基于GEO数据库食管癌关键基因和信号通路分析

doi:10.24920/004148

Abstract

Objective To screen antigen targets for immunotherapy by analyzing over-expressed genes, and to identify significant pathways and molecular mechanisms in esophageal cancer by using bioinformatic methods such as enrichment analysis, protein-protein interaction (PPI) network, and survival analysis based on the Gene Expression Omnibus (GEO) database.

Methods By screening with highly expressed genes, we mainly analyzed proteins MUC13 and EPCAM with transmembrane domain and antigen epitope from TMHMM and IEDB websites. Significant genes and pathways associated with the pathogenesis of esophageal cancer were identified using enrichment analysis, PPI network, and survival analysis. Several software and platforms including Prism 8, R language, Cytoscape, DAVID, STRING, and GEPIA platform were used in the search and/or figure creation.

Results Genes MUC13 and EPCAM were over-expressed with several antigen epitopes in esophageal squamous cell carcinoma (ESCC) tissue. Enrichment analysis revealed that the process of keratinization was focused and a series of genes were related with the development of esophageal cancer. Four genes including ALDH3A1, C2, SLC6A1,and ZBTB7C were screened with significant P value of survival curve.

Conclusions Genes MUC13 and EPCAM may be promising antigen targets or biomarkers for esophageal cancer. Keratinization may greatly impact the pathogenesis of esophageal cancer. Genes ALDH3A1, C2, SLC6A1,and ZBTB7C may play important roles in the development of esophageal cancer.

Key words: GEO, esophageal cancer, antigen, enrichment analysis, survival curve, signaling pathway

An-Yi Song, Lan Mu, Xiao-Yong Dai, Li-Jun Wang, Lai-Qiang Huang. Analysis of Significant Genes and Pathways in Esophageal Cancer Based on Gene Expression Omnibus Database[J].Chinese Medical Sciences Journal, 2023, 38(1): 20-28.

Figures/Tables 8

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References 21

[1]	Liu JX, Zhao L, Zhang XE, et al. Examination of the expression and prognostic significance of DLGAPs in gastric cancer using the TCGA database and bioinformatics analysis. Mol Med Rep 2018; 18 (6): 5621-9. doi: 10.3892/mmr.2018.9574. doi: 10.3892/mmr.2018.9574
[2]	Luo Q, Cui M, Deng QF, et al. Comprehensive analysis of differentially expressed profiles and reconstruction of a competing endogenous RNA network in papillary renal cell carcinoma. Mol Med Rep 2019; 19 (6): 4685-96. doi: 10.3892/mmr.2019.10138. doi: 10.3892/mmr.2019.10138 pmid: 30957192
[3]	Enzinger PC, Mayer RJ. Esophageal Cancer. N Engl J Med 2003; 349: 2241-52. doi: 10.1056/NEJMra035010. doi: 10.1056/NEJMra035010
[4]	Li M, Wang K, Pang Y, et al. SPP1 and FN1 associated with progression and prognosis of esophageal cancer identified by integrated expression profiles analysis. Med Sci Monit 2020; 26: e920355. doi: 10.12659/MSM.920355. doi: 10.12659/MSM.920355
[5]	Li Y, Shi R, Zhu G, et al. Construction of a circular RNA-microRNA-messenger RNA regulatory network of hsa_circ_0043256 in lung cancer by integrated analysis. Thorac Cancer 2022; 13:61-75. doi: 10.1111/1759-7714.14226. doi: 10.1111/1759-7714.14226
[6]	Andrea F, Damian S, Sune F, et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 2013; 41: D808-15. doi: 10.1093/nar/gks1094. doi: 10.1093/nar/gks1094
[7]	Yang R. Application of BiNGO and DAVID in Biological Enrichment Analysis of miR-155 Target Genes. J Fujian Med Univ 2012; 46 (6):408-14.
[8]	Minoru K, Yoko S, Masayuki K, et al. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res 2016; 44: D457-62. doi:10.1093/nar/gkv1070. doi: 10.1093/nar/gkv1070
[9]	Randi V, Overton J A, Greenbaum J A, et al. The immune epitope database (IEDB) 3.0. Nucleic Acids Res 2015; 43: D405-12. doi: 10.1093/nar/gky1006. doi: 10.1093/nar/gky1006
[10]	Liyan Huang, Sheng Zhang. Research progress in MUC13. J Clin Pathol Res 2014; 34 (3):312-6. doi: 10.11714/j.issn.2095-6959.2014.03.018. doi: 10.11714/j.issn.2095-6959.2014.03.018
[11]	Osta W. A. EpCAM Is overexpressed in breast cancer and is a potential target for breast cancer gene therapy. Cancer Res 2004; 64 (16): 5818-24. doi: 10.1158/0008-5472.CAN-04-0754. doi: 10.1158/0008-5472.CAN-04-0754 pmid: 15313925
[12]	Yao QG. Analysis of transmembrane structure of rice aquaporin ospip2: 6 using TMHMM software. Henan Agr 2017; 000(029): 59. doi: CNKI:SUN:NYHN.0.2017-29-037. doi: CNKI:SUN:NYHN.0.2017-29-037
[13]	Wei W, Wang K. Screening and bioinformatics analysis of myocardial infarction markers Based on GEO database chip. J Clin Pathol Res 2019; 39 (1): 27-32.
[14]	Elias PM. Epidermal lipids, membranes, and keratinization. Int J Dermatol 1982; 20 (1): 1-19. doi: 10.1111/ijd.1981.20.issue-1
[15]	Li CY, Jiang FB, Liang Y. Mutations in the filaggrin gene in Chinese patients with ichthyosis vulgaris. Chin J Mod Med 2010; 20 (7): 113-8. doi: 10.1111/j.1365-2133.2010.09740.x. doi: 10.1111/j.1365-2133.2010.09740.x
[16]	Raghunath M. SPINK5 and Netherton syndrome: novel mutations,demonstration of missing LEKTI, and differential expression of transglutaminases. J Invest Dermatol 2004; 123 (3): 474-83. doi: 10.1111/j.0022-202X.2004.23220.x. doi: 10.1111/j.0022-202X.2004.23220.x pmid: 15304086
[17]	Cuil T, Chen Y, Yang L, et al. DSC3 expression is regulated by p53, and methylation of DSC3 DNA is a prognostic marker in human colorectal cancer. Brit J Cancer 2011; 104: 1013-9. doi: 10.1038/bjc.2011.28. doi: 10.1038/bjc.2011.28 pmid: 21364582
[18]	Anaya J. OncoLnc: Linking TCGA survival data to mRNAs, miRNAs, and lncRNAs. Peer J Computer Science 2016; 2(2): e67. doi: 10.7287/PEERJ.PREPRINTS.1780. doi: 10.7287/PEERJ.PREPRINTS.1780
[19]	Patel M, Lu L, Eander DS, et al. ALDH1A1 and ALDH3A 1 expression in lung cancers: correlation with histologic type and potential precursors. Lung Cancer 2008; 59: 340-9. doi: 10.1016/j.lungcan.2007.08.033. doi: 10.1016/j.lungcan.2007.08.033
[20]	Thoeringer CK, Ripke S, Unschuld PG. The GABA transporter 1 (SLC6A1): a novel candidate gene for anxiety disorders. J Neural Transm 2009; 116(6): 649-57. doi: 10.1007/s00702-008-0075-y doi: 10.1007/s00702-008-0075-y pmid: 18607529
[21]	Jeon BN, Kim MK, Choi WI, et al. KR-POK Interacts with p53 and Represses Its Ability to Activate Transcription of p21WAF1/CDKN1A. Cancer Res 2012; 72(5): 1137-48. doi: 10.1158/0008-5472.CAN-11-2433. doi: 10.1158/0008-5472.CAN-11-2433

ID	P value	Log FC	Gene	Gene title
8090180	2.57E-09	-5.84111565	MUC13	Mucin 13, cell surface associated
7964927	1.31E-07	-5.53654183	TSPAN8	Tetraspanin 8
7969288	2.14E-06	-5.3224096	OLFM4	Olfactomedin 4
8098439	1.48E-11	-4.7634735	EPCAM	Epithelial cell adhesion molecule
8114964	5.03E-08	-4.653026	SPINK1	Serine peptidase inhibitor, Kazal type 1
7909164	3.89E-06	-4.58246132	CTSE	Cathepsin E
8125149	1.85E-09	-4.55285303	SLC44A4	Solute carrier family 44 member 4
8135033	7.60E-07	-4.53832027	MUC12	Mucin 12, cell surface associated
8138381	2.84E-06	-4.51515693	AGR2	Anterior gradient 2
8135015	2.25E-09	-4.48195073	MUC3B	Mucin 3B, cell surface associated

Term	Count	%	P value	Benjamini
Epidermis development	23	9.5	3.10E-23	2.70E-20
Keratinocyte differentiation	22	9.1	6.80E-23	3.00E-20
Peptide cross-linking	16	6.6	2.80E-17	8.30E-15
Keratinization	15	6.2	5.10E-16	1.10E-13
Establishment of skin barrier	7	2.9	6.00E-08	1.10E-05
Negative regulation of endopeptidase activity	9	3.7	1.50E-04	2.20E-02
Negative regulation of peptidase activity	4	1.7	1.20E-03	1.50E-01
Wound healing	6	2.5	3.30E-03	3.70E-01
Proteolysis	15	6.2	4.40E-03	4.10E-01
Defense response to fungus	4	1.7	4.60E-03	4.10E-01

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Gene	Antigen epitopes
MUC13	(1) ACDCKSDLQRP; (2) GLACDCKSDLQRP; (3) DLHSEITSLFK; (4) INKAIRSSSSNFLNYD
EPCAM	(1) KPYDSKSLRTAL; (2) AATATFAAA; (3) DVDIADVAY; (4) RAKPEGALQNNDGLYD; (5) IIIELKHKAREK

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