Effect of MET overexpression on the prognosis of patients with pancreatic cancer based on bioinformatics analysis_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

HE Jincheng ¹ , WANG Jun ² , JIANG Lei ² , MIN Guangtao ² ,  YAO Nan ²

1. The First School of Clinical Medicine of Lanzhou University, Lanzhou 730000, P. R. China;
2. Sixth Department of General Surgery, The First Hospital of Lanzhou University, Lanzhou 730000, P. R. China;

Corresponding author：

YAO Nan, Email: 981016396@qq.com

Keywords：

pancreatic cancer; mesenchymal epithelial transition factor; prognosis; bioinformatics

DOI：

10.7507/1007-9424.202012064

Video：

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Objective To explore the significance of mesenchymal epithelial transition factor (MET) as a clinical prognostic evaluation index for patients with pancreatic cancer based on bioinformatics analysis.Methods The GSE28735 and GSE62452 gene chips from GEO database were downloaded and the difference of MET gene expression between cancer and adjacent cancerous tissues were analyzed by bioinformatics. We downloaded pancreatic cancer gene chip from TCGA database to analyze the correlation between MET gene expression and clinicopathological features of pancreatic cancer patients and prognosis risk. Finally, the possible molecular mechanism of MET involved in pancreatic carcinogenesis was analyzed by GO and KEGG enrichment analysis.Results The expression level of MET gene in pancreatic cancer tissues was significantly higher than that in adjacent cancerous tissues (P<0.001). The overall survival and disease-free survival of pancreatic cancer patients in the high MET gene expression group were lower than those in the low expression group (P<0.001). The expression level of MET gene was related to the age of pancreatic cancer patients, T stage, and histological grading of tumors (P<0.05), and high MET gene expression, age >65 years, and N1 stage were independent risk factors affecting the prognosis of pancreatic cancer patients. KEGG enrichment analysis showed that MET was mainly related to PI3K/AKT signaling pathway, FAK signaling pathway, and cancer transcription dysregulation and so on.Conclusion MET may be a valuable tumor marker for pancreatic cancer and can predict the poor prognosis of patients with pancreatic cancer.

Citation： HE Jincheng, WANG Jun, JIANG Lei, MIN Guangtao, YAO Nan. Effect of MET overexpression on the prognosis of patients with pancreatic cancer based on bioinformatics analysis. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2021, 28(9): 1171-1176. doi: 10.7507/1007-9424.202012064 Copy

1.	Huang J, Lok V, Ngai CH, et al. Worldwide burden of, risk factors for, and trends in pancreatic cancer. Gastroenterology, 2021, 160(3): 744-754.
2.	Makohon-Moore A, Brosnan JA, Iacobuzio-Donahue CA. Pancreatic cancer genomics: insights and opportunities for clinical translation. Genome Med, 2013, 5(3): 26.
3.	Vogelstein B, Papadopoulos N, Velculescu VE, et al. Cancer genome landscapes. Science, 2013, 339(6127): 1546-1558.
4.	Lucito R, Suresh S, Walter K, et al. Copy-number variants in patients with a strong family history of pancreatic cancer. Cancer Biol Ther, 2007, 6(10): 1592-1599.
5.	荆薇, 杨向红. 基因异常对胰腺癌预后影响的研究进展. 实用医学杂志, 2019, 35(5): 838-841.
6.	Yang M, Zeng L, Ke NW, et al. World Health Organization grading classification for pancreatic neuroendocrine neoplasms: a comprehensive analysis from a large Chinese institution. BMC Cancer, 2020, 20(1): 906.
7.	杨美文, 张雷达. 胰腺癌患病情况及其危险因素分析. 中国医学前沿杂志(电子版), 2017, 9(4): 122-124.
8.	Imura Y, Nakai T, Yamada S, et al. Functional and therapeutic relevance of hepatocyte growth factor/c-MET signaling in synovial sarcoma. Cancer Sci, 2016, 107(12): 1867-1876.
9.	Zhang Y, Xia M, Jin K, et al. Function of the c-Met receptor tyrosine kinase in carcinogenesis and associated therapeutic opportunities. Mol Cancer, 2018, 17(1): 45.
10.	Park S, Choi YL, Sung CO, et al. High MET copy number and MET overexpression: poor outcome in non-small cell lung cancer patients. Histol Histopathol, 2012, 27(2): 197-207.
11.	Duplaquet L, Kherrouche Z, Baldacci S, et al. The multiple paths towards MET receptor addiction in cancer. Oncogene, 2018, 37(24): 3200-3215.
12.	Chen TH, Chan PC, Chen CL, et al. Phosphorylation of focal adhesion kinase on tyrosine 194 by Met leads to its activation through relief of autoinhibition. Oncogene, 2011, 30(2): 153-166.
13.	Maulik G, Kijima T, Ma PC, et al. Modulation of the c-Met/hepatocyte growth factor pathway in small cell lung cancer. Clin Cancer Res, 2002, 8(2): 620-627.
14.	Hamacher R, Schmid RM, Saur D, et al. Apoptotic pathways in pancreatic ductal adenocarcinoma. Mol Cancer, 2008, 7: 64.
15.	Symeonides SN, Anderton SM, Serrels A. FAK-inhibition opens the door to checkpoint immunotherapy in Pancreatic Cancer. J Immunother Cancer, 2017, 5: 17.
16.	Asano T, Yao Y, Shin S, et al. Insulin receptor substrate is a mediator of phosphoinositide 3-kinase activation in quiescent pancreatic cancer cells. Cancer Res, 2005, 65(20): 9164-9168.
17.	González MN, de Mello W, Butler-Browne GS, et al. HGF potentiates extracellular matrix-driven migration of human myoblasts: involvement of matrix metalloproteinases and MAPK/ERK pathway. Skelet Muscle, 2017, 7(1): 20.
18.	Pascale RM, Feo F, Calvisi DF. An infernal cross-talk between oncogenic β-catenin and c-Met in hepatocellular carcinoma: Evidence from mouse modeling. Hepatology, 2016, 64(5): 1421-1423.
19.	Lee KH, Hyun MS, Kim JR. Growth factor-dependent activation of the MAPK pathway in human pancreatic cancer: MEK/ERK and p38 MAP kinase interaction in uPA synthesis. Clin Exp Metastasis, 2003, 20(6): 499-505.
20.	Corso S, Giordano S. Cell-autonomous and non-cell-autonomous mechanisms of HGF/MET-driven resistance to targeted therapies: from basic research to a clinical perspective. Cancer Discov, 2013, 3(9): 978-992.
21.	Ghiso E, Giordano S. Targeting MET: why, where and how? Curr Opin Pharmacol, 2013, 13(4): 511-518.
22.	Maroun CR, Rowlands T. The Met receptor tyrosine kinase: a key player in oncogenesis and drug resistance. Pharmacol Ther, 2014, 142(3): 316-338.
23.	Blagotinsek K, Rozman D. Targeting signalling pathways in hepatocellular carcinoma. Curr Pharm Des, 2017, 23(1): 170-175.
24.	Bouattour M, Raymond E, Qin S, et al. Recent developments of c-Met as a therapeutic target in hepatocellular carcinoma. Hepatology, 2018, 67(3): 1132-1149.

1. Huang J, Lok V, Ngai CH, et al. Worldwide burden of, risk factors for, and trends in pancreatic cancer. Gastroenterology, 2021, 160(3): 744-754.
2. Makohon-Moore A, Brosnan JA, Iacobuzio-Donahue CA. Pancreatic cancer genomics: insights and opportunities for clinical translation. Genome Med, 2013, 5(3): 26.
3. Vogelstein B, Papadopoulos N, Velculescu VE, et al. Cancer genome landscapes. Science, 2013, 339(6127): 1546-1558.
4. Lucito R, Suresh S, Walter K, et al. Copy-number variants in patients with a strong family history of pancreatic cancer. Cancer Biol Ther, 2007, 6(10): 1592-1599.
5. 荆薇, 杨向红. 基因异常对胰腺癌预后影响的研究进展. 实用医学杂志, 2019, 35(5): 838-841.
6. Yang M, Zeng L, Ke NW, et al. World Health Organization grading classification for pancreatic neuroendocrine neoplasms: a comprehensive analysis from a large Chinese institution. BMC Cancer, 2020, 20(1): 906.
7. 杨美文, 张雷达. 胰腺癌患病情况及其危险因素分析. 中国医学前沿杂志(电子版), 2017, 9(4): 122-124.
8. Imura Y, Nakai T, Yamada S, et al. Functional and therapeutic relevance of hepatocyte growth factor/c-MET signaling in synovial sarcoma. Cancer Sci, 2016, 107(12): 1867-1876.
9. Zhang Y, Xia M, Jin K, et al. Function of the c-Met receptor tyrosine kinase in carcinogenesis and associated therapeutic opportunities. Mol Cancer, 2018, 17(1): 45.
10. Park S, Choi YL, Sung CO, et al. High MET copy number and MET overexpression: poor outcome in non-small cell lung cancer patients. Histol Histopathol, 2012, 27(2): 197-207.
11. Duplaquet L, Kherrouche Z, Baldacci S, et al. The multiple paths towards MET receptor addiction in cancer. Oncogene, 2018, 37(24): 3200-3215.
12. Chen TH, Chan PC, Chen CL, et al. Phosphorylation of focal adhesion kinase on tyrosine 194 by Met leads to its activation through relief of autoinhibition. Oncogene, 2011, 30(2): 153-166.
13. Maulik G, Kijima T, Ma PC, et al. Modulation of the c-Met/hepatocyte growth factor pathway in small cell lung cancer. Clin Cancer Res, 2002, 8(2): 620-627.
14. Hamacher R, Schmid RM, Saur D, et al. Apoptotic pathways in pancreatic ductal adenocarcinoma. Mol Cancer, 2008, 7: 64.
15. Symeonides SN, Anderton SM, Serrels A. FAK-inhibition opens the door to checkpoint immunotherapy in Pancreatic Cancer. J Immunother Cancer, 2017, 5: 17.
16. Asano T, Yao Y, Shin S, et al. Insulin receptor substrate is a mediator of phosphoinositide 3-kinase activation in quiescent pancreatic cancer cells. Cancer Res, 2005, 65(20): 9164-9168.
17. González MN, de Mello W, Butler-Browne GS, et al. HGF potentiates extracellular matrix-driven migration of human myoblasts: involvement of matrix metalloproteinases and MAPK/ERK pathway. Skelet Muscle, 2017, 7(1): 20.
18. Pascale RM, Feo F, Calvisi DF. An infernal cross-talk between oncogenic β-catenin and c-Met in hepatocellular carcinoma: Evidence from mouse modeling. Hepatology, 2016, 64(5): 1421-1423.
19. Lee KH, Hyun MS, Kim JR. Growth factor-dependent activation of the MAPK pathway in human pancreatic cancer: MEK/ERK and p38 MAP kinase interaction in uPA synthesis. Clin Exp Metastasis, 2003, 20(6): 499-505.
20. Corso S, Giordano S. Cell-autonomous and non-cell-autonomous mechanisms of HGF/MET-driven resistance to targeted therapies: from basic research to a clinical perspective. Cancer Discov, 2013, 3(9): 978-992.
21. Ghiso E, Giordano S. Targeting MET: why, where and how? Curr Opin Pharmacol, 2013, 13(4): 511-518.
22. Maroun CR, Rowlands T. The Met receptor tyrosine kinase: a key player in oncogenesis and drug resistance. Pharmacol Ther, 2014, 142(3): 316-338.
23. Blagotinsek K, Rozman D. Targeting signalling pathways in hepatocellular carcinoma. Curr Pharm Des, 2017, 23(1): 170-175.
24. Bouattour M, Raymond E, Qin S, et al. Recent developments of c-Met as a therapeutic target in hepatocellular carcinoma. Hepatology, 2018, 67(3): 1132-1149.

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Effect of MET overexpression on the prognosis of patients with pancreatic cancer based on bioinformatics analysis

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