Pancreatic adenocarcinoma (PAAD) is one of the most common malignant tumors with high mortality rates and a poor prognosis. There is an urgent need to determine the molecular mechanism of PAAD tumorigenesis and identify promising biomarkers for the diagnosis and targeted therapy of the disease.
Three GEO datasets (GSE62165, GSE15471 and GSE62452) were analyzed to obtain differentially expressed genes (DEGs). The PPI networks and hub genes were identified through the STRING database and MCODE plugin in Cytoscape software. GO and KEGG enrichment pathways were analyzed by the DAVID database. The GEPIA database was utilized to estimate the prognostic value of hub genes. Furthermore, the roles of MMP14 and COL12A1 in immune infiltration and tumor-immune interaction and their biological functions in PAAD were explored by TIMER, TISIDB, GeneMANIA, Metascape and GSEA.
A total of 209 common DEGs in the three datasets were obtained. GO function analysis showed that the 209 DEGs were significantly enriched in calcium ion binding, serine-type endopeptidase activity, integrin binding, extracellular matrix structural constituent and collagen binding. KEGG pathway analysis showed that DEGs were mainly enriched in focal adhesion, protein digestion and absorption and ECM-receptor interaction. The 14 genes with the highest degree of connectivity were defined as the hub genes of PAAD development. GEPIA revealed that PAAD patients with upregulated MMP14 and COL12A1 expression had poor prognoses. In addition, TIMER analysis revealed that MMP14 and COL12A1 were closely associated with the infiltration levels of macrophages, neutrophils and dendritic cells in PAAD. TISIDB revealed that MMP14 was strongly positively correlated with CD276, TNFSF4, CD70 and TNFSF9, while COL12A1 was strongly positively correlated with TNFSF4, CD276, ENTPD1 and CD70. GSEA revealed that MMP14 and COL12A1 were significantly enriched in epithelial mesenchymal transition, extracellular matrix receptor interaction, apical junction, and focal adhesion in PAAD development.
Our study revealed that overexpression of MMP14 and COL12A1 is significantly correlated with PAAD patient poor prognosis. MMP14 and COL12A1 participate in regulating tumor immune interactions and might become promising biomarkers for PAAD.
Citation: Yuexian Li, Zhou Su, Biwei Wei, Mengbin Qin, Zhihai Liang. Bioinformatics analysis identified MMP14 and COL12A1 as immune-related biomarkers associated with pancreatic adenocarcinoma prognosis[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 5921-5942. doi: 10.3934/mbe.2021296
Pancreatic adenocarcinoma (PAAD) is one of the most common malignant tumors with high mortality rates and a poor prognosis. There is an urgent need to determine the molecular mechanism of PAAD tumorigenesis and identify promising biomarkers for the diagnosis and targeted therapy of the disease.
Three GEO datasets (GSE62165, GSE15471 and GSE62452) were analyzed to obtain differentially expressed genes (DEGs). The PPI networks and hub genes were identified through the STRING database and MCODE plugin in Cytoscape software. GO and KEGG enrichment pathways were analyzed by the DAVID database. The GEPIA database was utilized to estimate the prognostic value of hub genes. Furthermore, the roles of MMP14 and COL12A1 in immune infiltration and tumor-immune interaction and their biological functions in PAAD were explored by TIMER, TISIDB, GeneMANIA, Metascape and GSEA.
A total of 209 common DEGs in the three datasets were obtained. GO function analysis showed that the 209 DEGs were significantly enriched in calcium ion binding, serine-type endopeptidase activity, integrin binding, extracellular matrix structural constituent and collagen binding. KEGG pathway analysis showed that DEGs were mainly enriched in focal adhesion, protein digestion and absorption and ECM-receptor interaction. The 14 genes with the highest degree of connectivity were defined as the hub genes of PAAD development. GEPIA revealed that PAAD patients with upregulated MMP14 and COL12A1 expression had poor prognoses. In addition, TIMER analysis revealed that MMP14 and COL12A1 were closely associated with the infiltration levels of macrophages, neutrophils and dendritic cells in PAAD. TISIDB revealed that MMP14 was strongly positively correlated with CD276, TNFSF4, CD70 and TNFSF9, while COL12A1 was strongly positively correlated with TNFSF4, CD276, ENTPD1 and CD70. GSEA revealed that MMP14 and COL12A1 were significantly enriched in epithelial mesenchymal transition, extracellular matrix receptor interaction, apical junction, and focal adhesion in PAAD development.
Our study revealed that overexpression of MMP14 and COL12A1 is significantly correlated with PAAD patient poor prognosis. MMP14 and COL12A1 participate in regulating tumor immune interactions and might become promising biomarkers for PAAD.
| [1] |
H. Zhu, T. Li, Y. Du, M. Li, Pancreatic cancer: challenges and opportunities, BMC Med., 16 (2018), 214. doi: 10.1186/s12916-018-1215-3
|
| [2] |
R. L. Siegel, K.D. Miller, A. Jemal, Cancer statistics, 2019, CA Cancer J. Clin., 69 (2019), 7-34. doi: 10.3322/caac.21551
|
| [3] |
J. Kleeff, M. Korc, M. Apte, C. La Vecchia, C. D. Johnson, A. V. Biankin, et al., Pancreatic cancer, Nat. Rev. Dis. Primers, 2 (2016), 16022. doi: 10.1038/nrdp.2016.22
|
| [4] |
A. Martín-Blázquez, C. Jiménez-Luna, C. Díaz, J. Martínez-Galán, J. Prados, F. Vicente, et al., Discovery of Pancreatic Adenocarcinoma Biomarkers by Untargeted Metabolomics, Cancers, 12 (2020), 1002. doi: 10.3390/cancers12041002
|
| [5] |
W. Lu, N. Li, F. Liao, Identification of key genes and pathways in pancreatic cancer gene expression profile by integrative analysis, Genes, 10 (2019), 612. doi: 10.3390/genes10080612
|
| [6] |
C. von Mering, M. Huynen, D. Jaeggi, S. Schmidt, P. Bork, B. Snel, STRING: a database of predicted functional associations between proteins, Nucleic Acids Res., 31 (2003), 258-261. doi: 10.1093/nar/gkg034
|
| [7] |
P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, et al., Cytoscape: a software environment for integrated models of biomolecular interaction networks, Genome Res., 13 (2003), 2498-2504. doi: 10.1101/gr.1239303
|
| [8] |
D. W. Huang, B. T. Sherman, Q. Tan, J. Kir, D. Liu, D. Bryant, et al., DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists, Nucleic Acids Res., 35 (2007), W169-W175. doi: 10.1093/nar/gkm415
|
| [9] |
Gene Ontology Consortium, The Gene Ontology Resource: 20 years and still GOing strong, Nucleic Acids Res., 47 (2019), D330-D338. doi: 10.1093/nar/gky1055
|
| [10] |
M. Kanehisa, Y. Sato, M. Kawashima, M. Furumichi, M. Tanabe, KEGG as a reference resource for gene and protein annotation, Nucleic Acids Res., 44 (2016), D457-D462. doi: 10.1093/nar/gkv1070
|
| [11] |
Z. Tang, C. Li, B. Kang, G. Gao, C. Li, Z. Zhang, GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses, Nucleic Acids Res., 45 (2017), W98-W102. doi: 10.1093/nar/gkx247
|
| [12] |
T. Li, J. Fan, B. Wang, N. Traugh, Q. Chen, J.S. Liu, et al., TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells, Cancer Res., 77 (2017), e108-e110. doi: 10.1158/0008-5472.CAN-17-0307
|
| [13] |
B. Ru, C. N. Wong, Y. Tong, J. Y. Zhong, S. S. W. Zhong, W. C. Wu, et al., TISIDB: an integrated repository portal for tumor-immune system interactions, Bioinformatics, 35 (2019), 4200-4202. doi: 10.1093/bioinformatics/btz210
|
| [14] |
M. Franz, H. Rodriguez, C. Lopes, K. Zuberi, J. Montojo, G. D. Bader, et al., GeneMANIA update 2018, Nucleic Acids Res., 46 (2018), W60-W64. doi: 10.1093/nar/gky311
|
| [15] |
Y. Zhou, B. Zhou, L. Pache, M. Chang, A. H. Khodabakhshi, O. Tanaseichuk, et al., Metascape provides a biologist-oriented resource for the analysis of systems-level datasets, Nat. Commun., 10 (2019), 1523. doi: 10.1038/s41467-019-09234-6
|
| [16] |
A. Subramanian, H. Kuehn, J. Gould, P. Tamayo, J.P. Mesirov, GSEA-P: a desktop application for Gene Set Enrichment Analysis, Bioinformatics, 23 (2007), 3251-3253. doi: 10.1093/bioinformatics/btm369
|
| [17] |
H. Läubli, L. Borsig, Altered cell adhesion and glycosylation promote cancer immune suppression and metastasis, Front. Immunol., 10 (2019), 2120. doi: 10.3389/fimmu.2019.02120
|
| [18] |
M. Janiszewska, M.C. Primi, T. Izard, Cell adhesion in cancer: Beyond the migration of single cells, J. Biol. Chem., 295 (2020), 2495-2505. doi: 10.1074/jbc.REV119.007759
|
| [19] |
C. Walker, E. Mojares, A. Del Río Hernández, Role of Extracellular Matrix in Development and Cancer Progression, Int. J. Mol. Sci., 19 (2018), 3028. doi: 10.3390/ijms19103028
|
| [20] | M. Wyganowska-Świątkowska, M. Tarnowski, D. Murtagh, E. Skrzypczak-Jankun, J. Jankun, Proteolysis is the most fundamental property of malignancy and its inhibition may be used therapeutically (Review), Int. J. Mol. Med., 43 (2019), 15-25. |
| [21] |
S. Perumal, O. Antipova, J. P. Orgel, Collagen fibril architecture, domain organization, and triple-helical conformation govern its proteolysis, Proc. Natl. Acad. Sci., 105 (2008), 2824-2829. doi: 10.1073/pnas.0710588105
|
| [22] |
S. Xu, H. Xu, W. Wang, S. Li, H. Li, T. Li, et al., The role of collagen in cancer: from bench to bedside, J. Transl. Med., 17 (2019), 309. doi: 10.1186/s12967-019-2058-1
|
| [23] |
A. Bastidas-Ponce, K. Scheibner, H. Lickert, M. Bakhti, Cellular and molecular mechanisms coordinating pancreas development, Development, 144 (2017), 2873-2888. doi: 10.1242/dev.140756
|
| [24] |
M. Singh, N. Yelle, C. Venugopal, S. K. Singh, EMT: Mechanisms and therapeutic implications, Pharmacol. Ther., 182 (2018), 80-94. doi: 10.1016/j.pharmthera.2017.08.009
|
| [25] |
S. P. Turunen, O. Tatti-Bugaeva, K. Lehti, Membrane-type matrix metalloproteases as diverse effectors of cancer progression, Biochim. Biophys. Acta Mol. Cell Res., 1864 (2017), 1974-1988. doi: 10.1016/j.bbamcr.2017.04.002
|
| [26] | J. F. Wang, Y. Q. Gong, Y. H. He, W. W. Ying, X. S. Li, X. F. Zhou, et al., High expression of MMP14 is associated with progression and poor short-term prognosis in muscle-invasive bladder cancer, Eur. Rev. Med. Pharmacol. Sci., 24 (2020), 6605-6615. |
| [27] |
A. Kasurinen, S. Gramolelli, J. Hagström, A. Laitinen, A. Kokkola, Y. Miki, et al., High tissue MMP14 expression predicts worse survival in gastric cancer, particularly with a low PROX1, Cancer Med., 8 (2019), 6995-7005. doi: 10.1002/cam4.2576
|
| [28] | Y. Jin, Z. Y. Liang, W. X. Zhou, L. Zhou, High MMP14 expression is predictive of poor prognosis in resectable hepatocellular carcinoma, Pathology, 52 (2020), 359-365. |
| [29] |
F. Duan, Z. Peng, J. Yin, Z. Yang, J. Shang, Expression of MMP-14 and prognosis in digestive system carcinoma: a meta-analysis and databases validation, J. Cancer, 11 (2020), 1141-1150. doi: 10.7150/jca.36469
|
| [30] | O. R. Grafinger, G. Gorshtein, T. Stirling, M. I. Brasher, M. G. Coppolino, β1 integrinmediated signaling regulates MT1-MMP phosphorylation to promote tumor cell invasion, J. Cell Sci., 133 (2020), jcs239152. |
| [31] |
W. Jiang, Y. Zhang, K. T. Kane, M. A. Collins, D. M. Simeone, M. P. di Magliano, et al., CD44 regulates pancreatic cancer invasion through MT1-MMP, Mol. Cancer Res., 13 (2015), 9-15. doi: 10.1158/1541-7786.MCR-14-0076
|
| [32] |
D. R. Gerecke, P. F. Olson, M. Koch, J. H. Knoll, R. Taylor, D. L. Hudson, et al., Complete primary structure of two splice variants of collagen XⅡ, and assignment of alpha 1(XⅡ) collagen (COL12A1), alpha 1(IX) collagen (COL9A1), and alpha 1(XIX) collagen (COL19A1) to human chromosome 6q12-q13, Genomics, 41 (1997), 236-242. doi: 10.1006/geno.1997.4638
|
| [33] |
J. Sapudom, T. Pompe, Biomimetic tumor microenvironments based on collagen matrices, Biomater. Sci., 6 (2018), 2009-2024. doi: 10.1039/C8BM00303C
|
| [34] |
Y. H. Xu, J. L. Deng, L. P. Wang, H. B. Zhang, L. Tang, Y. Huang, et al., Identification of Candidate Genes Associated with Breast Cancer Prognosis, DNA Cell Biol., 39 (2020), 1205-1227. doi: 10.1089/dna.2020.5482
|
| [35] |
Y. Chen, W. Chen, X. Dai, C. Zhang, Q. Zhang, J. Lu, Identification of the collagen family as prognostic biomarkers and immune-associated targets in gastric cancer, Int. Immunopharmacol., 87 (2020), 106798. doi: 10.1016/j.intimp.2020.106798
|
| [36] |
Y. Wu, Y. Xu, Integrated bioinformatics analysis of expression and gene regulation network of COL12A1 in colorectal cancer, Cancer Med., 9 (2020), 4743-4755. doi: 10.1002/cam4.2899
|
| [37] |
Z. Xiang, J. Li, S. Song, J. Wang, W. Cai, W. Hu, et al., A positive feedback between IDO1 metabolite and COL12A1 via MAPK pathway to promote gastric cancer metastasis, J. Exp. Clin. Cancer Res., 38 (2019), 314. doi: 10.1186/s13046-019-1318-5
|
| [38] |
R. Januchowski, M. Świerczewska, K. Sterzyńska, K. Wojtowicz, M. Nowicki, M. Zabel, Increased Expression of Several Collagen Genes is Associated with Drug Resistance in Ovarian Cancer Cell Lines, J. Cancer, 7 (2016), 1295-1310. doi: 10.7150/jca.15371
|
| [39] |
D. Öhlund, O. Franklin, E. Lundberg, C. Lundin, M. Sund, Type Ⅳ collagen stimulates pancreatic cancer cell proliferation, migration, and inhibits apoptosis through an autocrine loop, BMC Cancer, 13 (2013), 154. doi: 10.1186/1471-2407-13-154
|
| [40] |
M. A. Shields, S. Dangi-Garimella, S. B. Krantz, D. J. Bentrem, H. G. Munshi, Pancreatic cancer cells respond to type I collagen by inducing snail expression to promote membrane type 1 matrix metalloproteinase-dependent collagen invasion, J. Biol. Chem., 286 (2011), 10495-10504. doi: 10.1074/jbc.M110.195628
|
| [41] |
A. Habtezion, M. Edderkaoui, S.J. Pandol, Macrophages and pancreatic ductal adenocarcinoma, Cancer Lett., 381 (2016), 211-216. doi: 10.1016/j.canlet.2015.11.049
|
| [42] |
M. Yu, R. Guan, W. Hong, Y. Zhou, Y. Lin, H. Jin, et al., Prognostic value of tumorassociated macrophages in pancreatic cancer: a meta-analysis, Cancer Manag. Res., 11 (2019), 4041-4058. doi: 10.2147/CMAR.S196951
|
| [43] |
A. Ocana, C. Nieto-Jiménez, A. Pandiella, A. J. Templeton, Neutrophils in cancer: prognostic role and therapeutic strategies, Mol. Cancer, 16 (2017), 137. doi: 10.1186/s12943-017-0707-7
|
| [44] |
A. Deicher, R. Andersson, B. Tingstedt, G. Lindell, M. Bauden, D. Ansari, Targeting dendritic cells in pancreatic ductal adenocarcinoma, Cancer Cell Int., 18 (2018), 85. doi: 10.1186/s12935-018-0585-0
|
| [45] |
C. Yang, H. Cheng, Y. Zhang, K. Fan, G. Luo, Z. Fan, et al., Anergic natural killer cells educated by tumor cells are associated with a poor prognosis in patients with advanced pancreatic ductal adenocarcinoma, Cancer Immunol. Immunother., 67 (2018), 1815-1823. doi: 10.1007/s00262-018-2235-8
|
| [46] |
S. Quintero-Fabián, R. Arreola, E. Becerril-Villanueva, J.C. Torres-Romero, V. AranaArgáez, J. Lara-Riegos, et al., Role of Matrix Metalloproteinases in Angiogenesis and Cancer, Front. Oncol., 9 (2019), 1370. doi: 10.3389/fonc.2019.01370
|
| [47] |
R. Shimizu-Hirota, W. Xiong, B. T. Baxter, S. L. Kunkel, I. Maillard, X.W. Chen, et al., MT1-MMP regulates the PI3Kδ·Mi-2/NuRD-dependent control of macrophage immune function, Genes Dev., 26 (2012), 395-413. doi: 10.1101/gad.178749.111
|
| [48] |
A. M. H. Larsen, D. E. Kuczek, A. Kalvisa, M. S. Siersbæk, M. L. Thorseth, A. Z. Johansen, et al., Collagen Density Modulates the Immunosuppressive Functions of Macrophages, J. Immunol., 205 (2020), 1461-1472. doi: 10.4049/jimmunol.1900789
|
| [49] |
D. E. Kuczek, A. M. H. Larsen, M. L. Thorseth, M. Carretta, A. Kalvisa, M. S. Siersbæk, et al., Collagen density regulates the activity of tumor-infiltrating T cells, J. Immunother. Cancer, 7 (2019), 68. doi: 10.1186/s40425-019-0556-6
|
| [50] |
E. L. Hopewell, C. Cox, S. Pilon-Thomas, L. L. Kelley, Tumor-infiltrating lymphocytes: Streamlining a complex manufacturing process, Cytotherapy, 21 (2019), 307-314. doi: 10.1016/j.jcyt.2018.11.004
|
| [51] |
H. Du, K. Hirabayashi, S. Ahn, N. P. Kren, S. A. Montgomery, X. Wang, et al., Antitumor Responses in the Absence of Toxicity in Solid Tumors by Targeting B7-H3 via Chimeric Antigen Receptor T Cells, Cancer Cell, 35 (2019), 221-237. doi: 10.1016/j.ccell.2019.01.002
|
| [52] |
J. Jacobs, V. Deschoolmeester, K. Zwaenepoel, C. Rolfo, K. Silence, S. Rottey, et al., CD70: An emerging target in cancer immunotherapy, Pharmacol. Ther., 155 (2015), 1-10. doi: 10.1016/j.pharmthera.2015.07.007
|
| [53] |
P. Yin, L. Gui, C. Wang, J. Yan, M. Liu, L. Ji, et al., Targeted delivery of CXCL9 and OX40L by mesenchymal stem cells elicits potent antitumor immunity, Mol. Ther., 28 (2020), 2553-2563. doi: 10.1016/j.ymthe.2020.08.005
|
| [54] |
J. Wu, Y. Wang, Z. Jiang, Immune induction identified by TMT proteomics analysis in autoinducer-2 treated macrophages, Expert Rev. Proteomics, 17 (2020), 175-185. doi: 10.1080/14789450.2020.1738223
|
| [55] |
C. Liang, J. Xu, Q. Meng, B. Zhang, J. Liu, J. Hua, et al., TGFB1-induced autophagy affects the pattern of pancreatic cancer progression in distinct ways depending on SMAD4 status, Autophagy, 16 (2020), 486-500. doi: 10.1080/15548627.2019.1628540
|
| [56] |
K. C. Ohaegbulam, A. Assal, E. Lazar-Molnar, Y. Yao, X. Zang, Human cancer immunotherapy with antibodies to the PD-1 and PD-L1 pathway, Trends Mol. Med., 21 (2015), 24-33. doi: 10.1016/j.molmed.2014.10.009
|
| [57] |
S. S. Potter, Single-cell RNA sequencing for the study of development, physiology and disease, Nat. Rev. Nephrol., 14 (2018), 479-492. doi: 10.1038/s41581-018-0021-7
|
| [58] |
J. Cheng, J. Zhang, Z. Wu, X. Sun, Inferring microenvironmental regulation of gene expression from single-cell RNA sequencing data using scMLnet with an application to COVID-19, Brief. Bioinform., 22 (2021), 988-1005. doi: 10.1093/bib/bbaa327
|
| [59] |
J. Zhang, M. Guan, Q. Wang, J. Zhang, T. Zhou, X. Sun, Single-cell transcriptome-based multilayer network biomarker for predicting prognosis and therapeutic response of gliomas, Brief. Bioinform., 21 (2020), 1080-1097. doi: 10.1093/bib/bbz040
|
| [60] |
J. Han, R. A. DePinho, A. Maitra, Single-cell RNA sequencing in pancreatic cancer, Nat. Rev. Gastroenterol. Hepatol., 18 (2021), 451-452. doi: 10.1038/s41575-021-00471-z
|
| [61] |
Q. Luo, Q. Fu, X. Zhang, H. Zhang, T. Qin, Application of Single-Cell RNA Sequencing in Pancreatic Cancer and the Endocrine Pancreas, Adv. Exp. Med. Biol., 1255 (2020), 143-152. doi: 10.1007/978-981-15-4494-1_12
|
Figures(8) / Tables(3)
Yuexian Li, Zhou Su, Biwei Wei, Mengbin Qin, Zhihai Liang. Bioinformatics analysis identified MMP14 and COL12A1 as immune-related biomarkers associated with pancreatic adenocarcinoma prognosis[J]. Mathematical Biosciences and Engineering, 2021, 18(5): 5921-5942. doi: 10.3934/mbe.2021296
DownLoad: