Systematic Identification of Genomic Markers for Guiding Iron Oxide Nanoparticles in Cervical Cancer Based on Translational Bioinformatics

Figures & data

Table 1 Correlation Between the Clinicopathologic Variables and the Four Key Genes mRNA Expression in Cervical Cancer

Parameters	Variables	N	SMAD7 mRNA Expression				PTK2B mRNA Expression				IL1B mRNA Expression				PPFIA4 mRNA Expression
			High	Low	χ2	P-value	High	Low	χ2	P-value	High	Low	χ2	P-value	High	Low	χ2	P-value
Age	<50	180	85	95	1.3621	0.2432	97	83	2.6701	0.1023	89	91	0.054481	0.8154	74	106	13.951	0.0002
	≥50	124	67	57			55	69			63	61			78	46
Histologic_grade	G1	18	10	8	1.0883	0.78	9	9	0.058273	0.9963	7	11	1.1143	0.7737	9	9	6.6653	0.0834
	G2	135	66	69			67	68			68	67			64	71
	G3	118	61	57			57	61			57	61			56	62
	GX	24	10	14			12	12			13	11			18	6
Clinical_stage	I	162	74	88	3.7713	0.2873	85	77	1.7353	0.6292	70	92	6.4383	0.0922	56	106	36.292	0.0001
	II	69	37	32			33	36			39	30			45	24
	III	45	27	18			23	22			26	19			29	16
	IV	21	12	9			8	13			13	8			18	3
Pathologic_T	T1	140	65	75	3.5204	0.4748	71	69	2.2404	0.6917	58	82	9.2134	0.056	45	95	34.644	0.0001
	T2	71	35	36			35	36			36	35			41	30
	T3	20	12	8			7	13			13	7			16	4
	T4	10	7	3			6	4			8	2			8	2
	TX	17	7	10			8	9			9	8			13	4
Pathologic_N	N0	133	60	73	2.2422	0.326	69	64	0.76542	0.682	63	70	0.89922	0.6379	47	86	29.142	0.0001
	N1	60	34	26			29	31			27	33			27	33
	NX	66	33	33			30	36			35	31			50	16
Treatment regimen	Pla	106	64	42	3.7894	0.4353	57	49	7.5074	0.1114	58	48	5.8814	0.2082	58	48	3.8754	0.4232
	Pla+Pac	9	4	5			2	7			5	4			5	4
	Pla+Fluo	5	2	3			1	4			3	2			2	3
	Pla+Gem	3	1	2			2	1			0	3			0	3
	Pac	2	2	0			0	2			0	2			1	1
Radiation treatment	NO	48	19	29	1.2041	0.2724	25	23	0.035041	0.8515	22	26	0.48151	0.4878	16	32	11.001	0.0009
	YES	108	53	55			58	50			56	52			67	41
Vital status	Living	244	115	129	4.0701	0.0437	129	115	4.0701	0.0437	114	130	5.3161	0.0211	112	132	8.3061	0.004
	Deceased	60	37	23			23	37			38	22			40	20
Relapse	NO	201	92	109	5.1911	0.0227	107	94	4.5371	0.0332	95	106	1.6401	0.2004	81	120	21.591	0.0001
	YES	77	47	30			30	47			43	34			55	22

Figure 1 Properties of MNP in cervical cancer cells. (A and B) TEM images of MNPs in 25,000× (A), 80,000× (B), 600,000× (C). (D) MNP exhibited room temperature superparamagnetism with good magnetic field responsivity towards the external magnet. Effect of the MNP on the viability of the HeLa (E and F) and SiHa (H and I) cells via the MTT assay. Cells were treated with MNP for 0–72h at a concentration range 0–400μg/mL, H₂O₂ (0.5mM) were employed as positive control. HeLa (G) and SiHa (J) cells treated with MNP (100μg/mL) for 24 and 72 h were stained with FITC-labeled annexinV/PI and subsequently subjected to flow cytometry analysis.

Figure 2 Biological effects of MNP in lysosome of cervical cancer cells. (A) Colocalization of MNP-C6 with lysosomes in HeLa cells. Gray-value calculation and quantitative colocalization analysis was carried out using ImageJ software. CTSB activity in HeLa (B) and SiHa (C) cells was measured as RFU with CTSB substrate Z-FR-AMC. After MNP treatment, CTSB activity decreased significantly (p<0.001). Fluorescence value of lysotracker decreased obviously after MNP treatment in HeLa (D) and SiHa (E) cells. HeLa (F) and SiHa (G) cells treated with MNP exhibit a shift of granular red fluorescence to diffused green fluorescence of AO. Images at 1000× magnification were also shown (row 2, 4). Scale bar = 20 μm. ***p < 0.001.

Abbreviations: MNP, magnetic iron oxides nanoparticle; C6, coumarin 6; CTSB, cathepsin B; RFU, relative fluorescence units; AO, acridine orange.

Figure 3 Four key genes were associated with overall survival time and clinical features in cervical cancer patients. Survival analysis of PPFIA4 (A), SMAD7 (B), IL1B (C), and PTK2B (D) expression levels associating with overall survival time in 310 TCGA cervical cancer patients using Kaplan–Meier curves. The patients were divided into 2 groups, based on median gene expression value as a cut-off point. (E–G) The expression of PPFIA4 was highly associated with clinical stage with a p value of 2e-7 (E), and tumor (T) classification with a p value of 7.7e-06 (F). The expression of IL1B was significantly associated with tumor (T) classification with a p value of 0.0072 (G). The association between genes expression level and clinical features was estimated by Kruskal–Wallis test. (see also Figure S2).

Figure 4 Somatic mutations of four key genes that were detected in the TCGA cervical cancer patients. (A) Oncoplot showed somatic mutation patterns of the four key genes. Each column in the figure represented an individual cancer sample. (B and C) Plots showed mutation distribution and the protein domains for the mutated proteins of PPFIA4 (B) and PTK2B (C). (D) Top 30 most recurrent mutated genes in cervical cancer patients were represented. The bar plot on the top panel represented the total number of mutations. (E) PPFIA4 and PTK2B were in significant co-occurrence of mutation with 7 of the top mutated genes (□). p < 0.05; *p < 0.01.

Figure 5 Correlations of key genes expression with immune infiltration level in TCGA cervical cancer patients. (A) PTK2B expression was significantly negatively related to tumor purity, and had significant positive correlations with infiltrating levels of CD8⁺ T cells, neutrophils, and dendritic cells in cervical cancer patients. (B) PPFIA4 expression had significant negative correlations with infiltrating levels of CD8⁺ T cells. (C) SMAD7 expression had weak positive correlations with infiltrating levels of neutrophils and dendritic cells, and a weak negative correlation with tumor purity. (D) IL1B expression had significant positive correlations with infiltrating levels of neutrophils and dendritic cells. Each dot represented a single tumor sample.

Figure 6 Correlations of key genes with lysosome. GSEA results showed KEGG term “lysosome” is differentially enriched for the expression of PTK2B (A) and PPFIA4 (B) in cervical cancer. (C) PPFIA4 and PTK2B were in significant co-occurrence of mutation with 26 lysosomal genes in KEGG term “lysosome” gene set. (D–F) Correlation analyses of gene expression levels were also performed, and strong correlations between key genes and certain lysosomal genes were observed. The results were displayed as Pearson’s correlation coefficient bubble-plots (D), heatmap (E) and lysosomal genes with top highest Pearson’s r with key genes were shown in scatter-plots (F).

Abbreviations: GSEA, Gene Set Enrichment Analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes.

Figure 7 Network of key genes and anti-cancer drugs. The network contained 50 genes (4 key genes and their 46 most frequently altered neighbor genes). Relationships between these genes and anti-cancer drugs (hollow hexagon) were also illustrated. Yellow hexagons indicate the FDA approved anti-cancer drugs. PTK2B and IL1B were the targets of anti-cancer drugs including genistein, leflunomide and gallium nitrate.

Figure 8 PIK3R2 as a common hub genes in MNP treatment and cisplatin resistance (A). mRNA expression levels of key genes in HeLa (B) and SiHa (C) cells by qRT-PCR. Synergistic killing effects of MNP with cisplatin in HeLa (D) and SiHa (E) cells using MTT assay. (F) mRNA expression levels of key genes after combination of cisplatin and MNP in HeLa cells by qRT-PCR. Data were presented as mean ± SEM. *p < 0.05; **p < 0.01; ***p < 0.001.