Screening of core genes in pediatric hepatoblastoma based on omics data mining and co-expression network model
10.3760/cma.j.cn431274-20200228-00207
- VernacularTitle:基于组学数据挖掘及共表达网络模型的小儿肝母细胞瘤核心基因筛选研究
- Author:
Xuandong WEI
;
Ailian WANG
;
Jun QIU
;
Peijun JIA
;
Fang QU
;
Jisha ZHANG
;
Xiaoyu ZHOU
;
Chunxiang LUO
- From:
Journal of Chinese Physician
2021;23(2):240-244
- CountryChina
- Language:Chinese
-
Abstract:
Objective:Based on the microarray data mining method, the function and pathway of differential genes were analyzed after the differential genes were screened. At the same time, the core genes that determine the prognosis of pediatric hepatoblastoma were screened by coexpression network, and their predictive ability was evaluated.Methods:The microarray expression profile of pediatric hepatoblastoma used in this study was from the European Institute of bioinformatics (http: //www.ebi.ac.uk/embl/). The deadline for data collection was December 31, 2018. Firstly, the differentially expressed genes (gene expression level increased to 2 times or decreased to 1/2 of the original) were screened by SAM method, then the core genes were screened by coexpression network model based on dimension reduction principle, and the gene regulation evaluation score was calculated by MCODE algorithm to evaluate its regulation ability in the whole network model.Results:According to the enrichment results of 213 differentially expressed genes, the highest enrichment degree of signal pathway was metabolic pathways (2 122.529). The misjudgment rate of signal pathway enrichment results was less than 0.001, and the misjudgment rate was statistically significant by SAM method ( P<0.001). A total of 213 differentially expressed genes in different prognosis groups were used as the basis for the construction of the coexpression network. A total of 12 differentially expressed genes were included in the coexpression network. Using the poor prognosis group as the experimental group, and the better prognosis group as the control group, the MCODE algorithm was used to calculate the gene regulatory ability score. The results showed that the highest gene for determining the prognosis control ability of children hepatblastoma was ADH1A gene with a score of 19. In addition, the regulatory ability scores of HAO1, ADH1B, ALDOB and DPYS genes were higher than or close to 5, so they could be considered as the core genes in the coexpression network module. Conclusions:According to the results of coexpression network model, ADH1A gene is closely related to the occurrence and development of hepatoblastoma in children, and its molecular biological evidence needs to be further explored to guide the clinical development of tumor targeted intervention therapy.
