|Year : 2018 | Volume
| Issue : 2 | Page : 45-54
Commonly expressed genes among cancer stem cells induced from hiPSCs and Obtained from cancer tissues or cell lines
Akimasa Seno1, Masaharu Seno2
1 Laboratory of Nano-Biotechnology, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama, Japan; Okayama University Research Laboratory for Stem Cell Engineering in Detroit, Integrative Biosciences Center, Wayne State University, Detroit, MI, USA
2 Laboratory of Nano-Biotechnology, Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, Okayama, Japan; Okayama University Research Laboratory for Stem Cell Engineering in Detroit, Integrative Biosciences Center, Wayne State University, Detroit, MI, USA, Japan
|Date of Web Publication||4-Feb-2019|
Dr. Masaharu Seno
Room 460, Bldg Eng-6, Tsushima-Naka 3-1-1, Kita-Ku, Okayama 700-8530
Source of Support: None, Conflict of Interest: None
Introduction: Cancer is one of the serious health problems in worldwide. For the development of radical cancer treatment, cancer stem cells (CSCs) are getting an issue of focus. CSCs are thought to be resistant to chemotherapy and cause metastasis. Although it is very important to understand their characters in detail, the knowledge so far is not sufficient due to their low ratio in tumor tissues. Subjects and Methods: We have induced CSCs from induced pluripotent stem cells (iPSCs) culturing in the conditioned media from cancer-derived cells without introducing genes or mutations. These induced CSCs actually have CSCs-like properties of self-renewal, differentiation potential, and tumorigenicity. In this study, their gene expression data are compared with more than 1000 sets of data from normal stem cells, our CSCs, CSCs obtained from cancer tissues or cell lines and cancer cells, which obtained from Gene Expression Omnibus. Results: Although there were no known cancer stem cell markers which can distinguish CSCs from other cell types, clustering with spherical self-organizing map revealed the expression of NTNG1, ABLIM2, DNM3, EDN1, XLOC_001990, and ISY1-RAB43 are significantly high in CSCs. Conclusion: Their expression should help to find CSCs as the markers in the future. Simultaneously, the function of these genes should become important to be clarified.
Keywords: Actin-binding LIM protein 2, cancer stem cell, dynamin 3, endothelin 1, netrin-G1, spherical self-organizing map
|How to cite this article:|
Seno A, Seno M. Commonly expressed genes among cancer stem cells induced from hiPSCs and Obtained from cancer tissues or cell lines. Tumor Microenviron 2018;1:45-54
|How to cite this URL:|
Seno A, Seno M. Commonly expressed genes among cancer stem cells induced from hiPSCs and Obtained from cancer tissues or cell lines. Tumor Microenviron [serial online] 2018 [cited 2021 Nov 28];1:45-54. Available from: http://www.TMEResearch.org/text.asp?2018/1/2/45/251573
| Introduction|| |
Although cancer stem cells (CSCs) and their importance are reported in many papers,,,,,,,, their analysis is not sufficiently advanced because of their very small population in cancer tissues and difference depending on patients. During the decade, induced pluripotent stem cells (iPSCs) have been made,,,,, and CSCs could be induced from them without gene manipulation.,, In this report, induced CSCs and CSCs found in cancer cell tissues or cancer cell lines were compared with their origins to figure out their differences and similarities using a clustering procedure of spherical self-organizing map (sSOM).,,,, This would provide a novel method to understand the relationships of CSCs from different origin.
| Subjects and Methods|| |
Acquisition of data
All data were obtained from Gene Expression Omnibus () in April 2017. Agilent microarray data (Platform GPL16699 or GPL17077) analyzed for human iPSCs or cancer was chosen because the same platform was used for OCC-CSCs (GSE83880). A total of 1170 gene expression data sets, in each of which consisted from 50,170 probes, were obtained from different cells. To eliminate the data sets which are not related to stem cells or cancer and to make comparison clearer, 1036 data sets from the cells were reassigned into four groups: stem cell, OCC-CSC, CSC, and cancer [Table 1]. With all data sets from the four groups, digitalized intensity data were normalized with Bioconductor, package agilp (ver. 3.6.0, https://bioconductor.org/packages/release/bioc/html/agilp.html) as directed by maintainer's manual. Briefly, the raw intensity data were mapped to the same ID with IDswop. Mapped data were trimmed with equalizer to include only the set of probes that are common to all data. Then, a baseline file was generated by Baseline, and a set of probe expression data files were normalized by AALoess. Through these procedures, 50,032 probes for 40,105 genes were assessed for the experiments. Expressions of housekeeping genes (ACTB, ATP5F1, GAPDH, GAPDHS, GUSB, HPRT1, PGK1, PPIA, PPIAL4A, RPLP0, PRLP1, RPLP2, RPS18, TBP, TBPL1, TBPL2, TFRC, and YWHAZ) were tested and confirmed expressed equally among all groups [Supplementary Figure 1 [Additional file 1]].
|Table 1: Cell types obtained from gene expressionomnibus and result of grouping|
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Analyses by sphere self-organizing map
To find genes exhibiting significant expression among each group, averages of normalized expression value from each probe were compared. If it is maximum or minimum among the four groups and the difference from other groups is more than 1, the corresponding gene was nominated as a candidate marker gene of the group. The probes found following this rule were used to map the nodes depicted by the sSOM software “Blossom” (SOM Japan; https://bioconductor.org/packages/release/bioc/html/agilp.html). A virtual probe corresponding to an ideal marker gene has been set maximum or minimum value as normalized expression value in one representative group and set the opposite values in the rest of the groups. Nonsignificant distance (NSD) was calculated between each node on the sphere drawn by sSOM under the default parameters.
The significance of gene expression of four groups was tested by one-way ANOVA and Tukey-Kramer method. The critical range (qα, α = 0.05) of four groups consisted with 1,036 samples is 3.64. qs values are shown in figures when it is >qα.
| Results|| |
Expressions of known cancer stem cell markers are not always useful to specify cancer stem cells
To evaluate the expression of known CSC marker genes among our OCC-CSCs or other CSCs, their expression levels were analyzed first. A total of 77 probes corresponding to known CSC marker genes were included in the normalized data [Supplementary Figure 2[Additional file 2]] and the representative data are shown in [Figure 1]. Since the CSCs analyzed by Agilent microarray were limited to those derived from ovarian cancer tissue or cell line, we had to compare our OCC-CSCs with the CSCs. In spite of limited origin, the CSCs did not highly express ovarian CSC markers (CD44, CD117, ALDH, CD133, or CD24) or ovarian cancer markers (CA-125, beta-hCG, AFP, CEA, or SCC). Some CSC marker genes such as CDKN2A, ICAM1, and ITGB3 were found highly expressed in this CSC group. This implies these cells were sharing CSC properties and they could be considered as representatives of common CSCs. On the other hand, the gene expression of CD24, KIT, NANOG, POU5F1, and SOX2 was higher in OCC-CSC group than that in the other CSC group and was almost equivalent to that of stem cell group. KIT, KLF4, NCAM1, and some other genes were similarly expressed in OCC-CSC and CSC groups; however, there was no significant difference between the groups of stem cells and OCC-CSC or of CSC and cancer. CD44 and KLF4 are similarly expressed in all groups other than stem cell group. Although some combinations of the gene expression could evoke malignancy of cancer cells, no known “CSC marker” gene commonly appeared expressed in OCC-CSCs and other CSCs. Therefore, evaluation of the gene expression to find which gene is truly expressed as CSC marker gene, which should commonly be expressed in OCC-CSC and CSC in the level higher or lower than in the other cell groups.
|Figure 1: Gene expression of typical cancer stem cell marker genes. The expression of 16 typical cancer stem cell marker genes is shown as Box-and-whisker plot in each group. If the P value in one-way ANOVA is <0.05, all qs values are indicated when it is more than qα|
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Spherical self-organizing map revealed marker genes for each group
Normalized average expression values from the probes in each group were compared. When it is higher or lower than that of other groups and the difference is more than 1, it was chosen as a candidate probe for a marker representing the group. After this process, they were analyzed by sSOM with a virtually defined ideal probe (IP), which was set with the highest or the lowest expression value in one of the groups. Each IP is shown as a red dot at the center of each sphere [Figure 2]a. Expression similarity between probes is shown as a NSD in [Supplementary Table 1[Additional file 3]] and expressions of five probes which were assigned at the shortest NSD from the IP were shown in [Figure 2]b and [Figure 2]c [summarized in [Table 2]]. Compared with the expression of the genes, they should be marker genes for each group although no known CSC marker gene was listed in OCC-CSC or CSC group.
Distinguishable cancer stem cell marker genes nominated with spherical self-organizing map
Although the expression of known CSC marker genes does not always distinguish CSCs from cancer cells or stem cells, OCC-CSCs and CSCs focused in this study have been characterized as CSCs.,,, Then, a simple question comes up, “Are there any genes commonly expressed among these CSCs?” To identify genes commonly expressed, their gene expression profiles were reevaluated with modified conditions. Namely, OCC-CSC or CSC group had the genes with the first or the second highest/lowest expression value and as searched for marker genes, there was a difference more than one between the averages of the second and the third groups, when the normalized expression values were compared. As the result, 49 positively expressed genes and 17 negatively expressed genes were nominated in all CSCs [Supplementary Table 2[Additional file 4]]. Especially, eight genes were assigned at a NSD shorter than two, while all other genes were assigned farther apart at more than 2.4. Among the genes, significance is assessed with Tukey-Kramer method. As the result, the expression of six genes for NTNG1, ABLIM2, DNM3, EDN1, XLOC_001990, and ISY1-RAB43 was significant in OCC-CSC and CSC when compared with other two groups [Table 3] and [Figure 3].
|Figure 3: Expression data of commonly expressed genes among cancer stem cells. Their expression is shown as Box-and-whisker plot in each group. All the P values in one-way ANOVA are <0.05 and qs values are indicated when it is more than qα|
Click here to view
| Discussion|| |
The expression of 40,105 genes in total in 1036 data sets from different cells was compared in this study and six genes, NTNG1, ABLIM2, DNM3, EDN1, XLOC_001990, and ISY1-RAB43 were successfully nominated as the candidates of CSC markers. Among these six genes, little is known about the characteristic expression of the four genes, NTNG1, ABLIM2, DNM3, and EDN1, in cancer or stem cells while the information of XLOC_001990 and ISY1-RAB43 is still obscure.
NTNG1 encodes netrin-G1. This protein was found in P21 mouse cerebellum and thought to be involved in nervous system development. The mutated gene was found in the patient of Rett syndrome. Regarding its relationship with cancer, although it could be a prognostic gene,, nothing was revealed yet. ABLIM2, actin-binding LIM protein 2, is only known to bind to F-actin and enhance serum response factor activation. This gene might be involved in metastasis of some cancer cells. Dynamin 3, encoded in DNM3, is a member of guanosine triphosphate-binding protein. When methylation and expression of DNM3 in hepatocellular carcinoma (HCC) cells from 48 patients were compared, the decreased expression of DNM3 was found to correlate with the poor prognosis of the disease. Overexpression of DNM3 was reported to inhibit cell cycle and induce cellular apoptosis in HCC. In breast cancer lines, shRNA of DNM3 inhibited cell motility was inhibited. These reports may imply the function of this gene should depend on the type of cancer. In this paper, the expression of DNM3 was commonly found in all CSCs. OCC-CSCs were induced from human iPSCs in the presence of the conditioned medium of cancer cells derived from various cancer cell lines including both HCC and breast cancer-derived cells. The CSCs were obtained from ovarian cancer cell line or tissue., The function of DNM3 in CSC may not depend on the tissue types of cancer although the function is not clear, and further study is needed.
EDN1 is well-studied gene both in cancer and stem cells among the four genes. This gene codes endothelin-1 (ET-1), which is generally produced by vascular endothelial cells and is considered to be related with the cause of hypertension. The transgenic mice carrying hepatitis B virus antigen are reported to develop HCC with upregulation of EDN1, and, overexpression of EDN1 causes HCC in zebrafish. Moreover, hence, the blockade of ET-1 receptor is reported effective on chemotherapy-resistant ovarian cancer, melanoma, and myeloma.,, Not only cancer cells ET-1 but also has various effects on mesenchymal stem cells. Pourjafar et al. showed ET-1 effect on survival, angiogenesis, and migration of those cells in vitro, of which properties are also important in CSCs. Taking our results with these reports into consideration, ET-1 should exert a strong effect on CSCs derived not only from iPSCs but also from cancer tissues. The effect of ET-1 on CSCs should be further studied in vitro in the near future.
The authors would like to thank Dr. Heizo Tokutaka for developing sSOM software. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus (GSE113533).
Financial support and sponsorship
This study was financially supported by the followings:
- JSPS Grant-in-Aid for Scientific Research (A) No. 25242045 (MS)
- JSPS Grant-in-Aid for Challenging Exploratory Research No. 26640079 (MS)
- Long-Range-Research-Initiative from Japan Chemical Industry Association.
Conflicts of interest
There are no conflicts of interest.
| References|| |
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.
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.
Singh SK, Clarke ID, Terasaki M, Bonn VE, Hawkins C, Squire J, et al.
Identification of a cancer stem cell in human brain tumors. Cancer Res 2003;63:5821-8.
Richardson GD, Robson CN, Lang SH, Neal DE, Maitland NJ, Collins AT, et al.
CD133, a novel marker for human prostatic epithelial stem cells. J Cell Sci 2004;117:3539-45.
Xin L, Lawson DA, Witte ON. The sca-1 cell surface marker enriches for a prostate-regenerating cell subpopulation that can initiate prostate tumorigenesis. Proc Natl Acad Sci U S A 2005;102:6942-7.
Ricci-Vitiani L, Lombardi DG, Pilozzi E, Biffoni M, Todaro M, Peschle C, et al.
Identification and expansion of human colon-cancer-initiating cells. Nature 2007;445:111-5.
Prince ME, Sivanandan R, Kaczorowski A, Wolf GT, Kaplan MJ, Dalerba P, et al.
Identification of a subpopulation of cells with cancer stem cell properties in head and neck squamous cell carcinoma. Proc Natl Acad Sci U S A 2007;104:973-8.
Li C, Heidt DG, Dalerba P, Burant CF, Zhang L, Adsay V, et al.
Identification of pancreatic cancer stem cells. Cancer Res 2007;67:1030-7.
Takahashi K, Yamanaka S. Induction of pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. Cell 2006;126:663-76.
Takahashi K, Tanabe K, Ohnuki M, Narita M, Ichisaka T, Tomoda K, et al.
Induction of pluripotent stem cells from adult human fibroblasts by defined factors. Cell 2007;131:861-72.
Espejel S, Roll GR, McLaughlin KJ, Lee AY, Zhang JY, Laird DJ, et al.
Induced pluripotent stem cell-derived hepatocytes have the functional and proliferative capabilities needed for liver regeneration in mice. J Clin Invest 2010;120:3120-6.
Mae SI, Shono A, Shiota F, Yasuno T, Kajiwara M, Gotoda-Nishimura N, et al.
Monitoring and robust induction of nephrogenic intermediate mesoderm from human pluripotent stem cells. Nat Commun 2013;4:1367.
Fukusumi H, Shofuda T, Bamba Y, Yamamoto A, Kanematsu D, Handa Y, et al.
Establishment of human neural progenitor cells from human induced pluripotent stem cells with diverse tissue origins. Stem Cells Int 2016;2016:7235757.
Morizane A, Kikuchi T, Hayashi T, Mizuma H, Takara S, Doi H, et al.
MHC matching improves engraftment of iPSC-derived neurons in non-human primates. Nat Commun 2017;8:385.
Chen L, Kasai T, Li Y, Sugii Y, Jin G, Okada M, et al.
A model of cancer stem cells derived from mouse induced pluripotent stem cells. PLoS One 2012;7:e33544.
Nair N, Calle AS, Zahra MH, Prieto-Vila M, Oo AK, Hurley L, et al.
A cancer stem cell model as the point of origin of cancer-associated fibroblasts in tumor microenvironment. Sci Rep 2017;7:6838.
Calle AS, Nair N, Oo AK, Prieto-Vila M, Koga M, Khayrani AC, et al.
A new PDAC mouse model originated from iPSCs-converted pancreatic cancer stem cells (CSCcm). Am J Cancer Res 2016;6:2799-815.
Seno A, Kasai T, Ikeda M, Vaidyanath A, Masuda J, Mizutani A, et al.
Characterization of gene expression patterns among artificially developed cancer stem cells using spherical self-organizing map. Cancer Inform 2016;15:163-78.
Ikeda M, Kumon K, Omoto K, Sugii Y, Mizutani A, Vaidyanath A. Spherical self-organizing map detects MYBL 1 as candidate gene for triple-negative breast cancer. Neurosci Biomed Eng 2015;3:94-101.
Yuh S, Takayuki K, Takayuki O, Masashi I, Heizo T, Masaharu S. Clustering genes, tissues, cells and bioactive chemicals by Sphere SOM. In: Igadwa MJ, editor. Self Organizing Maps – Applications and Novel Algorithm Design. United Kingdom: InTech; 2011. p. 371-86.
Sugii Y, Kasai T, Ikeda M, Vaidyanath A, Kumon K, Mizutani A, et al.
A C procedure to identify cell surface markers through a spherical self-organizing map applied to DNA microarray analysis. Biomark Cancer 2016;8:17-23.
Tuoya, Sugii Y, Satoh H, Yu D, Matsuura Y, Tokutaka H, et al.
Spherical self-organizing map as a helpful tool to identify category-specific cell surface markers. Biochem Biophys Res Commun 2008;376:414-8.
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, et al.
Bioconductor: Open software development for computational biology and bioinformatics. Genome Biol 2004;5:R80.
Huber W, Carey VJ, Gentleman R, Anders S, Carlson M, Carvalho BS, et al.
Orchestrating high-throughput genomic analysis with bioconductor. Nat Methods 2015;12:115-21.
Chain B. Agilp: Agilent Expression Array Processing Package, R Package version 3.2.0; 2012.
Ishiguro T, Sato A, Ohata H, Ikarashi Y, Takahashi RU, Ochiya T, et al.
Establishment and characterization of an in vitro
model of ovarian cancer stem-like cells with an enhanced proliferative capacity. Cancer Res 2016;76:150-60.
Vathipadiekal V, Saxena D, Mok SC, Hauschka PV, Ozbun L, Birrer MJ, et al.
Identification of a potential ovarian cancer stem cell gene expression profile from advanced stage papillary serous ovarian cancer. PLoS One 2012;7:e29079.
Nakashiba T, Ikeda T, Nishimura S, Tashiro K, Honjo T, Culotti JG, et al.
Netrin-G1: A novel glycosyl phosphatidylinositol-linked mammalian netrin that is functionally divergent from classical netrins. J Neurosci 2000;20:6540-50.
Ricciardi S, Ungaro F, Hambrock M, Rademacher N, Stefanelli G, Brambilla D, et al.
CDKL5 ensures excitatory synapse stability by reinforcing NGL-1-PSD95 interaction in the postsynaptic compartment and is impaired in patient iPSC-derived neurons. Nat Cell Biol 2012;14:911-23.
Sho S, Court CM, Winograd P, Russell MM, Tomlinson JS. A prognostic mutation panel for predicting cancer recurrence in stages II and III colorectal cancer. J Surg Oncol 2017;116:996-1004.
Andrew AS, Baron JA, Butterly LF, Suriawinata AA, Tsongalis GJ, Robinson CM, et al.
Hyper-methylated loci persisting from sessile serrated polyps to serrated cancers. Int J Mol Sci 2017;18. pii: E535.
Barrientos T, Frank D, Kuwahara K, Bezprozvannaya S, Pipes GC, Bassel-Duby R, et al.
Two novel members of the ABLIM protein family, ABLIM-2 and -3, associate with STARS and directly bind F-actin. J Biol Chem 2007;282:8393-403.
Inokawa Y, Nomoto S, Hishida M, Hayashi M, Kanda M, Nishikawa Y, et al.
Dynamin 3: A new candidate tumor suppressor gene in hepatocellular carcinoma detected by triple combination array analysis. Onco Targets Ther 2013;6:1417-24.
Zhang Z, Chen C, Guo W, Zheng S, Sun Z, Geng X, et al.
DNM3 attenuates hepatocellular carcinoma growth by activating P53. Med Sci Monit 2016;22:197-205.
Marino N, Collins JW, Shen C, Caplen NJ, Merchant AS, Gökmen-Polar Y, et al.
Identification and validation of genes with expression patterns inverse to multiple metastasis suppressor genes in breast cancer cell lines. Clin Exp Metastasis 2014;31:771-86.
Kwon AY, Kim GI, Jeong JY, Song JY, Kwack KB, Lee C, et al.
VAV3 overexpressed in cancer stem cells is a poor prognostic indicator in ovarian cancer patients. Stem Cells Dev 2015;24:1521-35.
Yanagisawa M, Kurihara H, Kimura S, Tomobe Y, Kobayashi M, Mitsui Y, et al.
A novel potent vasoconstrictor peptide produced by vascular endothelial cells. Nature 1988;332:411-5.
Lu JW, Hsia Y, Yang WY, Lin YI, Li CC, Tsai TF, et al.
Identification of the common regulators for hepatocellular carcinoma induced by hepatitis B virus X antigen in a mouse model. Carcinogenesis 2012;33:209-19.
Lu JW, Liao CY, Yang WY, Lin YM, Jin SL, Wang HD, et al.
Overexpression of endothelin 1 triggers hepatocarcinogenesis in zebrafish and promotes cell proliferation and migration through the AKT pathway. PLoS One 2014;9:e85318.
Rosanò L, Cianfrocca R, Tocci P, Spinella F, Di Castro V, Caprara V, et al.
Endothelin A receptor/β-arrestin signaling to the wnt pathway renders ovarian cancer cells resistant to chemotherapy. Cancer Res 2014;74:7453-64.
Smith MP, Rowling EJ, Miskolczi Z, Ferguson J, Spoerri L, Haass NK, et al.
Targeting endothelin receptor signalling overcomes heterogeneity driven therapy failure. EMBO Mol Med 2017;9:1011-29.
Russignan A, Spina C, Tamassia N, Cassaro A, Rigo A, Bagnato A, et al.
Endothelin-1 receptor blockade as new possible therapeutic approach in multiple myeloma. Br J Haematol 2017;178:781-93.
Pourjafar M, Saidijam M, Mansouri K, Malih S, Ranjbar Nejad T, Shabab N, et al.
Cytoprotective effects of endothelin-1 on mesenchymal stem cells: An in vitro
study. Clin Exp Pharmacol Physiol 2016;43:769-76.
Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 2002;30:207-10.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3]