Study On Related Genes Of Liver Cancer

Contact: Audrey Hu (Whatsapp:008613880143964) Email: audrey.hu@wecistanche.com

Hepatocellular carcinoma (hepatocellular carcinoma, HCC) is currently the third malignant tumor in the world in terms of mortality [1]. According to the latest cancer report, in China, the incidence of liver cancer is only after lung cancer and gastric cancer [2]. The key to improving the prognosis of liver cancer patients is early diagnosis and screening, so as to avoid delay in diagnosis and treatment. On the other hand, diabetes is a common metabolic and endocrine disease, and it is also another major threat to human health [3].

Diabetes can cause damage to multiple organs in the body, leading to various complications, such as diabetic nephropathy, diabetic eye disease, diabetic foot, and diabetic cardiovascular complications. At the same time, the number of diabetic patients in China is increasing year by year. As of 2013, the prevalence of diabetes among Chinese adults was about 10.9% [4].

A number of meta-analyses and clinical studies have observed the effect of diabetes on the survival of patients with liver cancer. The analysis results suggest that diabetes is an independent risk factor for the prognosis of liver cancer [5,6], but its specific mechanism is still unclear. In recent years, with the development of high-throughput chip technology, it is possible to use gene expression profiling to study the core genes of liver cancer and diabetes [7]. In this study, we downloaded the gene expression matrices of liver cancer and type 2 diabetes from public databases, screened out overlapping differential genes by comparing the disease group and healthy control group, and then mined the common differential genes and organisms that lead to type 2 diabetes and liver cancer. To provide a reference for the study of diabetes-related liver cancer, the specific physiological and pathological mechanisms need further experimental verification.

1 Materials and methods

1.1 Data source and preprocessing: Gene expression data were downloaded from the GEO (Gene Expression Omnibus) database of the National Center for Biotechnology Information (NCBI). The database GSE7014 includes 20 type 2 diabetes samples and 6 healthy control samples [8], and the database GSE45267 includes 48 liver cancer samples in situ and 39 normal tissue samples. The database GSE121248 contains 69 liver cancer samples and 38 paracancerous samples, all of which are based on GPL570 (Affymetrix Human Genome U133 Plus 2. 0 Array). Data was preprocessed using the "Affy" R package.

1.2 Screening differential genes: We used the "limma" R package to screen differentially expressed genes (DEGs) between the disease group and the normal control group [9]. The threshold is set as FDR (false discovery rate) <0.05, |log2FC|>1.5. Heatmaps of DEGs were drawn using the "heatmap" R package.

1.3 Gene Ontology (GO), Protein ⁃ Protein Interaction (PPI) and core gene identification Feature enrichment analysis, plotted using the "ggplot2" R package. The threshold was set to FDR<0.05. Then use the STRING database (https://string-db.org) to study the interaction between common differential genes and proteins. Then import Cytoscape software to construct the PPI network, the threshold score > 0.7 is the critical value, and the core node protein is screened by the MCODE plug-in. Finally, the core genes were verified by the online database ONCOMINE (https://oncomine.org).

1.4 Survival analysis: Survival analysis of core genes was performed using the online database GEPIA (http://gepia.cancer-pku.cn/).

2 results

2.1 Differential gene screening results

The results of screening the liver cancer database GSE45267 showed that 1 694 DEGs were obtained (921 up-regulated, 773 down-regulated), and the liver cancer database GSE121248 was also screened to obtain 1 925 DEGs (1 009 up-regulated, 914 down-regulated, Figure 1A). Among them, there are 1235 intersection DEGs (Fig. 1B). The diabetes database GSE7014 was then screened, and a total of 2 508 differential genes (1 253 up-regulated, 1 254 down-regulated, Figure 1C) were obtained. The intersection of liver cancer DEGs and diabetes DEGs were obtained to obtain 328 overlapping DEGs, of which 78 were common DEGs. Up-regulated DEGs1 and 98 down-regulated DEGs3. Furthermore, 82 genes downregulated in type 2 diabetes (DEGs2) and 70 genes downregulated in liver cancer (DEGs4) were considered to be DEGs independent of liver cancer or diabetes.

2.2 Gene enrichment analysis

We then performed GO analysis on the four types of DEGs. The results showed that among the common differential genes, the biological functions of DEGs1 were mainly enriched in regulating DNA damage and repair, and the functions of DEGs3 were mainly chemical stimulation, defense response, inflammatory response, and apoptosis. and programmed death. Among the independent differential genes, DEGs2 analysis results were not statistically different (FDR>0.05), while DEGs4 may be related to the catabolism of organic nitrogen compounds.

2.3 Validation of protein interaction network and core genes

Next, we picked the common differential genes (DEGs1 and DEGs3) for PPI analysis using the STRING database, and screened out 8 core node proteins, which were serine protease family E member 1 (serpin family E member 1, SERPINE1), clusterin ( clusterin, CLU), thrombospondin 1 (thrombospon-)

2.4 Prognostic analysis of core genes

We performed survival analysis on 8 core genes based on the gene expression data and clinical information of liver cancer patients in the GEPIA database. Among them, high expression of MSH2, OIP5, AURKA, SMC4, and SERPINE1 predicts poor prognosis, and low expression of CLU is associated with poor prognosis. sexual differences. Combined with the box plots of the expression of 8 core genes in normal liver tissue and liver cancer tissue in ONCOMINE database, compared with normal tissue, MSH2, OIP5, AURKA, SMC4 are highly expressed in liver cancer, and CLU is low expressed in liver cancer, indicating poor prognosis The low expression of SERPINE1 in liver cancer indicates a better prognosis.

Serglycin (SRGN), structural maintenance of chromosomes (SMC4), aurora kinaseA (AURKA), OPA interacting protein 5 (OIP5), MUTS homolog 2 (mutS homolog 2, MSH2). Verified by the ONCOMINE database, the expression of the above-mentioned core genes in liver cancer is consistent with our prediction results, and there are significant differences.

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