Journal of Shanghai Jiao Tong University (Medical Science) >

2021 , Vol. 41 >Issue 9: 1197 - 1206

DOI: https://doi.org/10.3969/j.issn.1674-8115.2021.09.010

Basic research

Construction of a metastasis prediction model of microsatellite instability-high colorectal cancer based on differentially expressed gene assembly

  • Ying XU ,
  • Yi-min CHU ,
  • Da-ming YANG ,
  • Ji LI ,
  • Hai-qin ZHANG ,
  • Hai-xia PENG
Expand
  • Endoscopy Center, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200336, China
PENG Hai-xia, E-mail: phx1101@shtrhospital.com.

Received date: 2021-03-10

  Online published: 2021-08-24

Supported by

Fund of Shanghai Municipal Science and Technology Committee(18ZR1434900);General Program of Science and Technology Commission of Changning District of Shanghai(CNKW2018Y02);Interdisciplinary Program of Shanghai Jiao Tong University(ZH2018QNB24);Research Fund of Shanghai Sixth People′s Hospital Medical Group(ly202003)

Fold

Abstract

Objective

·To explore the potential key genes and the gene expression characteristics of microsatellite instability-high (MSI-H) colorectal cancer (CRC) with metastasis at the transcriptome level, and establish a metastasis prediction gene model.

Methods

·The transcriptome data of MSI-H CRC patients was obtained from The Cancer Genome Atlas database. The patients were divided into metastatic group (21 patients) and non-metastatic group (42 patients). The differentially expressed genes (DEGs) between the two groups were analyzed by Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA) to annotate, and cluster DEGs and enrich the signaling pathways. STRING and Cytoscape were used to select the hub genes. Nomogram was drawn based on the selected DEGs. The cross validation of the model was performed by Bootstrap method. Survival analysis was done to explore the influences of each gene in the nomogram on progression-free survival (PFS) of MSI-H CRC.

Results

·A total of 245 DEGs were obtained from the metastatic group and non-metastatic group, among which 204 genes were up-regulated and 41 genes were down-regulated. GO analysis showed that DEGs were mainly clustered in ion transmembrane transport, chloride transmembrane transport and chloride channel activity in terms of biological process and molecular function. In terms of cellular component, DEGs were mainly clustered in extracellular region and extracellular space. GSEA showed that the neuroactive ligand-receptor interaction and metabolic pathways were enriched in the up-regulated genes. The top 10 hub genes in the protein-protein interaction network of the up-regulated genes were screened by Cytoscape. The metastasis prediction gene model, which was set up based on the top 10 DEGs with the lowest adjusted P value and high physiological relevance to tumor, had certain predictive efficiency [area under curve (AUC)=0.975 for training, AUC=0.920 for validation]. The expression levels of AC078993.1 and IGLJ2 (immunoglobulin lambda joining 2) were significantly negatively correlated with PFS of MSI-H CRC (P=0.011, P=0.005).

Conclusion

·The changes in ion channels and extracellular environment may have important impacts on metastasis of MSI-H CRC. Neuroactive ligand-receptor interaction and metabolic pathways may be two important signaling pathways for metastasis of MSI-H CRC. A metastasis prediction gene model is established, which can provide reference for the follow-up related clinical researches.

Key words： colorectal cancer (CRC); microsatellite instability-high (MSI-H); metastasis; differentially expressed gene (DEG); bioinformatics; nomogram

Cite this article

Ying XU , Yi-min CHU , Da-ming YANG , Ji LI , Hai-qin ZHANG , Hai-xia PENG . Construction of a metastasis prediction model of microsatellite instability-high colorectal cancer based on differentially expressed gene assembly[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2021 , 41(9) : 1197 -1206 . DOI: 10.3969/j.issn.1674-8115.2021.09.010

References

1 Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2018, 68(6): 394-424.
2 郑荣寿, 孙可欣, 张思维, 等. 2015年中国恶性肿瘤流行情况分析[J]. 中华肿瘤杂志, 2019, 41(1): 19-28.
3 Schreuders EH, Ruco A, Rabeneck L, et al. Colorectal cancer screening: a global overview of existing programmes[J]. Gut, 2015, 64(10): 1637-1649.
4 Siegel RL, Miller KD, Fedewa SA, et al. Colorectal cancer statistics, 2017[J]. CA Cancer J Clin, 2017, 67(3): 177-193.
5 Edwards BK, Ward E, Kohler BA, et al. Annual report to the nation on the status of cancer, 1975?2006, featuring colorectal cancer trends and impact of interventions (risk factors, screening, and treatment) to reduce future rates[J]. Cancer, 2010, 116(3): 544-573.
6 Sargent D, Sobrero A, Grothey A, et al. Evidence for cure by adjuvant therapy in colon cancer: observations based on individual patient data from 20, 898 patients on 18 randomized trials[J]. J Clin Oncol, 2009, 27(6): 872-877.
7 Copija A, Waniczek D, Witko? A, et al. Clinical significance and prognostic relevance of microsatellite instability in sporadic colorectal cancer patients[J]. Int J Mol Sci, 2017, 18(1): E107.
8 Vilar E, Gruber SB. Microsatellite instability in colorectal cancer-the stable evidence[J]. Nat Rev Clin Oncol, 2010, 7(3): 153-162.
9 Boland CR, Thibodeau SN, Hamilton SR, et al. A National Cancer Institute Workshop on Microsatellite Instability for cancer detection and familial predisposition: development of international criteria for the determination of microsatellite instability in colorectal cancer[J]. Cancer Res, 1998, 58(22): 5248-5257.
10 Cohen R, Svrcek M, Dreyer C, et al. New therapeutic opportunities based on DNA mismatch repair and BRAF status in metastatic colorectal cancer[J]. Curr Oncol Rep, 2016, 18(3): 18.
11 Latham A, Srinivasan P, Kemel Y, et al. Microsatellite instability is associated with the presence of lynch syndrome pan-cancer[J]. J Clin Oncol, 2019, 37(4): 286-295.
12 S?reide K, Nedreb? BS, S?reide JA, et al. Lymph node harvest in colon cancer: influence of microsatellite instability and proximal tumor location[J]. World J Surg, 2009, 33(12): 2695-2703.
13 Buckowitz A, Knaebel HP, Benner A, et al. Microsatellite instability in colorectal cancer is associated with local lymphocyte infiltration and low frequency of distant metastases[J]. Br J Cancer, 2005, 92(9): 1746-1753.
14 Malesci A, Laghi L, Bianchi P, et al. Reduced likelihood of metastases in patients with microsatellite-unstable colorectal cancer[J]. Clin Cancer Res, 2007, 13(13): 3831-3839.
15 Kim CG, Ahn JB, Jung M, et al. Effects of microsatellite instability on recurrence patterns and outcomes in colorectal cancers[J]. Br J Cancer, 2016, 115(1): 25-33.
16 Venderbosch S, Nagtegaal ID, Maughan TS, et al. Mismatch repair status and BRAF mutation status in metastatic colorectal cancer patients: a pooled analysis of the CAIRO, CAIRO2, COIN, and FOCUS studies[J]. Clin Cancer Res, 2014, 20(20): 5322-5330.
17 Kawakami H, Zaanan A, Sinicrope FA. Microsatellite instability testing and its role in the management of colorectal cancer[J]. Curr Treat Options Oncol, 2015, 16(7): 30.
18 Colle R, Cohen R, Cochereau D, et al. Immunotherapy and patients treated for cancer with microsatellite instability[J]. Bull Cancer, 2017, 104(1): 42-51.
19 Liu Y, Sethi NS, Hinoue T, et al. Comparative molecular analysis of gastrointestinal adenocarcinomas[J]. Cancer Cell, 2018, 33(4): 721-735.e8.
20 Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data[J]. Bioinformatics, 2010, 26(1): 139-140.
21 Shannon P, Markiel A, Ozier O, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks[J]. Genome Res, 2003, 13(11): 2498-2504.
22 Wang SD, Yang L, Ci B, et al. Development and validation of a nomogram prognostic model for SCLC patients[J]. J Thorac Oncol, 2018, 13(9): 1338-1348.
23 Markowitz SD, Bertagnolli MM. Molecular origins of cancer: molecular basis of colorectal cancer[J]. N Engl J Med, 2009, 361(25): 2449-2460.
24 Popat S, Hubner R, Houlston RS. Systematic review of microsatellite instability and colorectal cancer prognosis[J]. J Clin Oncol, 2005, 23(3): 609-618.
25 Koslowski M, Türeci O, Huber C, et al. Selective activation of tumor growth-promoting Ca2+channel MS4A12 in colon cancer by caudal type homeobox transcription factor CDX2[J]. Mol Cancer, 2009, 8: 77.
26 Bie FL, Wang GH, Qu X, et al. Loss of FGL1 induces epithelial?mesenchymal transition and angiogenesis in LKB1 mutant lung adenocarcinoma[J]. Int J Oncol, 2019, 55(3): 697-707.
27 Nayeb-Hashemi H, Desai A, Demchev V, et al. Targeted disruption of fibrinogen like protein-1 accelerates hepatocellular carcinoma development[J]. Biochem Biophys Res Commun, 2015, 465(2): 167-173.
28 Chai ZB, Wang L, Zheng YB, et al. PADI3 plays an antitumor role via the Hsp90/CKS1 pathway in colon cancer[J]. Cancer Cell Int, 2019, 19: 277.
29 Chang XT, Chai ZB, Zou JR, et al. PADI3 induces cell cycle arrest via the Sirt2/AKT/p21 pathway and acts as a tumor suppressor gene in colon cancer[J]. Cancer Biol Med, 2019, 16(4): 729-742.
30 Prevarskaya N, Skryma R, Shuba Y. Ion channels in cancer: are cancer hallmarks oncochannelopathies?[J]. Physiol Rev, 2018, 98(2): 559-621.
31 Lui VC, Lung SS, Pu JK, et al. Invasion of human glioma cells is regulated by multiple chloride channels including ClC-3[J]. Anticancer Res, 2010, 30(11): 4515-4524.
32 Siveen KS, Raza A, Ahmed EI, et al. The role of extracellular vesicles as modulators of the tumor microenvironment, metastasis and drug resistance in colorectal cancer[J]. Cancers (Basel), 2019, 11(6): E746.
33 la Vecchia S, Sebastián C. Metabolic pathways regulating colorectal cancer initiation and progression[J]. Semin Cell Dev Biol, 2020, 98: 63-70.
34 Kasprzak A, Adamek A. The neuropeptide system and colorectal cancer liver metastases: mechanisms and management[J]. Int J Mol Sci, 2020, 21(10): 3494.
35 Qiu SY, Nikolaou S, Zhu J, et al. Characterisation of the expression of neurotensin and its receptors in human colorectal cancer and its clinical implications[J]. Biomolecules, 2020, 10(8): 1145.
36 Liu Y, He JJ, Xu JH, et al. Neuroendocrine differentiation is predictive of poor survival in patients with stage Ⅱ colorectal cancer[J]. Oncol Lett, 2017, 13(4): 2230-2236.
37 Yamamoto N, Oshima T, Yoshihara K, et al. Clinicopathological significance and impact on outcomes of the gene expression levels of IGF-1, IGF-2 and IGF-1R, IGFBP-3 in patients with colorectal cancer: overexpression of the IGFBP-3 gene is an effective predictor of outcomes in patients with colorectal cancer[J]. Oncol Lett, 2017, 13(5): 3958-3966.
38 Yang LS, Li JY, Fu SZ, et al. Up-regulation of insulin-like growth factor binding protein-3 is associated with brain metastasis in lung adenocarcinoma[J]. Mol Cells, 2019, 42(4): 321-332.
39 Loboda A, Nebozhyn MV, Watters JW, et al. EMT is the dominant program in human colon cancer[J]. BMC Med Genomics, 2011, 4: 9.
40 Schell MJ, Yang ML, Missiaglia E, et al. A composite gene expression signature optimizes prediction of colorectal cancer metastasis and outcome[J]. Clin Cancer Res, 2016, 22(3): 734-745.
Options
Outlines

/

〈 〉