Abstract

   In cancer cells, the nuclear transport system is often disrupted,
   leading to abnormal localization of nuclear proteins and altered gene
   expression. This disruption can arise from various mechanisms such as
   mutations in genes that regulate nuclear transport, altered expression
   of transport proteins, and changes in nuclear envelope structure.
   Oncogenic protein build-up in the nucleus due to the disturbance in
   nuclear transport can also boost tumor growth and cell proliferation.
   In this study, we performed bioinformatic analyses of 23 key nuclear
   transport receptors using genomic and transcriptomic data from
   pancancer and head and neck squamous cell carcinoma (HNSCC) datasets
   from The Cancer Genome Atlas (TCGA) and Cancer Cell Line Encyclopedia
   and found that the total alteration frequency of 23 nuclear transport
   receptors in 2691 samples of the PCAWG Consortium was 42.1% and a high
   levels of genetic alterations was significantly associated with poor
   overall survival. Amplification was the most common type of genetic
   alterations, and results in the overexpression of nuclear transport
   receptors in HNSCC compared to normal tissues. Furthermore, our study
   revealed that seven out of eight cell cycle genes (CDK1, CDK2, CDK4,
   CDK6, CCNA1, CCNB1, and CCNE2) were significantly and positively
   correlated with nuclear transport receptor genes in TCGA pancancer and
   CCLE datasets. Additionally, functional enrichment analysis showed that
   nuclear transport receptor genes were mainly enriched in the adhesion
   junction, cell cycle, ERBB, MAPK, MTOR and WNT signaling pathways.

Introduction

   Genetic mutations and chromosomal abnormalities are hallmarks of human
   cancers, including head and neck squamous cell carcinoma (HNSCC); HNSCC
   ranks as the sixth most prevalent cancer worldwide, with an annual
   incidence of approximately 930,000 cases and a mortality rate of
   approximately 50% [[36]1]. While conventional treatments, such as
   radiation therapy, chemotherapy, surgery, novel immunotherapies, and
   combination therapies, are available, recurrence occurs in 50% of
   patients. In addition, tumor resection surgery can diminish patients’
   physical function after surgery, but many patients still experience
   recurrence and metastasis [[37]1,[38]2]. Thus, the 5-year overall
   survival rate of HNSCCs remains unsatisfactory.

   Nuclear transport receptors including importins and exportins are
   proteins that transport molecules from the cytoplasm to the nucleus and
   vice versa, which is crucial for many cellular processes, including
   gene expression, DNA replication, and repair [[39]3]. In humans, there
   are at least 23 nuclear transport receptor family members
   [[40]4,[41]5], while the yeast Saccharomyces cerevisiae has 14 members.
   Nuclear transport receptors facilitate either the import or the export
   of molecules [[42]3]. When proteins are imported into the nucleus, they
   interact with importin β directly or via an adapter molecule called
   importin α [[43]6,[44]7]. Importin α binds the nuclear localization
   signal (NLS) and forms a trimeric complex with importin β, which then
   targets the nuclear pore complex (NPC). The complex undergoes a series
   of interactions with the NPC before translocating into the nucleus
   [[45]8,[46]9]. Upon reaching the nucleus, the cargo is released to
   carry out its function, while the other transport factors are recycled
   for future transport cycles.

   Cancer development, progression, and resistance to treatment have been
   linked to malfunctions in the nuclear-cytoplasmic transport system
   [[47]10]. Specifically, karyopherin nuclear transport receptors, a
   crucial component of this system, are responsible for stabilizing
   chromosomes and supporting mitosis [[48]10]. As a result, these
   receptors can impact the location of tumor suppressors and
   proto-oncogenes, thereby influencing the tumorigenesis process and drug
   sensitivity of cancer cells [[49]10]. Given their importance in the
   development of cancer, there is a need for comprehensive molecular
   analysis of nuclear transport, which is currently lacking in the
   literature.

Materials and methods

Genetic alteration analysis

   To investigate the genetic characteristics of nuclear transport
   receptor genes, we accessed the cBioPortal database
   ([50]https://www.cbioportal.org/) [[51]11,[52]12], selected “TCGA Pan
   Cancer Atlas Studies” in the “Quick select” section and searched for
   gene names of 22 main nuclear transport receptor genes (S1 Table in
   [53]S1 File) in (i) 2691 samples of PCAWG Consortium [[54]13], (ii) 850
   tumor cell lines of Cancer Cell Line Encyclopedia (CCLE) [[55]14],
   (iii) 523 samples of TCGA-HNSCC (Pancancer atlas) [[56]11,[57]12], and
   (iv) 56 HNSCC and 78 non-cancerous cell lines of CCLE [[58]14]. Within
   the “Cancer Types Summary” module, we reviewed the mutation type,
   alteration frequency, and CNA data across datasets. The tab OncoPrint
   showed an overview of the genetic alterations present in the 23 main
   nuclear transport receptor genes. Furthermore, the “Mutation” module
   provided a schematic diagram of the protein structure and detailed
   information on mutated sites.

Analysis of gene alteration on patient survival

   The correlation between gene alterations and patient survival was
   analyzed using the cBioPortal database [[59]11,[60]12]. We analyzed
   survival data for all tumor samples with or without genetic alterations
   in the “Comparison/Survival” module, with a log-rank P value < 0.05
   indicating statistical significance.

Gene set enrichment analysis (GSEA)

   GSEA is a computational method for assessing whether a set of
   previously defined genes differ statistically significantly and
   consistently between two biological states [[61]15]. GSEA was performed
   by GSEA software (version 4.3.0) to further investigate the functional
   enrichment of the genome under high expression conditions of 22 nuclear
   transport receptors (defined as higher than the median of mRNA levels).
   False detection rate (FDR) < 25% and nominal p < 0.05 were defined as
   the cutoff criteria.

Statistical analysis

   Independent-samples t test and Mann-Whitney U test were used to assess
   normal and skewed variables, respectively. Categorical variables were
   analyzed using the chi-square test or Fisher’s exact test, as
   appropriate. GraphPad Prism 9.3.1 software was used as the tool to
   visualize the results. P < 0.05 was considered statistically
   significant.

Results

Genetic alterations in nuclear transport receptor genes are widespread across
cancers

   Nuclear transport is dynamically mediated by nuclear transport
   receptors including importins and exportins. We initially used
   cBioPortal to evaluate genetic alterations in 23 main nuclear transport
   receptors (S1 Table in [62]S1 File). This was conducted for 2691
   samples from the PCAWG Consortium [[63]13], and the tumour entities for
   these samples are listed in S2 Table in [64]S1 File. We found that the
   total alteration frequency of this dataset was 42.1% (1133/2691). The
   frequencies of mutation, amplification, and deep deletion were 4.46%
   (120/2691), 30.17% (812/2691), and 3.27% (88/2691), respectively. Only
   4.2% (113/2691) of these cases had two or more alterations ([65]Fig 1A
   and S2 Table in [66]S1 File).

Fig 1. Genetic alterations of 23 nuclear transport receptors in pan-cancer.

   [67]Fig 1
   [68]Open in a new tab

   (A) in 2691 samples of PCAWG Consortium (B) in 850 tumor cell lines of
   CCLE dataset. (C) Genetic alterations of each nuclear transport
   receptor (KPNA1, KPNA2, KPNA3, KPNA4, KPNA5, KPNA6, KPNA7, IPO5, IPO7,
   IPO8, IPO9, IPO11, IPO13, TNPO1, TNPO2, TNPO3, XPOT, XPO4, XPO5, XPO6,
   and XPO7) in 2691 samples of PCAWG Consortium. (D) Comparation of
   overall survival rate between genetic altered group and unaltered
   group. (E) The significantly changed genes in genetic altered group and
   unaltered group.

   In addition, we conducted similar genetic alteration analysis in 850
   samples from CCLE [[69]14], of which the tumour entities are listed in
   S3 Table in [70]S1 File. The genetic alteration profiles of nuclear
   transport receptors for tumours in the CCLE database showed that
   alteration frequencies of mutation, structural variation, amplification
   and deep deletion were 15.76% (134/850), 1.41% (12/850), 19.88%
   (169/850), and 19.88% (169/850) respectively. A total of 29.65%
   (252/850) of these cell lines had two or more alterations ([71]Fig 1B
   and S3 Table in [72]S1 File). Among the types of genetic alterations,
   amplification was the most common type in nuclear transport receptors
   (58%-96.18%) ([73]Fig 1C and S4 Table in [74]S1 File), particularly in
   the karyopherin α family (KPNA1, KPNA2, KPNA3, KPNA4, KPNA5, KPNA6, and
   KPNA7). Among 23 main nuclear transport receptors, deletion was common
   in XPO7 (68.35%), while mutation was common in IPO7 (52.63%).

Impact of genetic alterations of nuclear transport receptors on overall
survival (OS)

   Next, we analyzed the impact of genetic alterations in nuclear
   transport receptors on the survival rates of patients. The patients in
   the PCAWG pancancer dataset were categorized into two groups: the
   alteration group and no alteration group. To compare the survival rates
   between 2 groups, the log-rank (Mantel-Cox) test was applied, and
   Kaplan-Meier survival curves were generated. Interestingly, a high
   frequency of genetic alterations in 23 main nuclear transport receptors
   was significantly associated with poor OS ([75]Fig 1D). Furthermore,
   TP53, CSMD3, FLG, TTN, PKHD1L1, RYR2, MYC, USH2A, SPTA1, and COL14A1
   were significantly upregulated in the group with genetic alterations in
   the nuclear transport receptors compared to the group without
   alterations in nuclear transport receptors compared to the group
   without alterations ([76]Fig 1E).

   Among 23 main nuclear transport receptor genes, the genetic alteration
   percentage in the PCAWG Consortium ranged from 1.9% (in KPNA6) to 10%
   (in IPO9) ([77]Fig 2A), and in the CCLE dataset the genetic alteration
   percentage ranged from 5% (in KPNA1) to 21% (in XPO7) ([78]Fig 2B). We
   interestingly found that cases with alterations in nuclear transport
   receptor genes also had mutations in top driver genes like TP53, TTN,
   and MUC16 ([79]Fig 2A and 2B). Furthermore, analysis of mutations in
   the transport receptors and top driver genes showed that some mutations
   tended to co-occur, while others appeared to be mutually exclusive
   ([80]Table 1). Somatic mutations including missense mutations,
   truncating mutations (nonsense, nonstop, frameshift deletion,
   frameshift insertion, splice site), in-frame mutations (in-frame
   deletion, in-frame insertion) and all other mutations in all the
   transporters, are shown in S1 Fig in [81]S1 File and S5 Table in [82]S1
   File.

Fig 2.

   [83]Fig 2
   [84]Open in a new tab

   Genetic alteration of 23 selected nuclear transport receptors in (A) 27
   tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG)
   Consortium of the International Cancer Genome Consortium (ICGC) and The
   Cancer Genome Atlas (TCGA) and (B) 850 cell lines of Cancer Cell Line
   Encyclopedia. Cell cycle regulation genes predominantly show a positive
   correlation with nuclear transport receptors. Heatmap showing positive
   (co-expression) or negative (mutual exclusivity) correlation between
   cell cycle genes and nuclear transport receptor genes in (C) the TCGA
   pan-cancer cohort and (D) CCLE. Spearman’s rank correlation coefficient
   is shown as colour scale at right side.

Table 1. Co-occurring and mutually exclusion mutations in nuclear transport
receptors and top mutated genes in PCAWG pancancer dataset.

   A B Neither A Not B B Not A Both Log2 Odds Ratio p-Value q-Value
   Tendency
   KPNA3 XPO4 696 61 81 75 >3 <0.001 <0.001 Co-occurrence
   KPNA7 TNPO3 736 59 61 57 >3 <0.001 <0.001 Co-occurrence
   KPNA3 IPO5 701 71 76 65 >3 <0.001 <0.001 Co-occurrence
   IPO5 XPO4 673 84 99 57 2.206 <0.001 <0.001 Co-occurrence
   KPNA1 KPNA4 805 25 61 22 >3 <0.001 <0.001 Co-occurrence
   IPO11 TNPO1 806 57 28 22 >3 <0.001 <0.001 Co-occurrence
   KPNA2 KPNB1 805 37 51 20 >3 <0.001 <0.001 Co-occurrence
   TNPO2 MUC16 428 13 405 67 2.445 <0.001 <0.001 Co-occurrence
   KPNA6 IPO13 811 46 38 18 >3 <0.001 <0.001 Co-occurrence
   TTN MUC16 169 272 107 365 1.084 <0.001 <0.001 Co-occurrence
   IPO8 MUC16 410 31 389 83 1.497 <0.001 <0.001 Co-occurrence
   KPNA5 XPO5 767 81 45 20 2.073 <0.001 <0.001 Co-occurrence
   IPO9 XPO7 680 43 158 32 1.679 <0.001 <0.001 Co-occurrence
   XPO7 MUC16 375 66 348 124 1.018 <0.001 <0.001 Co-occurrence
   KPNB1 XPO7 681 42 161 29 1.546 <0.001 0.001 Co-occurrence
   KPNB1 IPO9 783 55 59 16 1.949 <0.001 0.001 Co-occurrence
   IPO4 XPO7 654 69 151 39 1.292 <0.001 0.001 Co-occurrence
   KPNA2 IPO9 795 43 61 14 2.085 <0.001 0.002 Co-occurrence
   IPO9 XPO6 784 60 54 15 1.86 <0.001 0.004 Co-occurrence
   TNPO1 TNPO3 761 34 102 16 1.812 <0.001 0.004 Co-occurrence
   KPNA3 IPO4 699 106 78 30 1.343 <0.001 0.004 Co-occurrence
   IPO9 IPO11 775 59 63 16 1.738 <0.001 0.004 Co-occurrence
   IPO9 XPO5 787 61 51 14 1.824 <0.001 0.006 Co-occurrence
   TNPO1 XPO5 809 39 54 11 2.079 <0.001 0.006 Co-occurrence
   KPNB1 XPOT 792 58 50 13 1.828 <0.001 0.008 Co-occurrence
   IPO11 TNPO3 737 58 97 21 1.46 <0.001 0.008 Co-occurrence
   IPO11 TNPO2 770 63 64 16 1.611 <0.001 0.008 Co-occurrence
   KPNA4 TP53 214 8 616 75 1.703 <0.001 0.008 Co-occurrence
   KPNA6 KPNB1 791 51 58 13 1.798 <0.001 0.008 Co-occurrence
   TNPO2 XPO5 782 66 51 14 1.702 <0.001 0.008 Co-occurrence
   KPNB1 XPO5 790 58 52 13 1.768 <0.001 0.008 Co-occurrence
   KPNA6 IPO4 758 47 91 17 1.591 <0.001 0.008 Co-occurrence
   IPO11 MUC16 417 24 417 55 1.196 <0.001 0.009 Co-occurrence
   IPO4 XPO5 757 91 48 17 1.559 <0.001 0.009 Co-occurrence
   IPO9 XPOT 788 62 50 13 1.724 0.001 0.009 Co-occurrence
   KPNA2 XPOT 804 46 52 11 1.886 0.001 0.009 Co-occurrence
   IPO7 TNPO2 768 65 64 16 1.563 0.001 0.012 Co-occurrence
   IPO7 XPO7 671 52 161 29 1.217 0.001 0.012 Co-occurrence
   IPO7 XPO6 777 67 55 14 1.562 0.002 0.014 Co-occurrence
   IPO11 XPO5 782 66 52 13 1.567 0.002 0.019 Co-occurrence
   KPNB1 IPO13 797 60 45 11 1.699 0.003 0.02 Co-occurrence
   KPNA5 IPO4 726 79 86 22 1.233 0.003 0.021 Co-occurrence
   KPNB1 TNPO2 776 57 66 14 1.53 0.003 0.024 Co-occurrence
   KPNA4 TNPO1 791 72 39 11 1.632 0.004 0.026 Co-occurrence
   TNPO1 XPO7 692 31 171 19 1.311 0.004 0.027 Co-occurrence
   KPNA2 XPO7 687 36 169 21 1.246 0.004 0.027 Co-occurrence
   KPNA1 XPO4 726 31 140 16 1.42 0.004 0.029 Co-occurrence
   IPO9 TNPO2 772 61 66 14 1.425 0.004 0.029 Co-occurrence
   KPNB1 TTN 265 11 577 60 1.325 0.004 0.029 Co-occurrence
   IPO11 TTN 263 13 571 66 1.226 0.005 0.03 Co-occurrence
   IPO4 TNPO2 743 90 62 18 1.261 0.006 0.035 Co-occurrence
   IPO8 IPO11 738 96 61 18 1.182 0.007 0.043 Co-occurrence
   KPNB1 XPO6 785 59 57 12 1.486 0.008 0.047 Co-occurrence
   IPO5 IPO8 686 113 86 28 0.983 0.008 0.047 Co-occurrence
   KPNA7 IPO4 712 93 85 23 1.051 0.008 0.048 Co-occurrence
   XPOT XPO4 713 44 137 19 1.168 0.008 0.048 Co-occurrence
   [85]Open in a new tab

Genetic alteration of nuclear transport receptor genes is significantly
associated with cell cycle activation

   Cell cycle activation is a crucial aspect of cancer development, and
   its regulation is governed by various genes [[86]16]. Thus, we
   evaluated the relationship between the expression of nuclear transport
   receptors and cell cycle regulation genes (CDK1, CDK2, CDK4, CDK6,
   CCNA1, CCNB1, CCND1, and CCNE2) using cBioPortal. Most nuclear
   transport receptor genes showed a significant positive correlation with
   seven of eight cell cycle genes (CDK1, CDK2, CDK4, CDK6, CCNA1, CCNB1,
   and CCNE2) in the TCGA pancancer and CCLE datasets ([87]Fig 2C and 2D)
   with q-values shown in S6 and S7 Tables in [88]S1 File.

Genetic alterations in nuclear transport receptor genes are widespread in
HNSCC

   Furthermore, we again performed Oncoprint analysis through cBioportal’s
   OncoPrint tool to assess 23 nuclear receptor genes in 523 primary head
   and neck tumour samples, and then compared the mutational landscape of
   transport-related genes in HNSCC. The genetic alteration percentage in
   HNSCC ranged from 0.8% (in TNPO3) to 10% (in KPNA4) ([89]Fig 3A).
   Somatic mutations in 23 transporters in HNSCC are shown in S8 Table in
   [90]S1 File and [91]Fig 3A. Interestingly, we found that the cases in
   which nuclear transport receptor genes were altered also expressed the
   top mutated driver genes, including TP53, PIK3CA and TP63 ([92]Fig 3A).
   Moreover, among the alterations in several transport receptors and
   these top mutated genes, some mutations were co-occurring and some were
   mutual exclusive ([93]Table 2).

Fig 3.

   [94]Fig 3
   [95]Open in a new tab

   Genetic alteration of 23 nuclear transport receptors in HNSCC patient
   samples of TCGA dataset (A). Their expression in tumor tissue vs.
   normal tissues (B) and in HNSCC cell lines of CCLE dataset (C).

Table 2. Co-occurring and mutually exclusion mutations in nuclear transport
receptors and top mutated genes in HNSCC.

   A B Neither A not B B not A Both Log2 Odds Ratio p-Value q-Value
   Tendency
   KPNA4 TP63 398 11 48 39 >3 <0.001 <0.001 Co-occurrence
   KPNA4 PIK3CA 347 4 99 46 >3 <0.001 <0.001 Co-occurrence
   PIK3CA TP63 329 80 22 65 >3 <0.001 <0.001 Co-occurrence
   KPNA1 KPNA4 439 7 38 12 >3 <0.001 <0.001 Co-occurrence
   KPNA1 TP63 403 6 74 13 >3 <0.001 <0.001 Co-occurrence
   KPNA1 PIK3CA 345 6 132 13 2.502 <0.001 0.019 Co-occurrence
   KPNA7 TP53 143 0 331 22 >3 <0.001 0.021 Co-occurrence
   IPO4 TNPO3 488 4 2 2 >3 <0.001 0.029 Co-occurrence
   KPNA2 TNPO1 486 5 3 2 >3 0.002 0.061 Co-occurrence
   KPNB1 XPOT 476 2 16 2 >3 0.007 0.233 Co-occurrence
   KPNA4 IPO4 443 47 3 3 >3 0.016 0.459 Co-occurrence
   XPOT XPO6 474 16 4 2 >3 0.017 0.464 Co-occurrence
   IPO4 IPO8 473 4 17 2 >3 0.019 0.476 Co-occurrence
   TP53 TP63 126 283 17 70 0.874 0.022 0.504 Co-occurrence
   KPNA1 IPO8 461 16 16 3 2.434 0.031 0.679 Co-occurrence
   XPO5 TP63 405 4 83 4 2.287 0.035 0.715 Co-occurrence
   TNPO1 TNPO3 488 4 3 1 >3 0.04 0.762 Co-occurrence
   [96]Open in a new tab

Gene expression analysis

   As shown in the above results, amplification was the most common
   genetic alteration in nuclear transport receptor genes. It is well
   known that gene amplification is a common feature in many human
   cancers, and overexpression of genes due to amplification is a frequent
   occurrence in cancer [[97]17]. Thus, we examined the expression
   patterns of nuclear transport receptors in normal tissues and the
   tumors of the TCGA-HNSCC dataset, and the results are shown in [98]Fig
   3B. The expression of KPNA1, KPNA2, KPNA4, KPNA7, KPNB1, IPO9, TNPO2,
   XPOT, XPO5 and XPO6 was significantly higher in tumour tissue than in
   normal tissues, whereas the expression of KPNA3, KPNA5, IPO5, IPO11 and
   XPO4 was significantly lower in tumor tissues than in the normal
   tissues ([99]Fig 3B). Next, we analyzed the relative expression of
   nuclear transport receptor genes in 56 HNSCC and 78 non-cancerous cell
   lines from CCLE. Our analysis revealed KPNA7 as a nuclear transport
   receptor overexpressed in HNSCC tumors relative to normal tissues,
   based on the TCGA-HNSCC dataset. Likewise, we also found KPNA7 to
   exhibit higher expression in HNSCC cell lines compared to non-cancerous
   cell lines ([100]Fig 3C).

Functional enrichment analysis

   To gain insight into the known biological processes involved in HNSCC,
   cancer hallmark and KEGG pathway enrichment analysis (GSEA) were
   performed on expression data of selected nuclear transport receptor
   genes. According to the hallmark results, these genes were mainly
   enriched in E2F targets, G2M checkpoint and mitotic spindle ([101]Fig
   4A).

Fig 4.

   [102]Fig 4
   [103]Open in a new tab

   Functional enrichment analysis of 23 nuclear transport receptors in
   HNSCC (A) Enrichment analyses of Hallmarks (B) Enrichment analyses of
   KEGG.

   According to the results of KEGG signaling pathway analysis, nuclear
   transport receptor genes were mainly enriched in the adhesion junction
   (AJ), cell cycle (CC), ERBB, MAPK, MTOR and WNT signaling pathways
   ([104]Fig 4B).

Impact of genetic alterations of nuclear transport receptors on overall
survival (OS)

   Next, we analyzed the impact of genetic alterations of each nuclear
   transport receptor on the survival rates of HNSCC patients.
   Interestingly, we found that alterations in some of the members, such
   as KPNA7 and KPNB1, showed a significant correlation with the patient
   survival ([105]Fig 5).

Fig 5. Meier-Kaplan plots showing the correlation between the expression of
23 nuclear transport receptors and overall HNSCC patient survival.

   [106]Fig 5
   [107]Open in a new tab

Discussion

   Eukaryotic cells are characterized by a nuclear membrane that separates
   the nuclear and cytoplasmic components, which require a set of
   specialized transporters that transport molecules to and from the
   nucleus to the cytoplasm to ensure cellular homeostasis. Nuclear
   transport receptors play a crucial role in regulating the cell cycle by
   interacting with chromatin and genes associated with cell cycle
   progression; for example, p53, a protein crucial to the stress
   response, must be localized in the nuclear to function, and its nuclear
   localization is tightly regulated by both nuclear import and nuclear
   export of p53 [[108]18,[109]19]. Although p53 is synthesized in the
   cytoplasm, it regulates transcription in the nucleus. However, the
   precise signals or proteins that direct p53’s movement from the
   cytoplasm to the nucleus remain unclear. Many tumor types exhibit the
   abnormal cytoplasmic sequestration of p53 and display poor responses to
   chemotherapy and radiation therapies, which has led researchers to
   explore which of the major skeletal filament systems (such as actin
   filaments, intermediate filaments, or microtubules) could serve as a
   cytoplasmic anchor for p53 molecules [[110]20–[111]22]. p53 molecules
   are imported into the nucleus via their three nuclear localization
   signals (NLS) [[112]23,[113]24] and exported via their two nuclear
   export signals (NES) [[114]25,[115]26]. Following DNA damage, p53 is
   imported into the nucleus through its NLS [[116]27]. Recently, importin
   α3 was discovered to regulate the nucleocytoplasmic shuttling and
   activity of p53 [[117]28].

   EGFR, a renowned receptor tyrosine kinase, can be translocated into
   different organelles, including the nucleus and mitochondrion, upon
   stimuli such as ligand binding, radiation, and EGFR-targeted therapy
   [[118]29]. Nuclear EGFR is a multifunctional regulator with roles as a
   transcriptional regulator, tyrosine kinase, and mediator of other
   physiological processes [[119]29]. Studies have shown that nuclear EGFR
   is an indicator of poor clinical outcomes in cancer patients
   [[120]30,[121]31]. Moreover, nuclear EGFR has been shown to contribute
   to resistance to various cancer therapies, such as radiation,
   cisplatin, and cetuximab [[122]32–[123]34].

   In a previous study, we observed that a significant increase in FGFR1
   nuclear localization in HNSCC corresponded with high-grade
   histopathology, abundant nuclear polymorphisms and a high-grade
   invasion pattern [[124]35]. Stachowiak et al revealed that nuclear
   FGFR1 facilitates the transition from G0/G1 to S phase of the cell
   cycle [[125]36]. Moreover, nuclear FGFR1 initiates the release of
   CREB-binding protein (CBP) from its inactive complex with RSK1
   [[126]37], thereby increasing gene activities linked to cellular
   differentiation. Additionally, the protease granzymeB (GrB) is
   responsible for the cleavage of FGFR1, leading to the nuclear
   localization of FGFR1 cleavage and the invasion of breast cancer cells
   into the stroma [[127]38].

   Various studies have highlighted that nuclear transport receptors
   including XPO1, KPNA2, and KPNA4, exhibit hyperactivity in cancer and
   facilitate the export of vital tumor suppressors to the cytoplasm
   [[128]39] or the import of oncogenes to the nucleus [[129]40]. Although
   there have been no reports on the impact of their mutations and
   amplifications and mutations on outcomes in NSCLC, XPO1 has been shown
   to be involved in the development of other cancer types [[130]5].
   Researchers have been exploring several approaches to target nuclear
   transport, including the use of small molecule inhibitors such as
   KPT-330 and KPT-8602, which have shown promise in preclinical models of
   various cancers [[131]41,[132]42]. Additionally, inhibiting nuclear
   pore complex proteins such as Nup98 and Nup214 has emerged as a
   potential therapeutic approach [[133]43]. Gene therapy approaches such
   as siRNA-mediated knockdown of XPO1 and CRISPR-Cas9 technology have
   also shown promise in preclinical models of head and neck cancer
   [[134]43–[135]45]. Finally, the nuclear transport of the immune
   checkpoint molecule PD-L1 has been linked to the regulation of T-cell
   activity in the tumor microenvironment [[136]46].

   In conclusion, the significance of nuclear transport in cancer biology
   cannot be overstated. It is involved in key processes such as gene
   expression, DNA repair, cell cycle regulation, and immunotherapy.
   Dysregulation of nuclear transport is a defining characteristic of
   cancer and can lead to the advancement of tumors. Focusing on nuclear
   transport as a therapeutic target can lead to the development of
   innovative cancer treatments, further improving patient outcomes.

Supporting information

   S1 File

   (DOCX)
   [137]pone.0300446.s001.docx^ (636.2KB, docx)

Data Availability

   [138]https://doi.org/10.1038/s41586-020-1969-6
   [139]https://doi.org/10.1038/s41586-019-1186-3
   [140]https://doi.org/10.1126/scisignal.2004088
   [141]https://doi.org/10.1158/2159-8290.CD-12-0095.

Funding Statement

   This work was supported in part by (1) grants-in-aid from the Japan
   Society for the Promotion of Science to P.T. Nguyen and T. Sasahira;
   (2) grant-in-aid from Hirose foundation to P.T. Nguyen; (3) the MEXT
   Support Program for the Development of Human Resource in Science and
   Technology “Initiative for realizing diversity in the research
   environment (Leading type) to P.T. Nguyen. The funders had no role in
   study design, data collection and analysis, decision to publish, or
   preparation of the manuscript.

References