A Pan-Cancer Analysis of the Role of Selenoprotein P mRNA in Tumorigenesis

Figures & data

Table 1 Summary of Detailed Information for 33 Tumor Types in TCGA Database

Primary Disease Type	ID	Total N	Primary Tumor	Normal Tissue
Adrenocortical cancer	ACC	79	79	0
Bladder urothelial carcinoma	BLCA	430	411	19
Breast invasive carcinoma	BRCA	1217	1104	113
Cervical & endocervical cancer	CESC	309	306	3
Cholangiocarcinoma	CHOL	45	36	9
Colon adenocarcinoma	COAD	512	471	41
Diffuse large B-cell lymphoma	DLBC	48	48	0
Esophageal carcinoma	ESCA	173	162	11
Glioblastoma multiforme	GBM	173	168	5
Head & neck squamous cell carcinoma	HNSC	546	502	44
Kidney chromophobe	KICH	89	65	24
Kidney clear cell carcinoma	KIRC	607	535	72
Kidney papillary cell carcinoma	KIRP	321	289	32
Acute myeloid leukemia	LAML	151	151	0
Brain lower grade glioma	LGG	529	529	0
Liver hepatocellular carcinoma	LIHC	424	374	50
Lung adenocarcinoma	LUAD	585	526	59
Lung squamous cell carcinoma	LUSC	550	501	49
Mesothelioma	MESO	86	86	0
Ovarian serous cystadenocarcinoma	OV	379	379	0
Pancreatic adenocarcinoma	PAAD	182	178	4
Pheochromocytoma & paraganglioma	PCPG	186	183	3
Prostate adenocarcinoma	PRAD	551	499	52
Rectum adenocarcinoma	READ	177	167	10
Sarcoma	SARC	265	263	2
Skin cutaneous melanoma	SKCM	472	471	1
Stomach adenocarcinoma	STAD	407	375	32
Testicular germ cell tumor	TGCT	156	156	0
Thyroid carcinoma	THCA	568	510	58
Thymoma	THYM	121	119	2
Uterine corpus endometrioid carcinoma	UCEC	583	548	35
Uterine carcinosarcoma	UCS	56	56	0
Uveal melanoma	UVM	80	80	0

Figure 1 Expression level of SELENOP in different tumors. (A) SELENOP expression in 33 tumor types (based on TCGA database). (B) The corresponding normal tissues of the GTEx database were included as controls, compared with ACC, DLBC, LAML, LGG, OV, SKCM, TGCT, UCS tumor tissues in TCGA database. (C) The expression level of SeP total protein in 6 tumors (based on CPTAC dataset). *P < 0.05, **P < 0.01, ***P < 0.001.

Abbreviations: ACC, adrenocortical cancer; DLBC, diffuse large B-cell lymphoma; LAML, acute myeloid leukemia; LGG, brain lower grade glioma; OV, ovarian serous cystadenocarcinoma; SKCM, skin cutaneous melanoma; TGCT, testicular germ cell tumor; UCS, uterine carcinosarcoma.

Figure 2 Association between SELENOP expression and OS, PFS and DSS. (A) Forest plot of OS associations in 33 types of tumor. (B) Forest plot of PFS associations in 33 types of tumor. (C) Forest plot of DSS associations in 33 types of tumor.

Abbreviations: OS, overall survival; PFS, progression-free survival; DSS, disease-specific survival.

Figure 3 Kaplan–Meier analysis of the association between SELENOP expression and OS. (A) Kaplan–Meier plot of high and low SELENOP expression in KIRC patients. (B) and in LGG patients. (C) and in LUAD patients. (D) and in UCEC patients.

Abbreviations: KIRC, kidney clear cell carcinoma; LGG, brain lower grade glioma; LUAD, lung adenocarcinoma; UCEC, uterine corpus endometrioid carcinoma.

Figure 4 Mutation feature of SELENOP in different tumor types of TCGA. (A) The genetic alteration type and frequency of SELENOP in various tumors (based on the cBioPortal tool). (B) The potential correlation between mutation status and disease-specific survival of UCEC. (C) and progression-free survival of UCEC. (D) and overall survival of UCEC. (E) disease-free survival of UCEC.

Abbreviation: UCEC, uterine corpus endometrioid carcinoma.

Figure 5 Associations between SELENOP expression and microsatellite instability (MSI), tumor mutational burden (TMB) and mismatch repair (MMR) in 33 tumor types. (A) Results of correlation analysis between SELENOP expression in 33 tumor types and MSI. (B) and TMB. The P-value is supplied. The correlation coefficient values of +0.4 and – 0.4 are marked. (C) and five MMR genes. The top left triangle represents the P-value, and the bottom right triangle represents the correlation coefficient. *P < 0.05, **P < 0.01 and ***P < 0.001.

Table 2 Correlation Between SELENOP and Macrophage Gene Markers Based on GEPIA Online Tool, Including TAM and M1 and M2 Macrophages

Tumors	TAM						M1 Macrophage						M2 Macrophage
	CD80		CD86		HLA-G		CD36		IL-6		NOS2		TGFβ		STAT6		IL-10
	R	P	R	P	R	P	R	P	R	P	R	P	R	P	R	P	R	P
ACC	0.17	0.14	0.3	*	0.14	0.22	0.44	***	0.23	*	0.21	0.061	0.26	*	0.45	***	0.37	**
BLCA	0.11	*	0.22	***	0.11	*	0.4	***	0.084	0.093	0.17	**	0.066	0.19	0.23	***	0.21	***
BRCA	0.049	0.11	0.16	***	−0.099	*	0.39	***	0.085	*	0.033	0.28	0.12	***	0.31	***	0.24	***
CESC	0.12	*	0.15	*	−0.044	0.44	0.23	***	−0.18	*	0.28	***	−0.054	0.34	0.35	***	0.1	0.067
CHOL	−0.3	0.076	−0.22	0.2	−0.13	0.44	0.062	0.72	0.075	0.67	0.21	0.22	−0.2	0.23	0.29	0.09	−0.16	0.34
COAD	0.24	***	0.29	***	0.15	*	0.36	***	0.2	*	0.029	0.63	0.2	***	0.15	*	0.34	***
DLBC	0.18	0.22	0.2	0.19	0.083	0.58	0.2	0.19	−0.11	0.48	−0.038	0.8	0.22	0.14	0.25	0.086	−0.15	0.33
ESCA	0.036	0.63	0.032	0.67	0.027	0.72	0.28	***	0.0066	0.93	0.23	*	−0.045	0.54	0.16	*	0.0036	0.96
GBM	0.2	*	0.42	***	−0.028	0.73	0.22	*	0.032	0.69	0.044	0.58	0.15	0.055	0.34	***	0.41	***
HNSC	0.25	***	0.31	***	−0.032	0.47	0.32	***	0.09	*	0.31	***	0.015	0.73	0.27	***	0.27	***
KICH	0.31	*	0.31	*	0.11	0.37	0.31	*	−0.031	0.81	0.18	0.15	0.23	0.068	0.37	*	0.28	*
KIRC	0.2	***	0.36	***	0.042	0.34	0.49	***	−0.11	*	0.42	***	0.057	0.19	0.35	***	0.4	***
KIRP	0.48	***	0.54	***	0.19	**	0.52	***	0.29	***	0.25	***	0.28	***	0.25	***	0.6	***
LAML	0.31	***	−0.18	*	−0.088	0.25	−0.1	0.17	0.1	0.18	−0.17	*	−0.11	0.16	−0.082	0.29	−0.035	0.65
LGG	0.15	**	0.47	***	0.064	0.15	0.0086	0.85	0.12	*	0.053	0.23	0.28	***	0.32	***	0.28	***
LIHC	−0.087	0.094	−0.085	0.1	0.02	0.71	0.46	***	−0.029	0.57	0.28	***	−0.34	***	0.26	***	0.058	0.27
LUAD	0.3	***	0.34	***	0.13	*	0.51	***	−0.04	0.38	0.12	*	0.14	*	0.32	***	0.44	***
LUSC	0.15	**	0.33	***	0.11	*	0.5	***	0.08	0.078	0.2	***	0.095	*	0.14	*	0.35	***
MESO	0.18	0.1	0.21	*	−0.21	0.052	0.32	*	0.12	0.26	0.3	*	0.15	0.18	0.071	0.51	0.3	*
OV	0.17	**	0.22	***	0.11	*	0.24	***	0.097	*	0.22	***	0.22	***	0.19	***	0.25	***
PAAD	0.19	*	0.43	***	−0.18	*	0.59	***	0.27	**	0.2	*	−0.15	*	0.065	0.39	0.4	***
PCPG	0.4	***	0.57	***	0.16	*	0.48	***	0.38	***	0.11	0.14	0.32	***	0.27	**	0.61	***
PRAD	0.23	***	0.33	***	0.0078	0.86	0.59	***	0.14	*	0.12	*	0.0012	0.98	0.38	***	0.33	***
READ	0.25	*	0.29	*	0.16	0.13	0.33	*	0.077	0.46	0.22	*	0.11	0.3	−0.018	0.86	0.28	*
SARC	0.37	***	0.56	***	0.21	**	0.6	***	0.31	***	0.24	***	0.18	*	0.12	*	0.56	***
SKCM	0.54	***	0.61	***	−0.025	0.6	0.53	***	0.22	***	0.11	*	0.3	***	0.15	*	0.51	***
STAD	0.19	**	0.31	***	0.036	0.47	0.59	***	0.12	*	0.038	0.44	0.29	***	0.3	***	0.33	***
TGCT	−0.071	0.41	0.025	0.77	−0.14	0.11	0.58	***	−0.34	***	0.46	***	0.51	***	0.6	***	0.15	0.082
THCA	0.17	***	0.23	***	0.071	0.11	0.27	***	0.24	***	0.32	***	0.16	**	0.51	***	0.34	***
THYM	0.34	**	0.55	***	0.24	*	0.47	***	0.39	***	0.21	*	0.32	**	0.57	***	0.67	***
UCEC	0.17	*	0.11	0.15	0.11	0.15	0.26	**	0.064	0.4	0.15	0.055	−0.00051	0.99	0.37	***	0.12	0.1
UCS	0.54	***	0.63	***	0.28	*	0.33	*	0.29	*	0.25	0.057	0.28	*	0.37	*	0.48	**
UVM	0.45	***	0.62	***	0.16	0.17	0.37	***	0.36	*	0.092	0.42	0.22	*	0.42	***	0.65	***

Notes: *P < 0.05, **P < 0.01, and ***P < 0.001.

Abbreviation: TAM, Tumor-Associated Macrophage.

Figure 6 Associations between SELENOP expression and tumor immune infiltration. (A) Correlation analysis between expression levels of SELENOP and immune cell infiltration in KIRC. (B) and in LGG. (C) and in LUAD. (D) and in SKCM.

Abbreviations: KIRC, kidney clear cell carcinoma; LGG, brain lower grade glioma; LUAD, lung adenocarcinoma; SKCM, skin cutaneous melanoma.

Figure 7 Six tumors with the highest correlation coefficients between SELENOP expression and the tumor microenvironment. (A) Correlation between SELENOP and stromal scores in TGCT, SKCM, SARC, PCPG, KIRP and PAAD. (B) Correlation between SELENOP and immune scores in SARC, SKCM, PAAD, GBM, PCPG and KIRP.

Abbreviations: TGCT, testicular germ cell tumor; SKCM, skin cutaneous melanoma; SARC, sarcoma; PCPG, pheochromocytoma and paraganglioma; KIRP, kidney papillary cell carcinoma; PAAD, pancreatic adenocarcinoma; GBM, glioblastoma multiforme.

Figure 8 PPI network for SELENOP based on GeneMANIA online tool. Different colors of the network edge indicate the bioinformatics methods applied: physical interaction, co-expression, predicted, colocalization, pathway, genetic interaction, and shared protein domains.

Abbreviation: PPI, protein–protein interaction.

Figure 9 GO functional annotation of GSEA of SELENOP in various tumor types. (A) A common feature in LGG and UCEC was that SELENOP negatively regulated vascular endothelial cell proliferation. (B) SELENOP expression was negatively correlated with epidermal cell differentiation, keratinization and epidermis development in KIRC, LUAD and STAD. (C) In COAD, PCPG and SKCM, the process of upregulation was related to immune function, including T cell activation, B cell mediated immunity, adaptive immune response and immune response regulation cell surface receptor signaling pathways.

Abbreviations: COAD, colon adenocarcinoma; STAD, stomach adenocarcinoma; KIRC, kidney clear cell carcinoma; LGG, brain lower grade glioma; LUAD, lung adenocarcinoma; SKCM, skin cutaneous melanoma; UCEC, uterine corpus endometrioid carcinoma; PCPG, pheochromocytoma and paraganglioma.

Figure 10 Summary of the transport function of SeP. Dietary Se is absorbed through the small intestine, and various Se compounds synthesize selenoprotein in the liver via circulation of portal vein, including SeP. Plasma SeP preferentially transports Se to various target tissues, such as the brain, kidney and testis. SeP transport in the brain is complicated because Se passes through multiple cell membranes before eventually reaching neurons. The neuronal SeP synthesis and ApoER2/Lrp8-mediated SeP reuptake in the brain are called the SeP cycle. Testicular function is also dependent on ApoER2/Lrp8-mediated SeP uptake. Megalin/Lrp2 is expressed in renal tubular epithelium and is involved in the reabsorption of SeP from glomerular filtrate.

Abbreviations: SeP, Selenoprotein P; Se, Selenium; ApoER2, apolipoprotein E receptor 2.