Check for updates
Pan-cancer analysis reveals the roles of XPO1 in predicting prognosis and tumorigenesis
Lei Zhao1, Baiwei Luo2, Liang Wang3, Wei Chen1, Manyu Jiang2, Nengwei Zhang4
1Department of Critical Care Medicine, Beijing Shijitan Hospital, Capital Medical University, Beijing, China; 2The First Clinical Medical School of Guangdong Medical University, Zhanjiang, China; ‘Department of Hematology, Beijing TongRen Hospital, Capital Medical University, Beijing, China; 4Surgery Centre of Diabetes Mellitus, Beijing Shijitan Hospital, Capital Medical University, Beijing, China
Contributions: (I) Conception and design: N Zhang, L Zhao; (II) Administrative support: L Wang; (III) Provision of study materials or patients: N Zhang, L Zhao, L Wang; (IV) Collection and assembly of data: B Luo; (V) Data analysis and interpretation: B Luo; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.
Correspondence to: Nengwei Zhang, MD. Surgery Centre of Diabetes Mellitus, Beijing Shijitan Hospital, Capital Medical University, No. 10 Tieyi Rd, Haidian District, Beijing 100038, China. Email: znw@bjsjth.cn.
Background: Exportin 1 (XPO1), a nuclear export protein, participates in many biological processes, including mRNA transport, nucleocytoplasmic transport, nuclear protein export, regulation of mRNA stability, and drug response. XPO1 plays key roles in many cancer types and may serve as a potential biomarker. It is significant to systematically elucidate the roles of XPO1 in various cancer types in terms of function, molecular biology, immunology, and clinical relevance.
Methods: Data from UCSC Xena, CCLE, and CBioPortal were analyzed for the investigation of the differential expression of XPO1 across multiple cancer types. Clinical data were acquired to analyze the influence of XPO1 on the clinical characteristics of patients, such as survival outcome and clinical stage. The roles of XPO1 in the onset and progression of multiple cancers were expounded in terms of genetic changes at the molecular level [including tumor mutational burden (TMB), microsatellite instability (MSI), copy number variation (CNV), methylation, and gene co-expression], biological pathway changes, and the immune microenvironment.
Results: XPO1 was overexpressed in various tumor types, which may be related to CNV. Clinical data analysis revealed that XPO1 may serve as a risk factor in tumors, such as adrenocortical carcinoma, liver hepatocellular carcinoma, and low-grade glioma, thereby affecting patient prognosis. XPO1 in multiple tumor types was also substantially correlated with clinical stage, patient gender, and patient age. In certain tumors, the expression level of XPO1 exerted a greater influence on TMB and MSI. It was also found that XPO1 inhibited the activity of immune cells in the tumor immune microenvironment, such as CD8+ T cells, and affected biological pathways, such as the cell cycle and oxidative phosphorylation, and drove the expression of cancer driver genes, immune checkpoint genes, and highly mutated genes.
Conclusions: XPO1 is a potential pan-cancer risk factor as it may jointly promote tumor onset and progression by inhibiting the immune response, influencing relevant biological pathways, and promoting mutations in other genes.
Keywords: Exportin 1 (XPO1); pan-cancer; tumor mutational burden (TMB); microsatellite instability (MSI); immune microenvironment
Submitted Aug 15, 2021. Accepted for publication Oct 14, 2021.
doi: 10.21037/tcr-21-1646
View this article at: https://dx.doi.org/10.21037/tcr-21-1646
Introduction
The nuclear export protein Exportin 1 (XPO1) plays a key role in the onset and progression of both solid tumors and hematological malignancies and is associated with a poor prognosis in patients with various cancers, including pancreatic, lung, gastric, prostate, and colorectal cancers (1). Selective inhibitors of XPO1, with selinexor being one of the most representative drugs, have been widely tested in solid tumors and hematological malignancies and approved for the treatment of relapsed/refractory multiple myeloma and diffuse large B-cell lymphoma (2,3). Some studies have suggested that XPO1 may promote tumor cell proliferation by influencing the sub-cellular localization of nuclear export signal-containing oncogenes, tumor suppressor proteins, control of the mitotic apparatus, chromosome segregation, stability of nuclear and chromosomal structures (4). Notably, XPO1 is the major transporter of many types of nuclear proteins, including tumor suppressor proteins and oncoproteins, such as Rb, APC, p53, p21, p27, BRCA1/2, eIF4E, and survivin (1,4,5). This indicates that XPO1 plays a critical role in the progression of many tumors. Besides directly influencing the expression of proto-oncogenes and tumor suppressor genes, XPO1 can also indirectly promote tumor onset and progression by affecting vascular endothelial growth factor, epidermal growth factor receptor, Cox-2, c-Myc, and HIF-1 (5). Based on these findings, it is evident that XPO1 does not affect tumor progression by a single route but exerts biological effects on tumors through multiple pathways. Therefore, elucidating the roles of XPO1 in various tumors based on clinical-omics and genomics is essential to provide theoretical guidance for future drug development and clinical treatment.
We present the following article in accordance with the REMARK reporting checklist (available at https://dx.doi. org/10.21037/tcr-21-1646).
Methods
Data sources
The mRNA expression data, clinical data, and methylation data of 33 tumor types and normal tissues were downloaded from UCSC Xena (https://xena.ucsc.edu/). The tumor types investigated in this study included adrenocortical carcinoma (ACC), urothelial bladder carcinoma (BLCA), infiltrating ductal carcinoma (BRCA), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), diffuse large B-cell lymphoma (DLBC),
esophageal carcinoma (ESCA), glioblastoma multiforme (GBM), head and neck squamous cell carcinoma (HNSC), kidney chromophobe renal cell carcinoma (KICH), kidney clear cell renal cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), acute myeloid leukemia (LAML), low-grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), mesothelioma (MESO), ovarian serous cystadenocarcinoma (OV), pancreatic adenocarcinoma (PAAD), pheochromocytoma/paraganglioma (PCPG), prostate adenocarcinoma (PRAD), rectum adenocarcinoma (READ), sarcoma (SARC), skin cutaneous melanoma (SKCM), stomach adenocarcinoma (STAD), testicular germ cell tumor (TGCT), thyroid carcinoma (THCA), thymic carcinoma (THYM), uterine corpus endometrial carcinoma (UCEC), uterine carcinosarcoma (UCS), and uveal melanoma (UVM). The mRNA expression data of various cancer cell lines were downloaded from CCLE (https://portals.broadinstitute.org/ccle), and the copy number variation (CNV) data of XPO1 in the 33 types of tumors were downloaded from CBioPortal (https://www. cbioportal.org/). All procedures performed in this study involving human participants were in accordance with the Declaration of Helsinki (as revised in 2013).
Differential expression of XPO1
To determine the differential expression of XPO1 in normal and tumor tissues, the data of XPO1 expression in the 33 tumor types downloaded from UCSC Xena were first transformed to the normalized transcripts per million format, and boxplots were plotted using the ggpubr and ggplot2 R packages (6). The statistical significance of differential XPO1 expression in the various tumors was determined using the Wilcoxon signed-rank test.
Correlations of XPO1 with clinical characteristics
To evaluate the effects of XPO1 expression on overall survival (OS), disease-specific survival (DSS), disease- free interval (DFI), and progression-free interval (PFI) in patients, tumor samples were divided into the two following groups: the high expression group and low expression group, based on the median XPO1 expression level among the various tumor types. Subsequently, survival analysis was performed using the log-rank test in the survival R package, and results were visualized using the survminer R package. A Cox proportional hazards model was also computed
in the survival R package for survival analysis, and forest plots were drawn using the forestplot R package for the visualization of analysis results. The influence of XPO1 on patient survival in various cancers was investigated using the Kaplan-Meier Plotter (https://kmplot.com/analysis/) (7).
Correlations of XPO1 with patient age (≤18 years: adolescence; 19-30 years: adulthood; 31-50 years: middle- aged; >51 years: older), sex, and clinical stage were investigated. Boxplots were drawn using the ggpubr and ggplot2 R packages (6), and the statistical significance of the potential clinical correlations was determined using the Wilcoxon signed-rank test.
Characteristics of molecular-level changes of XPO1 and their molecular effects
Data regarding the CNVs of XPO1, including amplifications and deletions, were downloaded from CBioPotal (https://www. cbioportal.org/) for the calculation of CNV frequency among the various tumor types. Correlations between CNV and XPO1 expression level were determined using the Wilcoxon signed-rank test and visualized using the ggpubr R package. The Spearman’s rank correlation coefficients for correlations of XPO1 expression with tumor mutational burden (TMB) and microsatellite instability (MSI) in the various tumors were separately calculated, and radar charts were plotted using the fmsb R package. The statistical significance of differences in XPO1 methylation level in normal and tumor tissues was determined using the Wilcoxon signed-rank test, and results were visualized using the ggplot2 R package (6). Based on the median XPO1 methylation level, tumor samples were divided into the two following groups: the high methylation group and low methylation group, and survival analysis was performed using the survival R package. Mutations of XPO1 in the 33 tumor types were recorded, and the Spearman’s rank correlation coefficients between XPO1 expression and the expression of the top 30 highly mutated genes among the various tumor types were separately calculated.
Effects of XPO1 on the immune microenvironment
To determine the effects of XPO1 on the immune microenvironment in various tumor types, the stromal and immune scores of the tumors were evaluated using the ESTIMATE algorithm (8). The Spearman’s rank correlation coefficients for correlations of XPO1 expression with the stromal scores and the immune scores were separately calculated. Relative contents of 22 types of
immune cells in the various tumor types were calculated using the CIBERSORT algorithm (9). Then the Spearman’s rank correlation coefficients between XPO1 expression and the various immune cell types were calculated.
Co-expression of XPO1 and specific genes
Co-expression analyses of XPO1 with immune-related genes and cancer driver genes were separately performed for further determination of XPO1 functions. The Pearson correlation coefficients between expression levels of XPO1 and the various immune checkpoint genes were calculated. We used the edgeR R package (10,11) to perform differential analysis between normal and tumor group. Next, we chose five genes with the highest log FC values among the 568 driver genes (12) for each tumor type, and the driver genes with the greatest difference were determined through the union of data for the various tumor types. The Pearson correlation coefficients between XPO1 expression and the driver genes with the greatest difference were calculated. Based on the median value of XPO1 expression, tumor samples were divided into the high expression group and low expression group, and differential analysis was performed using the edgeR R package (10,11). Gene set enrichment analysis (GSEA) was subsequently performed using the clusterProfiler R package (13). The top five genes with the highest absolute values for the normalized enrichment score and P<0.05 were identified for each tumor type, and the results were visualized.
Statistical analysis
All analyses were carried out in R version 3.6.1 and corresponding packages.
Results
XPO1 expression
Differential expression analysis was performed between tumor tissue and the adjacent normal tissue for each tumor type to investigate the changes in XPO1 expression patterns. Results indicated that XPO1 was significantly overexpressed in most tumor types and significantly underexpressed in a small number of tumors. XPO1 was significantly overexpressed in BLCA, BRCA, CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUSC, READ, SARC, and STAD and significantly underexpressed in KICH and THCA (Figure 1A). This indicated that XPO1
A
Type Normal Tumor
ns
ns
*
*
ns
ns
ns
| XPO1 log2 (TPM+1) | C | XPO1 log2 (TPM+1) | B | XPO1 log2 (TPM+1) | ||||
|---|---|---|---|---|---|---|---|---|
| 5 | 7 6 | 8 | 2 | 6 4 | 8 | 2 | 8 6 4 | |
| Giant cell tumour | Blood | ACC | ||||||
| Upper aerodigestive kidney | Heart | BLCA | ||||||
| Multiple myeloma | Liver | BRCA | ||||||
| Liver | Muscle | CESC | ns | |||||
| Thyroid | Kidney | CHOL | ||||||
| Esophagus | Pancreas | COAD | ||||||
| CML Urinary tract | Stomach | DLBC ESCA | *** | |||||
| Colorectal Breast | Salivary Gland Brain | GBM | * | |||||
| Bile duct | Adrenal Gland | HNSC | *** | |||||
| Endometrium | Colon | KICH | *** *** | |||||
| Ovary Pancreas | Esophagus | KIRC KIRP | *** | |||||
| Lymphoma DLBCL | <not provided> | LAML | ||||||
| Lymphoma other Melanoma | Adipose Tissue | LGG | ||||||
| Meningioma | F | Blood Vessel Prostate | LIHC | *** | ||||
| Lung NSC AML | Breast | LUAD LUSC | *** *** | |||||
| Glioma | Small Intestine | MESO | ||||||
| Lymphoma burkitt Stomach | Lung | OV | ||||||
| Ewings sarcoma | Thyroid | PAAD | ns | |||||
| Lymphoma hodgkin | Cervix Uteri | PCPG | ||||||
| Osteosarcoma | Bladder | PRAD | ||||||
| Medulloblastoma | Skin | READ | ||||||
| Mesothelioma | Vagina | SARC | ||||||
| Other | Spleen | SKCM | ||||||
| Soft tissue Prostate | Pituitary | STAD | ||||||
| T-cell ALL | Nerve | TGCT | ||||||
| Neuroblastoma | Fallopian Tube | THCA | ||||||
| B-cell ALL | Uterus | THYM | - | |||||
| Leukemia other Lung small cell | Ovary | UCEC | ||||||
| Chondrosarcoma | Testis | UCS | ||||||
| NA | Bone Marrow | UVM |
Figure 1 XPO1 expression in normal tissues, tumor tissues, and cancer cell lines. (A) Differential expression analysis of XPO1 in normal and tumor samples of 33 tumor types (*, P<0.05; *** , P<0.001. The P value of each tumor: BLCA: 5.61e-04; BRCA: 2.97e-09; CESC: 0.08; CHOL: 2.26e-09; COAD: 4.87e-16; ESCA: 1.2e-06; GBM: 0.013; HNSC: 7.85e-15; KICH: 6.58e-09; KIRC: 3.14e-08; KIRP: 2.35e- 05; LIHC: 1.73e-20; LUAD: 2.22e-18; LUSC: 1.71e-25; PAAD: 0.966; PCPG: 0.265; PRAD: 0.852; READ: 0.02; SARC: 0.023; SKCM: 0.883; STAD: 1.27e-15; THCA: 7e-04; THYM: 0.251; UCEC: 0.98). (B) XPO1 expression in normal tissues (based on data from the GTEx database). (C) XPO1 expression in tumor cell lines (based on data from the CCLE database). XPO1, Exportin 1; BLCA, bladder carcinoma; BRCA, infiltrating ductal carcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; ESCA, esophageal carcinoma; GBM, glioblastoma multiforme; HNSC, head and neck squamous cell carcinoma; KICH, kidney chromophobe renal cell carcinoma; KIRC, kidney clear cell renal cell carcinoma; KIRP, kidney renal papillary cell carcinoma; LIHC, liver hepatocellular carcinoma; LUAD, lung adenocarcinoma; LUSC, lung squamous cell carcinoma; PAAD, pancreatic adenocarcinoma; PCPG, pheochromocytoma/paraganglioma; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; SARC, sarcoma; SKCM, skin cutaneous melanoma; STAD, stomach adenocarcinoma; THCA, thyroid carcinoma; THYM, thymic carcinoma; UCEC, uterine corpus endometrial carcinoma.
played different roles in different tumor types and might provide promoting or inhibiting effects during different stages of tumor onset and progression. With selective gene expression, the expression of the same gene in different tissues may not be completely identical, and differences may exist in the biological effects of the gene on various tissues. By utilizing GTEx sample data stored at UCSC Xena, we analyzed the expression of XPO1 in normal tissues. Results indicated that XPO1 expression was relatively higher in the ovary, uterus, bone marrow, testis, and nerve tissues, and relatively lower in the heart and blood tissues (Figure 1B). The analysis of XPO1 expression in tumor cell lines revealed that XPO1 was significantly overexpressed in B-cell acute lymphoblastic leukemia (B-ALL), T-cell ALL, small- cell lung carcinoma and neuroblastoma. This suggested that XPO1 might play a key role in lung cancer, ALL, and neuroblastoma (Figure 1C).
Correlations of XPO1 with clinical characteristics
To investigate the correlations of XPO1 expression with patient prognosis, we performed survival analysis on OS, DSS, DFI, and PFI using the two following methods: the log-rank test and Cox proportional hazards model (Figure 2A-2G, Figure S1A-S1I). When the analysis results of the two methods were consolidated, we observed the following: (I) XPO1 was a risk factor for OS in ACC, LGG, and LIHC, higher XPO1 expression was associated with shorter OS; (II) XPO1 underexpression in ACC, LGG, LIHC, PRAD was associated with longer DSS, but shorter in THYM; (III) XPO1 underexpression in ACC, LIHC, LUAD, and PRAD was associated with longer PFI, and GBM was converse; and (IV) XPO1 underexpression in ACC was associated with longer DFI. Kaplan-Meier Plotter also got similar results (Figure S2A-S2L). To further elucidate the relationships between XPO1 and the various clinical characteristics, the effects of XPO1 on patient age, sex, and clinical stage were investigated (Figure 3A-3I). Results indicated that XPO1 expression was closely related to clinical stage in ACC, BRCA, LIHC, PAAD, SKCM, and THCA. Differences existed in XPO1 expression in BRCA, THCA, and READ among the different age groups, and significant differential expression of XPO1 in LIHC, HNSC, and LUSC also existed between the two sexes. These results suggested that XPO1 expression exerted certain effects on cancer progression, which was in agreement with the observations of XPO1 overexpression
in the majority of tumor types.
Characteristics of molecular-level changes of XPO1 and their correlations with TMB and MSI
We determined the CNV (including amplifications and deletions) frequencies of XPO1 to investigate the diversity of genetic variations of XPO1 among the various tumor types. Results indicated that XPO1 had relatively high CNV frequencies (>5%), with the frequencies of copy number amplifications being generally higher than that of copy number deletions (Figure 4A). When the effects of CNV on XPO1 expression were investigated, we found that XPO1 expression was generally significantly correlated with CNV in tumors, such as BLCA, BRCA, CESC, KIRC, and LICH. Therefore, XPO1 overexpression appeared to be intricately linked to copy number amplification (Figure 4B). Notably, the frequency of copy number deletions of XPO1 in KICH was substantially higher than amplifications, which was consistent with the significant underexpression of XPO1 in KICH mentioned earlier. When the effects of XPO1 on MSI in the various tumor types were investigated, it was found that XPO1 was significantly positively correlated with MSI in ACC, STAD, READ, and CHOL (P<0.05, Spearman’s rank correlation coefficient >0.25) and significantly negatively correlated with MSI in DLBC (P<0.05, Spearman’s rank correlation coefficient ← 0.25) (Figure 4℃). As many studies have reported an important relationship between TMB and the outcome of immune checkpoint inhibitor therapy, we also performed a correlation analysis of XPO1 and TMB in various tumor types and found that XPO1 expression was significantly positively correlated with TMB in ACC, STAD, READ, LUAD, and LGG (P<0.05, Spearman’s rank correlation coefficient >0.25) (Figure 4D). The results described above further demonstrated that CNV was closely associated with XPO1 overexpression and suggested that XPO1 might affect the prognosis of various cancers through TMB and MSI.
Clinical prognostic value of XPO1 methylation
XPO1 methylation in normal and tumor tissues was measured. The result showed that XPO1 in BLCA, KIRP, LIHC, PRAD and THCA was highly methylated compared with XPO1 in normal tissues (Figure 5A). In particular, survival rate was better with low-methylated
©
A
B
OS
DFI
C
DSS
D
PFI
9.948(3
Hazard ratio
ACC ALCA
pvalue
Hazard ratio
-001
04916
20.505(1.755-239.623)
ACC
0.001
10.021(3
28763
ACC.
0.001
Hazard ratio
20.186(7.309 59-10
0.793
1.111(0.504-2.449)
A
BRO
”
9.893
- 19810 684-1.546
1-93818-910-3738
BRCA CESC
1.19910 860-1 19310.869-3-393
9
482 144
BECA
0.124
3.98518-888-2-6153
CESC
0.941
0.8.50
1:3718.740-
21-1.224
CHOL COAD
1.033(0.433-2.463)
4
.876
8 85218 298 1.183
CHOL COAD
0.982
0.79
0.94710.716-1.252 1.9470-110-3609
80
DLBC
0,351
7:368(0,708-2.645)
LẠC
0.68810.418-1-121
0.886
0.801(0.038-16,826)
ESCA GBM
0.628
0.149
9.67110-120 2.905 1 31218 850-2.993
PLAC
1.296
GBM
0.007
1.445(0.920-2 241
12-0.820
5.
19820
ESCA
0.982
0.4990
5.440(1.003-29.493
.495
HNSC
0.991(0.428-2.292)
0.792/0.454-1-284
-29.5
0.486
1.291(0.629-2.647)
1
HINSC
RICH
635 8.873
0.030
0.9590.108-1-301 6.211(1.196-32.25
1.064(0.824-1-15? 3.24210.896-11.734
1.
184
869-3.35
KICH
KIRC KIRE
0.562
1.971(0.199-19.490)
0.113
505410 845 4 846
-
8.299
KIRG
216
LGG
1087 0.532-2.223
810.665-1.899
LGG
901(1.308-2.762 403/1.089-1.80
882
0.001
LGG
1.247 0.890-1.747
0.003
1.10}
A
0.484
0.049
1.495(0.485-4.610)
-8-898
LUAR
0.209 8.209
LUAR
-2 5-1
HVAD LUAD LUSC
0.039
1.3080.002-1.70m
9-485
0.242
MESO
0.739 0,511 0.223 0.00
1:2191 941-2.239)
0.8918-8-2.38 1.4450.840-45
351
1.075(0.704-1.642)
1 90018 888-3.268
‘9421
1.639(0,376-7.148)
SAAD
0.788(0.536-1,157)
8.887
OV
RAD
PAAD PCPG
8.818
19-18319-896-178:818
BAAD SSAB
866-3.112
980 405-32 32
5.971(1.298-27.469)
PSA3
8.681
59239:348-3:438
READ
3.241 0 966-10.87
0.028
0.926
PRAD
1.140(0.071-18 303
READ
0.282 0771
1.54810.698-9 3
REAR
302/0 220-7.709
ERCM
616-2
9.999% 379-1.848
GARC
1 37710 879-2157
STAR SGCT
0.235
0.8/10.694-1.42%
1.377(0.879-2.157)
1.750
&KCM
0.487
1.443(0)
ÈGCT
F
STAD
0.162
112 -08-13.286
0.732(0.407-1.318)
8.491
han 748-1.440
THCA
1-1920 317-17.367
0.797(0.418-1.520
311
3
S
TGCI
0.238
THCA UCEC UČS
0.490
0.10910.588-1-204
0.027
THCA
0.789
ÚČS
0.090
0.141(0.025-0.801
0.86310.294-2439
0.065
2.194(0.850-9.181)
1.490(0.975-2.278)
12/1-245-3.215
UČS
0.005
0.477 0.005
1.539(1.
ŮVM
0.045
1-2
0.492
0.565(0.111-2.883)
UVM
0.048
1.777(1.004-3.145)
UVM
2.106(
5
0.12 0.50 2.0 8.0 32.0
0.062 1e+00 2e+01
0.031 0.500 8e+00 1e+02 Hazard ratio
0.25 1.0 4.0 16.0 Hazard ratio
Hazard ratio
Hazard ratio
E
Cancer: ACC
XPO1 levels+High-Low
Disease-free interval
XPO1 levels-High-Low
Disease-specific survival
XPO1 levels- High- Low
Progression-free interval
XPO1 levels + High-Low
1.00 .
1.00
-
1.00
-
1.00 -
Overall survival
0.75
0.75
0.75
0.75
0.50
0.50
0.50
0.50
4
0.25
P<0.001
0.25
P=0.005
0.25
P<0.001
0.25
P<0.001
0.00
0.00
0.00
0.00
0 1 2 3 4 5 6 7 8 9 10 11 12
0 1 2 3 4 5 6 7 8 9 10 11 12
0 1 2 3 4 5 6 7 8 9 10 11 12
0 1 2 3 4 5 6 7 8 9 10 11 12
XPO1 levels
Times, years
XPO1 levels
Times, years
XPO1 levels
Times, years
XPO1 levels
Times, years
High
-
20
35
23
22 8
6
2
2
2
0
0
High Low
-
2ª
19
14
21
9 16
6
6
2
2
2
2
1
2 0
0
High Low
~ -
34
28
8
Low
40
13
23
23
12
39
39
21
18 6
2
2
2
2
0
0
High Low
-
39
31
23
5
3
1
1
0
9
6
5
2
2
9
6
4
3
2
2
13
9
6
5
2
2
2
an
40
28
17
1
7
5
4
2
2
2
T
T
1
T
T
T
1
T
T
1
0
1
2
3
4
5
6
7
8
9 10 11 12
0
1
2
3
4
5
6 7 8
9 10 11 12
0
1
2
3
4
5
6
7
8
9 10 11 12
0
1
2
3
4
5
6
7
8
10 11 12
Times, years
Times, years
F
Times, years
Times, years
Cancer: LGG
Disease-specific survival
Cancer: KIRC
XPO1 levels-High-Low
XPO1 levels+High-Low
XPO1 levels -High-Low
XPO1 levels- High-Low
1.00.
1.00
-
1.00
1.00
-
Overall survival
Disease-free interval
Progression-free interval
0.75
0.75
0.75
0.75
0.50
0.50
0.50
0.50
0.25
P=0.027
0.25
P=0.012
0.25
P=0.018
0.25
P=0.032
0.00
0.00
0.00
0.00
0 1 2 3 4 5 6 7 8 910 11 12 13 14 15 16 17 18 19 20
0 1 2 3 4 5 6 7 8 9 1011 12 13 14 15 16 17 18 1920
0 1 2 3 4 5 6 7 8 9 10 11 12
XPO1 levels
Times, years
0 1 2 3 4 5 6 7 8 9 10 11 12
XPO1 levels
Times, years
XPO1 levels
Times, years
XPO1 levels
Times, years
High LOW
-
202 125 86 86 46 35 28 23 1
8
4
3
2
1
1
1
0
0
9 C
High LOW
7
257 200 124 7 45 66 26 20 13 10 10
258 198 120 81
0
High
1
67 66
GAN
&&
88
80 0
AZ
Ad
11
9
5
Low
2.
I
300
安庆
MN
28
a
Na
5
0
8
7
5
3
3
2
0
0
0
9
5
0
5
0
0
0 1 2 3 4 5 6 7 8 910 11 12 13 14 15 16 17 18 19 20
0 1 2 3 4 5 6 7 8 910 11 12 13 14 15 16 17 18 19 20
0
1
2
3
4
5
6
7
8
9 10 11 12
0
1
2
3
4
5
6
7
8
9 10 11 12
Times, years
Times, years
Times, years
Times, years
A
ACC ns
B
BRCA ns
C
LIHC
6
ns
Value
XPO1 expression
6
*
**
XPO1 expression
Value
6
ns
*
Adolescence
Adulthood
XPO1 expression
Value
Adolescence
5
Adulthood
Middle age
Middle age
5
Older
5
Adulthood
Middle age
Older
Male
4
Male
Female
4
Stage
4
Older
Male
Female
Stage II
Female
3
Stage I
Stage II
Stage III
3
Stage
Stage III
3
Stage IV
Stage II
2
Stage IV
Stage III
2
Stage IV
Age
Gender
Stage
Age
Gender
Stage
Age
Gender
Stage
D
PAAD ns
E
SKCM
F
THCA
6
ns
*
Value
6
ns
ns
**
Value
6
ns
**
Value
XPO1 expression
Middle age
Adolescence
Adolescence
5
Older
XPO1 expression
Adulthood
XPO1 expression
Male
Female
Middle age
5
Adulthood
4
# Older
Middle age
Older
4
Stage
Stage II
Male
Male
9
Stage III
Female
4
Female
Stage
3
Stage IV
Stage
Stage II
Stage II
2
Stage III
Stage IV
3
Stage III
2
Stage IV
Age
Gender
Stage
Age
Gender
Stage
Age
Gender
Stage
G
HNSC
H
LUSC *
I
READ
6
ns
*
ns
ns
ns
6
**
ns
ns
XPO1 expression
Value
XPO1 expression
6
Value
XPO1 expression
Value
5
Adulthood
Middle age
Middle age
5
Middle age
Older
Older
Older
Male
5
Male
4
E
Male
Female
Female
Female
4
Stage I
Stage
Stage II
Stage II
3
Stage I
Stage II
Stage III
Stage III
3
Stage III
4
Stage IV
Stage IV
2
Stage IV
:
Age
Gender
Stage
Age
Gender
Stage
Age
Gender
Stage
XPO1 than with high-methylated XPO1 in BLCA and UCEC (Figure 5B,5C); the converse was found to be true in CHOL and KIRC (Figure 5D,5E).
Correlations of XPO1 with genomic mutations
The occurrence of malignancies usually involves mutations in multiple genes. The mutation frequencies of XPO1 in the various tumors were generally low (except for a mutation frequency of >5% in UCEC) (Figure 6A). Recently, there was a report in the literature that XPO1 pathogenic mutations could contribute to a poor survival in NSCLC (14). To further elucidate the potential
biological roles of XPO1 in genomic mutations, we analyzed the co-expression of XPO1 and highly mutated genes of the various tumor types. The result suggested that XPO1 expression generally showed significantly positive correlations with the highly mutated genes of the different tumors (Figure 6B-6L, Figure S3A-S3V). This suggested that XPO1 could indirectly promote tumor onset and progression by influencing mutation in other genes.
Potential significance of XPO1 in immune microenvironment changes
The immune microenvironment plays a crucial role in
Percentage, %
A
20
40
60
0
ACC
BLCA
BRCA
CESC
CHOL
COAD
DLBC
ESCA
GBM
HNSC
KICH
KIRC
KIRP
LAML
Amplification
LGG
LIHC
LUAD
LUSC
Deletion
MESO
OV
PAAD
PCPG
PRAD
READ
SARC
SKCM
STAD
TGCT
THCA
THYM
UCEC
UCS
UVM
| log2 (XPO1 RSEM) | B | ||||
|---|---|---|---|---|---|
| 9 | 11 13 | ||||
| ACC | ns | ||||
| BLCA | **** | ||||
| BRCA | **** | ||||
| CESC | **** | ||||
| CHOL | * | ||||
| COAD | **** | Type | |||
| DLBC | ** | ||||
| ESCA | **** | ||||
| GBM | ns | Deep | |||
| HNSC | **** | Deletion | |||
| KICH | ns | ||||
| KIRC | **** | ||||
| KIRP | **** | ||||
| LAML | ns | Shallow | |||
| LGG | 0 | ** | |||
| LIHC | ** | Deletion | |||
| LUAD | **** | ||||
| LUSC | **** | ||||
| MESO | ns | Diploid | |||
| OV | **** | ||||
| PAAD | ** | ||||
| PCPG | |||||
| PRAD | *** | Gain | |||
| ns | |||||
| READ | * | ||||
| SARC | **** | ||||
| SKCM | **** | ||||
| STAD | |||||
| TGCT | **** ns | Amplification | |||
| THCA | |||||
| THYM | ns | ||||
| UCEC | ns | ||||
| UCS | **** | ||||
| UVM | ns ns | ||||
C
BLCA ACC* UVM
D
BRCA
UCS
BLCA *** ACC *** UVM
BRCA ***
UCS
CESC
0.5
UCEC ***
CESC
0.
UCEC
CHOL*
0.2
THYM
CHOL
0.25
THYM*
COAD
THCA
COAD*
THCA
0
0
DLBC*
TGCT
DLBC
TGCT
-0.25
-0.25
ESCA
STAD ***
ESCA
STAD ***
-0.5
-0.5
GBM
SKCM
GBM
SKCM **
HNSC
SARC
HNSC ***
SARC
KICH
READ ***
KICH
READ **
KIRC
PRAD
KIRC
PRAD ***
KIRP
PCPG
KIRP
PCPG
LAML
PAAD
LAML
PAAD*
LGG
OV
LGG ***
OV **
LIHC
LUAD ***
LUAD LUSC ***
MESO
LIHC
LUSC ***
MESO*
A
Type # Normal
Tumor
**
**
6
-log2 (beta value)
1
1
I
A
4
S
2
LUAD
GBM
OV
LUSC
PRAD
UCEC
BLCA
TGCT
ESCA
PAAD
KIRP
LIHC
CESC
SARC
BRCA
THYM
MESO
COAD
STAD
SKCM
CHOL
KIRC
THCA
HNSC
LAML
READ
LGG
DLBC
KICH
UCS
ACC
PCPG
UVM
B
C
1.00
Methy levels - High - Low
1.00
1
Methy levels -High -Low
Overall survival
0.75
Overall survival
0.75
0.50
0.50
0.25
0.25
0.00
P<0.001
0.00
P<0.001
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
0
1
2
3
4
5
6
7
8
9
0 11 12 13 14 15 16 17
18
19 20
Methy levels
Time, years
Methy levels
Time, years
High
205
38 54 35 27
16 9
8
5
4
2
0
0
0
0
0
High
216164111 57 39 25 15 8 4
2
0 000
0 0
0
0
0
0
0
Low
06 155 88
55
43
32
18
13
8
5
4
3
3
3
0
0
Low
7189118 77 53 35
26
8
11
7
4
3
2
2
2
2
1
1 4
1
0
0
T
Y
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
0
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Time, years
Time, years
D
E
1.00
Methy levels High - Low
Overall survival
1.00
Methy levels -High -Low
2
0.75
Overall survival
0.75
0.50
0.50
0.25
P=0.003
0.25
0.00
P=0.003
0.00
0
1
2
3
4
5
0
1
2
3
4
5 6 7 8 9 101
11 12
Methy levels
Time, years
Methy levels
Time, years
High
18
7
Low
18
16
5
11
9
6
4
0
2
0
High Low
59128
09
92
45
26
20
17
160121 86
80
62
12
3
1
T
T
T
T
T
T
64
48
30
21
13
6
5
1
0
0
T
T
r
T
T
T
T
T
T
T
T
T
T
0
1
2
3
4
5
0
1 2 3 4 5 6 7 8 9 10 11 12
Time, years
Time, years
Percentage, %
Percentage, %
G
Percentage, %
D
Percentage, %
J
4
5
6
25
50
75
10
10
15
20
0
1
2
3
0
5
0
5
ACC
0
.
PBRM1
APC
DNMŢ3A
ARID 1A
BLCA
BRCA
TP53
FLTS
BAP1
TIN
NPM
RUNX1
EPHA2
CESC
KRAS
IDH1
CHOL
TP53
MUC16
COAD CUAD
SYNE1
DLBC
FAT4
CHDI MUCSB
MAP1B
ESCA GBM
MUC 16
FBXW
MUC16
LAPLP
ELF3
FOR2
HNSC
LAP1B
NRAS
TP53
KMT2D
READ
LAML LAML
CHOL
KICH
IDH1
KIRC KIRP
SMAD4
KRAS
NEB
CSMD1 PIK3CA
CMYA5
DNAH5
PCLO
LAML LIGG LGG
ALB
FAT3 HVA1
CEBER
APOB
LIHC
LUAD
MACE
AHINAK
DNAH11
MACF
LUSC
OB SON
SME
FGER
UBRA
BIRRA
MESO
ASXL
BOOK
OV
ISNEB
USHOA
BOORL
LAMA3
PAAD
PCPG
DDAD
COL6A3
ANK2
MACF1
SYNE
DNAH17 KIF19
PRAD
ARCA13
DCAFAL2
READ
SARC
SLITRK5
PRUNE2
AHMAN
DNAKS
SKCM
CEMDE CSMDS
UNC13C ZNF804B
GRILLA GRIK
OLDU98
ADAM30
STAD
LRP1B
TGCT
Cor
P value
Cor
.
P value
THCA
A
.
·
Cor
THYM
A
·
P
0.0
0.2
0.4
0.6
<0.0001
<0.001
<0.01
<0.05
>0.05
value
0.0
0.2
0.4
<0.0001
<0.001
<0.01
<0.05
>0.05
-0.5
0.0
0.5
<0.0001
<0.001
<0.01
<0.05
>0.05
UCEC
UCS
UVM
B
Percentage, %
K
Percentage, %
H
Percentage, %
E
Percentage,
%
10
15
10
20
30
20
40
60
10
20
30
40
50
0
0
0
0
5
APC
TP53
KIT
BAP1
TP53
KMT2D
KRAS MUC4
NEZ
TP53
TIN KA IN
TTN
KOMOA
NRAS
SETD2
MUC16
SYNET
ARID1A
PIK3CA
SRCAP
LAIPT
PIK3CA
MUC5B
FAT4
FAT4
SYRE
BIRC6 PCLŐ
PTCH1
CCDC168 LAMAS
DESON
RYR2
KMIZE
45836
LRP18
NCOR1 PARM
DNAH5
EP300
BLCA
NISCH
PBRMI
PCLO
COAD COAD
COAD
MACF1
TRIP 12
TGCT TGCT TGCT
MESO MESO
CSMUS LAP18
ELG
NOTCH3
DST ARID2
BATA
KNIC
ZNF469
ABCA13
DNAH11
DNEAT3
FOXPA
OGDHL RANGA BAZ2B
USH2A USHZA
CSMUS
DMD
ELIS ATM
ATADE
CSMDI
LENERI HECTD4
DNHD1
ALPK3 NDSTO
HYHS RYR1
CBN
EMID1 PIK3CA SACS
FBXW
CREBBP
HTR7
H
LAP2
NEB
KMT2D KMVER
ZEUXA
ANKRD50
FLG
NLRP7
DNAHS
NEB
SVINE
EML5
CCDC168
SY NE
NLMP9
MUC4
ADSONE
AHLANG
HMCN1
BRAF
OBSUN
MUC17
DOPO
MROH2B
FLG
ANKRD30A
Cor
·
Cor
Cor
.
·
P
-0.50
-0.25
0.00
0.25
0.50
0.75
<0.0001
<0.001
<0.01
<0.05
>0.05
value
-0.2
0.0
0.2
0.4
<0.0001
<0.001
<0.01
<0.05
>0.05
P value
0.0
0.2
0.4
<0.0001
<0.001
<0.01
<0.05
>0.05
P value
0.0
0.2
0.4
Cor
<0.0001
<0.001
<0.01
<0.05
. >0.05
P value
Percentage, %
Percentage,
di
F
Percentage,
de
C
Percentage, % L
20
30
Zhao et al. Pan-cancer analysis of XPO1
I
10
0
20
40
60
8
12
10
20
30
4
0
0
0
TP53
PTEN
TTN
PIK3CA KMI2C
PTEN
SPOP
15291
PIK3CA
MUC16
FOXA1
EGFR-
MUCHO
MUSS
ARIDIA
KMT2D
TP53
AMOR
MUCHO
DMD ELG
MUC16
SPA1
NF1-
RYR2
PIK351
SPIA
SPTA1
EP300
KMT2D KMUSE
LAP1B
RB1
FBXW7
CSMD3
AIM
ATRX
CSMD3
PIKORA
PORRET
MILES
MUCIL
CTNMBS
GBM GBM
CESC
HYR2
PTEN
LRP1B
HYR2
SYNE1 MUC5B
PRAD PRAD
USH2A ADGRV1
PREBA LRP2
FAT4 ZEMX4 OBSČÍN DCLO POER
UCEC UCEC UCEC
PRAD
OBSCN
HMCIH
CSMD1
KMT2B
CACINATE
KDMBA
APOB. FLG2.
SYNE PCLO
RYB1 FAT3
COL6A3-
AP
DNAH5-
DST
NEP
KMCO-
MUC5B
MACF1
ZMYMS
OBSCN
ELG
MACE
AHNAN
DNADE
NEB
USH2A
O
DNAHZ LRP1B
HMC
DNAH3
PRKDC
DNAHE
AHNAK2
“MON1
CHD4
DNAH5 FAT3
CDKY PCLO
DNAHB-
USH2A-
DNAH2
COL11A1
MUC17-
LRP2
Cor
P value
Cor
. >0.05
P value
0.0
0.2
3
Cor
· >0.05
P value
CCDC168
0.0
0.2
0.4
.4
<0.0001
<0.001
<0.01
<0.05
-0.2
0.0
0.2
0.4
0.6
Cor
<0.0001
<0.001
<0.01
<0.05
.>0.05
P value
0.0
0.2
0.4
0.6
<0.0001
<0.001
<0.01
<0.05
>0.05
-0.2
<0.0001
<0.001
<0.01
<0.05
Figure 6 Genetic mutations of XPO1. (A) Mutation frequencies of XPO1 in the 33 tumor types. Correlations of XPO1 expression with expression levels of the top 30 highly mutated genes in (B) BLCA, (C) CESC, (D) CHOL, (E) COAD, (F) GBM, (G) LAML, (H) MESO, (I) PRAD, (J) READ, (K) TGCT, and (L) UCEC. Circle size indicates the magnitude of the P value and shade indicates the magnitude of the Spearman’s rank correlation coefficient. XPO1, Exportin 1; BLCA, bladder carcinoma; CESC, cervical squamous cell carcinoma and endocervical adenocarcinoma; CHOL, cholangiocarcinoma; COAD, colon adenocarcinoma; GBM, glioblastoma multiforme; LAML, acute myeloid leukemia; MESO, mesothelioma; PRAD, prostate adenocarcinoma; READ, rectum adenocarcinoma; TGCT, testicular germ cell
tumor onset and progression as well as patient prognosis. Therefore, correlation analysis was performed on XPO1 expression and the stromal and immune scores of the various tumor types were calculated using the ESTIMATE algorithm. We found that XPO1 expression exhibited tumor; UCEC, uterine corpus endometrial carcinoma.
negative correlations with both the stromal and immune scores. In particular, XPO1 was significantly negatively correlated with both the stromal and immune scores of ACC, GBM, LUSC, SARC, and STAD (P<0.05, R ← 0.25) (Figure 74-7E), significantly negatively correlated with
A
Cancer: ACC
Cancer: ACC
B
Cancer: GBM
Cancer: GBM
R =- 0.39, P=0.00041
R =- 0.41, P=0.00017
5.0
‘R =- 0.41, P=3.4e-08
5.0
R =- 0.42, P=2.5e-08
:
%
4
4
4.5
4.5
XPO1
XPO1
XPO1
XPO1
3
3
4.0
4.0
3.5
3.5
2
2
-1000
0
1000
2000
-1000
0
1000
-1000
0
1000
2000
-1500-1000-500
0
500
1000 1500
Immune score
Stromal score
Immune score
Stromal score
C
Cancer: LUSC
Cancer: LUSC
D
Cancer: SARC
Cancer: SARC
5.5
6
R =- 0.31
P=2:1e-12
5.5
6
R =- 0.38
P<2.2e-
$6
R =- 0.48, P<2.2e-16
R =- 0.55, P<2.2e-16
5.0
5.0
4.5
4.5
XPO1
5
XPO1
5
XPO1
4.0
XPO1
4.0
4
4
3.5
3.5
3.0
3.0
2.5
2.5
-1000
0
1000
2000
3000
-2000
-1000
0
1000
2000
-1000
0
1000 2000 3000
-1000
0
1000
2000
Immune score
Stromal score
Immune score
Stromal score
E
Cancer: STAD
Cancer: STAD
F
Neutrophils Eosinophils
Mast cells activated
Mast cells resting
Dendritic cells activated
R =- 0.27, P=2e-07
R =- 0.37, P=1.7e-13
Dendritic cells resting
P value
5
5
Macrophages M2
<0.05
Macrophages M1
<0.01
Macrophages MO
<0.001
Monocytes
<0.0001
XPO1
NK cells activated
4
XPO1
4
NK cells resting
Cor
T cells gamma delta
T cells regulatory (Tregs)
0.6
T cells follicular helper
0.3
0.0
3
3
T cells CD4 memory activated
T cells CD4 memory resting
-0.3
T cells CD4 naive
-0.6
T cells CD8
Plasma cells
B cells memory
-1000
0
1000
2000
3000
-2000
-1000
0
1000
2000
B cells naive
Immune score
Stromal score
ACC BICA
BRCA
CES CHOL
COAD
DLBO
ESCA
GBM
HNCC
HNSC
RP
LAML
LGG
MESO
PAAD
PCPG
PRAD
READ
SARC
KCM
STAR
THCA
THYM
UCEC
UVM
immune score in CESC, ESCA, KIRP, LAML, PCPG, UCS, and MESO (P<0.05, R ← 0.25), and significantly negatively correlated with stromal score in OV and TGCT (P<0.05, R ← 0.25) (Figure S4A-S4R). The result obtained from the CIBERSORT algorithm showed that XPO1 was generally negatively correlated with the various immune cell contents (except for M1 macrophages, activated memory Th cells and resting memory Th cells) (Figure 7F). Therefore, XPO1 could also promote tumor onset and progression by influencing the tumor immune microenvironment.
Interactions of XPO1 with other genes and XPO1 molecular functions
To further investigate the molecular mechanisms by which XPO1 affected the tumor microenvironment, we performed co-expression analyses of XPO1 with immune checkpoint genes and cancer driver genes. XPO1 generally showed significantly positive correlations with the expression of immune co-inhibitory receptors, such as ADOPA2A, BTLA, CD160, CD200, CD200R1, PDCD1LG2, and
A
B
ZBTB16
VTCN1
VSIR
TNFSF9
UGT
TNFSF4
TNFSF18-
TNFSF15-
TNFSF14
TNERSF9.
SA
TNFRSF8.
TNFRSF4-
PRI
TNFRSF25-
INFRSF18
NY
TNFRSF14
TMIGD2
TIGIT
MY
PDCD1LG2
PDCD1 NRP1
LGALS9
LAIR1
Cor
Log FC
LAG3
KIR3DL1
5.0
IDO2
0.6
M
IDO1
ICOSLG
cos
0.3
2.5
HHLA2
0.0
HAVCR2
CTLA4 CD86
0.0
FG
CD80.
-0.3
ES
-2.5
CD70-
EBE
-5.0
CD48
CD44
CD40LG
P value
CD40
<0.05
P value
CD28.
CD276
DCS
CD274- CD27.
<0.01
· < 0.05
CD244.
<0.01
CD200R1
<0.001
SOL
· < 0.001
CD200-
CD160.
<0.0001
<0.0001
BTNL2
BTLA
ADORA2A
C
D
HO)
CD
SP
PE
NK
CY
P value
P value
NA
<0.05
· <0.01
<0.05
<0.001
<0.01
<0.0001
NES
UG
Cor
3
0.50
2
1
0.25
0
E
马V
0.0
HOX
-0.25
DOSTA
-0.50
EP
w
HOXC13
CTLA4, and negative correlations with immune co- stimulatory receptors, such as TNFRSF14, TNFRSF18, TNFRSF4, and TMIGD2 (Figure 8A). Mutations in cancer driver genes favor the promotion of tumor onset and progression. To further investigate the effects of XPO1 on gene mutations, co-expression analysis was performed with XPO1 and driver genes with high differential expression. XPO1 expression was highly positively correlated with driver gene expression, which indicated that XPO1 may serve as a potential driving factor of tumor onset and
progression (Figure 8B,8C). GSEA revealed that cell cycle- targeting genes encoding E2F transcription factors, G2/M checkpoint genes, and genes associated with the assembly of the mitotic spindle apparatus were downregulated in the XPO1 overexpression group. Genes related to coagulation system components, genes encoding complement system components, genes related to oxidative phosphorylation, genes encoding proteins involved in the processing of drugs and other xenobiotics, and genes that upregulate adipocyte differentiation were upregulated in the XPO1
underexpression group (Figure 8D). Therefore, XPO1 might affect the tumor immune microenvironment by regulating the expression of immune checkpoint genes and served as a driving factor for the promotion of mutations in cancer driver genes, thereby promoting tumor onset. The effects of XPO1 on the tumor microenvironment might also be exerted through influences on biological processes, such as the cell cycle and oxidative phosphorylation.
Discussion
The inhibition of XPO1 expression can serve as a potential treatment strategy for a wide variety of tumors, such as malignant tumors of the bladder, FLT3 mutation-induced acute myeloid leukemia, melanoma, stomach cancer, cervical cancer, ovarian cancer, pancreatic ductal adenocarcinoma, glioma, and osteosarcoma (15-17). In this study, we performed a systematic description of XPO1 expression across multiple cancer types by analyzing the transcriptome expression data of 33 types of tumors from the UCSC Xena database. Our results indicated that XPO1 was significantly overexpressed in BLCA, BRCA, CHOL, COAD, ESCA, GBM, HNSC, KIRC, KIRP, LIHC, LUSC, READ, SARC, and STAD. To further determined if XPO1 was generally overexpressed in tumors, we described XPO1 expression by utilizing normal tissue transcriptome data from GTEx and tumor cell line transcriptome data from CCLE. The mean expression of XPO1 in the various tumor cell lines was higher than that of normal tissues. XPO1 overexpression is a key characteristic of lung cancer, osteosarcoma, glioma, pancreatic ductal adenocarcinoma, cervical cancer, ovarian cancer, renal cell carcinoma, esophageal cancer, stomach cancer, hepatocellular carcinoma, acute myeloid/ lymphocytic leukemia, multiple myeloma, and colon cancer. XPO1 plays a crucial role in tumor progression by influencing p53 phosphorylation or survivin protein expression (17-29). Our results also indicated that XPO1 overexpression was an important characteristic of many tumor types and might influence tumor progression.
TMB, which refers to the number of gene mutations present in tumor cells, can be utilized as a biomarker of certain tumor types and predicts the effects of immunotherapy. TMB has been used as a novel biomarker for the prediction of the clinical benefits of nivolumab combined with ipilimumab in the treatment of non-small cell lung cancer (30). MSI is present in various cancers, such as colon, endometrial, cervical, esophageal, skin, and breast cancers (31). Patients with colon cancer with characteristics
of MSI have a better prognosis compared with those without MSI, and differences exist in the response to chemotherapy between the two groups (32). Our results indicated that XPO1 expression was significantly correlated with the presence of TMB and MSI in many tumor types, such as ACC and STAD. Therefore, we deduced that the molecular mechanisms of the effects of XPO1 on tumors might be related to its role in nuclear protein transport. By affecting or altering the transport of certain proteins, XPO1 might exert effects on biological processes, such as the cell cycle, DNA replication, and transcription, thereby indirectly inducing mutations and changes in expression in certain genes.
The results of GSEA indicated that XPO1 expression affected genes related to the G2/M checkpoints and oxidative phosphorylation, causing disturbances to related signaling pathways or biological processes. XPO1 expression also showed significantly positive correlations with the expression of highly mutated genes and cancer driver genes, suggesting that XPO1 could play a role in the early stages of tumor onset. Therefore, the early use of XPO1 inhibitors might provide better prognostic effects.
During the tumor onset and development process, tumor progression is determined by the continuous interactions between the tumor and host immune response (33). In other words, the progression and survival outcome of a tumor is largely dictated by its immune microenvironment. To elucidate the relationships of XPO1 with host immunity, we utilized the ESTIMATE and CIBERSORT algorithms to evaluate immunity-related characteristics and found that XPO1 expression was generally negatively correlated with immune and stromal scores, negatively correlated with the contents of regulatory T cells, CD8+ T cells, and plasma cells, and positively correlated with M1 macrophages, Tfh cells, and activated and resting CD4+ T cells. Correlation analysis of the expression of XPO1 and immune checkpoint genes revealed that XPO1 was generally positively correlated with immune co-inhibitory receptors and negatively correlated with immune co-stimulatory receptors. This indicated that XPO1 overexpression could regulate the tumor immune microenvironment and inhibited the host immune response, ultimately causing tumor immune evasion and progression.
Conclusions
We utilized pan-cancer data for the multi-dimensional analysis of the expression and molecular mechanisms of
XPO1 in clinical characteristics and genomics. We also systematically elucidated the biological roles of XPO1 in various cancers. The results of this study provided a theoretical basis for clinical applications of XPO1 inhibitors.
Acknowledgments
Thank all the datasets’ owners who made the valuable data public.
Funding: None.
Footnote
Reporting Checklist: The authors have completed the REMARK reporting checklist. Available at https://dx.doi. org/10.21037/tcr-21-1646
Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi. org/10.21037/tcr-21-1646). The authors have no conflicts of interest to declare.
Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. All procedures performed in this study involving human participants were in accordance with the Declaration of Helsinki (as revised in 2013).
Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non- commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.
References
1. Azizian NG, Li Y. XPO1-dependent nuclear export as a target for cancer therapy. J Hematol Oncol 2020;13:61.
2. XPO1 Inhibitor Approved for Multiple Myeloma. Cancer Discov 2019;9:1150-1.
3. Azmi AS, Uddin MH, Mohammad RM. The nuclear export protein XPO1 - from biology to targeted therapy. Nat Rev Clin Oncol 2021;18:152-69.
4. Gravina GL, Senapedis W, McCauley D, et al. Nucleo- cytoplasmic transport as a therapeutic target of cancer. J Hematol Oncol 2014;7:85.
5. Sun Q, Chen X, Zhou Q, et al. Inhibiting cancer cell hallmark features through nuclear export inhibition. Signal Transduct Target Ther 2016;1:16010.
6. Valero-Mora PM. ggplot2: Elegant Graphics for Data Analysis. Journal of Statal Software 2010;35.
7. Nagy A, Lánczky A, Menyhárt O, et al. Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets. Sci Rep 2018;8:9227.
8. Yoshihara K, Shahmoradgoli M, Martínez E, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun 2013;4:2612.
9. . Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 2015;12:453-7.
10. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 2010;26:139-40.
11. McCarthy DJ, Chen Y, Smyth GK. Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation. Nucleic Acids Res 2012;40:4288-97.
12. Martínez-Jiménez F, Muiños F, Sentís I, et al. A compendium of mutational cancer driver genes. Nat Rev Cancer 2020;20:555-72.
13. Yu G, Wang LG, Han Y, et al. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 2012;16:284-7.
14. Nagasaka M, Asad MFB, Al Hallak MN, et al. Impact of XPO1 mutations on survival outcomes in metastatic non-small cell lung cancer (NSCLC). Lung Cancer 2021;160:92-8.
15. Zhang W, Ly C, Ishizawa J, et al. Combinatorial targeting of XPO1 and FLT3 exerts synergistic anti-leukemia effects through induction of differentiation and apoptosis in FLT3-mutated acute myeloid leukemias: from concept to clinical trial. Haematologica 2018;103:1642-53.
16. Yang J, Bill MA, Young GS, et al. Novel small molecule XPO1/CRM1 inhibitors induce nuclear accumulation of TP53, phosphorylated MAPK and apoptosis in human melanoma cells. PLoS One 2014;9:e102983.
17. Gao W, Lu C, Chen L, et al. Overexpression of CRM1: A Characteristic Feature in a Transformed Phenotype of Lung
Carcinogenesis and a Molecular Target for Lung Cancer Adjuvant Therapy. J Thorac Oncol 2015;10:815-25.
18. Yao Y, Dong Y, Lin F, et al. The expression of CRM1 is associated with prognosis in human osteosarcoma. Oncol Rep 2009;21:229-35.
19. Shen A, Wang Y, Zhao Y, et al. Expression of CRM1 in human gliomas and its significance in p27 expression and clinical prognosis. Neurosurgery 2009;65:153-9; discussion 159-60.
20. Huang WY, Yue L, Qiu WS, et al. Prognostic value of CRM1 in pancreas cancer. Clin Invest Med 2009;32:E315.
21. van der Watt PJ, Maske CP, Hendricks DT, et al. The Karyopherin proteins, Crm1 and Karyopherin beta1, are overexpressed in cervical cancer and are critical for cancer cell survival and proliferation. Int J Cancer 2009;124:1829-40.
22. Chen Y, Camacho SC, Silvers TR, et al. Inhibition of the Nuclear Export Receptor XPO1 as a Therapeutic Target for Platinum-Resistant Ovarian Cancer. Clin Cancer Res 2017;23:1552-63.
23. Inoue H, Kauffman M, Shacham S, et al. CRM1 blockade by selective inhibitors of nuclear export attenuates kidney cancer growth. J Urol 2013;189:2317-26.
24. van der Watt PJ, Zemanay W, Govender D, et al. Elevated expression of the nuclear export protein, Crm1 (exportin 1), associates with human oesophageal squamous cell carcinoma. Oncol Rep 2014;32:730-8.
25. Subhash VV, Yeo MS, Wang L, et al. Anti-tumor efficacy of Selinexor (KPT-330) in gastric cancer is dependent on nuclear accumulation of p53 tumor suppressor. Sci Rep
Cite this article as: Zhao L, Luo B, Wang L, Chen W, Jiang M, Zhang N. Pan-cancer analysis reveals the roles of XPO1 in predicting prognosis and tumorigenesis. Transl Cancer Res 2021;10(11):4664-4679. doi: 10.21037/tcr-21-1646
2018;8:12248.
26. Zheng Y, Gery S, Sun H, et al. KPT-330 inhibitor of XPO1-mediated nuclear export has anti-proliferative activity in hepatocellular carcinoma. Cancer Chemother Pharmacol 2014;74:487-95.
27. Tai YT, Landesman Y, Acharya C, et al. CRM1 inhibition induces tumor cell cytotoxicity and impairs osteoclastogenesis in multiple myeloma: molecular mechanisms and therapeutic implications. Leukemia 2014;28:155-65.
28. Kojima K, Kornblau SM, Ruvolo V, et al. Prognostic impact and targeting of CRM1 in acute myeloid leukemia. Blood 2013;121:4166-74.
29. Aladhraei M, Kassem Al-Thobhani A, Poungvarin N, et al. Association of XPO1 Overexpression with NF-KB and Ki67 in Colorectal Cancer. Asian Pac J Cancer Prev 2019;20:3747-54.
30. Hellmann MD, Ciuleanu TE, Pluzanski A, et al. Nivolumab plus Ipilimumab in Lung Cancer with a High Tumor Mutational Burden. N Engl J Med 2018;378:2093-104.
31. Dudley JC, Lin MT, Le DT, et al. Microsatellite Instability as a Biomarker for PD-1 Blockade. Clin Cancer Res 2016;22:813-20.
32. Vilar E, Gruber SB. Microsatellite instability in colorectal cancer-the stable evidence. Nat Rev Clin Oncol 2010;7:153-62.
33. Bruni D, Angell HK, Galon J. The immune contexture and Immunoscore in cancer prognosis and therapeutic efficacy. Nat Rev Cancer 2020;20:662-80.