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Screening and validating the core biomarkers in patients with pancreatic ductal adenocarcinoma

  • Received: 20 May 2019 Accepted: 24 September 2019 Published: 06 November 2019
  • Pancreatic ductal adenocarcinoma (PAAD) is one of the most common malignant tumors in digestive system. To find the new therapeutic targets and explore potential mechanisms underlying PAAD, the bioinformatics has been performed in our study. The PAAD gene expression profile GSE28735 was chosen to analyze the differentially expressed genes (DEGs) between PAAD carcinoma tissues and normal adjacent tissues from 45 patients with PAAD. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, a protein-protein interaction (PPI) network was also constructed to help us screen the top 20 hub genes in this profile and demonstrated the underlying interactions among them. The Gene Expression Profiling Interactive Analysis (GEPIA) was further performed in order to valid the mRNA levels of top5 up-regulated and top5 down-regualted DEGs, apart from exploring their association with survival rate as well as tumor stage. Finally, Q-PCR was further employed to valid the top5 up-regulated and top5 down-regulated genes in patients with PAAD. In our study, there were a total of 444 DEGs captured (271 up-regulated genes and 173 down-regulated genes). Among these DEGs, the top5 up-regulated genes were CEACAM5, SLC6A14, LAMC2, GALNT5 and TSPAN1 while the top5 down-regulated genes were GP2, CTRC, IAPP, PNLIPRP2 and PNLIPRP1. GO analysis disclosed that the DEGs were predominantly enriched in cell adhesion, lipid metabolism, integrin binding, proteolysis and calcium ion binding. KEGG analysis disclosed that the enriched pathway included pancreatic secretion, protein digestion and absorption, fat digestion and absorption, ECM-receptor interaction, focal adhesion and PI3K-Akt signaling pathway. Survival analysis unveiled that the high expression levels of SLC6A14, GALNT5 and TSPAN1 may correlate with the poor prognosis while high expression levels of IAPP may contribute to a better prognosis in patients with PAAD. Additionally, the levels of CEACAM5, SLC6A14, LAMC2 and GALNT5 were also associated with tumor stage. Furthermore, according to the connectivity degree of these DEGs, we selected the top20 hub genes, namely ALB, FN1, EGF, MMP9, COL1A1, COL3A1, FBN1, CXCL12, POSTIN, BGN, VCAN, THBS2, KRT19, MET, MMP14, COL5A2, GCG, MUC1, MMP1 and CPB1, which were expected to be promising therapeutic targets in PAAD. Collectively, our bioinformatics analysis showed that DEGs and hub genes may be defined as new biomarkers for diagnosis and for guiding the therapeutic strategies of PAAD.

    Citation: Yan Li, Yuzhang Zhu, Guiping Dai, Dongjuan Wu, Zhenzhen Gao, Lei Zhang, Yaohua Fan. Screening and validating the core biomarkers in patients with pancreatic ductal adenocarcinoma[J]. Mathematical Biosciences and Engineering, 2020, 17(1): 910-927. doi: 10.3934/mbe.2020048

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  • Pancreatic ductal adenocarcinoma (PAAD) is one of the most common malignant tumors in digestive system. To find the new therapeutic targets and explore potential mechanisms underlying PAAD, the bioinformatics has been performed in our study. The PAAD gene expression profile GSE28735 was chosen to analyze the differentially expressed genes (DEGs) between PAAD carcinoma tissues and normal adjacent tissues from 45 patients with PAAD. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed using Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, a protein-protein interaction (PPI) network was also constructed to help us screen the top 20 hub genes in this profile and demonstrated the underlying interactions among them. The Gene Expression Profiling Interactive Analysis (GEPIA) was further performed in order to valid the mRNA levels of top5 up-regulated and top5 down-regualted DEGs, apart from exploring their association with survival rate as well as tumor stage. Finally, Q-PCR was further employed to valid the top5 up-regulated and top5 down-regulated genes in patients with PAAD. In our study, there were a total of 444 DEGs captured (271 up-regulated genes and 173 down-regulated genes). Among these DEGs, the top5 up-regulated genes were CEACAM5, SLC6A14, LAMC2, GALNT5 and TSPAN1 while the top5 down-regulated genes were GP2, CTRC, IAPP, PNLIPRP2 and PNLIPRP1. GO analysis disclosed that the DEGs were predominantly enriched in cell adhesion, lipid metabolism, integrin binding, proteolysis and calcium ion binding. KEGG analysis disclosed that the enriched pathway included pancreatic secretion, protein digestion and absorption, fat digestion and absorption, ECM-receptor interaction, focal adhesion and PI3K-Akt signaling pathway. Survival analysis unveiled that the high expression levels of SLC6A14, GALNT5 and TSPAN1 may correlate with the poor prognosis while high expression levels of IAPP may contribute to a better prognosis in patients with PAAD. Additionally, the levels of CEACAM5, SLC6A14, LAMC2 and GALNT5 were also associated with tumor stage. Furthermore, according to the connectivity degree of these DEGs, we selected the top20 hub genes, namely ALB, FN1, EGF, MMP9, COL1A1, COL3A1, FBN1, CXCL12, POSTIN, BGN, VCAN, THBS2, KRT19, MET, MMP14, COL5A2, GCG, MUC1, MMP1 and CPB1, which were expected to be promising therapeutic targets in PAAD. Collectively, our bioinformatics analysis showed that DEGs and hub genes may be defined as new biomarkers for diagnosis and for guiding the therapeutic strategies of PAAD.


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    [1] V. Canzoniert, M. Berretta, A. Buonadonna, et al., Solid pseudopapillary tumour of the pancreas, Lancet Oncol., 4 (2003), 225-6.
    [2] M. Noë, N. Rezaee, K. Asrani, et al., Immunolabeling of Cleared Human Pancreata Provides Insights into Three-dimensional Pancreatic Anatomy and Pathology, Am. J. Pathol., 188 (2018), 1530-1535.
    [3] N. Martinez-Bosch, J. Vinaixa and P. Navarro, Immune Evasion in Pancreatic Cancer: From Mechanisms to Therapy, Cancers, 10 (2018), 6.
    [4] C. Napoli, N. Sperandio, R. T. Lawlor, et al., Urine metabolic signature of pancreatic ductal adenocarcinoma by 1H nuclear magnetic resonance: Identification, mapping, and evolution, J. Proteome Res., 11 (2011), 1274-1283.
    [5] B. J. Raphael, R. H. Hruban, A. J. Aguirre, et al., Integrated Genomic Characterization of Pancreatic Ductal Adenocarcinoma, Cancer Cell, 32 (2017), 185-203.
    [6] V. P. Groot, N. Rezaee, W. C. Wu, et al., Patterns, Timing, and Predictors of Recurrence Following Pancreatectomy for Pancreatic Ductal Adenocarcinoma, Ann. Surg., 267(2018), 936-945.
    [7] J. Kim, W. R. Bamlet, A. L. Oberg, et al., Detection of early pancreatic ductal adenocarcinoma with thrombospondin-2 and CA19-9 blood markers, Sci. Trans. Med., 9 (2017), eaah5583.
    [8] P. T. Nelson, D. A. Baldwin, L. M. Scearce, et al., Microarray-based, high-throughput gene expression profiling of microRNAs, Nat. Methods, 1 (2004), 155-161.
    [9] N. Li, L. Li, and Y. Chen, The Identification of Core Gene Expression Signature in Hepatocellular Carcinoma, Oxid. Med. Cell. Longevity, 2018 (2018), 1-15.
    [10] M. A. Fernández, C. Rueda and S. D. Peddada, Identification of a core set of signature cell cycle genes whose relative order of time to peak expression is conserved across species,Nucleic Acids Res., 40 (2012), 2823-2832.
    [11] S. Yao, and T. Liu, Analysis of differential gene expression caused by cervical intraepithelial neoplasia based on GEO database, Oncol. Lett., 15 (2018), 8319-8324.
    [12] M. D. Young, M. J. Wakefield, J. K. Smyth, et al., Gene ontology analysis for RNA-seq: Accounting for selection bias, Genome Biol., 11 (2010), R14.
    [13] E. Altermann, and T. R. Klaenhammer, Pathway Voyager: Pathway mapping using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, BMC Genomics,6 (2005), 60.
    [14] G. Dennis, B. T. Sherman, D. A. Hosack, et al., DAVID: Database for Annotation, Visualization, and Integrated Discovery, Genome Biol., 4 (2003), R60.
    [15] 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., 45 (2017), W98-W102.
    [16] H. Feng, J. Chen, H. Wang, et al., Roflumilast reverses polymicrobial sepsis-induced liver damage by inhibiting inflammation in mice, Lab. Invest., 97 (2017), 1008-1019. doi: 10.1038/labinvest.2017.59
    [17] Z. Zhou, Y. Cheng, Y. Jiang, et al., Ten hub genes associated with progression and prognosis of pancreatic carcinoma identified by co-expression analysis, Int. J. Biol. Sci., 14 (2018), 124-136.
    [18] A. Adamska, A. Domenichini, and M. Falasca, Pancreatic Ductal Adenocarcinoma: Current and Evolving Therapies, Int. J. Mol. Sci., 18 (2017), 1338.
    [19] E. S. Knudsen, P. Vail, U. Balaji, et al., Stratification of Pancreatic Ductal Adenocarcinoma: Combinatorial Genetic, Stromal, and Immunological Markers, Clin. Cancer Res., 23 (2017), 4429-4440.
    [20] S. Govindan, T. Cardillo, C. D'Souza, et al., Therapy of human colonic and lung cancer xenografts with SN-38 conjugates of anti-CEACAM5 and anti-EGP-1 humanized monoclonal antibodies, Mol. Cancer Ther., 6 (2007).
    [21] R. D. Blumenthal, E. Leon, H. J. Hansen, et al., Expression patterns of CEACAM5 and CEACAM6 in primary and metastatic cancers, BMC Cancer, 7 (2007), 2.
    [22] E. Powell, J. Shao, H. M. Picon, et al., A functional genomic screen in vivo identifies CEACAM5 as a clinically relevant driver of breast cancer metastasis, NPJ. Breast Cancer, 4 (2018), 9.
    [23] F. Gebauer, D. Wicklein, J. Horst, et al., Carcinoembryonic antigen-related cell adhesion molecules (CEACAM) 1, 5 and 6 as biomarkers in pancreatic cancer, Plos One, 9 (2014), e113023.
    [24] S. Bröer and U. Gether, The solute carrier 6 family of transporters, Br. J. Pharmacol., 167 (2012), 256-278.
    [25] A. R. Penheiter, S. Erdogan, S. J. Murphy, et al., Transcriptomic and Immunohistochemical Profiling of SLC6A14 in Pancreatic Ductal Adenocarcinoma, Biomed. Res. Int., 2015 (2015), 593572.
    [26] L. Samluk, M. Czeredys, K. Skowronek, et al., Protein kinase C regulates amino acid transporter ATB0,+,Biochem. Biophys. Res. Commun., 422 (2012), 64-69. doi: 10.1016/j.bbrc.2012.04.106
    [27] A. P. Singh, S. Arora, A. Bhardwaj, et al., CXCL12/CXCR4 protein signaling axis induces sonic hedgehog expression in pancreatic cancer cells via extracellular regulated kinase- and Akt kinase-mediated activation of nuclear factor κB: Implications for bidirectional tumor-stromal interactions, J. Biol. Chem., 287 (2012), 39115-39124.
    [28] E. L. Heinrich, W. Lee, J. Lu, et al., Chemokine CXCL12 activates dual CXCR4 and CXCR7-mediated signaling pathways in pancreatic cancer cells, J. Transl. Med., 10 (2012), 68.
    [29] F. Sieero, C.Biben, L. Martínez-Muñoz, et al., Disrupted cardiac development but normal hematopoiesis in mice deficient in the second CXCL12/SDF-1 receptor, CXCR7, Proc. Natl. Acad. Sci. U. S. A., 104 (2007), 14759-14764.
    [30] K. Cui, W. Zhao, C. Wang, et al., The CXCR4-CXCL12 Pathway Facilitates the Progression of Pancreatic Cancer Via Induction of Angiogenesis and Lymphangiogenesis, J. Surg. Res., 171 (2011), 143-150.
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