Identification of DEGs
The GSE25097 dataset was processed with R, DEGs with adj.p value <0.05 and |logFC| > 2, including 790 genes were screened for further investigation(Figure 1, Supplement table 1). The TCGA LIHC dataset was analysed with R ×64 3.6.1, using the package DEGseq2, adj.p value <0.05 and |FC| > 2 were regarded as the cut-off criteria. We got 2162 genes that met the standards(Figure 1, Supplement table 2). To confirm the reliability of DEGs in liver cancer, we obtained Common DEGs of the two datasets, including 102 genes (Figure 1, Table 1). The volcano map (Figure 2A, Figure 2C) and heat map (Figure 2B, Figure 2D) were drawn based on the differential genes obtained from data set GSE25097 and TCGA LIHC, respectively..
GO and Reactome pathway analysis of the DEGs
We used GO analysis and Reactome Pathway analysis to conduct enrichment analysis of 102 Common DEGs. GO analysis includes biological process(BP) analysis, cellular component(CC) analysis and molecular function(MF) analysis(Figure 3a). BP analysis showed that liver cancer had changes in hormone metabolism(Cellular hormone metabolic process, Hormone metabolic process), cell reaction to copper, cadmium ions and inorganic substances, and detoxification function(Cellular response to cadmium ion, Cellular response to metal ion, Cellular response to inorganic substance, Cellular response to copper ion, Detoxification of copper ion, Detoxification). CC analysis showed that the Collagen trimer and Collagen-containing extracellular matrix of liver cancer cells was changed. Moreover, the MF analysis showed that the patients with liver cancer had an abnormal expression of oxidoreductase activity and molecular binding function(Glycosaminoglycan binding,Cytokine receptor binding,iron ion binding,extracellular matrix binding,carbohydrate binding). The results showed that the changes of cellular collagen were observed at the cellular level, the changes of hormone metabolism, reaction to metal ions and detoxification were observed at the biological function, and the changes of molecular binding and oxidoreductase activity were observed at the molecular level.
Through Reactome enrichment analysis(Figure 3B), we found that liver cancer changes in biological oxidations, reactions and conjugation ability to metal ions(phase II-conjugation of compounds, metallothioneins bind metals, response to metal ions), and also affects growth hormone receptor signalling.
Comparing the results of the two enrichment analyses, we found that the information obtained by the two enrichment analyses was consistent, that is, the two analyses were enriched with changes in hormone metabolism, biological oxidation, cell reaction to metal ions and other aspects in patients with liver cancer.
PPI network analysis and screening for hub Genes
102 the DEGs were input into STRING to build PPI network (Figure 4A). Then the PPI network diagram was imported into Cytoscape(3.2.1). CytoHubba of app plug-in was used to calculate the Degree Value and other parameter values(Supplement table 3). Genes whose Degree Value is greater than or equal to 5 are taken as Hub genes, and a total of 22 Hub genes were obtained(Table 2). See Figure 4B for the relationship between 22 Hub genes.
Expression of Hub genes in patients with liver cancer
The expression of 22 Hub genes in liver cancer and normal liver tissues was analysed, and it was found that SPP1, AURKA, NQO1, NUSAP1, TOP2A, UBE2C, AFP, GMNN, PTTG1, RRM2, UBE2T, GPC3, SPARCL1 etc.(Figure 5A) 13 genes were highly expressed in liver cancer tissues. However, ESR1, CXCL12, FOS, DCN, EGR1, SOCS3, CYP1A2, FOSB, PCK1 etc. 9 genes were low expressed in liver cancer tissues(Figure 5B).
ROC curve analysis of Hub genes
ROC curve analysis was performed on 22 Hub genes using the package pROC. AUC > 90% was taken as the cutoff value, and it was found that 16 of the 22 Hub genes with AUC > 90% were SPP1, AURKA, CXCL12, FOS, NUSAP1, TOP2A, UBE2C, AFP, DCN, GMNN, PTTG1, RRM2, SOCS3, FOSB, PCK1, SPARCL1 respectively. The expression level of these genes has high accuracy in distinguishing normal tissue from liver cancer tissue, and could be a potential "tumour biomarker". At the same time, it can be used as a biomarker for the diagnosis of liver cancer, which has important significance for the accurate diagnosis of liver cancer(Figure 6).
The survival curve of Hub genes
Survival curves were plotted from Kaplan-Meier estimations(Figure 7), The Cox regression model was used to calculate Hazard Ratio(HR) of Hub genes for liver cancer patients. The results showed that among these Hub genes, the expression levels of ESR1, SPP1 and FOSB genes were closely related to the survival time of liver cancer patients, with statistically significant differences(p<0.05). HR values were 0.88, 1.1 and 0.88, respectively, that is, ESR1 and FOSB were low-risk factors, while SPP1 was a high-risk factor.
GSEA revealed the biological function that affects the survival time of liver cancer
Single-gene GSEA was used to investigate biological pathways and biological functions related to survival time(Figure 8). Figure 8A shows all the related pathways of ESR1, FOSB and SSP1 genes. Figure 8B shows the commonly related pathways of ESR1, FOSB and SSP1 genes. Figure 8B a1, b1 and c1 are the three common pathways of ESR1, FOBS and SPP1 genes. Figure 8B a2 and c2 are the seven common pathways of ESR1 and SPP1 genes, and Figure 8B a3 and b3 are the four common pathways of ESR1 and FOSB genes.
The three common pathways enriched by ESR1, FOBS and SPP1 genes are HALLMARK MYC TARGETS V1, HALLMARK G2M CHECKPOINT and HALLMARK E2F TARGETS pathways(Figure 8Ba, b, c). According to the information in Figure 8B, we can find that high expression of ESR1 and FOBS can activate these three pathways, while high expression of SPP1 can inhibit these three pathways. However, in liver cancer tissues, ESR1 and FOBS genes were low in expression, while SPP1 genes were high in expression (see Figure 5). Therefore, changes in the expression levels of ESR1, FOBS and SPP1 genes in liver cancer inhibited all three pathways.
Seven common pathways were obtained by enrichment analysis of ESR1 and SPP1 genes. They are HALLMARK PANCREAS BETA CELLS, HALLMARK ESTROGEN RESPONSE LATE, HALLMARK ADIPOGENESIS, HALLMARK FATTY ACID METABOLISM, HALLMARK BILE ACID METABOLISM, HALLMARK XENOBIOTIC METABOLISM and HALLMARK PEROXISOME pathway, the high expression of ESR1 gene can activate the HALLMARK PANCREAS BETA CELLS and HALLMARK ESTROGEN RESPONSE LATE pathways. Five pathways, namely, HALLMARK ADIPOGENESIS, HALLMARK FATTY ACID METABOLISM, HALLMARK BILE ACID METABOLISM, HALLMARK XENOBIOTIC METABOLISM, and HALLMARK PEROXISOME, were inhibited, while SPP1 gene was just opposite to ESR1 gene (Figure 8Ba2, c2). In liver cancer, the ESR1 gene is a low expression, while the SPP1 gene is a high expression (see Figure 5). Therefore, changes in ESR1 and SPP1 gene expression in liver cancer activated the HALLMARK ADIPOGENESIS, HALLMARK FATTY ACID METABOLISM, HALLMARK BILE ACID METABOLISM, HALLMARK XENOBIOTIC METABOLISM, and HALLMARK PEROXISOME pathway. While both the HALLMARK PANCREAS BETA CELLS and HALLMARK ESTROGEN RESPONSE LATE pathway were suppressed.
The four common pathways enriched by ESR1 and FOSB genes are respectively HALLMARK MYC TARGETS V2, HALLMARK HEME METABOLISM, HALLMARK COAGULATION and HALLMARK UV RESPONSE DN pathways. High expression of ESR1 and FOSB can activate the HALLMARK MYC TARGETS V2 pathway and inhibit the three pathways, including HALLMARK HEME METABOLISM, HALLMARK COAGULATION and HALLMARK UV RESPONSE_DN(Figure 8B a3, b3). However, in liver cancer, both ESR1 and FOBS genes were low expressed(see Figure 5). Therefore, changes in the expression levels of ESR1 and FOBS genes in liver cancer inhibited HALLMARK MYC TARGETS V2 pathway, while HALLMARK HEME METABOLISM, HALLMARK COAGULATION, HALLMARK UV RESPONSE_DN three pathways were activated.