摘要
目的:从生物信息学角度探索与子宫颈癌(CC)相关的生物标记物,为早期诊断提供依据;同时为中医药治疗提供生物信息学证据并为中药治疗的靶点探索提供方向。方法:从基因表达综合数据库(GEO)中检索和下载CC微阵列数据集,鉴定差异表达基因(DEGs)。采用STRING数据库构建宫颈癌DEGs蛋白-蛋白相互作用网络图(PPI网络)。利用Cytoscape中的MCODE识别PPI网络中最显著模块获得hub基因,并使用DAVID数据库对其进行GO分析和KEGG分析。此外,复习当今中医药治疗CC的机制研究,与生物信息学预测的生物标记物进行对比分析,从生物信息学角度验证其有效性。结果:共得到3个微阵列数据集(GSE63514、GSE7410、GSE7803),与非癌组织比较,CC组织中有65个DEGs,其中hub基因15个(degree>10)。GO分析和KEGG分析显示,这些基因的生物学功能主要富集于细胞分裂、细胞周期和DNA复制等。复习当今文献发现许多中药能作用于上述标记物,可从细胞周期阻滞、干扰DNA复制等途径干预肿瘤的发展,与生信分析结果相一致。此外还有一些生物标记物缺少动物、细胞或人体实验的验证,是未来中药抗肿瘤药物的研究方向。结论:微阵列分析可预测早期诊断CC的标记物,为中药的抗肿瘤功效提供生信依据,并为未来中药抗肿瘤的研发提供方向。
Objective: To explore biomarkers related to cervical cancer(CC) from the perspective of bioinformatics,and to provide evidence for the treatment of traditional Chinese medicine(TCM) and provide direction for the exploration of therapeutic targets of TCM. Methods: CC microarray dataset were retrieved and downloaded from gene expression synthesis database(GEO) to identify differentially expressed genes(DEGs). The protein-protein interaction network(PPI network) of DEGs in CC was constructed by STRING database. The key genes(hub genes) were obtained by MCODE in Cytoscape to identify the most important modules in PPI network, and DAVID database was used for gene ontology(GO) analysis and KEGG analysis. In addition, the mechanism of TCM in the treatment of CC was reviewed and compared with bioinformatics markers to verify its effectiveness from the perspective of bioinformatics. Results: A total of 3 microarray datasets(GSE63514, GSE7410, GSE7803) were obtained. Compared with non-cancerous tissues, 65 DEGs were found in CC tissues, among which 15 were hub genes(degree>10) were obtained. GO analysis and KEGG analysis showed that the biological functions of these genes were mainly concentrated in cell division, cell cycle and DNA replication. By reviewing the current literature, we found that many TCMs could act on the above-mentioned markers to inhibit the development of CC, which was consistent with the results of bioinformatics analysis. In addition, some biomarkers were lack of the verification of animal, cell or human experiments, which were also valuable research direction of anti-tumor drugs in the field of Chinese medicine. Conclusion: Microarray analysis can help us predict biomarkers for early diagnosis of CC, provide a basis for the anti-tumor efficacy of TCM, and provide a direction for the research and development of anti-tumor of TCM in the future.
作者
赵小萱
冯晓玲
ZHAO Xiao-xuan;FENG Xiao-ling(Heilongjiang University of Chinese Medicine,Harbin 150040,China;First Affiliated Hospital,Heilongjiang University of Chinese Medicine,Harbin 150040,China)
出处
《中华中医药杂志》
CAS
CSCD
北大核心
2021年第3期1352-1357,共6页
China Journal of Traditional Chinese Medicine and Pharmacy
基金
国家自然科学基金项目(No.81973894,No.81373673)
黑龙江中医药大学研究生创新科研项目(No.2020yjscx003)。
关键词
子宫颈癌
生物标记物
生物信息学
中医药治疗
微阵列
差异表达基因
关键基因
启示
Cervical cancer(CC)
Biomarkers
Bioinformatics
TCM treatment
Microarray
Differentially expressed genes(DEGs)
Key genes
Enlightenment
