University of Hyderabad, Hyderabad, India
3 Molecular Reproduction, Development and Genetics, Indian Institute of Science, Bangalore, Karnataka, India; Women's Malignancies Branch, Center for Cancer Research, National Cancer Institute, National Institute of Health, Bethesda, MD, USA
4 Molecular Reproduction, Development and Genetics, Indian Institute of Science, Bangalore, Karnataka, India; Department of Biomedicine, University of Basel, Basel, Switzerland
5 Medical Services, CFS Group, Centre for Sight, Hyderabad, India
6 Dacryology Services, Department of Ophthalmic Plastics and Facial Aesthetic Surgery; Department of Orbits and Ocular Oncology, L.V.Prasad Eye Institute, Hyderabad, India
7 Molecular Reproduction, Development and Genetics, Indian Institute of Science, Bangalore, Karnataka, India

Date of Web Publication9-Sep-2019

Correspondence Address:
Prof. Geeta K Vemuganti
School of Medical Sciences, University of Hyderabad, Hyderabad
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jrcr.jrcr_7_19

Rights and Permissions
  Abstract 


Purpose: Retinoblastoma (Rb) is the most common intraocular malignant tumor, which is not only unique but also has unraveled many novel aspects of tumor-suppressor genes. Genetic mutations of Rb, loss of phosphorylation, and many other factors resulted in uncontrolled cell division of the retinal cells resulting in tumor progression. In this study, we have analyzed the gene expression patterns of unilateral tumors (n = 11) in comparison to the normal-appearing retina (n = 2) from Rb patients who underwent enucleation for advanced Rb. With recent advances in the knowledge of the role of stem cells in these tumors, it is important to evaluate and understand the self-renewal signaling involved in these tumors. Here, in this study, we particularly aimed at evaluating the aberrant self-renewal signaling pathways in human Rb tumors and genes which show differential expression in cases with and without histologic risk factors (HRF). Materials and Methods: Freshly unfixed eyeballs (n = 11) were obtained. Normal-appearing retinas were pooled together (n = 2) and used as a control for microarray experiments. Total RNA was isolated from tumors and control tissues, and expression of genes was evaluated by hybridizing to expression arrays. Using real-time polymerase chain reaction (PCR), the results, thus, obtained were validated (for expression of N-Myc, HMGA2, LIN-28b, and Activin receptor 1C [ACVR1C]) in tissues compared to two control retinas latter obtained from enucleated Rb eyeballs without tumor. Furthermore, immunohistochemistry (IHC) was done on retrospective (n = 19) cases to confirm the expression of ACVR1C. Results: In Rb tumors, 5593 genes were upregulated and 4864 genes were downregulated (P ≤ 0.05 and fold change ≥1.5 folds). Changes in N-Myc, HMGA2, LIN28b, and ACVR1C expression detected by microarray were validated by real-time PCR. The analysis shows significant up-regulation of HMGA2 and its downstream regulator LIN-28b, which is involved in self-renewal pathway of fetal neural stem cells. ACVR1C is one of the markers, which shows differential expression between histological subtypes of tumors as evident in IHC. CBLB (P ≤ 0.05) and MAPK 8 (P ≤ 0.05) were shown to be highly upregulated in tumors without HRF compared to cases with HRF. Conclusions: This study showed up-regulation of genes involved in neural stem cell self-renewal and marginally in notch signaling. While other stem cell pathways such as Wnt and sonic hedgehog (SHH) pathways were upregulated in these tumors. Targeting these self-renewal pathways would aid in eliminating the resistant cells in this tumor and thus may help in treating the recurrence. Genes such as CBLB, MAPK 8, and ACVR1C can be used as potential biomarkers in this tumor to prognosticate cases with or without HRFs and differentiation of the tumors.

Keywords: Biomarkers, cancer pathways, gene microarray, retinoblastoma


How to cite this article:
S Balla MM, Nair RM, Khan I, Reddy Kalathur RK, Honavar SG, Mohammed JA, Kondaiah P, Vemuganti GK. Gene expression analysis of retinoblastoma tissues with clinico-histopathologic correlation. J Radiat Cancer Res 2019;10:85-95

How to cite this URL:
S Balla MM, Nair RM, Khan I, Reddy Kalathur RK, Honavar SG, Mohammed JA, Kondaiah P, Vemuganti GK. Gene expression analysis of retinoblastoma tissues with clinico-histopathologic correlation. J Radiat Cancer Res [serial online] 2019 [cited 2019 Sep 15];10:85-95. Available from: http://www.journalrcr.org/text.asp?2019/10/2/85/266120




  Introduction Top


Retinoblastoma (Rb) is a childhood malignant tumor, which generally develops within 5 years of age. This tumor is the result of mutations in RB1, a tumor-suppressor gene and it may be manifested in genetic or sporadic forms. RB1 gene, taken part in various mechanisms such as anti-apoptosis, differentiation, cell cycle regulation, DNA repair, and DNA replication.[1]

Previous studies demonstrated the presence of a small population of cells in many solid tumors that retain the characteristics of self-renewal, differentiation ability in vitro, and tumor-forming ability in vivo, and these were referred to as cancer stem cells.[2],[3],[4],[5],[6],[7],[8],[9],[10],[11] In Rb, recent reports showed that tumor cells express the markers of stem cells such as MDR1, ABCG2, Oct4, Nanog, ALDH1, and CD44.[6],[12],[13],[14],[15],[16],[17],[18] Subpopulations within this tumor were also evaluated for differential expression of markers and shown that FSClo/SSClo population is primitive when compared to that of FSChi/SSClo.[6] In addition to the phenotypic characterization, it was suggested that loss of Wnt signaling is responsible for the formation of Rb tumor and activating this signal by lithium chloride treatment resulted in the increase of stem-like cells.[19] Evidences from other tumors showed that the cells in tumors have elevated stem cell pathway regulators and as a result may exhibit the resistance to chemotherapy.[20],[21],[22],[23],[24],[25] Hence, the evaluation of various signaling pathways specific for stem cells in Rb tumor may aid in designing better therapeutic strategies for targeting stem-like cells to overcome resistance in this tumor.

We evaluated the expression of various stem cell markers and signaling pathways in Rb tumors as compared to pooled samples of normal retinas. Although HMGA2 was reported earlier,[26],[27] its role in stem cell self-renewal was not emphasized in these tumors. Hence, in this study, we specifically looked at HMGA2 and Lin28b gene expression and analyzed its self-renewal signaling in these tumors. In addition, we looked for novel biomarkers expressed in different types of Rb tumors. The expression of these genes was correlated with histopathologic risk factors in these tumors.


  Materials and Methods Top


Tissue collection

Unfixed prospective tumor samples were obtained (n = 11) with the approval of the study by the Institutional Review Board and in accordance with Helsinki guidelines. Histologically, normal retinas were collected from enucleated eyeballs of Rb patient for use as controls (two samples pooled together) in microarray experiments. Two other control retinas obtained from Rb patients without tumor were used for Real-time polymerase chain reaction (PCR) assays to validate results of microarray experiments.

Extraction, semi-quantitative, and real-time polymerase chain reaction

Total RNA was extracted from human tissues, which was also used in MA procedure (Rb tumor and normal retina) using TRI-reagent (Sigma-Aldrich, St. Louis, USA) according to the manufacturer's protocol. Two micrograms of RNA was reverse transcribed using a cDNA synthesis kit (Applied Biosystems, USA) and 1/100th of the reaction product was used per 20 μL Real-time PCR reactions. Real-time PCR was performed using Dynamo™SYBRgreen 2X mix (Finnzymes, Finland) in triplicates and quantitation was performed in ABI Prism 7000 sequence detection system and analyzed with SDS 2.1 software (Applied Biosystems, USA). The expression of Glyceraldehyde adenosine phosphate dehydrogenase (GAPDH) gene was the internal control and differential expression was determined by the formula:

δCt = CtGene-Ct GAPDH

δδCt = δCt treated-δCt untreated

Fold change = 2δδCt

Unpaired t test was used to get the P value. The sequence of primers used in this study is given in [Table 1].
Table 1: Real Time PCR primer sequences for the genes studied

Click here to view


Microarray protocols and data analysis

Microarray was performed using whole human genome (4 × 44k) cDNA arrays (Agilent Technologies, USA). For labeling reactions, 500 ng each of RNA from 11 Rb patients and from two control tissues pooled into one was used. Labeling of the probes was done using the Low RNA Input Linear Amplification Kit (Agilent Technologies, USA) where total RNA is first converted to cDNA using T7-oligo d (T) primers. From this cDNA, labeled cRNA was generated through anin vitro transcription reaction using T7 RNA polymerase and Cy3 (for one normal retina RNA) or Cy5 (Rb RNA) CTP, respectively. Labeled cRNA was purified using RNAeasy columns (QIAGEN GmbH, Germany). Labeled probes were quantified using spectrophotometer (NanoDrop-1000, Thermo Scientific, USA) and resolved on 1% Agarose gel and scanned using the Typhoon 9210 scanner (GE Life Sciences, USA), to assess the integrity. Samples with higher labeling efficiency (specific activity ≥8 pmol Cy3 or Cy5/μg cRNA) were selected for competitive hybridization as per the manufacturer's protocol. 825 ng each of cyanine 5 and cyanine 3 labeled cRNAs from Rb patients and from normal tissue were mixed, added to hybridization buffer and placed on the array. Hybridizations were done in a hybridization chamber (Agilent Technologies, USA) for 17 h at 65°C with gentle rotation. After hybridization, washing of the slides was performed using wash buffer kit (Agilent Technologies, USA) as per the manufacturer's protocols and dried at room temperature. The slides were scanned in a scanner (Agilent Technologies, USA). The image analysis was performed using Feature extraction tool version 9.5.3.1 (Agilent Technologies, USA) and data analysis was performed using GeneSpring version 10 (Agilent Technologies, USA). The background corrected raw intensity values were used for analysis. LOWESS algorithm was used to normalize the data and Box-whisker plot was created for all samples with normalized intensity values. Fold change (Fc) was calculated based on the ratio of Cy5/Cy3 intensities. For statistical analysis, Student's t-test against zero was performed using Benjamini-Hochberg multiple testing correction.

Immunohistochemistry

For immunohistochemistry (IHC) eleven more cases were taken in addition to the MA samples (n = 11). These include formalin-fixed paraffin-embedded sections of 4-μm thickness of well (n = 6), moderately (n = 7), poorly differentiated (n = 7), and retinocytoma (n = 2) tumors. Hepatocellular carcinoma was used as a positive control for the expression of Activin receptor 1C (ACVR1C). After alcohol and xylene washes, peroxide blocking was done for 30 min. The antibody used was rabbit anti-human ACVR1C (Abcam, U. K) in the dilution of 1:40. Epitope retrieval was done in the microwave at 100°C in Tris-ethylenediaminetetraacetic acid buffer (pH 9.0) for 18 min. Before adding primary antibody, sections were blocked for nonspecific binding using phosphate buffered saline-bovine serum albumin (1%) for 30 min. After overnight incubation of primary antibody at 4°C, sections were washed and then incubated with peroxidase-conjugated Envision FLEX substrate buffer polymer detection kit (Dako, Denmark) for 30 min at 4°C. DAB (3', 3'-Diamino benzidine tetra HCl, Sigma-Aldrich, USA) was added for 5 min. Then, the sections were mounted using DPX mount and visualized under a microscope. Grading of tumors was done by an ocular pathologist (GKV) for the expression of ACVR1C marker.

Microarray image and data analysis

Array image analysis was done using Agilent's Feature Extraction Software, Agilent Technologies, USA. Filtering and compilation of data have been done using R Software (http://www.r-project.org/). Spots of compromised quality and with low intensity were eliminated from the analysis, background subtracted signal intensities are normalized using LOWESS method (Gene spring version 10). T-test was performed to identify differentially regulated genes in Rb samples in comparison to normal and Cy5:Cy3 ratios are calculated. The genes showed consistent regulation in at least 75% of the samples and showed fold change greater than ±1.5 fold were considered for functional enrichment analysis and further for validation using real-time PCR. For genes that were deregulated in subtypes of tumor (WD/MD vs. PD) and tumors with histologic risk factors (HRF) vs. without HRF Fc greater than ±2 fold were considered and Fisher's exact test was applied to get P value.

Enrichment analysis of biological functional groups and pathways

We evaluated the significance of differentially regulated genes on the biological processes using DAVID bioinformatics resources.[27] To eliminate general biological processes, we chose the gene ontology level 5 and also picked those biological processes that are highly enriched and showed P ≤ 0.05 [Figure 1]. Various pathway analysis was performed using the Kyoto Encyclopedia of Genes and Genomes pathway database.[28]
Figure 1: Pie diagram representing number of genes involved in specific pathways in retinoblastoma tumors

Click here to view



  Results Top


Clinical features

The clinical features of 22 Rb cases are summarized in [Supplementary Table 1 [Additional file 1]]. The mean age of patients was 2.4 years in which 15 patients were male and nine were female. Out of 22 cases, there were four eyeballs from bilateral cases and remaining from unilateral. None of the cases has bone marrow/cerebrospinal fluid involvement. Adjuvant chemotherapy was given to seven cases after enucleation and in one case enucleation was done post-chemotherapy. Eleven eyeballs were categorized as ICIR Group D and five as ICIR Group E

Histopathology examination

Twelve tumors were well and moderately differentiated, whereas eight were poorly differentiated, retinocytoma-like areas were present in one case and also in one MD case. Histopathologic high-risk factors were noted in 11/22 cases, which include 3/11 cases with optic nerve and lamina cribrosa involvement, 4/11 pre laminar cribrosa, 1/11 cases with full-thickness choroid, 2/11 cases with lamina cribrosa and full-thickness choroid involvement, and 1/11 cases with optic nerve and anterior layers of cribrosa involvement. None of the cases showed bone marrow/CSF involvement.

Gene expression in retinoblastoma tumors

There was up-regulation of 5593 genes (≥1.5 fold) and down-regulation of 4864 genes (≤1.5 fold). A comprehensive analysis of the microarray data revealed genes belonging to cell cycle, DNA repair, aminoacyl-t-RNA biosynthesis. In neuronal tissues such as negative regulation of neuron apoptosis, vasculogenesis, somitogenesis, and neural crest cell development and differentiation. The list of top 25 highly upregulated and downregulated genes is mentioned in [Table 2] and [Table 3]. We identified vascular endothelial growth factor (VEGF) signaling pathway (P = 0.0019), transforming growth factor (TGF)-beta signaling pathway (P = 0.009), p53 signaling pathway (P = 0.017), insulin signaling pathway (P = 0.032), chemokine signaling pathway (P = 0.011), and Wnt signaling pathway (P = 0.05) that are highly deregulated in Rb tumors [Figure 1] and [Table 4]. The pathways were then compared with the histologic features for correlation [Table 5] and [Table 6]. It was observed that TGF-β, p53, and cancer signaling pathways were upregulated significantly, whereas chemokine signaling pathway along with VEGF, WNT, and senescence were downregulated predominantly in cases of poorly differentiated tumors [Figure 2]. The genes with differential expression in five PD cases and 6 MD/WD tumors, i.e., ≥ or ≤2-fold change are enlisted in [Table 5].
Table 2: List of top 25 genes highly upregulated in primary Rb tumors

Click here to view
Table 3: List of top 25 genes highly downregulated in primary Rb tumors

Click here to view
Table 4: List of signaling pathways deregulated in primary Rb tumors

Click here to view
Table 5: List of genes differentially expressed in MD vs. PD tumors

Click here to view
Table 6: Significant difference in gene expression between cases with and without HRF

Click here to view
Figure 2: Pathways deregulated in primary retinoblastoma with histologic correlation (histologic risk factors, no histologic risk factors, well or moderately differentiated tumor, poorly differentiated tumor,P<0.05 **/*)

Click here to view


Microarray results were confirmed by real-time PCR for MYCN, HMGA2, Lin 28b, and ACVR1C in 11 RB tumors compared to n = 2 control retina tissues [Figure 3]a,[Figure 3]b,[Figure 3]c,[Figure 3]d. The mRNA expression levels of MYC N, HMGA2, and Lin28-b were statistically significant and the P values of the genes were 0.0014, 0.0007, and 0.0008, respectively. The P value for ACVR1C is statistically significant (P = 0.015) by microarray analysis [Table 5], which was further validated using real-time PCR analysis and IHC as shown in [Figure 3]d and [Figure 4]. The expression of ACVR1C in WD tumors, especially in areas of rosettes is very less as compared to PD areas. In retinocytoma areas, the expression is very less/almost absent as shown in [Figure 4]d,[Figure 4]e,[Figure 4]f,[Figure 4]g,[Figure 4]j and [Figure l].
Figure 3: Real-time expression data of genes (a) HMGA2, (b) LIN28B, (c) MYCN, and (d) ACVR1C in retinoblastoma and control retina samples

Click here to view
Figure 4: Activin receptor 1C expression in (a and b): Hepatocellular carcinoma, used as a positive control (c): Normal retina (internal control) and D: Differentiated areas of MD tumor (e and f): Poorly differentiated tumor (g): Retina and tumor of PD Rb tumor. (h and i) Retina of Poorly differentiated tumor (j): Retinocytoma area of retinoblastoma tumor. (k) Retina of WD tumor. (l) Rosettes were shown to be negative for activin receptor 1C in the same WD tumor (k), a, e, f: ×10 b-d, g-l: ×40

Click here to view


HMGA2 signaling

Microarray results showed that the fold expression of HMGA2 is higher in PD compared to MD tumors (mean expression in five each of MD and PD were 45.66 and 52.4, respectively). Real-time PCR validation showed that mRNA expression levels of HMGA2 and LIN-28B in 11 tumor samples were significantly upregulated and P were 0.0007 and 0.0008, respectively [Figure 3]a,[Figure 3]b. Our results suggest that HMGA2 signaling is highly elevated in human Rb tumors.

Regulators of various other signaling pathways that were deregulated, i.e., ≥ or ≤1.5-fold change is listed in [Table 4]. The genes that had differential expression in five PD cases and 6 MD/WD tumors, i.e., ≥ or ≤2 Fc are listed in [Table 5].

ACVR1C nodal signaling regulator is highly upregulated in PD compared to MD/WD and retinocytoma tumors as shown in [Figure 3]d and [Figure 4] and [Table 5]. In addition to this pro-apoptotic regulator BAD is significantly downregulated in MD/WD compared to PD tumors (P = 0.015). Other gene which is downregulated in MD/WD tumors and unregulated in PD tumors was GNB2 and the P = 0.015 value was shown to be statistically significant. RASA1 was upregulated in 50% of the MD/WD cases, while it is unregulated in all of the PD tumors. RRM2 was upregulated more than 2 folds in 60% of PD tumors and unregulated in 83% of MD/WD cases. SHH was downregulated <2 folds in 60% of the PD tumors and unregulated in 83% of WD/MD tumors. SMAD3 was upregulated more than 2 folds in 83.7% of MD/WD tumors and unregulated in 60% of PD tumors [Table 5].

CBLB (P ≤ 0.05) and MAPK8 (P ≤ 0.05) were shown to be highly upregulated in tumors without HRF compared to cases with HRF [Table 6]. The senescence markers such as TOP2B (1.56), NPY5R (2.4), ATM (4.62), and P53 (2.23) were found to be upregulated in all tumors with higher expression in PD tumors. However, there was no significant difference in the expression of senescence markers between HRF and no HRF cases. The downregulated senescence-related genes in the cohort were SLIT2 (-2.02), CCNA1(-2.41), PTEN (-1.86), and MIF (-2.12).


  Discussion Top


Rb is the most common intraocular childhood tumor arising from mutations in both alleles of the RB1 gene. Few studies have demonstrated the presence of markers expressed by slow cycling self-renewal stem-like cells in Rb tumors.[6],[12],[13],[14],[16],[17],[18],[28] As there is a limited amount of literature regarding the gene expression profile in these tumors, the present study aimed at analyzing stem cell pathway gene expression profiles in these tumors. The results of our study show that HMGA2, which is a fetal neural stem cell signaling, is highly upregulated. HMGA2 gene and its downstream regulator LIN 28b were highly upregulated in Rb tumor samples in comparison to normal retina. Microarray results have shown that there is an average of 48.6-fold up-regulation of the HMGA2 gene. Whereas the downstream regulators of HMGA2 signaling like LIN-28b were significantly deregulated. The expression of LIN-28b gene is upregulated by 2.1 folds. These results suggest the hypothesis that fetal neural stem cell self-renewal signaling is highly upregulated in Rb tumors.

In humans, HMGA2 is strongly expressed in early stages of development and its expression is restricted to particular cell types. In adults, the function of HMGA2 gene is replaced by Bmi-1 in certain tissues.[29],[30] Recent studies have shown that HMGA2 gene is involved in self-renewal of fetal neural stem cells and retinal stem/progenitor cells in mouse models.[31],[32] It was shown that HMGA2 deficient mice have defects in the self-renewal ability of fetal stem cells. This study has suggested that HMGA2 loss do not result in overall decrease in cellular proliferation but specifically affects stem cell self-renewal.[32] Expression of HMG family of genes was correlated in various other tumors such as neuroblastomas, breast, small-cell lung carcinoma, and human prolactinomas.[33],[34],[35],[36],[37],[38],[39] It was reported earlier that HMGA2 de-repression was observed in Rb tumors and was shown to be expressed in high-risk metastatic tumors.[26],[27],[40] Moreover, it was shown that micro RNAs like let-7b was downregulated in 39% of Rb tumors.[40] In addition, results of this study show that there is an average 2.1-fold up-regulation of LIN 28b which suggests indirectly that let-7b is not activated and hence, self-renewal signaling is high in these tumors. Further validation of LIN28b is warranted as it might prove to be a therapeutic target. It would be worthwhile to explore the expression of these genes within the Rb CSCs in comparison to nonCSCs in a larger cohort. This data suggest that stem cell signaling like HMGA2 was shown to be activated in these tumors and further supports the hypothesis of the presence of cancer stem cells.

This data also suggest that nodal signaling is highly deregulated in these tumors as higher ACVR1C expression is observed in PD tumors, the downstream regulators like Smad2/3 was unregulated in 60% of PD tumors. BAD, a pro-apoptotic gene is highly downregulated in MD/WD tumors suggesting that apoptosis is minimal in these subtypes.

In the present study, CBLB and MAPK8 which are insulin signaling pathway regulators showed significantly upregulated (P = 0.024) in tumors without HRF compared to the tumors with HRF. Studies have shown that CBLB reduces the invasive ability of the by preventing EMT and the gene being upregulated in Rb cases without any risk factors for metastasis confirms the same.[41] MAPK8 or JNK1 has been shown to be both involved in tumor promotion as well as tumor suppression. Further studies are warranted in Rb to decipher the role of MAPK8 specific to Rb.[42]

Microarray results also revealed that other stem cell self-renewal regulators like Notch pathway, for example, Notch1, EP300, RBPJ, and MAML were marginally upregulated and the negative regulators of this signaling like NUMB and HDCA2 were unregulated. We have also evaluated Wnt signaling pathway regulators, and they were observed to be downregulated in our data set which is in agreement with the study of Silva et al.[19] This data suggest that stem cell signaling like HMGA2 and Notch were shown to be activated in these tumors and further supports the hypothesis of the presence of cancer stem cells.

Our data also revealed that retinal specific genes that were differentially regulated. The microarray data were submitted to the RetChip database, and results showed that ten genes related to retina were highly upregulated which were-NPTX1, MYO10, GALNT13, PFTK1, CNTN1, PCSK2, PLAGL1, SSTR2, MBP, and FZD8, respectively (P < 0.05). The genes that were highly downregulated are STAT4, HES1, CLDN5, PAX6, FBOX2, IRX5, GDNF, ROM1, PDE6A, and ARR3 (P < 0.05). Kapatai et al. have also analyzed the retinal-specific gene expression in human Rb tissues when compared to normal fetal retina.[43] Their study identified two Rb subgroups, one with expression of multiple retinal cell type-specific genes and the other with only cone photoreceptor gene expression. Our study is in agreement with the findings of their study; however, differentiation and HRF appeared to be associated with a few pathways we did not identify a single retinal cell-type-specific gene expression in our cohort.

Previous two gene expression studies have shown contradicting results regarding the deregulation of PI3K/AKT/mTOR (insulin pathway) pathway.[44],[45] Arun kumar et al. have shown that there is up-regulation of this signaling, which is marginally upregulated in the present data set. In contrast, this signaling was not regulated in study conducted by Ganguly andShields.[45] In the present study, the primary regulators of this pathway such as TP53 ( 2.23-fold), RPS6KB1 ( 1.5), ACACA (2.22), PCK2 ( 5.26), IRS1 (2.65), PRKX (3.47), CBLB (2.34), TSC1 (2.1), MAPK8 (2.02), CRK (2.0), AKT3 (2.71), and SHC4 ( 2.44) were upregulated.

The other reported proto-oncogenes in Rb tumors such as MYCN and its target gene, MDM2, and RXRG were observed to be upregulated in the cohort.[18],[45] In this study, MYCN is highly upregulated in all tumors as confirmed by several other studies. MDM2 and its transcriptional activator, RXRG which are notably involved in P53 inactivation are marginally up-regulated in two cases and unregulated in other tumors. Both CRX and THRB which are crucial for cone cell differentiation[46],[47] were downregulated in six cases. LHX1 (LIM homeobox gene, horizontal cell marker), which was shown to be present in Rb cell of origin (horizontal cells) in mouse models of Rb[48] was observed to be downregulated by 2.5-fold in the human Rb samples. This hints at a ubiquitous progenitor gene signature within the Rb cells that suppresses retinal differentiation, which is concordant with the findings by McEvoy et al.[49]

Cell senescence is an important mechanism crucial in aging and cancer that causes irreversible differentiation to cells preparing them for programmed cell death. However, in cancers, it has been observed that a variety of senescence-related genes are repressed or expressed. This can either be as a cancer-inhibiting or promoting strategy which has been debated by several groups.[50],[51] Senescence markers such as TOP2B, ARF1, and P53, which have been well explored in several tumors, have found to be upregulated in the cohort, which indicate that there is an intrinsic mechanism to suppress senescence within the tumor in spite of these genes getting activated to control tumor progression. This could be explained by the down-regulation of other senescence related genes such as-SLIT2, CCNA1, PTEN, and MIF. These genes were downregulated to a larger extent in HRF (P = 0.2) and WD cases (P = 0.3). Senescence in Rb is a relatively less explored phenomenon and we believe that this can be translated into a targeted therapy to induce tumor differentiation in cases where there is high cellular activity and proliferation.[52]

Limitations

In many tumors, there is vast amount of gene expression data available in databases, but in Rb, only a couple of reports are available. In one report, they have compared the expression of genes in tumors against adult retina[44] in the other, they have compared against normal appearing retina in the Rb eye[45],[46] Due to the nonavailability of age-matched human infant/juvenile retina, this study attempted to evaluate gene expression profiles of human Rb tumors compared to the retina collected from nontumor calotte of eyeball diagnosed for Rb.


  Conclusions Top


In summary, fetal neural stem cell signaling is highly upregulated in human Rb tumors. Notch signaling is marginally upregulated and other stem cell self-renewal pathways such as Wnt, SHH were downregulated. Nodal signaling is also deregulated in these tumors. Genes such as CBLB, MAPK 8, and ACVR1C can be used as potential biomarkers in this tumor to prognosticate cases with or without HRFs and differentiation of the tumors.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Burkhart DL, Sage J. Cellular mechanisms of tumour suppression by the retinoblastoma gene. Nat Rev Cancer 2008;8:671-82.  Back to cited text no. 1
    
2.
Kondo T, Setoguchi T, Taga T. Persistence of a small subpopulation of cancer stem-like cells in the C6 glioma cell line. Proc Natl Acad Sci U S A 2004;101:781-6.  Back to cited text no. 2
    
3.
Collins AT, Berry PA, Hyde C, Stower MJ, Maitland NJ. Prospective identification of tumorigenic prostate cancer stem cells. Cancer Res 2005;65:10946-51.  Back to cited text no. 3
    
4.
Al-Hajj M, Clarke MF. Self-renewal and solid tumor stem cells. Oncogene 2004;23:7274-82.  Back to cited text no. 4
    
5.
Al-Hajj M, Wicha MS, Benito-Hernandez A, Morrison SJ, Clarke MF. Prospective identification of tumorigenic breast cancer cells. Proc Natl Acad Sci U S A 2003;100:3983-8.  Back to cited text no. 5
    
6.
Balla MM, Vemuganti GK, Kannabiran C, Honavar SG, Murthy R. Phenotypic characterization of retinoblastoma for the presence of putative cancer stem-like cell markers by flow cytometry. Invest Ophthalmol Vis Sci 2009;50:1506-14.  Back to cited text no. 6
    
7.
Bapat SA, Mali AM, Koppikar CB, Kurrey NK. Stem and progenitor-like cells contribute to the aggressive behavior of human epithelial ovarian cancer. Cancer Res 2005;65:3025-9.  Back to cited text no. 7
    
8.
Ben-Porath I, Thomson MW, Carey VJ, Ge R, Bell GW, Regev A, et al. An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors. Nat Genet 2008;40:499-507.  Back to cited text no. 8
    
9.
Bonnet D, Dick JE. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat Med 1997;3:730-7.  Back to cited text no. 9
    
10.
Fang D, Nguyen TK, Leishear K, Finko R, Kulp AN, Hotz S, et al. Atumorigenic subpopulation with stem cell properties in melanomas. Cancer Res 2005;65:9328-37.  Back to cited text no. 10
    
11.
Kusumbe AP, Mali AM, Bapat SA. CD133-expressing stem cells associated with ovarian metastases establish an endothelial hierarchy and contribute to tumor vasculature. Stem Cells 2009;27:498-508.  Back to cited text no. 11
    
12.
Krishnakumar S, Mallikarjuna K, Desai N, Muthialu A, Venkatesan N, Sundaram A, et al. Multidrug resistant proteins: P-glycoprotein and lung resistance protein expression in retinoblastoma. Br J Ophthalmol 2004;88:1521-6.  Back to cited text no. 12
    
13.
Mohan A, Kandalam M, Ramkumar HL, Gopal L, Krishnakumar S. Stem cell markers: ABCG2 and MCM2 expression in retinoblastoma. Br J Ophthalmol 2006;90:889-93.  Back to cited text no. 13
    
14.
Seigel GM, Campbell LM, Narayan M, Gonzalez-Fernandez F. Cancer stem cell characteristics in retinoblastoma. Mol Vis 2005;11:729-37.  Back to cited text no. 14
    
15.
Xu XL, Fang Y, Lee TC, Forrest D, Gregory-Evans C, Almeida D, et al. Retinoblastoma has properties of a cone precursor tumor and depends upon cone-specific MDM2 signaling. Cell 2009;137:1018-31.  Back to cited text no. 15
    
16.
Zhong X, Li Y, Peng F, Huang B, Lin J, Zhang W, et al. Identification of tumorigenic retinal stem-like cells in human solid retinoblastomas. Int J Cancer 2007;121:2125-31.  Back to cited text no. 16
    
17.
Nair RM, Balla MM, Khan I, Kalathur RK, Kondaiah P, Vemuganti GK, et al. In vitro characterization of CD133lo cancer stem cells in retinoblastoma Y79 cell line. BMC Cancer 2017;17:779.  Back to cited text no. 17
    
18.
Seigel GM, Hackam AS, Ganguly A, Mandell LM, Gonzalez-Fernandez F. Human embryonic and neuronal stem cell markers in retinoblastoma. Mol Vis 2007;13:823-32.  Back to cited text no. 18
    
19.
Silva AK, Yi H, Hayes SH, Seigel GM, Hackam AS. Lithium chloride regulates the proliferation of stem-like cells in retinoblastoma cell lines: A potential role for the canonical Wnt signaling pathway. Mol Vis 2010;16:36-45.  Back to cited text no. 19
    
20.
Rossi DJ, Weissman IL. Pten, tumorigenesis, and stem cell self-renewal. Cell 2006;125:229-31.  Back to cited text no. 20
    
21.
Kolligs FT, Bommer G, Göke B. Wnt/beta-catenin/tcf signaling: A critical pathway in gastrointestinal tumorigenesis. Digestion 2002;66:131-44.  Back to cited text no. 21
    
22.
Hopfer O, Zwahlen D, Fey MF, Aebi S. The notch pathway in ovarian carcinomas and adenomas. Br J Cancer 2005;93:709-18.  Back to cited text no. 22
    
23.
Katano M. Hedgehog signaling pathway as a therapeutic target in breast cancer. Cancer Lett 2005;227:99-104.  Back to cited text no. 23
    
24.
Liu S, Dontu G, Mantle ID, Patel S, Ahn NS, Jackson KW, et al. Hedgehog signaling and Bmi-1 regulate self-renewal of normal and malignant human mammary stem cells. Cancer Res 2006;66:6063-71.  Back to cited text no. 24
    
25.
Dean M, Fojo T, Bates S. Tumour stem cells and drug resistance. Nat Rev Cancer 2005;5:275-84.  Back to cited text no. 25
    
26.
Chau KY, Manfioletti G, Cheung-Chau KW, Fusco A, Dhomen N, Sowden JC, et al. Derepression of HMGA2 gene expression in retinoblastoma is associated with cell proliferation. Mol Med 2003;9:154-65.  Back to cited text no. 26
    
27.
Venkatesan N, Kandalam M, Pasricha G, Sumantran V, Manfioletti G, Ono SJ, et al. Expression of high mobility group A2 protein in retinoblastoma and its association with clinicopathologic features. J Pediatr Hematol Oncol 2009;31:209-14.  Back to cited text no. 27
    
28.
Kyritsis AP, Tsokos M, Triche TJ, Chader GJ. Retinoblastoma – Origin from a primitive neuroectodermal cell? Nature 1984;307:471-3.  Back to cited text no. 28
    
29.
He S, Nakada D, Morrison SJ. Mechanisms of stem cell self-renewal. Annu Rev Cell Dev Biol 2009;25:377-406.  Back to cited text no. 29
    
30.
Molofsky AV, He S, Bydon M, Morrison SJ, Pardal R. Bmi-1 promotes neural stem cell self-renewal and neural development but not mouse growth and survival by repressing the p16Ink4a and p19Arf senescence pathways. Genes Dev 2005;19:1432-7.  Back to cited text no. 30
    
31.
Lord-Grignon J, Abdouh M, Bernier G. Identification of genes expressed in retinal progenitor/stem cell colonies isolated from the ocular ciliary body of adult mice. Gene Expr Patterns 2006;6:992-9.  Back to cited text no. 31
    
32.
Nishino J, Kim I, Chada K, Morrison SJ. Hmga2 promotes neural stem cell self-renewal in young but not old mice by reducing p16Ink4a and p19Arf expression. Cell 2008;135:227-39.  Back to cited text no. 32
    
33.
Fedele M, Battista S, Kenyon L, Baldassarre G, Fidanza V, Klein-Szanto AJ, et al. Overexpression of the HMGA2 gene in transgenic mice leads to the onset of pituitary adenomas. Oncogene 2002;21:3190-8.  Back to cited text no. 33
    
34.
Finelli P, Pierantoni GM, Giardino D, Losa M, Rodeschini O, Fedele M, et al. The high mobility group A2 gene is amplified and overexpressed in human prolactinomas. Cancer Res 2002;62:2398-405.  Back to cited text no. 34
    
35.
Hisaoka M, Sheng WQ, Tanaka A, Hashimoto H. HMGIC alterations in smooth muscle tumors of soft tissues and other sites. Cancer Genet Cytogenet 2002;138:50-5.  Back to cited text no. 35
    
36.
Hunter DS, Klotzbücher M, Kugoh H, Cai SL, Mullen JP, Manfioletti G, et al. Aberrant expression of HMGA2 in uterine leiomyoma associated with loss of TSC2 tumor suppressor gene function. Cancer Res 2002;62:3766-72.  Back to cited text no. 36
    
37.
Quade BJ, Weremowicz S, Neskey DM, Vanni R, Ladd C, Dal Cin P, et al. Fusion transcripts involving HMGA2 are not a common molecular mechanism in uterine leiomyomata with rearrangements in 12q15. Cancer Res 2003;63:1351-8.  Back to cited text no. 37
    
38.
Van Dorpe J, Dal Cin P, Weremowicz S, Van Leuven F, de Wever I, Van den Berghe H, et al. Translocation of the HMGI-C (HMGA2) gene in a benign mesenchymoma (chondrolipoangioma). Virchows Arch 2002;440:485-90.  Back to cited text no. 38
    
39.
Cerignoli F, Ambrosi C, Mellone M, Assimi I, di Marcotullio L, Gulino A, et al. HMGA molecules in neuroblastic tumors. Ann N Y Acad Sci 2004;1028:122-32.  Back to cited text no. 39
    
40.
Mu G, Liu H, Zhou F, Xu X, Jiang H, Wang Y, et al. Correlation of overexpression of HMGA1 and HMGA2 with poor tumor differentiation, invasion, and proliferation associated with let-7 down-regulation in retinoblastomas. Hum Pathol 2010;41:493-502.  Back to cited text no. 40
    
41.
Xu L, Zhang Y, Qu X, Che X, Guo T, Cai Y, et al. E3 ubiquitin ligase cbl-b prevents tumor metastasis by maintaining the epithelial phenotype in multiple drug-resistant gastric and breast cancer cells. Neoplasia 2017;19:374-82.  Back to cited text no. 41
    
42.
Bubici C, Papa S. JNK signalling in cancer: In need of new, smarter therapeutic targets. Br J Pharmacol 2014;171:24-37.  Back to cited text no. 42
    
43.
Kapatai G, Brundler MA, Jenkinson H, Kearns P, Parulekar M, Peet AC, et al. Gene expression profiling identifies different sub-types of retinoblastoma. Br J Cancer 2013;109:512-25.  Back to cited text no. 43
    
44.
Chakraborty S, Khare S, Dorairaj SK, Prabhakaran VC, Prakash DR, Kumar A, et al. Identification of genes associated with tumorigenesis of retinoblastoma by microarray analysis. Genomics 2007;90:344-53.  Back to cited text no. 44
    
45.
Ganguly A, Shields CL. Differential gene expression profile of retinoblastoma compared to normal retina. Mol Vis 2010;16:1292-303.  Back to cited text no. 45
    
46.
Ajioka I, Martins RA, Bayazitov IT, Donovan S, Johnson DA, Frase S, et al. Differentiated horizontal interneurons clonally expand to form metastatic retinoblastoma in mice. Cell 2007;131:378-90.  Back to cited text no. 46
    
47.
Bradford RL, Wang C, Zack DJ, Adler R. Roles of cell-intrinsic and microenvironmental factors in photoreceptor cell differentiation. Dev Biol 2005;286:31-45.  Back to cited text no. 47
    
48.
Laurie NA, Donovan SL, Shih CS, Zhang J, Mills N, Fuller C, et al. Inactivation of the p53 pathway in retinoblastoma. Nature 2006;444:61-6.  Back to cited text no. 48
    
49.
McEvoy J, Flores-Otero J, Zhang J, Nemeth K, Brennan R, Bradley C, et al. Coexpression of normally incompatible developmental pathways in retinoblastoma genesis. Cancer Cell 2011;20:260-75.  Back to cited text no. 49
    
50.
Rodier F, Campisi J. Four faces of cellular senescence. J Cell Biol 2011;192:547-56.  Back to cited text no. 50
    
51.
Lu WY, Bird TG, Boulter L, Tsuchiya A, Cole AM, Hay T, et al. Hepatic progenitor cells of biliary origin with liver repopulation capacity. Nat Cell Biol 2015;17:971-83.  Back to cited text no. 51
    
52.
Pérez-Mancera PA, Young AR, Narita M. Inside and out: The activities of senescence in cancer. Nat Rev Cancer 2014;14:547-58.  Back to cited text no. 52
    


    Figures

  [Figure 1], [Figure 2], [Figure 3], [Figure 4]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Materials and Me...
Results
Discussion
Conclusions
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed104    
    Printed0    
    Emailed0    
    PDF Downloaded22    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]