Analysis Of Differential Protein Expression In Hepatoblastoma By Proteomics Combined With Transcriptomics And Its Significance

Objective: Proteomics combined with transcriptomics was used to screen common differentially expressed factors,and to explore the internal mechanism and special biomarkers of hepatoblastoma occurrence and development.Methods: 1.The FFPE samples of hepatoblastoma and adjacent liver tissue were used for protein quantitative analysis using DIA technology,and R language was used for differential expression analysis to find differential proteins;2.In this study,the limma package of R language was used to analyze the differential expression of the GSE131329 matrix data in the CEO database to find differential m RNAs;3.Proteomics combined with transcriptomics to search for common differentially expressed factors,perform function and pathway enrichment analysis,use the STRING database to construct a protein-protein interaction(PPI)network,and use the Oncopression database to verify them.Results: 1.The proteomic analysis of hepatoblastoma and adjacent liver tissue screened out 217 differential proteins,of which 182 were up-regulated and 35 were down-regulated.2.A total of 1326 differential m RNAs were screened out by transcriptomic analysis using GEO data,of which 556 were up-regulated and 770 were down-regulated.3.A total of 11 common differentially expressed factors were obtained by combined proteomic and transcriptomic analysis,and their differential proteins and differential m RNA expression trends were the same.Enrichment analysis found that they were involved in the response and metabolism of xenobiotics,the detoxification of toxic compounds and the treatment of ethanol.The biological process of oxidative metabolism,has molecular functions of glutathione peroxidase activity,glutathione transferase activity,peroxidase activity,and participates in the regulation of P450 metabolism of xenobiotics,glutathione Peptide metabolism,chemical carcinogenesis of DNA adducts,platinum drug resistance and other signaling pathways.The protein interaction analysis of common differential factors found that ADH1 A,ADH4,GSTA1,GSTP1,ALDOB and ACLY formed a network.The Oncopression database was used to verify that the expression levels of 3down-regulated co-expression factors(excepting ADH1 A,including GSTA1,ALDOB and ADH4)in HB tissue samples were significantly decreased(P < 0.05),and The expression levels of 2 up-regulated co-expressed factors(including ACLY and GSTP1)in HB tissue was significantly increased(P < 0.05).Conclusion: In our study,we used proteomics combined with transcriptomics to analyze hepatoblastoma and paracancerous liver tissues for the first time.The analysis results found that GSTA1,GSTP1,ACLY,ALDOB and ADH4 may be potential biomarkers for the diagnosis and treatment of HB.These findings may provide new ideas and theoretical support for future HB research.