Bioinformation-based Analysis Of Key Genes And Pathways In Hepatocellular Carcinoma

Background and AimsHepatocellular carcinoma(HCC)is currently the third most common malignant tumor in the world.It is a complex heterogeneous tumor with high malignancy,morbidity and recurrence rate.Even if surgical intervention is performed on patients with early hepatocellular carcinoma,the recurrence rate is high.The most common cause of HCC is viral hepatitis,including hepatitis B and hepatitis C,followed by alcoholic hepatitis,autoimmune hepatitis,aflatoxin-related hepatitis,and other liver diseases.Epidemiological data show that China is a highly endemic area of Hepatitis B virus(HBV)infection.HBV infection is the main pathogenic factor of HCC,and its development generally follows the hepatitis-cirrhosis-hepatoma trilogy.Regularity;At present,the main type of HCC in China is hepatitis B virus-related liver cancer,which accounts for about 55% of the world's disease.The economic cost of hepatitis and liver cancer is a great economic and social burden for China's and even the world's public health care.The vast majority of patients with chronic HBV infection have poor compliance during the treatment,while HCC usually has no obvious symptoms in the early stage,and the disease progresses rapidly.Most patients with liver cancer have lost the conditions and timing of surgery,resulting in the survival rate of patients.And the quality of life has declined.HCC is less sensitive to radiotherapy and chemotherapy,and it is not possible to create conditions for surgery like neoadjuvant chemotherapy or radiotherapy in other cancer patients.The treatment options for patients with advanced liver cancer are even less.In addition,there are currently no effective drugs and measures to cure HCC,and advanced patients can only accept intervention or palliative treatment.These factors lead to low survival rate,short survival time and poor quality of life in patients with HCC.Therefore,the search for HCC-specific biomarkers or therapeutic targets is one of the major issues that need to be solved urgently.In recent years,with the perfection of genome sequencing,the spread of biochips,and the widespread use of big data in bioinformatics,sequencing genes in liver cancer tissues and analyzing differential genes expressed during tumor production,development,and metastasis,in the early stages of HCC Both clinical diagnosis and treatment have important clinical application value.In this study,the liver cancer-related microarray datasets(GSE25097,GSE54236)were downloaded from the public gene expression database(gene expression omnibus,GEO),and the differentially expressed genes(DEGs)were screened using the R language,followed by differentially expressed genes.Gene ontology(GO)functional enrichment analysis,Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway enrichment analysis,protein-protein interaction(PPI)network analysis At the same time,the clinical data in the Cancer Genome Atlas(TCGA)was used to analyze the survival of the selected core genes;it provided ideas for the diagnosis,treatment and evaluation of prognosis indicators of liver cancer.Materials and Methods1.Screen out the eligible liver cancer chip data sets GSE25097 and GSE54236 from the GEO database.The above data sets are all human-derived genome-wide RNA expression chips.After removing the normal donors and cirrhosis patients,the former contains 211 pairs of liver cancer tissue specimens and paracancerous tissue specimens,and the latter includes 77 pairs of liver cancer.Tissue and adjacent tissue specimens.2.The R data software package was used to preprocess the original data,and the differentially expressed genes were screened by |lg FC|>1.5,P<0.05.3.The online tool DAVID database(the Database for Annotation,Visualization,and Integrated Discovery,DAVID)was used to perform GO functional gene enrichment analysis and KEGG pathway enrichment analysis on the top ten differentially expressed genes in the two data sets.4.Use the String(the Search Tool for the Retrieval of Interacting Genes,String)database to map the protein-protein interaction network of the differentially expressed genes,and further screen out the target genes.5.Finally,the clinical data of liver cancer patients were downloaded from the cancer genome map,and the Kaplan-Meiter survival curve of target gene and prognosis was drawn using the online tool GEPIA(Gene Expression Profiling Interactive Analysis,GEPIA)database.Results1.Analysis of two data sets of GSE25097 and GSE54236,yielding 69 differentially expressed genes,including 16 up-regulated genes and 53 down-regulated genes.2.GO functional enrichment analysis showed that differentially expressed genes play a role in cell adhesion,steroid metabolism,mucopolysaccharide decomposition,etc.KEGG pathway enrichment analysis showed that differentially expressed genes play a regulatory role in cytochrome P450 metabolism and arachidonic acid metabolism.3.The PPI network map suggests that CYP1A2,CYP2E1,CYP2C8,CYP2C9,and TOP2 A may play important regulatory roles in the development of liver cancer.4.In HCC patients,the mRNA expression levels of CYP2E1,CYP2C8,CYP2C9,and TOP2 A may be correlated with overall survival(OS);while the expression of CYP1A2 mRNA is not correlated with OS.Conclusion1.TOP2 A is up-regulated in hepatocarcinoma tissues,and CYP1A2,CYP2E1,CYP2C8,and CYP2C9 are decreased in liver cancer tissues;2.In HCC patients,the expression levels of CYP2E1,CYP2C8,CYP2C9,and TOP2 A were correlated with OS.The higher the expression levels of CYP2E1,CYP2C8,and CYP2C9,the longer the patient's OS;the higher the TOP2 A expression,the shorter the patient's OS.The expression level of CYP1A2 was not significantly correlated with the patient's OS;3.These five target genes may play a vital role in the development of liver cancer,and may also become a new biomarker for the diagnosis or prognosis of liver cancer,and even more likely to become a new target for liver cancer treatment.Point may also provide a new idea for future basic research and targeted therapy.