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CC BY-NC-ND 4.0 · Horm Metab Res 2023; 55(06): 420-425
DOI: 10.1055/a-2007-2715
Original Article: Endocrine Research

Screening of Therapeutic Targets for Pancreatic Cancer by Bioinformatics Methods

Xiaojie Xiao
1   Department of Oncology and Vascular Interventional Radiology, Zhongshan Hospital Xiamen University, Xiamen, China
,
Zheng Wan
1   Department of Oncology and Vascular Interventional Radiology, Zhongshan Hospital Xiamen University, Xiamen, China
,
Xinmei Liu
2   Animal and Plant Inspection and Quarantine Technology Center Shenzhen Customs, Shenzhen Haiguan, Shenzhen, China
,
Huaying Chen
3   Zhongshan Hospital of Xiamen University, Zhongshan Hospital Xiamen University, Xiamen, China
,
Xiaoyan Zhao
3   Zhongshan Hospital of Xiamen University, Zhongshan Hospital Xiamen University, Xiamen, China
,
Rui Ding
3   Zhongshan Hospital of Xiamen University, Zhongshan Hospital Xiamen University, Xiamen, China
,
Yajun Cao
3   Zhongshan Hospital of Xiamen University, Zhongshan Hospital Xiamen University, Xiamen, China
,
Fangyuan Zhou
3   Zhongshan Hospital of Xiamen University, Zhongshan Hospital Xiamen University, Xiamen, China
,
Enqi Qiu
3   Zhongshan Hospital of Xiamen University, Zhongshan Hospital Xiamen University, Xiamen, China
,
Wenrong Liang
3   Zhongshan Hospital of Xiamen University, Zhongshan Hospital Xiamen University, Xiamen, China
,
Juanjuan Ou
3   Zhongshan Hospital of Xiamen University, Zhongshan Hospital Xiamen University, Xiamen, China
,
Yifeng Chen
3   Zhongshan Hospital of Xiamen University, Zhongshan Hospital Xiamen University, Xiamen, China
,
Xueting Chen
4   Wanbei Coal and Electricity Group General Hospital, Suzhou, China
,
Hongjian Zhang
1   Department of Oncology and Vascular Interventional Radiology, Zhongshan Hospital Xiamen University, Xiamen, China
› Institutsangaben Funding Information Natural Science Foundation of Fujian Province — 2022J05298
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Abstract

Pancreatic cancer (PC) has the lowest survival rate and the highest mortality rate among all cancers due to lack of effective treatments. The objective of the current study was to identify potential therapeutic targets in PC. Three transcriptome datasets, namely GSE62452, GSE46234, and GSE101448, were analyzed for differentially expressed genes (DEGs) between cancer and normal samples. Several bioinformatics methods, including functional analysis, pathway enrichment, hub genes, and drugs were used to screen therapeutic targets for PC. Fisher’s exact test was used to analyze functional enrichments. To screen DEGs, the paired t-test was employed. The statistical significance was considered at p <0.05. Overall, 60 DEGs were detected. Functional enrichment analysis revealed enrichment of the DEGs in “multicellular organismal process”, “metabolic process”, “cell communication”, and “enzyme regulator activity”. Pathway analysis demonstrated that the DEGs were primarily related to “Glycolipid metabolism”, “ECM-receptor interaction”, and “pathways in cancer”. Five hub genes were examined using the protein-protein interaction (PPI) network. Among these hub genes, 10 known drugs targeted to the CPA1 gene and CLPS gene were found. Overall, CPA1 and CLPS genes, as well as candidate drugs, may be useful for PC in the future.

Key words

pancreatic cancer - differentially expressed genes - targets - bioinformatics - treatment

Supplementary Material



Publikationsverlauf

Eingereicht: 05. Dezember 2022

Angenommen nach Revision: 21. Dezember 2022

Accepted Manuscript online:
04. Januar 2023

Artikel online veröffentlicht:
10. Februar 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Raimondi S, Maisonneuve P, Lowenfels AB.. Epidemiology of pancreatic cancer: an overview. Nat Rev Gastroenterol Hepatol 2009; 6: 699-708
    CrossrefPubMedGoogle Scholar
  • 2 Ansari D, Tingstedt B, Andersson B. et al. Pancreatic cancer: yesterday, today and tomorrow. Future Oncol 2016; 12: 1929-1946
    CrossrefPubMedGoogle Scholar
  • 3 Vincent A, Herman J, Schulick R. et al. Pancreatic cancer. Lancet 2011; 378: 607-620
    CrossrefPubMedGoogle Scholar
  • 4 Mizrahi JD, Surana R, Valle JW. et al. Pancreatic cancer. Lancet 2020; 395: 2008-2020
    CrossrefPubMedGoogle Scholar
  • 5 Parola C, Neumeier D, Reddy ST.. Integrating high-throughput screening and sequencing for monoclonal antibody discovery and engineering. Immunology 2018; 153: 31-41
    CrossrefPubMedGoogle Scholar
  • 6 Reuter JA, Spacek DV, Snyder MP.. High-throughput sequencing technologies. Mol Cell 2015; 58: 586-597
    CrossrefPubMedGoogle Scholar
  • 7 Liang W, Zhao Y, Huang W. et al. Non-invasive diagnosis of early-stage lung cancer using high-throughput targeted DNA methylation sequencing of circulating tumor DNA (ctDNA). Theranostics 2019; 9: 2056-2070
    CrossrefPubMedGoogle Scholar
  • 8 Feng Y, Jiang Y, Hao F.. GSK2126458 has the potential to inhibit the proliferation of pancreatic cancer uncovered by bioinformatics analysis and pharmacological experiments. J Transl Med 2021; 19: 373
    CrossrefPubMedGoogle Scholar
  • 9 Wu J, Li Z, Zeng K. et al. Key genes associated with pancreatic cancer and their association with outcomes: A bioinformatics analysis. Mol Med Rep 2019; 20: 1343-1352
    PubMedGoogle Scholar
  • 10 Jiang PF, Zhang XJ, Song CY. et al. S100P acts as a target of miR-495 in pancreatic cancer through bioinformatics analysis and experimental verification. Kaohsiung J Med Sci 2021; 37: 562-571
    CrossrefPubMedGoogle Scholar
  • 11 Kale VP, Habib H, Chitren R. et al. Old drugs, new uses: drug repurposing in hematological malignancies. Semin Cancer Biol 2021; 68: 242-248
    CrossrefPubMedGoogle Scholar
  • 12 Chapman-Shimshoni D, Yuklea M, Radnay J. et al. Simvastatin induces apoptosis of B-CLL cells by activation of mitochondrial caspase 9. Exp Hematol 2003; 31: 779-783
    CrossrefPubMedGoogle Scholar
  • 13 Cho SJ, Kim JS, Kim JM. et al. Simvastatin induces apoptosis in human colon cancer cells and in tumor xenografts, and attenuates colitis-associated colon cancer in mice. Int J Cancer 2008; 123: 951-957
    CrossrefPubMedGoogle Scholar
  • 14 Lin JJ, Ezer N, Sigel K. et al. The effect of statins on survival in patients with stage IV lung cancer. Lung Cancer 2016; 99: 137-142
    CrossrefPubMedGoogle Scholar
  • 15 Ma J, Cai Z, Wei H. et al. The anti-tumor effect of aspirin: what we know and what we expect. Biomed Pharmacother 2017; 95: 656-661
    CrossrefPubMedGoogle Scholar
  • 16 Liu H, Xiong C, Liu J. et al. Aspirin exerts anti-tumor effect through inhibiting Blimp1 and activating ATF4/CHOP pathway in multiple myeloma. Biomed Pharmacother 2020; 125: 110005
    CrossrefPubMedGoogle Scholar
  • 17 Dai X, Yan J, Fu X. et al. Aspirin inhibits cancer metastasis and angiogenesis via targeting heparanase. Clin Cancer Res 2017; 23: 6267-6278
    CrossrefPubMedGoogle Scholar
  • 18 Clough E, Barrett T.. The gene expression omnibus database. Methods Mol Biol 2016; 1418: 93-110
    CrossrefPubMedGoogle Scholar
  • 19 Zeng X, Shi G, He Q. et al. Screening and predicted value of potential biomarkers for breast cancer using bioinformatics analysis. Sci Rep 2021; 11: 20799
    CrossrefPubMedGoogle Scholar
  • 20 Deng JL, Xu YH, Wang G.. Identification of potential crucial genes and key pathways in breast cancer using bioinformatic analysis. Front Genet 2019; 10: 695
    CrossrefPubMedGoogle Scholar
  • 21 Chen S, Yang D, Lei C. et al. Identification of crucial genes in abdominal aortic aneurysm by WGCNA. Peer J 2019; 7: e7873
    CrossrefPubMedGoogle Scholar
  • 22 Rozeveld CN, Johnson KM, Zhang L. et al. KRAS controls pancreatic cancer cell lipid metabolism and invasive potential through the lipase HSL. Cancer Res 2020; 80: 4932-4945
    CrossrefPubMedGoogle Scholar
  • 23 Sunami Y, Rebelo A, Kleeff J.. Lipid metabolism and lipid droplets in pancreatic cancer and stellate cells. Cancers (Basel) 2017; 10
    PubMedGoogle Scholar
  • 24 Patra KC, Kato Y, Mizukami Y. et al. Mutant GNAS drives pancreatic tumourigenesis by inducing PKA-mediated SIK suppression and reprogramming lipid metabolism. Nat Cell Biol 2018; 20: 811-822
    CrossrefPubMedGoogle Scholar
  • 25 Sugar IP, Mizuno NK, Momsen MM. et al. Regulation of lipases by lipid-lipid interactions: implications for lipid-mediated signaling in cells. Chem Phys Lipids 2003; 122: 53-64
    CrossrefPubMedGoogle Scholar
  • 26 Xiao X, Ferguson MR, Magee KE. et al. The Arg92Cys colipase polymorphism impairs function and secretion by increasing protein misfolding. J Lipid Res 2013; 54: 514-521
    CrossrefPubMedGoogle Scholar
  • 27 Zhang G, He P, Tan H. et al. Integration of metabolomics and transcriptomics revealed a fatty acid network exerting growth inhibitory effects in human pancreatic cancer. Clin Cancer Res 2013; 19: 4983-4993
    CrossrefPubMedGoogle Scholar
  • 28 Weyrich P, Albet S, Lammers R. et al. Genetic variability of procolipase associates with altered insulin secretion in non-diabetic Caucasians. Exp Clin Endocrinol Diabetes 2009; 117: 83-87
    Artikel in Thieme ConnectPubMedGoogle Scholar
  • 29 Uhlig R, Contreras H, Weidemann S. et al. Carboxypeptidase A1 (CPA1) Immunohistochemistry is highly sensitive and specific for acinar cell carcinoma (ACC) of the pancreas. Am J Surg Pathol 2022; 46: 97-104
    CrossrefPubMedGoogle Scholar
  • 30 Kemik O, Kemik AS, Sumer A. et al. Serum procarboxypeptidase A and carboxypeptidase A levels in pancreatic disease. Hum Exp Toxicol 2012; 31: 447-451
    CrossrefPubMedGoogle Scholar
  • 31 Witt H, Beer S, Rosendahl J. et al. Variants in CPA1 are strongly associated with early onset chronic pancreatitis. Nat Genet 2013; 45: 1216-1220
    CrossrefPubMedGoogle Scholar