Exploring the role of VCAN in the prognosis of esophageal squamous cell carcinoma based on bioinformatics data_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

LU Sifen ¹ , WEI Xiaozhen ² , MOU Biqin ¹ , HU Qiongxia ¹ , DENG Zhujun ¹ ,  ZHANG Wengeng ¹

1. Precision Medicine Key Laboratory of Sichuan Province and Precision Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
2. Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;

Corresponding author：

ZHANG Wengeng, Email: wengeng.zhang@wchscu.cn

Keywords：

Esophageal squamous cell carcinoma; prognosis; versican (VCAN); bioinformation

DOI：

10.7507/1007-4848.202201072

Video：

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Objective To explore the role of versican (VCAN) in ESCC prognosis based on bioinformatics data. Methods First, three RNA microarray datasets of ESCC were downloaded from GEO database, which were then integrated and used to explore differentially expressed genes (DEGs). The subsequent analysis was conducted based on the results of these DEGs: (1) The STRING database was used to construct a protein-protein interaction (PPI) network; (2) molecular complex detection software was used to analyze the modules of the PPI network, of which the most significant modules were chosen, and hub genes were the genes included in the chosen modules; (3) high-throughput RNA sequencing data from TCGA and GTEx databases were used to verify the expression of these hub genes to confirm whether they were differentially expressed; (4) the survival curve analysis of confirmed DEGs was conducted to select genes that had significant influence on the survival of ESCC; (5) TIMER database was used to analyze the relationship between the gene expression of VCAN and the abundance of tumor-infiltrating immune cells (TIICs) and gene markers in these cells; (6) Targetscan and miRDB software were used to predict the miRNAs that could regulate VCAN, and Cytoscape software was used to construct the regulatory network. Results A total of 630 DEGs and 32 hub genes were found, of which VCAN was an up-regulated DEG, and high expression of VCAN was significantly associated with poor prognosis of ESCC. Moreover, VCAN could also play a role in the immune microenvironment of ESCC, which was mainly manifested by a significant positive correlation between the abundance of VCAN and the abundance of M2 macrophages gene markers, some of which had been reported to be associated with poor prognosis of ESCC. Finally, we also found that VCAN could be regulated by 15 miRNAs in ESCC, some of which had been reported to be associated with ESCC prognosis. Conclusion This study provides direct and indirect comprehensive evidence for the role of VCAN in ESCC prognosis. The direct evidence is that the survival curve shows that highly expressed VCAN is significantly associated with the poor prognosis of ESCC, and the indirect evidence is that VCAN is positively related to some markers which indicate poor prognosis in the ESCC immune microenvironment, and VCAN can be regulated by some prognostic miRNAs in ESCC.

Citation： LU Sifen, WEI Xiaozhen, MOU Biqin, HU Qiongxia, DENG Zhujun, ZHANG Wengeng. Exploring the role of VCAN in the prognosis of esophageal squamous cell carcinoma based on bioinformatics data. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2022, 29(8): 1031-1041. doi: 10.7507/1007-4848.202201072 Copy

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40.	Li CY, Zhang WW, Xiang JL, et al. Identification of microRNAs as novel biomarkers for esophageal squamous cell carcinoma: A study based on The Cancer Genome Atlas (TCGA) and bioinformatics. Chin Med J (Engl), 2019, 132(18): 2213-2222.
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42.	Liu J, Geng X, Hou J, et al. New insights into M1/M2 macrophages: Key modulators in cancer progression. Cancer Cell Int, 2021, 21(1): 389.

1. Wang L, Han H, Wang Z, et al. Targeting the microenvironment in esophageal cancer. Front Cell Dev Biol, 2021, 9: 684966.
2. Cao L, Wang X, Zhu G, et al. Traditional Chinese medicine therapy for rsophageal cancer: A literature review. Integr Cancer Ther, 2021, 20: 15347354211061720.
3. Yamamoto S, Kato K. Immuno-oncology for esophageal cancer. Future Oncol, 2020, 16(32): 2673-2681.
4. Munari FF, Santos WD, Evangelista AF, et al. Profile of esophageal squamous cell carcinoma mutations in Brazilian patients. Sci Rep, 2021, 11(1): 20596.
5. He S, Xu J, Liu X, et al. Advances and challenges in the treatment of esophageal cancer. Acta Pharm Sin B, 2021, 11(11): 3379-3392.
6. Papadas A, Arauz G, Cicala A, et al. Versican and versican-matrikines in cancer progression, inflammation, and immunity. J Histochem Cytochem, 2020, 68(12): 871-885.
7. Wight TN, Kang I, Evanko SP, et al. Versican—A 45critical extracellular matrix regulator of immunity and inflammation. Front Immunol, 2020, 11: 512.
8. Cheng Y, Sun H, Wu L, et al. VUp-Regulation of VCAN promotes the proliferation, invasion and migration and serves as a biomarker in gastric cancer. Onco Targets Ther, 2020, 13: 8665-8675.
9. Zhang Y, Zou X, Qian W, et al. Enhanced PAPSS2/VCAN sulfation axis is essential for Snail-mediated breast cancer cell migration and metastasis. Cell Death Differ, 2019, 26(3): 565-579.
10. Yang L, Wang L, Yang Z, et al. Up-regulation of EMT-related gene VCAN by NPM1 mutant-driven TGF-β/cPML signalling promotes leukemia cell invasion. J Cancer, 2019, 10(26): 6570-6583.
11. Li J, Chen Z, Tian L, et al. LncRNA profile study reveals a three-lncRNA signature associated with the survival of patients with oesophageal squamous cell carcinoma. Gut, 2014, 63(11): 1700-1710.
12. Li W, Zhang L, Guo B, et al. Exosomal FMR1-AS1 facilitates maintaining cancer stem-like cell dynamic equilibrium via TLR7/NFκB/c-Myc signaling in female esophageal carcinoma. Mol Cancer, 2019, 18(1): 22.
13. Li Y, Lu Z, Che Y, et al. Immune signature profiling identified predictive and prognostic factors for esophageal squamous cell carcinoma. Oncoimmunology, 2017, 6(11): e1356147.
14. Shi X, Chen Z, Hu X, et al. AJUBA promotes the migration and invasion of esophageal squamous cell carcinoma cells through upregulation of MMP10 and MMP13 expression. Oncotarget, 2016, 7(24): 36407-36418.
15. Liu J, Wang Y, Chu Y, et al. Identification of a TLR-induced four-lncRNA signature as a novel prognostic biomarker in Esophageal Carcinoma. Front Cell Dev Biol, 2020, 8: 649.
16. Shabalin AA, Tjelmeland H, Fan C, et al. Merging two gene-expression studies via cross-platform normalization. Bioinformatics, 2008, 24(9): 1154-1160.
17. Ritchie ME, Phipson B, Wu D, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res, 2015, 43(7): e47.
18. Li L, Zhu Z, Zhao Y, et al. FN1, SPARC, and SERPINE1 are highly expressed and significantly related to a poor prognosis of gastric adenocarcinoma revealed by microarray and bioinformatics. Sci Rep, 2019, 9(1): 7827.
19. Szklarczyk D, Gable AL, Lyon D, et al. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res, 2019, 47(D1): D607-D613.
20. Shannon P, Markiel A, Ozier O, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res, 2003, 13(11): 2498-2504.
21. Bader GD, Hogue CW. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 2003, 4(1): 2.
22. Xie C, Mao X, Huang J, et al. KOBAS 2.0: A web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res, 2011, 39(Web Server issue): W316-W322.
23. Benjamini Y, Hochberg Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol, 1995, 57(1): 289-300.
24. Tang Z, Li C, Kang B, et al. GEPIA: A web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res, 2017, 45(W1): W98-W102.
25. Li T, Fan J, Wang B, et al. TIMER: A web server for comprehensive analysis of tumor-infiltrating immune cells. Cancer Res, 2017, 77(21): e108-e110.
26. Li B, Severson E, Pignon JC, et al. Comprehensive analyses of tumor immunity: Implications for cancer immunotherapy. Genome Biol, 2016, 17(1): 174.
27. Agarwal V, Bell GW, Nam JW, et al. Predicting effective microRNA target sites in mammalian mRNAs. Elife, 2015, 4: e05005.
28. Lu J, Clark AG. Impact of microRNA regulation on variation in human gene expression. Genome Res, 2012, 22(7): 1243-1254.
29. Wong N, Wang X. miRDB: An online resource for microRNA target prediction and functional annotations. Nucleic Acids Res, 2015, 43(D1): D146-D152.
30. Lei Y, Jin X, Sun M, et al. miR-129-5p ameliorates ischemic brain injury by binding to SIAH1 and activating the mTOR signaling pathway. J Mol Neurosci, 2021, 71(9): 1761-1771.
31. 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(2): 1343-1352.
32. Feng J, Zhang H, Li F. Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model. BMC Bioinformatics, 2021, 22(1): 47.
33. Wang M, Liu D, Huang Y, et al. Identification of key genes related to the prognosis of esophageal squamous cell carcinoma based on chip re-annotation. Appl Sci, 2021, 11(7): 3229.
34. Zhang H, Zhong J, Tu Y, et al. Integrated bioinformatics analysis identifies Hub genes associated with the pathogenesis and prognosis of esophageal squamous cell carcinoma. Biomed Res Int, 2019, 2019: 2615921.
35. Harjunp H, Asens ML, Guenther C, et al. Cell adhesion molecules and their roles and regulation in the immune and tumor microenvironment. Front Immunol, 2019, 10: 1078.
36. Hu JM, Liu K, Liu JH, et al. CD163 as a marker of M2 macrophage, contribute to predict aggressiveness and prognosis of Kazakh esophageal squamous cell carcinoma. Oncotarget, 2017, 8(13): 21526-21538.
37. Shigeoka M, Urakawa N, Nakamura T, et al. Tumor associated macrophage expressing CD204 is associated with tumor aggressiveness of esophageal squamous cell carcinoma. Cancer Sci, 2013, 104(8): 1112-1119.
38. Mukaka MM. Statistics corner: A guide to appropriate use of correlation coefficient in medical research. Malawi Med J, 2012, 24(3): 69-71.
39. Quail DF, Joyce JA. Microenvironmental regulation of tumor progression and metastasis. Nat Med, 2013, 19(11): 1423-1437.
40. Li CY, Zhang WW, Xiang JL, et al. Identification of microRNAs as novel biomarkers for esophageal squamous cell carcinoma: A study based on The Cancer Genome Atlas (TCGA) and bioinformatics. Chin Med J (Engl), 2019, 132(18): 2213-2222.
41. Yamauchi N, Kanke Y, Saito K, et al. Stromal expression of cancer associated fibroblast related molecules, versican and lumican, is strongly associated with worse relapse free and overall survival times in patients with esophageal squamous cell carcinoma. Oncol Lett, 2021, 21(6): 445.
42. Liu J, Geng X, Hou J, et al. New insights into M1/M2 macrophages: Key modulators in cancer progression. Cancer Cell Int, 2021, 21(1): 389.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Exploring the role of VCAN in the prognosis of esophageal squamous cell carcinoma based on bioinformatics data

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