Identification of hepatocellular carcinoma-related genes associated with macrophage differentiation based on bioinformatics analyses

Figures & data

Table 1. The specific information of DEGs

ID	Description Name	Expression
PARP1	Poly(ADP-ribose) polymerase 1	Down
INHA	Inhibin subunit alpha	Down
IL31RA	Interleukin 31 receptor A	Down
MMP9	Matrix metallopeptidase 9	Down
NKX2-3	NK2 homeobox 3	Down
BMP4	Bone morphogenetic protein 4	Down
PRKCA	Protein kinase C alpha	Down
IL34	Interleukin 34	Down
TGFB1	Transforming growth factor beta 1	Down
CEBPA	CCAAT enhancer binding protein alpha	Down
CASP8	Caspase 8	Down
FADD	Fas associated via death domain	Down
EIF2AK1	Heme-regulated eIF2α kinase	Down
CDC42	Cell division cycle 42	Down
ID2	Inhibitor of DNA binding 2	UP
CD4	CD4 molecule	UP
TRIB1	Tribbles pseudokinase 1	UP

Figure 1. Heatmap and volcanic maps of differentially expressed MDGs from TCGA and ICGC data. (a and b) Differentially expressed MDGs obtained from the TCGA dataset. (c and d) Differentially expressed MDGs obtained from the ICGC dataset

Figure 2. Venn diagram showing the intersecting differentially expressed MDGs from two public databases. Blue area: TCGA dataset; yellow area: ICGC dataset; cross area: differentially expressed MDGs expressed in both databases

Figure 3. The expression of differentially expressed MDGs in HCC from TCGA and ICGC data. (a) The expression of differentially expressed MDGs in TCGA data. (b) The expression of differentially expressed MDGs in ICGC data

Figure 4. The relationship between MDG expression and the prognosis of HCC patients. (a–c) Kaplan-Meier estimated survival curves of CDC42, EIF2AK1 and MMP9 were constructed using TCGA clinical data. (d–f) Kaplan-Meier estimated survival curves of CDC42, EIF2AK1 and MMP9 were constructed using ICGC clinical data

Figure 5. ROC analysis of the CDC42, EIF2AK1 and MMP9 signatures in HCC. (a) ROC analysis of the TCGA database. (b) ROC analysis of the ICGC database

Table 2. Univariate and Multivariate analyses of factors associated with survival of patients with HCC in TCGA database and ICGC database

	Overall survival(TCGA)					Overall survival(ICGC)
	Univariate		Multivariate			Univariate		Multivariate
Variable	pvalue		HR (95% CI)	pvalue		pvalue		HR (95% CI)	pvalue
Age, years (≥53 vs. <53)	0.591		1.011(0.991–1.031)	NA		0.800		0.314(0.157–0.628)	NS
Gender (male vs. female)	0.301		1.03(0.609–1.743)	NA		0.031		0.996(0.963–1.031)	NA
Grade (well or moderate vs. poor)	0.914		1.027(0.732–1.443)	NA		-		-	-
Stage(Ⅰ/ⅡVSⅠⅠⅠ/Ⅳ)	<0.001		1.181(0.425–3.278)	NA		<0.001		2.021(1.382–2.954)	<0.001
T(1/2 vs. 3/4)	<0.001		1.444(0.569–3.667)	NA		-		-	-
Distant metastasis (positive vs. negative)	0.023		1.752(0.456–6.735)	NA		-		-	-
Lymph nodes (positive vs. negative)	0.328		1.398(0.205–9.509)	NA		-		-	-
CDC42 expression (low vs. high)	<0.001		1.017(1.002–1.033)	0.030		0.001		1.016(1.004–1.027)	0.007
EIF2AK1 expression (low vs. high)	0.005		1.017(0.996–1.038)	NA		0.007		1.016(0.995–1.038)	NA
MMP9 expression (low vs. high)	0.034		1.003(0.998–1.007)	NA		0.960		0.991(0.975–1.007)	NA

NS, not significant; NA, not available .

Figure 6. CDC42 expression is upregulated in primary HCC. (a) CDC42 expression in the Roessler liver dataset in Oncomine. (b) CDC42 expression in the Mas liver dataset in Oncomine. (c) CDC42 expression in the Wurmbach liver dataset in Oncomine

Figure 7. Immunohistochemistry confirmed the differential expression of CDC42. (a) The expression of CDC42 in normal tissue. (b) The expression of CDC42 in tumor tissue

Figure 8. The immune infiltration levels of six immune cell types associated with CDC42 expression were analyzed based on the TIMER database. (a–f) CDC42 expression positively correlates with the infiltration of B cells (a), CD4 + T cells (b), CD8 + T cells (c), dendritic cells (d), macrophages (e), and neutrophils (f)

Figure 9. Correlation analyses between CDC42 expression and M2 macrophage markers. (a–d) The expression of CDC42 was significantly correlated with M2 macrophage markers (CD163, MMP2, MMP9, and STAT3) in the TCGA database. (e–h) The expression of CDC42 was significantly correlated with M2 macrophage markers (CD163, MMP2, MMP9, and STAT3) in the ICGC database

Figure 10. Correlation analyses between CDC42 expression and immune checkpoint inhibitors

(a–c) The expression of CDC42 was positively correlated with PD-1 (PDCD1), PD-L1 (CD274), and CTLA4.

Figure 11. Enrichment plots from GSEA. (a) CDC42 expression positively correlated with the Wnt signaling pathway in the TCGA dataset. (b) CDC42 expression positively correlated with the Wnt signaling pathway in the ICGC dataset

Availability of data and materials

The datasets analyzed was acquired from The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/) and ICGC database (https://dcc.icgc.org/).