基于R语言子宫内膜癌脂代谢基因差异性分析及免疫组织化学验证
| 作者: |
1刘桐宇,
2胡丹,
3李杰萍,
2朱伟峰,
2许春伟,
1谢榕,
1杨琳,
1邹建平,
1嵇海舟,
1孙阳
1 福建医科大学附属福建省肿瘤医院妇科，福州 350014 2 福建医科大学附属福建省肿瘤医院病理科，福州 350014 3 武警福建总队医院检验科，福州 350003 |
| 通讯: |
孙阳
Email: doctorsunyang@sina.com |
| DOI: | 10.3978/j.issn.2095-6959.2021.04.016 |
| 基金: | 福建省卫生和计划生育委员会医学创新课题(2017-CX-9)；福建省科技计划项目(2018Y2003)。 |
摘要
目的：探讨R语言在基因表达数据库中筛选子宫内膜癌相关的脂质代谢关键分子的可行性。方法：从基因表达数据库(Gene Expression Omnibus，GEO)中筛选子宫内膜癌测序集GSE56087，基于R语言进行基因差异性分析及信号通路分析，从结果中选取可能有意义的高表达基因并进行免疫组织化学验证。结果：通过基因差异性分析、富集分析并结合信号通路分析，筛选出子宫内膜癌组织中代谢相关基因CYP3A4与NR1I2高表达。临床标本免疫组织化学显示CYP3A4与NR1I2在癌与癌旁组织的表达差异有统计学意义(P<0.05)，但均非高表达。CYP3A4与NR1I2在不同子宫内膜癌分期中的表达情况的差异无统计学意义(P>0.05)。结论：R语言对已有基因表达数据库进行基因差异性分析和信号通路分析是可行的。对GSE56087测序结果进行差异性基因分析发现，脂质代谢相关的CPY基因家族、NR基因家族有高表达，尤其CYP3A4与NR1I2，值得后续深入研究。
关键词：
R语言；脂质代谢；内膜癌；免疫组织化学
Differential analysis for lipid metabolism genes in endometrial cancer and immunohistochemical verification based on R language
CorrespondingAuthor: SUN Yang Email: doctorsunyang@sina.com
DOI: 10.3978/j.issn.2095-6959.2021.04.016
Foundation: This work is supported by the Medical Innovation Project of Fujian Provincial Health and Family Planning Commission (2017-CX-9) and the Science and Technology Program of Fujian Province (2018Y2003), China.
Abstract
Objective: To explore the feasibility of screening key molecules of lipid metabolism in endometrial cancer in gene expression database based on R language. Methods: Following screening the endometrial cancer sequence set GSE56087 from the Gene Expression Omnibus (GEO) database, differential analysis of genes and signal pathway analysis were performed based on R language. Potentially significant genes with high expression were selected from the results for immunohistochemical verification. Results: Through genetic difference analysis, enrichment analysis, and signal pathway analysis, high expression of metabolism-related genes CYP3A4 and NR1I2 in endometrial cancer tissues were screened out. Immunohistochemistry of clinical specimens showed that the expression of CYP3A4 and NR1I2 in cancer and adjacent tissues was statistically significant (P<0.05), but they were not highly expressed. There was no significant difference in expression of CYP3A4 and NR1I2 in different endometrial cancer stages (P>0.05). Conclusion: It is feasible to analyze the gene difference and signal pathway in the existing gene expression database with R language. Differential gene analysis of GSE56087 sequence results showed that the CPY gene family and NR gene family related to lipid metabolism were highly expressed, especially CYP3A4 and NR1I2, which are worthy of further research.
Keywords：
R language; lipid metabolism; endometrial cancer; immunohistochemistry