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Journal of Trace Elements in Medicine and Biology
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Journal of Trace Elements in Medicine and Biology
Clinical studies
Reduced expression of ferroportin1 and ceruloplasmin predicts poor prognosis in adrenocortical carcinoma
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Bo Zhuª,1, Qi Zhib,1, Qian Xieª, Xiaohui Wuª, Yanan Gaoª, Xiao Chen“, Liyun Shia,*
a Department of Microbiology and Immunology, School of Medicine and Life Sciences, Nanjing University of Chinese Medicine, Nanjing, 210023, PR China b Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, PR China
” Department of Pharmacology, School of Medicine and Life Sciences, Nanjing University of Chinese Medicine, Nanjing, 210023, PR China
ARTICLE INFO
Keywords:
Adrenocortical carcinoma Iron metabolism Prognosis Ferroportin1 Ceruloplasmin
ABSTRACT
Introduction: Iron metabolism is tightly controlled in human cells. Dysregulation of iron metabolism-related genes has been characterized as a promising prognostic biomarker in cancers. However, the expression patterns and prognostic roles of iron metabolism-related genes remain unknown in adrenocortical carcinoma (ACC). Objectives: The primary objective of this study was to explore the expression patterns and prognostic roles of iron metabolism-related genes in ACC using publicly available datasets.
Methods: In the present study, we compared the expression patterns of 36 iron metabolism-related genes be- tween ACC tumors (n = 77) and normal adrenal tissues (n = 128) based on The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) data. The associations between clinical variables (including survival rate and pathological stage) and expression levels of iron mentalism-related genes were further explored. All the bioinformatics analyses were performed using the GEPIA or the Metascape tool.
Results: Twelve iron metabolism-related genes were differentially expressed between ACC tumors and normal controls. Among them, reduced expression levels of ferroportin1 (FPN1) and ceruloplasmin (CP) were sig- nificantly correlated with poor survival of ACC patients. Specially, the expression levels of FPN1 were negatively correlated with the pathological stages of ACC. A pan-cancer analysis characterized the reduced expression of FPN1 and CP as an ACC-specific signature among 33 types of cancers. Functional enrichment analysis suggested that both FPN1 and CP might be implicated in several immune processes.
Conclusion: Reduced expression of FPN1 and CP was identified as a potential signature for poor prognosis of ACC in this study. Mechanisms underlying the prognostic value of FPN1 or CP in ACC deserve further experimental investigation.
Abbreviations: Tf, Transferrin; STEAP3, Six-transmembrane epithelial antigen of the prostate-3; HJV, Hemojuvelin; DMT1, Divalent metal transporter 1; Dcytb, Duodenal cytochrome b; SCARA5, Scavenger receptor class A member 5; ZIP, Zrt/IRT-like protein; FLVCR, Feline leukemic virus, sub-group C receptor; HRG-1, Heme-responsive gene-1; NTBI, Non transferrin bound iron; HO, Heme oxygenase; LIP, Labile iron pool; Mfrn, Mitoferrin; ABCB7, ATP-binding cassette B7; IRP, Iron regulatory protein; FPN1, Ferroportin1; CP, Ceruloplasmin; HEPH, Hephaestin; ALAS1, 5-aminolevulinic acid synthase 1; FtMt, Ferritin mitochondrial; FtH(L), Ferritin heavy (light) chain; PCBP, Poly(rC) binding protein; NCOA4, Nuclear receptor coactivator 4; FBXL5, F-box and leucine rich repeat protein 5; FAM96A, Family with sequence similarity 96 member A; TPM, Transcripts per million; ACC, Adrenocortical carcinoma; BLCA, Bladder urothelial carcinoma; BRCA, Breast invasive carcinoma; CESC, Cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, Cholangio carcinoma; COAD, Colon adenocarcinoma; DLBC, Lymphoid neoplasm diffuse large B-cell lymphoma; ESCA, Esophageal carcinoma; GBM, Glioblastoma multiforme; HNSC, Head and neck squamous cell carcinoma; KICH, Kidney chromophobe; KIRC, Kidney renal clear cell carcinoma; KIRP, Kidney renal papillary cell carcinoma; LAML, Acute myeloid leukemia; LGG, Brain lower grade glioma; LIHC, Liver hepatocellular carcinoma; LUAD, Lung adenocarcinoma; LUSC, Lung squamous cell carcinoma; MESO, Mesothelioma; OV, Ovarian serous cystadenocarcinoma; PAAD, Pancreatic adenocarcinoma; PCPG, Pheochromocytoma and paraganglioma; PRAD, Prostate adenocarcinoma; READ, Rectum adeno- carcinoma; SARC, Sarcoma; SKCM, Skin cutaneous melanoma; STAD, Stomach adenocarcinoma; TGCT, Testicular germ cell tumors; THCA, Thyroid carcinoma; THYM, Thymoma; UCEC, Uterine corpus endometrial carcinoma; UCS, Uterine carcinosarcoma; UVM, Uveal melanoma
* Corresponding author at: School of Medicine and Life Sciences, Nanjing University of Chinese Medicine, 138 Xianlin Road, Nanjing, 210023, PR China. E-mail address: shi_liyun@msn.com (L. Shi).
1 These authors contribute equally to this work.
https://doi.org/10.1016/j.jtemb.2019.07.009
1. Introduction
Iron is the most abundant trace element in the human body [1]. Systemic and cellular iron metabolism is tightly regulated, and nu- merous proteins are dedicated to the uptake, utilization, storage and export of iron [2]. Most dietary iron is in the ferric form, it needs to be reduced before it can be absorbed. After reduced by duodenal cyto- chrome b (Dcytb), the dietary iron enters enterocyte via divalent metal transporter 1 (DMT1) and is exported into the circulation via ferro- portin 1 (FPN1) [3]. Absorbed iron binds to plasma transferrin (Tf) and is distributed to tissues throughout the body [4]. Almost all nucleated cells are able to use Tf-bound iron. Diferric Tf binds to its receptor (TFR1) on cell membrane, and the complex is internalized in endo- somes. Iron freed from Tf is reduced by the ferrireductase STEAP3 (six- transmembrane epithelial antigen of the prostate-3) in the endosomes, and then released into the cytoplasm via DMT1 [5,6]. The hemochro- matosis protein HFE binds to TfR1 in competition with Tf, and thus reduces cellular iron uptake [7]. Some cells can also take up non transferrin-bound iron (NTBI), which enters cells via DMT1, Zrt/IRT- like protein 8 (ZIP8), or ZIP14, after being reduced to its ferrous form by Dcytb [8]. Heme oxygenases are the rate-limiting enzymes in the degradation of heme, releasing iron, biliverdin, and carbon monoxide, and play an important role in the acquisition of heme-iron [9]. Ad- ditionally, several other genes mediate iron uptake in a cell-specific manner, including the scavenger receptor class A member 5 (SCARA5), the feline leukemic virus, sub-group C receptor 2 (FLVCR2), CD163, CD91, and heme-responsive gene-1 (HRG1) [8,10-14]. Once iron enters into the cytosolic labile iron pool (LIP), it is utilized by intracellular iron proteins, stored in ferritin, or transported into mitochondrial via mitoferrin (Mfrn), or the ATP-binding cassette B7 (ABCB7) [15,16]. Intracellular iron homeostasis is mainly balanced by iron regulatory proteins (IRPs) [17]. Mechanisms underlying cellular iron export re- main not fully elucidated. FPN1 is the only known iron exporter in human cells and plays a critical role in regulating cellular iron home- ostasis [18]. As exported iron must be oxidized to its ferric form, FPN1 functions in concert with the ferroxidases hephaestin (HEPH) and cer- uloplasmin (CP) [19,20]. Previous studies have also indicated that hepcidin, a major regulator of body iron metabolism, inhibits iron ef- flux by binds to FPN1, and triggers the internalization and degradation of FPN1 [21].
Cancer cells often require a relatively high amount of intracellular iron to maintain rapid proliferation [22]. Expression pattern of iron metabolism-related genes varies in different types of cancer [8]. The expression signatures of iron metabolism-related genes have been identified as potential prognosis predictors or therapeutic targets in multiple types of cancer [8,23]. Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with poor prognosis and few therapeutic options [24]. Factors and mechanisms affecting the survival of ACC patients remain elusive. Therefore, there is a pressing need to explore novel prognosis predictors and therapeutic targets for ACC.
The association between adrenal glands and iron homeostasis has been reported for over half a century [25]. Subcutaneous injections of excess iron can cause marked damage in the adrenal cortex of guinea- pigs, indicating the potential role of iron homeostasis in adrenal gland diseases [26]. The expression patterns of iron metabolism-related genes in ACC remain unknown due to the limited availability of ACC samples. Nowadays, the convenient access to The Cancer Genome Atlas (TCGA) database allows large-scale global gene expression profiling of indicated genes in rare cancers, including ACC [27]. In the present study, we explored the expression patterns of 36 iron metabolism-related genes in ACC tumors and evaluated the prognostic value of 12 differentially expressed genes for ACC patients.
2. Methods
2.1. Differential expression analysis
GEPIA (http://gepia.cancer-pku.cn/) is a web server for cancer and normal gene expression profiling and interactive analyses based on TCGA and the GTEx projects [28]. Relative mRNA expression levels of 36 iron metabolism-related genes (including hepcidin, CP, HEPH, Tf, TfR1, TfR2, HFE, HJV, STEAP3, DMT1, Dcytb, ZIP14, ZIP8, SCARA5, FLVCR2, HRG-1, CD91, CD163, HO-1, Mfrn1, Mfrn2, frataxin, ABCB7, ALAS1, FLVCR1, FtMt, FtH, FtL, PCBP1, PCBP2, NCOA4, FPN, IRP1, IRP2, FBXL5, and FAM96A) in ACC tumors (n = 77) were compared with their normal counterparts (n = 128). The expression data are first log2 (TPM + 1) transformed for differential analysis and the log2FC is defined as median (Tumor) - median (Normal). An adjusted P-value (adj. P) < 0.01 and |log2FC| > 1 were set as the cut-off criteria.
2.2. Survival analysis
Patients with ACC were classified into two subgroups with the median value of the expression level of indicated gene as a cutoff. Overall survival and disease-free survival were calculated using the Kaplan-Meier method, and survival curves were compared using log- rank tests. The log-rank P value < 0.05 was selected as a significance threshold. The hazards ratio hazard ratios (HRs) with their 95% CIs were calculated based on Cox PH model.
2.3. Correlation analysis
The Spearman rank analysis was performed to evaluate the corre- lation between the expression levels of indicated genes. We used the non-log scale for calculation and the log-scale axis for visualization with the GEPIA tool, respectively. A P value < 0.05 was considered statis- tically significant.
2.4. Pan-cancer gene expression analysis
TCGA has profiled more than 10,000 samples derived from 33 types of cancer, including ACC, BLCA, BRCA, CESC, CHOL, COAD, DLBC, ESCA, GBM, HNSC, KICH, KIRC, KIRP, LAML, LGG, LIHC, LUAD, LUSC, MESO, OV, PAAD, PCPG, PRAD, READ, SARC, SKCM, STAD, TGCT, THCA, THYM, UCEC, UCS, and UVM (For full names of these cancer types, see Abbreviations). The pan-cancer gene expression analysis based on TCGA and GTEx data was performed using the GEPIA tool. The expression data are first log2 (TPM + 1) transformed for differ- ential analysis and the log2FC is defined as median (Tumor) - median (Normal). An adjusted P-value (adj. P) < 0.01 and |log2FC| > 1 were set as the cut-off criteria.
2.5. Functional and pathway enrichment analysis
The co-expression analysis was performed using the GEPIA tool, and 200 similarly expressed genes of the target gene were subjected to the functional and pathway enrichment analysis. The functional and pathway enrichment analysis was performed using the Metascape tool (www.metascape.org) with the following ontology sources: GO Biological Processes, KEGG pathway, Reactome Gene Sets, Canonical Pathways and CORUM [29]. All genes in the human genome were used as the enrichment background. Terms with an enrichment factor > 1.5, a minimum count of 3, and a P value < 0.01 were collected and grouped into clusters based on their membership similarities.
A
CP
B
HEPH
C
STEAP3
D
Dcytb
6
*
5
.
*
.
8
5
*
log2(TPM+1)
log2(TPM+1)
2
log2(TPM+1)
log2(TPM+1)
6
6
4
2
3
4
3
3
A
4
K
4
2
;
:
~
2
2
1
1
1
5
0
0
0
0
T
N
T
N
T
N
T
N
E
ZIP8
F
FLVCR2
G
CD91
H
CD163
5
5
8
8
log2(TPM+1)
4
log2(TPM+1)
4
log2(TPM+1)
log2(TPM+1)
H
6
A
3
SZ
6
.:
3
2
2
4
*
4
2
3
1
1
2
2
0
0
0
0
T
N
T
N
T
N
T
N
I
FtH
J
FPN1
K
IRP1
L
IRP2
15
ـــ
*
10
*
7
6
6
log2(TPM+1)
C
log2(TPM+1)
5
log2(TPM+1)
8
E
10
5
log2(TPM+1)
NE
4
?
6
4
:
4
GOT
3
3
5
2
2
2
1
1
0
0
0
0
T
N
T
N
T
N
T
N
A
CP
B
HEPH
C
STEAP3
D
Dcytb
1.0
P=0.037
1.0
P=0.14
1.0
P=0.0072
1.0
P=0.18
0.8
0.8
0.8
0.8
Low
High
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
0.2
0.2
0.2
0.0
0.0
0.0
0.0
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
E
ZIP8
F
FLVCR2
G
CD91
H
CD163
1.0
P=0.17
1.0
P=0.044.
1.0
P=0.12
1.0
P=0.55
0.8
0.8
0.8
0.8
0.6-
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
0.2
0.2
0.2
0.0
0.0
0.0
0.0
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
I
FtH
J
FPN1
K
IRP1
L
IRP2
Overall Survival
1.0
P=0.75
1.0
P=0.00025
1.0
P=0.69
1.0
P=0.47
0.8
0.8
0.8
0.8
0.6
0.6
0.6
0.6
0.4-
0.4
0.4
0.4
0.2
0.2
0.2
0.2
0.0
0.0
0.0
0.0
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
Month
Fig. 2. Association between the expression le- vels of 12 differentially expressed genes (re- lated to iron metabolism) and the overall sur- vival in patients with ACC. The patients with ACC (n = 77) were classified into two sub- groups based on the expression level of in- dicated genes, including CP (A), HEPH (B), STEAP3 (C), Dcytb (D), ZIP8 (E), FLVCR2 (F), CD91 (G), CD163 (H), FtH (I), FPN1 (J), IRP1 (K) and IRP2 (L). The overall survival prob- abilities in subgroups were determined with Kaplan-Meier survival analysis and compared using the log-rank test. The median TPM was selected as the cut-off level for subgrouping (Red line: high expression group; Blue line: low expression group). The hazards ratio was cal- culated based on Cox PH model, and 95% Confidence Interval was added as dotted line. All data were extracted from TCGA using the GEPIA tool. (For interpretation of the refer- ences to colour in this figure legend, the reader is referred to the web version of this article).
A
B
C
D
CP
HEPH
STEAP3
Dcytb
1.0
P=0.021
1.0
P=0.78
1.0
P=0.016
1.0
P=0.054
0.8
0.8
0.8
0.8
Low
High
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
0.2
0.2
0.2
0.0
0.0
0.0
0.0
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
E
ZIP8
F
FLVCR2
G
CD91
H
CD163
1.0
P=0.66
1.0
P=0.023
1.0
P=0.89
1.0
P=0.35
0.8
0.8
0.8
0.8
0.6
0.6
0.6
0.6
0.4.
0.4
0.4
0.4
0.2
0.2
0.2
0.2
0.0
0.0
0.0
0.0
0
50
100
150
0
50
100
150
0
50
100
150
0
50
100
150
I
FtH
J
FPN1
K
IRP1
L
IRP2
Disease Free Survival
1.0
P=0.83
1.0
P=0.0018
1.0
P=0.77
1.0
P=0.091
0.8
0.8
0.8
0.8
0.6
0.6
0.6
0.6
0.4
0.4
0.4
0.4
0.2
0.2
0.2
0.2
0.0
0.0
0.0
0.0
0
50
100
150
0
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100
150
0
50
100
150
0
50
100
150
Month
A
CP
B
HEPH
C
STEAP3
D
Dcytb
10
P=0.300
6
P=0.312
5
P=0.406
7
P=0.999
8
5
6
4
4
6
4
3
3
5
4
2
2
2
3
1
1
2
0
1
1
II
III
IV
II
III
IV
0
II
III
IV
II
III
IV
E
ZIP8
F
FLVCR2
G
CD91
H
CD163
6
P=0.0732
5
P=0.549
10
P=0.470
10
P=0.0537
5
8
4
4
8
3
3
6
6
2
2
4
4
1
1
2
2
I
II
III
IV
II
III
IV
II
III
IV
11
III
IV
I
FtH
J
FPN1
K
IRP1
L
IRP2
16
P=0.277
10
P=0.0179
7
P=0.795
5
P=0.557
14
8
6
log2(TPM+1)
4
12
6
5
4
3
10
4
3
2
2
2
8
1
1
II
III
IV
I
Il
III
IV
I
II
III
IV
I
11
III
IV
Pathological stage
3. Results
3.1. Reduced expression of CP, HEPH, STEAP3, Dcytb, FLVCR2, CD91, CD163, FtH, FPN1, IRP1 and IRP2, but increased expression of ZIP8 was identified in ACC tumors
We first compared the expression levels of 36 iron metabolism-re- lated genes between ACC tumors (n = 77) and normal adrenal gland tissues (n = 128), including 17 genes for iron uptake (hepcidin, Tf, TfR1, TfR2, HFE, HJV, STEAP3, DMT1, Dcytb, ZIP14, ZIP8, SCARA5,
FLVCR2, HRG-1, CD91, CD163, and HO-1), 12 genes for iron utilization and storage (Mfrn1, Mfrn2, frataxin, ABCB7, ALAS1, FLVCR1, FtMt, FtH, FtL, PCBP1, PCBP2, and NCOA4), 4 genes for intracellular iron balance (IRP1, IRP2, FBXL5, and FAM96A), 2 genes encoding ferrox- idases (CP and HEPH) and one iron exporter gene FPN1. We found that the expression levels of CP, HEPH, STEAP3, Dcytb, FLVCR2, CD91, CD163, FtH, FPN1, IRP1 and IRP2 were significantly decreased while the expression level of ZIP8 was significantly increased in ACC tumors compared to normal controls (Fig. 1).
A
Normal
B
ACC
3.5
3.0
P=0.45
P=0.0021
R =- 0.067
8
R=0.35
2.5
6
2.0
log2(CP TPM)
1.5
4
1.0
0.5
2
0.0
0
0
2
4
6
8
10
2
4
6
8
10
log2(FPN1 TPM)
3.2. Expression levels of FPN1 and CP were negatively correlated with the survival of patients with ACC
We then explored the association between the 12 differentially ex- pressed genes and patient survival using the Kaplan-Meier survival
analysis. We found that lower expression levels of FPN1 and CP, but higher expression levels of STEAP3 and FLVCR2 predicted poorer overall survival (Fig. 2) and disease free survival (Fig. 3) in patients with ACC. Considering that the expression levels of FPN1, CP, STEAP3 and FLVCR2 were all decreased in ACC tumors, FPN1 or CP might be more appropriate for prognostic prediction than STEAP3 or FLVCR2.
3.3. Decreased expression of FPN1 was correlated with the progression of ACC
In patients with ACC, the prognosis is depending on tumor stage. Once the tumor spreads outside the adrenal gland, 5-year survival drops from 58 to 66% (patients with intra-adrenal ACC) to 0-24% (patients with extra-adrenal ACC) [30]. The associations between 12 differential expressed genes related to iron metabolism and the stages of ACC were explored. We found a significant negative correlation between FPN1 levels and pathological stages of ACC (Fig. 4). These data suggested that FPN1 down-regulation might participate in the progression of ACC.
3.4. Expression levels of FPN1 and CP were positively correlated in ACC tumors, but not in normal controls
We then explored the correlations of FPN1 and CP in ACC tumors and normal controls, respectively. A weak but significant positive cor- relation between FPN1 and CP expression was observed in ACC tumors but not in normal controls (Fig. 5). These results suggested that the
A
1400
FPN1
Normal
1200
Cancer
Decreased in cancer
1000
Increased in cancer
TPM
800
600
400
200
0
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA THYM
UCEC
UC
UVM
B
3000
CP
Normal
2500
Cancer
Decreased in cancer
Increased in cancer
2000
TPM
1500
1000
500
0
ACC
BLCA
BRCA
CESC CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
LGG
LIHC
LUAD
LUSC
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCĘC
UCS
UVM
A
FPN1
GO: 0042445: hormone metabolic process-
GO: 0006069: ethanol oxidation-
GO: 0060021: roof of mouth development-
R-HSA-5686938: Regulation of TLR by endogenous ligand-
hsa05144: Malaria-
hsa00340: Histidine metabolism-
R-HSA-198933: Immunoregulatory interactions between a Lymphoid and a non-Lymphoid cell-
GO: 0043001: Golgi to plasma membrane protein transport-
GO: 0009617: response to bacterium-
GO: 0009791: post-embryonic development-
GO: 0009636: response to toxic substance-
GO: 0018958: phenol-containing compound metabolic process-
GO: 0051345: positive regulation of hydrolase activity-
M255: PID HIF1 TFPATHWAY-
M5885: NABA MATRISOME ASSOCIATED-
0
2
4
6
8
10
B
-log10(P)
CP
M5885: NABA MATRISOME ASSOCIATED-
GO:0043588: skin development-
GO:0052548: regulation of endopeptidase activity-
GO:0019221: cytokine-mediated signaling pathway-
GO:0030213: hyaluronan biosynthetic process-
hsa05146: Amoebiasis-
GO:0009617: response to bacterium-
R-HSA-5083625: Defective GALNT3 causes familial hyperphosphatemic tumoral calcinosis (HFTC)-
GO:0098542: defense response to other organism-
GO:0032652: regulation of interleukin-1 production-
GO:0042060: wound healing-
GO:0051607: defense response to virus-
GO:0031069: hair follicle morphogenesis-
M65: PID FRA PATHWAY-
GO:0030856: regulation of epithelial cell differentiation-
GO:2000116: regulation of cysteine-type endopeptidase activity-
GO:0010718: positive regulation of epithelial to mesenchymal transition-
GO:0035065: regulation of histone acetylation-
R-HSA-913531: Interferon Signaling-
GO:0007498: mesoderm development-
0
5
10
15
-log10(P)
positive correlation of FPN1 and CP expression might be tumor- or even ACC- specific.
3.5. Pan-cancer analysis identified reduced expression of FPN1 and CP as an ACC-specific expression signature
We further explored the expression pattern of FPN1 and CP in an- other 32 types of cancer. We found that FPN1 was down-regulated in BLCA, CESC, LUSC, OV, and UCS, while CP was down-regulated in COAD, HNSC, KICH, READ, SKCM and THCA, compared to their cor- responding normal controls. Moreover, the expression of FPN1 or CP increased in some cancer types. Among these 33 types of cancers, re- duced expression of both FPN1 and CP was only found in ACC tumors (Fig. 6). These data indicated that down-regulation of FPN1 and CP might be an ACC-specific expression signature.
3.6. The expression of FPN1 and CP might affect or be affected by the tumor immune microenvironment of ACC
Using the GEPIA tool, we identified 200 similarly expressed genes of FPN1 or CP, respectively. For each given gene list, the functional and
pathway enrichment was carried out using the Metascape tool, with the following ontology sources: KEGG Pathway, GO Biological Process, Reactome Gene Sets, Canonical Pathways and CORUM. The involve- ment of FPN1- and CP-coexpressed genes in immune regulation was demonstrated (Fig. 7). The co-expressed network of FPN1 was involved in TLR signaling, immune-regulatory interactions, and response to bacterium, and the co-expressed network of CP was involved in cyto- kine-mediated signaling pathway, response to bacterium, regulation of interleukin-1 production, defense response to virus, and interferon signaling. These results indicated that expression of FPN1 and CP might affect or be affected by the tumor immune microenvironment of ACC.
4. Discussion
Different expression signatures of iron metabolism-related genes have been identified to predict prognosis in several cancer types [8,23,31]. However, little is known on the expression pattern and function of iron metabolism-related genes in ACC. In this study, we demonstrated that reduced expression levels of FPN1 and CP were as- sociated with poor prognosis in patients with ACC.
Rapidly proliferating tumor cells often require increased amounts of
iron to maintain abnormally enhanced DNA synthesis [32]. FPN1, the only known iron exporter in mammalian cells, is down-regulated in various types of cancer, including prostate, lung, ovarian and breast cancer [33-38]. Reduced FPN1 may promote proliferation of cancer cells through restricting the efflux of iron. Presently, we demonstrated for the first time that FPN1 expression was significantly decreased in ACC tumors. Pinnix et al. have identified reduced FPN1 expression as an independent predictor of poor prognosis in breast cancer [33]. Consistently, we demonstrated that reduced FPN1 expression was as- sociated with poor prognosis in patients with ACC.
It is generally known that CP is predominantly synthesized by the liver and then released into the circulation under physiological condi- tions [39]. Increased CP level in serum has been described as a potential prognostic marker for cancers, including bile duct, breast, and colon cancer [40-43]. The transcriptional expression of CP in tumors and its prognostic roles remain unclear. Our findings suggested that reduced CP expression in tumors might predict poor prognosis in patients with ACC. The biological and prognostic roles of CP produced by liver or tumor tissues in ACC deserve further exploration, and it would be useful to know the level of CP transcript expression relative to that of hepa- tocytes, in normal and cortical tumor cells.
The expression level of FPN1 was weakly but significantly corre- lated with CP in ACC tumors but not in normal controls, suggesting the existence of a tumor-specific mechanism underlying iron metabolism. We further characterized reduced expression of FPN1 and CP as an ACC-specific signature by a pan-cancer analysis. Additionally, we found that FPN1 expression was continuously and significantly decreased with the increasing stages of ACC. These results suggested the potential of FPN1 and CP, especially FPN1, in the diagnosis and prognosis of ACC.
FPN1-mediated iron export has been characterized as a key com- ponent of innate immune responses during infections [44,45]. Zhang et al. have demonstrated that FPN1 deficiency enhances the production of pro-inflammatory cytokines such as TNF-a and IL-6 in mouse mac- rophages [44]. Conversely, Manfred et al. have found that reduced FPN1 impairs cellular iron homeostasis and attenuates inflammatory immune responses in macrophages during Salmonella infection [45]. These findings suggest that immune regulation involves FPN1. Simi- larly, CP protein levels in the circulation increase during infections and inflammation [46], as well as in cancer [40-43], suggesting its potential role in tumor immunity regulation. It remains unknown whether and how the CP-FPN1 system of iron export regulates the tumor immune environment. By the functional enrichment analysis, we demonstrated the involvement of FPN1- or CP-similarly expressed genes in several immune processes, suggesting their possible implications on tumor immune microenvironment in ACC. However, it could be that the op- posite is true, namely that the tumor or the microenvironment it pro- duces is altering the expression of FPN1 and CP. Several other tumor- associated biological processes or pathways were characterized, in- cluding wound healing, epithelial cell differentiation, histone acetyla- tion, hormone metabolic process, histidine metabolism and HIF-1a regulation. These findings may help clarify the mechanisms underlying the prognostic role of FPN1 or CP in ACC. The link between iron me- tabolism and tumor immune microenvironment deserves further ex- perimental exploration.
Another 10 differential expressed genes related to iron metabolism were identified in ACC tumors, including HEPH, STEAP3, Dcytb, ZIP8, FLVCR2, CD91, CD163, FtH, IRP1 and IRP2. The expression of ferritin, an iron-storage molecule, is increased in esophageal adenocarcinoma and glioblastoma but decreased in breast cancer [47-50]. The expres- sion of IRP1 is increased in breast cancer, and the expression of IRP2 is up-regulated in breast, colorectal, and lung cancer [51-53]. Differently, reduced expression of FtH, IRP1 and IRP2 in ACC tumors was demon- strated in the present study. As a CP homologue, low HEPH expression correlates with poor survival of breast cancer [54]. Consistently, we found reduced HEPH expression in ACC tumors compared to normal
controls. However, no association between the expression of FtH, IRP1, IRP2 or HEPH and survival of patients with ACC was found.
In summary, we identified the reduced expression of FPN1 and CP as a potential signature for poor prognosis of ACC patients based on the TCGA database. Mechanisms underlying the prognostic value of FPN1 and CP in ACC deserve further experimental exploration.
Declarations of interest
None.
Acknowledgement
This study was supported by National Natural Science Foundation of China (81802928).
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