Research article Special Issues

The identification of a common different gene expression signature in patients with colorectal cancer

  • Received: 03 January 2019 Accepted: 26 February 2019 Published: 10 April 2019
  • Colorectal cancer (CRC) is one of the most common malignancies, giving rise to serious financial burden globally. This study was designed to explore the potential mechanisms implicated with CRC and identify some key biomarkers. CRC-associated gene expression dataset (GSE32323) was downloaded from GEO database. The differentially expressed genes (DEGs) were selected out based on the GEO2R tool. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed to search the enriched pathways of these DEGs. Additionally, a protein-protein interaction (PPI) network was also constructed to visualize interactions between these DEGs. Quantitative Real-time PCR (qPCR) was further performed to valid the top5 up-regulated and top5 down-regulated genes in patients with CRC. Finally, the survival analysis of the top5 up-regulated and top5 down-regulated genes was conducted using GEPIA, aiming to clarify their potential effects on CRC. In this study, a total of 451 DEGs were captured (306 down-regulated genes and 145 up-regulated genes). Among these DEGs, the top5 up-regulated genes were DPEP1, KRT23, CLDN1, LGR5 and FOXQ1 while the top5 down-regulated genes were CLCA4, ZG16, SLC4A4, ADH1B and GCG. GO analysis revealed that these DEGs were mainly enriched in cell adhesion, cell proliferation, RNA polymerase Ⅱ promoter and chemokine activity. KEGG analysis disclosed that the enriched pathway included mineral absorption, chemokine signaling pathway, transcriptional misregulation in cancer, pathways in cancer and PPAR signaling pathway. Survival analysis showed that the expression level of ZG16 may correlate with the prognosis of CRC patients. Furthermore, according to the connectivity degree of these DEGs, we selected out the top15 hub genes, namely MYC, CXCR1, TOP2A, CXCL12, SST, TIMP1, SPP1, PPBP, CDK1, THBS1, CXCL1, PYY, LPAR1, BMP2 and MMP3, which were expected to be promising therapeutic target in CRC. Collectively, our analysis unveiled potential biomarkers and candidate targets in CRC, which could be helpful to the diagnosis and treatment of CRC.

    Citation: Zhongwei Zhao, Xiaoxi Fan, Lili Yang, Jingjing Song, Shiji Fang, Jianfei Tu, Minjiang Chen, Liyun Zheng, Fazong Wu, Dengke Zhang, Xihui Ying, Jiansong Ji. The identification of a common different gene expression signature in patients with colorectal cancer[J]. Mathematical Biosciences and Engineering, 2019, 16(4): 2942-2958. doi: 10.3934/mbe.2019145

    Related Papers:

  • Colorectal cancer (CRC) is one of the most common malignancies, giving rise to serious financial burden globally. This study was designed to explore the potential mechanisms implicated with CRC and identify some key biomarkers. CRC-associated gene expression dataset (GSE32323) was downloaded from GEO database. The differentially expressed genes (DEGs) were selected out based on the GEO2R tool. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were employed to search the enriched pathways of these DEGs. Additionally, a protein-protein interaction (PPI) network was also constructed to visualize interactions between these DEGs. Quantitative Real-time PCR (qPCR) was further performed to valid the top5 up-regulated and top5 down-regulated genes in patients with CRC. Finally, the survival analysis of the top5 up-regulated and top5 down-regulated genes was conducted using GEPIA, aiming to clarify their potential effects on CRC. In this study, a total of 451 DEGs were captured (306 down-regulated genes and 145 up-regulated genes). Among these DEGs, the top5 up-regulated genes were DPEP1, KRT23, CLDN1, LGR5 and FOXQ1 while the top5 down-regulated genes were CLCA4, ZG16, SLC4A4, ADH1B and GCG. GO analysis revealed that these DEGs were mainly enriched in cell adhesion, cell proliferation, RNA polymerase Ⅱ promoter and chemokine activity. KEGG analysis disclosed that the enriched pathway included mineral absorption, chemokine signaling pathway, transcriptional misregulation in cancer, pathways in cancer and PPAR signaling pathway. Survival analysis showed that the expression level of ZG16 may correlate with the prognosis of CRC patients. Furthermore, according to the connectivity degree of these DEGs, we selected out the top15 hub genes, namely MYC, CXCR1, TOP2A, CXCL12, SST, TIMP1, SPP1, PPBP, CDK1, THBS1, CXCL1, PYY, LPAR1, BMP2 and MMP3, which were expected to be promising therapeutic target in CRC. Collectively, our analysis unveiled potential biomarkers and candidate targets in CRC, which could be helpful to the diagnosis and treatment of CRC.


    加载中


    [1] K. Yang, F. Zhang, P. Han, et al., Metabolomics approach for predicting response to neoadjuvant chemotherapy for colorectal cancer, Metabolomics, 9 (2018), 110–121.
    [2] R. L. Siegel, K. D. Miller, S. A. Fedewa, et al., Colorectal cancer statistics, 2017. CA Cancer J. Clin., 3 (2017), 104–117.
    [3] P. M. Yang, Y. T. Li, C. T. Shun, et al., Zebularine inhibits tumorigenesis and stemness of colorectal cancer via p53-dependent endoplasmic reticulum stress, Sci. Rep., 11 (2013), 3219.4. S. A. Noonan, M. E. Morrissey, P. Martin, et al., Tumour vasculature immaturity, oxidative damage and systemic inflammation stratify survival of colorectal cancer patients on bevacizumab treatment, Oncotarget, 12 (2018), 10536–10548.
    [4] 5. Y. Liu, G. Wang, Y. Yang, et al., Increased TEADexpression and nuclear localization in colorectal cancer promote epithelial-mesenchymal transition and metastasis in a YAP-independent manner, Oncogene, 21 (2016), 2789–2795.
    [5] 6. A. G. Long, E. T. Lundsmith and K. E. Hamilton, Inflammation and colorectal cancer, Curr. Colorectal Cancer Rep., 4 (2017), 341–3.
    [6] 7. H. Zheng, L. Yu, S. Luo, et al., miR-29inhibits the metastasis and epithelial-mesenchymal transition of colorectal cancer by targeting S100A4, BMC Cancer, 1 (2017), 140–147.
    [7] 8. A. Mahasneh, F. Alshaheri and E. Jamal, Molecular biomarkers for an early diagnosis, effective treatment and prognosis of colorectal cancer: Current updates, Exp. Mol. Pathol., 3 (201, 475–483.
    [8] 9. N. Li, L. Li and Y. Chen, The identification of core gene expression signature in hepatocellular carcinoma, Oxid. Med. Cell Longev., 4 (201, 1–15.
    [9] 10. L. Ling, L. Ning, C. He, et al., Proteomic analysis of differentially expressed proteins in kidneys of brain dead rabbits, Mol. Med. Rep., 1 (2017), 215–223.
    [10] 11. T. Braunschweig, J. Y. Chung, S. M. Hewitt, Tissue microarrays: bridging the gap between research and the clinic, Expert Rev. Proteom., 3 (2005), 325–336.
    [11] 12. A. Khamas, T. Ishikawa, K. Shimokawa, et al., Screening for epigenetically masked genes in colorectal cancer Using 5-Aza-2'-deoxycytidine, microarray and gene expression profile. Cancer Genom. Proteom., 2 (2012), 67–7513. G. Dennis, B. T. Sherman, D. A. Hosack, et al., DAVID: Database for Annotation, visualization, and Integrated Discovery, Genome Biol., 5 (2003), 3.
    [12] 14. Z. Tang, C. Li, B. Kang, et al., GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses, Nucleic Acids Res., 1 (2017), 98–102.
    [13] 15. L. P. Sun, Y. Guo, Y. X. Zhang, et al., Antioxidant and anti-tyrosinase activities of phenolic extracts from rape bee pollen and inhibitory melanogenesis by cAMP/MITF/TYR pathway in B16 mouse melanoma cells, Front. Pharmaco., 176 (2017), 46–51.
    [14] 16. A. Ikin, C. Riveros, P. Moscato, et al., The Gene Interaction Miner: a new tool for data mining contextual information for protein–protein interaction analysis, Bioinformatics, 2 (2010), 283–284.
    [15] 17. M. Arnold, M. S. Sierra, M. Laversanne, et al., Global patterns and trends in colorectal cancer incidence and mortality, Gut, 4 (2017), 683–691.
    [16] 18. D. K. Rex, C. R. Boland, J. A. Dominitz, et al., Colorectal cancer screening: recommendations for physicians and patients from the U.S. multi-society task force on colorectal cancer, Gastroenterology, 7 (2017), 307–323.
    [17] 19. E. V. Cutsem, A. Cervantes, R. Adam, et al., ESMO consensus guidelines for the management of patients with metastatic colorectal cancer, Ann. Oncol., 8 (2016), 1386–1422.
    [18] 20. V. P. Deenadayalu and D. K. Rex, Colorectal cancer screening: a guide to the guidelines, Rev. Gasteroenterol. Di., 7 (2016), 204.
    [19] 21. M. Hui, W. Li, L. A. Boardman, et al., Loss of ZG16 is associated with molecular and clinicopathological phenotypes of colorectal cancer, BMC Cancer, 1 (2018), 433–441.
    [20] 22. P. A. Eisenach, E. Soeth, C. Röder, et al., Dipeptidase 1 (DPEP1) is a marker for the transition from low-grade to high-grade intraepithelial neoplasia and an adverse prognostic factor in colorectal cancer, Brit. J. Cancer., 3 (3), 694–703.
    [21] 23. S. Pyronnet, C. Bousquet, S. Najib, et al., Antitumor effects of somatostatin, Mol. Cell Endocrinol., 1 (2008), 230–237.
    [22] 24. K. Leiszter, F. Sipos, O. Galamb, et al., Promoter Hypermethylation-Related Reduced Somatostatin Production Promotes Uncontrolled Cell Proliferation in Colorectal Cancer, Plos One, 2 (2015), e0118332.
    [23] 25. Y. Mori, K. Cai, Y. Cheng, et al., A genome-wide search identifies epigenetic silencing of somatostatin, tachykinin-1, and 5 other genes in colon cancer, Gastroenterology, 3 (2006), 797–808.
    [24] 26. Y. Liu, H. C. Min, C. K. Tham, et al., Methylation of serum SST gene is an independent prognostic marker in colorectal cancer, Am. J. Cancer Res., 9 (2016), 2098–3008.
    [25] 27. N. Nagarsheth, M. S. Wicha and W. Zou, Chemokines in the cancer microenvironment and their relevance in cancer immunotherapy, Nat. Rev. Immunol., 9 (2017), 559–572.
    [26] 28. H. Verbeke, S. Struyf, G. Laureys, et al., The expression and role of CXC chemokines in colorectal cancer, Cytokine Growth F. R., 5 (2011), 345–358.
    [27] 29. O. Oladipo, S. Conlon, A. O'Grady, et al., The expression and prognostic impact of CXC-chemokines in stage II and III colorectal cancer epithelial and stromal tissue, Br. J. Cancer, 3 (2011),480–487.
    [28] 30. K. Rupertus, J. Sinistra, C. Scheuer, et al., Interaction of the chemokines I-TAC (CXCL11) and SDF-1 (CXCL12) in the regulation of tumor angiogenesis of colorectal cancer, Clin. Exp. Metastasis, 4 (2014), 447–459.
    [29] 31. X. Shen, A. Artinyan, D. Jackson, et al., Chemokine receptor CXCR4 enhances proliferation in pancreatic cancer cells through AKT and ERK dependent pathways, Pancreas, 1(2010), 81–87.
    [30] 32. Y. Fang, F. C. Henderson, Q. Yi, et al., Chemokine CXCL16 expression suppresses migration and invasiveness and induces apoptosis in breast cancer cells, Mediat. Inflamm., 8 (2014), 478641.
    [31] 33. J. Ehling and F. Tacke, Role of chemokine pathways in hepatobiliary cancer, Cancer Lett., 2 (2016), 173–183.
  • Reader Comments
  • © 2019 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(7442) PDF downloads(1157) Cited by(22)

Article outline

Figures and Tables

Figures(8)  /  Tables(4)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog