Advertisement

Stem Cell Reviews and Reports

, Volume 15, Issue 1, pp 23–34 | Cite as

Integrative Analysis of CD133 mRNA in Human Cancers Based on Data Mining

  • Gui-Min Wen
  • Fei-Fei Mou
  • Wei Hou
  • Dan Wang
  • Pu XiaEmail author
Article
  • 250 Downloads

Abstract

CD133 is a wildly used cancer stem cell marker. The purpose of this study was to explore the significance of CD133 mRNA in human cancers mainly based on The Cancer Genome Atlas (TCGA) database. Bioinformatic analyses were done by using public repositories, including BioGPS, SAGE Genie tools, Oncomine analysis, Regulome Explorer, COSMIC analysis, and Kaplan-Meier Plotter. The main findings in this study were: 1) High CD133 mRNA was correlated with a benign survival rate of gastric cancer and lung cancer; 2) Transmembrane protein 125 (TMEM125) in bladder urothelial carcinoma and intercellular adhesion molecule 2 (ICAM2) in ovarian serous cystadenocarcinoma were closely related to CD133 expression; 3) The location and the topological structure of CD133 protein were not determined by its transcript variant in cancer cells; 4) CD38 and CD200 may be used as novel surface markers for solid cancers. However, the mechanism of these findings is not completely clear, further studies have to be performed in the future.

Keywords

CD133 TCGA Cancer Bioinformatic analyses Mechanism 

Notes

Funding

This study was supported by National Natural Scientific Foundation of China (No.81502558) and Talents Introduction Projects of Liaoning Medical University.

Compliance with Ethical Standards

Conflict of Interest

None.

Supplementary material

12015_2018_9865_MOESM1_ESM.xls (53 kb)
ESM 1 (XLS 53 kb)
12015_2018_9865_MOESM2_ESM.xls (102 kb)
ESM 2 (XLS 102 kb)
12015_2018_9865_MOESM3_ESM.docx (2.4 mb)
ESM 3 (DOCX 2421 kb)

References

  1. 1.
    Gutman, D. A., Cobb, J., Somanna, D., Park, Y., Wang, F., Kurc, T., et al. (2013). Cancer digital slide archive: An informatics resource to support integrated in silico analysis of TCGA pathology data. Journal of the American Medical Informatics Association, 20, 1091–1098.CrossRefGoogle Scholar
  2. 2.
    Joshi, A., De Smet, R., Marchal, K., Van de Peer, Y., & Michoel, T. (2009). Module networks revisited: Computational assessment and prioritization of model predictions. Bioinformatics, 25, 490–496.CrossRefGoogle Scholar
  3. 3.
    Gonzalez, G. H., Tahsin, T., Goodale, B. C., Greene, A. C., & Greene, C. S. (2016). Recent advances and emerging applications in text and data Mining for Biomedical Discovery. Briefings in Bioinformatics, 17, 33–42.CrossRefGoogle Scholar
  4. 4.
    Siegel, R. L., Miller, K. D., & Jemal, A. (2016). Cancer statistics, 2016. CA: a Cancer Journal for Clinicians, 66, 7–30.Google Scholar
  5. 5.
    Ghazanfar, S., & Yang, J. Y. (2016). Characterizing mutation-expression network relationships in multiple cancers. Computational Biology and Chemistry, 63, 73–82.CrossRefGoogle Scholar
  6. 6.
    Gao, P., Zhou, X., Wang, Z. N., Song, Y. X., Tong, L. L., Xu, Y. Y., et al. (2012). Which is a more accurate predictor in colorectal survival analysis? Nine data mining algorithms vs. the TNM staging system. PLoS One, 7, e42015.CrossRefGoogle Scholar
  7. 7.
    Berger, M. F., Levin, J. Z., Vijayendran, K., Sivachenko, A., Adiconis, X., Maguire, J., et al. (2010). Integrative analysis of the melanoma transcriptome. Genome Research, 20, 413–427.CrossRefGoogle Scholar
  8. 8.
    Takahashi, M., Matsuoka, Y., Sumide, K., Nakatsuka, R., Fujioka, T., Kohno, H., et al. (2014). CD133 is a positive marker for a distinct class of primitive human cord blood-derived CD34-negative hematopoietic stem cells. Leukemia, 28, 1308–1315.CrossRefGoogle Scholar
  9. 9.
    Roudi, R., Korourian, A., Shariftabrizi, A., & Madjd, Z. (2015). Differential expression of cancer stem cell markers ALDH1 and CD133 in various lung cancer subtypes. Cancer Investigation, 33, 294–302.CrossRefGoogle Scholar
  10. 10.
    Zhou, Q., Chen, A., Song, H., Tao, J., Yang, H., & Zuo, M. (2015). Prognostic value of cancer stem cell marker CD133 in ovarian cancer: A meta-analysis. International Journal of Clinical and Experimental Medicine, 8, 3080–3088.Google Scholar
  11. 11.
    Jing, F., Kim, H. J., Kim, C. H., Kim, Y. J., Lee, J. H., & Kim, H. R. (2015). Colon cancer stem cell markers CD44 and CD133 in patients with colorectal cancer and synchronous hepatic metastases. International Journal of Oncology, 46, 1582–1588.CrossRefGoogle Scholar
  12. 12.
    Li, J., Chen, J. N., Zeng, T. T., He, F., Chen, S. P., & Ma, S. (2016). CD133+ liver cancer stem cells resist interferon-gamma-induced autophagy. BMC Cancer, 16, 15.CrossRefGoogle Scholar
  13. 13.
    Marusyk, A., Almendro, V., & Polyak, K. (2012). Intra-tumor heterogeneity: A looking glass for cancer. Nature Reviews. Cancer, 12, 323–334.CrossRefGoogle Scholar
  14. 14.
    Weitzel, J. N., Blazer, K. R., MacDonald, D. J., Culver, J. O., & Offit, K. (2011). Genetics, Genomics and Cancer risk assessment: State of the art and future directions in the era of personalized medicine. CA: a Cancer Journal for Clinicians, 61, 327–359.Google Scholar
  15. 15.
    Avgustinova, A., & Benitah, S. A. (2016). The epigenetics of tumour initiation: Cancer stem cells and their chromatin. Current Opinion in Genetics & Development, 36, 8–15.CrossRefGoogle Scholar
  16. 16.
    Xia, P. (2014). Surface markers of cancer stem cells in solid tumors. Current Stem Cell Research & Therapy, 9, 102–111.CrossRefGoogle Scholar
  17. 17.
    Jaime-Pérez, J. C., Villarreal-Villarreal, C. D., Vázquez-Garza, E., Méndez-Ramírez, N., Salazar-Riojas, R., & Gómez-Almaguer, D. (2016). Flow cytometry data analysis of CD34+/CD133+ stem cells in bone marrow and peripheral blood and T, B, and NK cells after hematopoietic grafting. Data in Brief, 7, 1151–1155.CrossRefGoogle Scholar
  18. 18.
    Long, H., Xiang, T., Qi, W., Huang, J., Chen, J., He, L., et al. (2015). CD133+ ovarian cancer stem-like cells promote non-stem cancer cell metastasis via CCL5 induced epithelial-mesenchymal transition. Oncotarget, 6, 5846–5859.Google Scholar
  19. 19.
    Shah, M. M., & Landen, C. N. (2014). Ovarian cancer stem cells: Are they real and why are they important? Gynecologic Oncology, 132, 483–489.CrossRefGoogle Scholar
  20. 20.
    Bonome, T., Levine, D. A., Shih, J., Randonovich, M., Pise-Masison, C. A., Bogomolniy, F., et al. (2008). A gene signature predicting for survival in suboptimally debulked patients with ovarian cancer. Cancer Research, 68, 5478–5486.CrossRefGoogle Scholar
  21. 21.
    Yoshihara, K., Tajima, A., Adachi, S., Quan, J., Sekine, M., Kase, H., et al. (2011). Germline copy number variations in BRCA1-associated ovarian cancer patients. Genes, Chromosomes & Cancer, 50(2001), 167–177.CrossRefGoogle Scholar
  22. 22.
    Lin, C. H., Liu, C. H., Wen, C. H., Ko, P. L., & Chai, C. Y. (2015). Differential CD133 expression distinguishes malignant from benign papillary lesions of the breast. Virchows Archiv, 466, 177–184.CrossRefGoogle Scholar
  23. 23.
    Ohtsubo, I., Ajiki, T., Hori, Y., Murakami, S., Shimizu, K., Itoh, T., et al. (2012). Distinctive expression of CD133 between intraductal papillary neoplasms of the bile duct and bile duct adenocarcinomas. Hepatology Research, 42, 574–582.CrossRefGoogle Scholar
  24. 24.
    Shimizu, K., Itoh, T., Shimizu, M., Ku, Y., & Hori, Y. (2009). CD133 expression pattern distinguishes intraductal papillary mucinous neoplasms from ductal adenocarcinomas of the pancreas. Pancreas, 38, e207–e214.CrossRefGoogle Scholar
  25. 25.
    Fellay, J., Ge, D., Shianna, K. V., Colombo, S., Ledergerber, B., Cirulli, E. T., et al. (2009). Common genetic variation and the control of HIV-1 in humans. PLoS Genetics, 5, e1000791.CrossRefGoogle Scholar
  26. 26.
    Irvin, M. R., Wineinger, N. E., Rice, T. K., Pajewski, N. M., Kabagambe, E. K., Gu, C. C., et al. (2011). Genome-wide detection of allele specific copy number variation associated with insulin resistance in African Americans from the HyperGEN study. PLoS One, 6, e24052.CrossRefGoogle Scholar
  27. 27.
    Liu, S., Xie, L., Yue, J., Ma, T., Peng, C., Qiu, B., et al. (2016). Whole-exome sequencing identifies a novel homozygous frameshift mutation in the PROM1 gene as a causative mutation in two patients with sporadic retinitis pigmentosa. International Journal of Molecular Medicine, 37, 1528–1534.CrossRefGoogle Scholar
  28. 28.
    Pohl, A., El-Khoueiry, A., Yang, D., Zhang, W., Lurje, G., Ning, Y., et al. (2013). Pharmacogenetic profiling of CD133 is associated with response rate (RR) and progression-free survival (PFS) in patients with metastatic colorectal cancer (mCRC), treated with bevacizumab-based chemotherapy. The Pharmacogenomics Journal, 13, 173–180.CrossRefGoogle Scholar
  29. 29.
    Wang, Q., Liu, H., Xiong, H., Liu, Z., Wang, L. E., Qian, J., et al. (2015). Polymorphisms at the microRNA binding-site of the stem cell marker gene CD133 modify susceptibility to and survival of gastric cancer. Molecular Carcinogenesis, 54, 449–458.CrossRefGoogle Scholar
  30. 30.
    Cheng, M., Yang, L., Yang, R., Yang, X., Deng, J., Yu, B., et al. (2013). A microRNA-135a/b binding polymorphism in CD133 confers decreased risk and favorable prognosis of lung cancer in Chinese by reducing CD133 expression. Carcinogenesis, 34, 2292–2299.CrossRefGoogle Scholar
  31. 31.
    Liu, Y., Ren, S., Xie, L., Cui, C., Xing, Y., Liu, C., et al. (2015). Mutation of N-linked glycosylation at Asn548 in CD133 decreases its ability to promote hepatoma cell growth. Oncotarget, 6, 20650–20660.Google Scholar
  32. 32.
    Aravantinos, G., Isaakidou, A., Karantanos, T., Sioziou, A., Theodoropoulos, G. E., Pektasides, D., et al. (2015). Association of CD133 polymorphisms and response to bevacizumab in patients with metastatic colorectal cancer. Cancer Biomarkers, 15, 843–850.CrossRefGoogle Scholar
  33. 33.
    Schroeder, M. P., Gonzalez-Perez, A., & Lopez-Bigas, N. (2013). Visualizing multidimensional cancer genomics data. Genome Medicine, 5, 9.CrossRefGoogle Scholar
  34. 34.
    Fabregat, I., Malfettone, A., & Soukupova, J. (2016). New insights into the crossroads between emt and stemness in the context of cancer. Journal of Clinical Medicine, 5, E37.CrossRefGoogle Scholar
  35. 35.
    Koren, A., Rijavec, M., Kern, I., Sodja, E., Korosec, P., & Cufer, T. (2016). BMI1, ALDH1A1, and CD133 transcripts connect epithelial-mesenchymal transition to cancer stem cells in lung carcinoma. Stem Cells International, 2016, 9714315.CrossRefGoogle Scholar
  36. 36.
    Lee, S. O., Yang, X., Duan, S., Tsai, Y., Strojny, L. R., Keng, P., et al. (2016). IL-6 promotes growth and epithelial-mesenchymal transition of CD133+ cells of non-small cell lung cancer. Oncotarget, 7, 6626–6638.Google Scholar
  37. 37.
    Zhi, Y., Mou, Z., Chen, J., He, Y., Dong, H., Fu, X., et al. (2015). B7H1 expression and epithelial-to-mesenchymal transition phenotypes on colorectal cancer stem-like cells. PLoS One, 10, e0135528.CrossRefGoogle Scholar
  38. 38.
    Drachsler, M., Kleber, S., Mateos, A., Volk, K., Mohr, N., Chen, S., et al. (2016). CD95 maintains stem cell-like and non-classical EMT programs in primary human glioblastoma cells. Cell Death & Disease, 27, e2209.CrossRefGoogle Scholar
  39. 39.
    Lin, C. W., Lin, P. Y., & Yang, P. C. (2016). Noncoding RNAs in tumor epithelial-to-mesenchymal transition. Stem Cells International, 2016, 2732705.CrossRefGoogle Scholar
  40. 40.
    Tsukasa, K., Ding, Q., Miyazaki, Y., Matsubara, S., Natsugoe, S., & Takao, S. (2016). miR-30 family promotes migratory and invasive abilities in CD133+ pancreatic cancer stem-like cells. Human Cell, 29, 130–137.CrossRefGoogle Scholar
  41. 41.
    Howard, S., Deroo, T., Fujita, Y., & Itasaki, N. (2011). A positive role of cadherin in Wnt/β-catenin signalling during epithelial-mesenchymal transition. PLoS One, 6, e23899.CrossRefGoogle Scholar
  42. 42.
    Czyzewska, J., Guzińska-Ustymowicz, K., Ustymowicz, M., Pryczynicz, A., & Kemona, A. (2010). The expression of E-cadherincatenin complex in patients with advanced gastric cancer: Role in formation of metastasis. Folia Histochemica et Cytobiologica, 48, 37–45.CrossRefGoogle Scholar
  43. 43.
    Yoshii, T., Miyagi, Y., Nakamura, Y., Kobayashi, O., Kameda, Y., & Ohkawa, S. (2013). Pilot research for the correlation between the expression pattern of E-cadherin-β-catenin complex and lymph node metastasis in early gastric cancer. Tumori, 99, 234–238.CrossRefGoogle Scholar
  44. 44.
    Mak, A. B., Nixon, A. M. L., Kittanakom, S., Stewart, J. M., Chen, G. I., Curak, J., Gingras, A. C., Mazitschek, R., Neel, B. G., Stagljar, I., & Moffat, J. (2012). Regulation of CD133 by HDAC6 promotes β-catenin signaling to suppress cancer cell differentiation. Cell Reports, 2, 951–963.CrossRefGoogle Scholar
  45. 45.
    Cuajungco, M. P., Podevin, W., Valluri, V. K., Bui, Q., Nguyen, V. H., & Taylor, K. (2012). Abnormal accumulation of human transmembrane (TMEM)-176A and 176B proteins is associated with cancer pathology. Acta Histochemica, 114, 705–712.CrossRefGoogle Scholar
  46. 46.
    Hrasovec, S., Hauptman, N., Glavac, D., Jelenc, F., & Ravnik-Glavac, M. (2013). TMEM25 is a candidate biomarker rmethylated and down-regulated in colorectal cancer. Disease Markers, 34, 93–104.CrossRefGoogle Scholar
  47. 47.
    Xu, X. Y., Zhang, L. J., Yu, Y. Q., Zhang, X. T., Huang, W. J., Nie, X. C., et al. (2014). Down-regulated MAC30 expression inhibits proliferation and mobility of human gastric cancer cells. Cellular Physiology and Biochemistry, 33, 1359–1368.CrossRefGoogle Scholar
  48. 48.
    Kim, S. T., Sohn, I., DO, I. G., Jang, J., Kim, S. H., Jung, I. H., et al. (2014). Transcriptome analysis of CD133-positive stem cells and prognostic value of survivin in colorectal cancer. Cancer Genomics Proteomics, 11, 259–266.Google Scholar
  49. 49.
    Wang, Y., Nathanson, L., & McNiece, I. K. (2011). Differential hematopoietic supportive potential and gene expression of stroma cell lines from midgestation mouse placenta and adult bone marrow. Cell Transplantation, 20, 707–726.CrossRefGoogle Scholar
  50. 50.
    Li, C., Wang, C., Xing, Y., Zhen, J., & Ai, Z. (2016). CD133 promotes gallbladder carcinoma cell migration through activating Akt phosphorylation. Oncotarget, 7, 17751–17759.Google Scholar
  51. 51.
    Tsukasa, K., Ding, Q., Yoshimitsu, M., Miyazaki, Y., Matsubara, S., & Takao, S. (2015). Slug contributes to gemcitabine resistance through epithelial-mesenchymal transition in CD133(+) pancreatic cancer cells. Human Cell, 28, 167–174.CrossRefGoogle Scholar
  52. 52.
    Hong, S. W., Hur, W., Choi, J. E., Kim, J. H., Hwang, D., & Yoon, S. K. (2016). Role of ADAM17 in invasion and migration of CD133-expressing liver cancer stem cells after irradiation. Oncotarget, 7, 23482–23497.Google Scholar
  53. 53.
    Feduska, J. M., Aller, S. G., Garcia, P. L., Cramer, S. L., Council, L. N., van Waardenburg, R. C., et al. (2015). ICAM-2 confers a non-metastatic phenotype in neuroblastoma cells by interaction with α-actinin. Oncogene, 34, 1553–1562.CrossRefGoogle Scholar
  54. 54.
    Yiming, L., Yunshan, G., Bo, M., Yu, Z., Tao, W., Gengfang, L., et al. (2015). CD133 overexpression correlates with clinicopathological features of gastric cancer patients and its impact on survival: A systematic review and meta-analysis. Oncotarget, 6, 42019–42027.CrossRefGoogle Scholar
  55. 55.
    Wang, W., Chen, Y., Deng, J., Zhou, J., Zhou, Y., Wang, S., et al. (2014). The prognostic value of CD133 expression in non-small cell lung cancer: A meta-analysis. Tumour Biology, 35, 9769–9775.CrossRefGoogle Scholar
  56. 56.
    Gottschling, S., Jensen, K., Herth, F. J., Thomas, M., Schnabel, P. A., & Herpel, E. (2013). Lack of prognostic significance of neuroendocrine differentiation and stem cell antigen co-expression in resected early-stage non-small cell lung cancer. Anticancer Research, 33, 981–990.Google Scholar
  57. 57.
    Hong, I., Hong, S. W., Chang, Y. G., Lee, W. Y., Lee, B., Kang, Y. K., et al. (2015). Expression of the cancer stem cell markers CD44 and CD133 in colorectal cancer: An immunohistochemical staining analysis. Annals of Coloproctology, 31, 84–91.CrossRefGoogle Scholar
  58. 58.
    Zhou, F., Mu, Y. D., Liang, J., Liu, Z. X., Chen, H. S., & Zhang, J. F. (2014). Expression and prognostic value of tumor stem cell markers ALDH1 and CD133 in colorectal carcinoma. Oncology Letters, 7, 507–512.CrossRefGoogle Scholar
  59. 59.
    Huang, M., Zhu, H., Feng, J., Ni, S., & Huang, J. (2015). High CD133 expression in the nucleus and cytoplasm predicts poor prognosis in non-small cell lung cancer. Disease Markers, 2015, 986095.Google Scholar
  60. 60.
    Cantile, M., Collina, F., D'Aiuto, M., Rinaldo, M., Pirozzi, G., Borsellino, C., et al. (2013). Nuclear localization of cancer stem cell marker CD133 in triple-negative breast cancer: A case report. Tumori, 99, e245–e250.CrossRefGoogle Scholar
  61. 61.
    Hashimoto, K., Aoyagi, K., Isobe, T., Kouhuji, K., & Shirouzu, K. (2014). Expression of CD133 in the cytoplasm is associated with cancer progression and poor prognosis in gastric cancer. Gastric Cancer, 17, 97–106.CrossRefGoogle Scholar
  62. 62.
    Sasaki, A., Kamiyama, T., Yokoo, H., Nakanishi, K., Kubota, K., Haga, H., et al. (2010). Cytoplasmic expression of CD133 is an important risk factor for overall survival in hepatocellular carcinoma. Oncology Reports, 24, 537–546.CrossRefGoogle Scholar
  63. 63.
    McKenzie, J. L., Gan, O. I., Doedens, M., & Dick, J. E. (2007). Reversible cell surface expression of CD38 on CD34-positive human hematopoietic repopulating cells. Experimental Hematology, 35, 1429–1436.CrossRefGoogle Scholar
  64. 64.
    Bonnet, D., & Dick, J. E. (1997). Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nature Medicine, 3, 730–737.CrossRefGoogle Scholar
  65. 65.
    Camacho Villa, A. Y., Reyes Maldonado, E., Montiel Cervantes, L. A., & Vela Ojeda, J. (2012). CD133+CD34+ and CD133+CD38+ blood progenitor cells as predictors of platelet engraftment in patients undergoing autologous peripheral blood stem cell transplantation. Transfusion and Apheresis Science, 46, 239–244.CrossRefGoogle Scholar
  66. 66.
    Gemei, M., Di Noto, R., Mirabelli, P., & Del Vecchio, L. (2013). Cytometric profiling of CD133+ cells in human colon carcinoma cell lines identifies a common core phenotype and cell type-specific mosaics. The International Journal of Biological Markers, 28, 267–273.CrossRefGoogle Scholar
  67. 67.
    Miao, Y., Fan, L., Wu, Y. J., Xia, Y., Qiao, C., Wang, Y., et al. (2016). Low expression of CD200 predicts shorter time-to-treatment in chronic lymphocytic leukemia. Oncotarget, 7, 13551–13562.Google Scholar
  68. 68.
    Rhodes, D. R., Kalyana-Sundaram, S., Mahavisno, V., Varambally, R., Yu, J., Briggs, B. B., et al. (2007). Oncomine 3.0: Genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles. Neoplasia, 9, 166–180.CrossRefGoogle Scholar
  69. 69.
    Gyorffy, B., Lanczky, A., & Szallasi, Z. (2012). Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients. Endocrine-Related Cancer, 19, 197–208.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Basic Nursing, College of NursingLiaoning Medical UniversityJinzhouPeople’s Republic of China
  2. 2.Shijiazhuang Medical CollegeShijiazhuangPeople’s Republic of China
  3. 3.Department of Medical Genetics, College of Basic Medical ScienceLiaoning Medical UniversityJinzhouPeople’s Republic of China
  4. 4.Department of Histology and Embryology, College of Basic Medical ScienceLiaoning Medical UniversityJinzhouPeople’s Republic of China
  5. 5.Department of Cell Biology, College of Basic Medical Science, and Biological Anthropology InstituteLiaoning Medical UniversityJinzhouPeople’s Republic of China

Personalised recommendations