Comprehensive bioinformatics analysis on exportins in lung adenocarcinoma and lung squamous cell carcinoma

doi:10.21203/rs.3.rs-1987101/v1

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Comprehensive bioinformatics analysis on exportins in lung adenocarcinoma and lung squamous cell carcinoma

https://doi.org/10.21203/rs.3.rs-1987101/v1

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Background

Lung cancer is one of the most common malignant tumors in the world. Exportins are closely associated with the cellular activity and disease progression in a variety of different tumors. However, the expression level, genetic variation, immune infiltration and biological function of different exportins in LUAD and LUSC and their relationship with the prognosis of LUAD and LUSC patients have not been fully clarified.

Methods

In this study, ONCOMINE, UALCAN, HPA, Kaplan-Meier plotter, cBioPortal, STRING, DAVID, TIMER and LinkedOmics databases were used to analyze the differential expression, prognostic value, genetic variation, biological function and immune cell infiltration of exportins in patients with LUAD and LUSC.

Results

The transcriptional and protein expression levels of CSE1L and XPO1 / 5 / 6 / 7 were increased in LUAD and LUSC patients, and the increased transcriptional levels of CSE1L and XPO5 / 6 / 7 were related to worse prognosis. The increased transcriptional level of XPO1 suggested a better prognosis. These results indicated that CSE1L and XPO1 / 5 / 6 / 7 may be potential prognostic biomarkers for the survival of patients with LUAD and LUSC. Besides, the high mutation rate of exportins in NSCLC was 50.48%, and the largest proportion of mutations was high mRNA expression. The expression of exportins was significantly correlated with the infiltration of various immune cells. Differentially expressed exportins could regulate the occurrence and development of LUAD and LUSC by involving a variety of miRNAs and transcription factor E2F1.

Conclusions

Our study could provide novel insights for the selection of prognostic biomarkers of exportins in LUAD and LUSC.

bioinformatics analysis

exportins

lung cancer

biomarkers

prognostic value

As one of the most common malignant tumors in the world and the most common cause of global cancer-related mortality, lung cancer can result in more than a million deaths every year¹. There are two main types of lung cancer, namely small cell lung cancer (SCLC) and non-small-cell lung cancer (NSCLC), among which, the most common subtypes of NSCLC are lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), accounting for about 85% of new lung cancer cases². Despite the progress of molecular targeted and immune-based checkpoint therapies, the 5-year overall survival (OS) of NSCLC patients remains less than 15%³, and cancer therapy still faces great challenges, including delayed diagnosis, recurrence, metastasis and cancer-related mutations ⁴. Therefore, it is crucial to determine more reliable tumor molecular markers for early screening, diagnosis, individualized treatment and prognosis of NSCLC.

Exportins are a group of nucleocytoplasmic transport receptor proteins that are widely distributed in eukaryotes. At present, seven exportins have been identified in the mammalian cell, namely XPO1 (also called CRM1), CSE1L (also called XPO2 or CAS), XPOT (also called XPO3), XPO4, XPO5, XPO6, and XPO7⁵; ⁶. These exportins belong to Karyopherin- β (Kap- β; also known as importin and exportin) family and are responsible for transporting most proteins and RNA in the nucleus across the nuclear membrane to the cytoplasm⁶; ⁷. Kaps protein sequences have low homology (10–20%), while have similar physical properties such as molecular weights (90–150 kDa) and isoelectric points (pI = 4.0–5.0). They both have conserved N-terminal Ran binding domain and HEAT Repeats domain. Through these special structures, biological macromolecules can be exported to the nucleus⁸. Kaps are also closely related to many cellular processes, such as gene differential expression, cell signal transduction, tumor immune response and the mechanism of tumorigenesis, because they interact with a large number of proteins with different functions and play a role in controlling a variety of protein localization ⁹. Some studies have found that exportins were associated with cell activity and progression of various cancers, such as liver cancer ¹⁰, breast cance¹¹, gastric cancer ¹², lung cancer ¹³, and prostate cancer¹⁴. In lung cancer, some studies have found that the stable overexpression of CRM1 in human bronchial epithelial cells leads to malignant cellular transformation¹⁵. Nuclear export protein CSE1L interacts with p65 and activates nuclear factor-κB (NF-κB). Mitogen-activated protein kinase (MAPK) signaling pathway promotes NSCLC proliferation and inhibits apoptosis¹⁶. However, the expression level, genetic variation, immune infiltration and biological function of different exportins in LUAD and LUSC and their relationship with prognosis of LUAD and LUSC patients have not been fully clarified.

With the rapid development of RNA-sequencing technology and microarrays, RNA and DNA exploration have become a significant constituent of biomedical and biological research¹⁷, which makes it possible to comprehensively analyze exportins. Based on various large public databases, our study expanded the relevant knowledge of LUAD and LUSC, and conducted a comprehensive bioinformatics analysis on the relationship between seven different exportins and the prognosis of LUAD and LUSC. We present the following article in accordance with the STREGA reporting checklist.

ONCOMINE analysis

ONCOMINE (www.ONCOMINE.org) is a publicly accessible and currently the world's largest oncogene chip database and comprehensive data mining platform, including 715 datasets and 86,733 samples¹⁸. The ONCOMINE database was used to determine the transcriptional levels of exportins in different types of cancer. In our study, a p-value < 1E-4, a fold change of 2, and a gene rank in the top 10% were set as the significance thresholds. t-test was used to analyze the differences in the expression of exportins in LUAD and LUSC, and statically significant differences were considered when a p-value < 0.05.

UALCAN analysis

UALCAN (http://ualcan.path.uab.edu/) mainly uses TCGA transcriptome and clinical patient data to help identify candidate biomarkers of specific cancer subclasses with diagnostic, prognostic or therapeutic significance, and it can also be used as a computer verification platform for target genes¹⁹. In our study, the "TCGA analysis" module and the " LUAD " and " LUSC " datasets were used to analyze the relationship between exportins transcription levels in LUAD and LUSC and normal tissues in the "expression" link. Statically significant differences were considered when a p-value < 0.05.

Human Protein Atlas (HPA) analysis

The Human Protein Atlas (HPA) (https://www.proteinatlas.org/) database is an online free database that provides a large number of proteomic and transcriptomic data of specific human tissues and cells²⁰. In our study, the protein expression levels of exportins in LUAD and LUSC and normal tissues were studied.

Kaplan–Meier Plotter analysis

As an important prognostic biomarker evaluation tool, Kaplan-Meier plotter (https://kmplot.com/analysis/index.php?p=background）can evaluate the correlation between the expression of the 54,000 genes on the survival rates in 21 different cancers ²¹. In our study, it was used to evaluate the prognostic value of exportins mRNA expression, in which cancer patients were split into high and low expression group based on median values of mRNA expression and validated by Kaplan-Meier survival curves. Information about the number-at-risk cases, median values of mRNA expression levels, 95% confidence interval (CI), risk ratio (HR) and p-values can be found at the Kaplan-Meier plotter webpage. Statically significant differences were considered when a p-value < 0.05.

cBioPortal analysis

cBioPortal (https://www.cbioportal.org/) is a comprehensive network resource that can visualize and analyze multidimensional cancer genomics data²². In our study, by using the cBioPortal online tool of LUAD (TCGA, firehose Legacy), the genome profiles of seven exportins members were analyzed, which contained mutations, putative copy-number alterations from GISTIC and mRNA Expression z-Scores (RNASeq V2 RSEM) with a z-score threshold of ±2.0. Co-expression genes of exportins members were performed using the “co-expression” module of cBioportal. The Pearson correlation coefficient was used to investigate the correlation between exportins members and co-expression genes, and top ten co-expression genes of each exportins with the largest Pearson’s correlation coefficient were listed.

PPI network construction and module analysis

STRING (Version: 11.5) (https://cn.string-db.org/) is an online database for analyzing the functional interactions between proteins encoded by different genes²³. Cytoscape（Version: 3.8.2）is an open bioinformatics software platform for visualizing gene interaction networks²⁴. The STRING database was used to construct the PPI network of 69 genes significantly related to exportins mutation, and then processed the details and module analysis in Cytoscape software.

DAVID analysis

Database for Annotation, Visualization and Integrated Discovery (DAVID; Version: 6.8) (https://david.ncifcrf.gov/tools.jsp）is a comprehensive bioinformatics website that can visually annotate biological functions²⁵. In our study, 69 co-expression genes significantly related to exportins mutations were analyzed by Gene Ontology (GO) and Kyoto Kyoto Encyclopedia of Genes and Genomes (KEGG). Statically significant differences were considered when a p-value < 0.05.

TIMER analysis

TIMER (https://cistrome.shinyapps.io/timer/）,a reliable and convenient online analysis tool, can systematically evaluate the infiltration of different immune cells ad their clinical effects²⁶. The “gene module” was used to evaluate the correlation between the epression of exportins and immune cell infiltration. Statically significant difference was considered when a p-value < 0.05.

LinkedOmics analysis

LinkedOmics (http://www.linkedomics.org/login.php） is a comprehensive multiomics database for the analysis of 32 TCGA cancer types²⁷. In our study, TCGA_LUAD dataset was used for the “LinkInterpreter” module. Gene Set Enrichment Analysis (GSEA) tool was selected to analyze miRNA target enrichment and transcription factor target enrichment of exportins, in which the rank criterion was the minimum number of genes (size) of 3 and the simulation of 500, and Pearman correlation test was used. Statically significant differences were considered when a p-value < 0.05.

Transcription Levels and Protein Expression of Exportins in Patients with LUAD and LUSC

The transcriptional and protein levels of different exportins members between LUAD, LUSC and normal tissues were compared through ONCOMINE, UALCAN and HPA databases. ONCOMINE differential expression analysis revealed that the transcriptional levels of XPO1, CSE1L, XPOT and XPO5 were up-regulated in patients with LUAD and LUSC (Figure 1 and Table 1). The transcription level of XPO1 was significantly higher in patients with LUAD and LUSC in three datasets²⁸; ²⁹. In Hou Lung dataset²⁸, XPO1 overexpression was detected in LUAD and LUSC compared with normal tissues with fold changes of 1.375 (p = 1.09e-09) and 2.828 (p = 6.44e-09), respectively. However, TCGA Lung 2 dataset found that XPO1 transcription level in LUAD and LUSC samples increased by 1.043 times (p = 1.78e-12) and 1.179 times (p= 3.11e-39), respectively. Moreover, Stearman Lung investigated a 1.35-fold increase in XPO1 mRNA expression in LUAD tissues (p = 1.95e-05)²⁹. CSE1L transcriptional level in LUAD and LUSC patients was also found to be elevated in three datasets. In the Hou Lung dataset²⁸, CSE1L was overexpressed in LUAD and LUSC compared with that in the normal samples, with a fold change of 1.487 (p = 2.01e-08) and 2.51 (p = 1.54e-17). In the Bhattacharjee Lung dataset³⁰, CSE1L was overexpressed in LUSC with fold changes of 7.468 (p = 7.67e-06), and the transcription level of CSE1L in LUAD was slightly higher than that in normal Lung tissue, but the p-value did not exceed 0.05. Furthermore, CSE1L was also overexpressed in LUAD and LUSC in Garber Lung dataset, with fold changes of 2.29 (p = 0.001) and 3.497 (p = 5.78e-05)³¹. Similarly, XPOT transcription level in LUAD and LUSC patients were also elevated in three data sets. In Hou Lung dataset²⁸ and Garber Lung dataset³¹, XPOT was significantly overexpressed in LUAD with fold changes 1.768 and p =0.002. In Bhattacharjee Lung dataset³⁰, the transcription level of XPOT in LUSC increased with a fold change of 4.078 (p = 9.25e-05), but the same change was not found in LUAD samples (p = 0.382), compared with normal tissues. A similar trend of XPO5 was shown in the Hou Lung dataset²⁸. XPO5 was significantly up-regulated in LUAD and LUSC with fold changes of 1.684 (p = 1.63e-10) and 2.044 (p = 1.72e-11) respectively. Similarly, the same results were displayed in Garber Lung dataset³¹.

Next, the transcriptional expression of exportins members between LUAD, LUSC and normal tissues was confirmed by using the UALCAN dataset (Figure 2). The results proved that the transcription levels of XPO1, CSE1L, XPOT, XPO5, XPO6 and XPO7 in LUAD and LUSC tissues were significantly higher than that in normal lung tissues, while the transcription level of XPO4 in normal lung tissues was higher than that in LUAD and LUSC tissues.

After analyzing the transcription levels of exportins in LUAD and LUSC, the protein expression levels of exportins in LUAD and LUSC patients and normal lung tissues were confirmed through HPA database（Figure 3）. It should be noted that XPO1 protein was expressed in both normal lung tissues and lung cancer tissues (Figure 3A). CSE1L, XPO5, XPO6 and XPO7 were not expressed in normal lung tissues, but were low- and medium- expressed in lung cancer tissues (Figure 3B-E). Moreover, there was no protein expression of XPOT and XPO4 in normal and lung cancer tissues. In summary, the results of the above three databases revealed that the transcriptional and translational expression levels of XPO1, CSE1L , XPO5, XPO6 and XPO7 genes were increased in LUAD and LUSC patients.

Prognostic Characteristics of Exportins Members in Patients with LUAD and LUSC

In order to evaluate the clinical significance of exportins, the publicly Kaplan-Meier plotter tool was used to explore the correlation between exportins members’ transcriptional levels and the survival of patients with LUAD and LUSC patients further. The main parameters of survival analysis included overall survival (OS) and progression-free survival (FP). According to the survival curves of Kaplan-Meier shown in Figure4 and Table 2, OS was negatively correlated with the transcriptional levels of CSE1L and XPOT / 4 / 5 / 6 / 7, but positively correlated with XPO1 (Figure4A -G). Moreover, the transcriptional levels of XPO4 / 6 / 7 in LUAD and LUSC were negatively correlated with FP, while the transcriptional levels of XPO1 and XPOT were positively correlated with FP, and there was no significant correlation between the transcriptional levels of CSE1L and XPO5 with FP (Figure4A -G). In summary, the elevated transcriptional levels of CSE1L and XPO5 / 6 / 7 are related to the worse prognosis of LUAD and LUSC patients, combined with the transcriptional and protein expression levels of exportins in the above databases and the results of Kaplan Meier plotter tool. In contrast, increased XPO1 transcription levels mean better prognosis in patients with LUAD and LUSC patients. Therefore, we have reasons to believe that CSE1L and XPO1 / 5 / 6 / 7 may be useful biomarkers to predict the survival rate of LUAD and LUSC patients.

The Genetic Alteration and Mutation Information of Exportins

The LUAD (TCGA, Firehose Legacy) module was used in cBioPortal online tool to analyze the mutations of exportins members. As shown in Figure 5A, 261 of 517 patients had gene mutations, with a mutation rate of 50.48%, of which CSE1L and XPO7 were the genes with the largest mutations, with the mutation rates of 17% and 18%, respectively. Furthermore, the mutation rates of XPO1, XPOT, XPO4, XPO5, and XPO6 genes were 9, 10, 1, 11, 2.7, and 12% in the LUAD samples, respectively. Gene mutations in exportins members included mRNA up-regulation (120 cases, 23.21%), multiple alterations (56 cases, 10.83%), mRNA down-regulation (44 cases, 8.51%), genetic amplification (18 cases, 3.48%), deep deletion (14 case, 2.71%) and mutation (9 cases, 1.74%). Among them, the highest proportion of mutation was mRNA up-regulation, especially in XPO1, CSE1L, XPOT and XPO6. However, the overexpression of mRNA was not detected in XPO5, but it had high frequency of genetic amplification of 2.7%. We collected mRNA expression data (RNA SEQ V2 rsem) of the exportins through the cBioPortal online tool (TCGA, firehose Legacy) of Pearson corrected LUAD. The results showed that XPO1 was significantly positively correlated with CSE1L, XPOT, XPO4, XPO6, and XPO7; CSE1L was positively correlated with XPOT, XPO5, XPO6 and XPO7; There was a significant positive correlation between XPOT and XPO6 (Figure 5B). In addition, the "mRNA expression z-scores relative to diploid samples (RNA Seq V2 RSEM)" option in the genome map was selected to analyze the correlation between exportins members through the "mutual exclusivity" module. The results Figure 5C showed that there was a correlation between the transcriptional levels of XPO1, CSE1L, XPOT and XPO6, and the transcriptional levels of XPO1 and CSE1L were also correlated with XPO7. Furthermore, the relationship between the mRNA expression and copy- number alterations (CAN） of exportins members was analyzed, and it was found that the mRNA expression of exportins members was positively correlated with CAN (Figure 6).

GO and KEGG Enrichment Analysis of Exportins and Their Co-expression Genes in LUAD and LUSC Patients

Subsequently, “co-expression” module of cBiopotal was used to list the top 10 co-expression genes significantly related to the mutation of each exportins member, with a total of 70 genes (Supplementary Table 1). After deleting duplicate genes, a total of 69 genes were selected. Afterwards, the STRING database was used to construct the PPI network of 69 co- expression genes significantly related to exportins mutations, and then the details and module analysis in Cytoscape software was processed. As shown in Figure 5C, the first 11 genes significantly associated with the exportins mutations were TOP2A, AURKA, BUB1, EXO1, TTK, MCM10, NCAPG, KIF2C, NEK2, KIF15 and TPX2. Next, the biological functions of 69 co- expression genes significantly associated with exportins mutations were further analyzed through GO enrichment analysis and KEGG pathway enrichment in DAVID. As shown in Figure 7 and Supplementary Table 2, it was found that BP such as GO: 0007067 (mitotic nuclear division), GO:0008283 (cell population proliferation), GO:1901796 (regulation of signal transduction by p53 class mediator), GO:0000086 (G2/M transition of mitotic cell cycle) ，and GO:0000086 (G2/M transition of mitotic cell cycle) were remarkably regulated by the exportins mutations in NSCLC (Figure 7A). Moreover, CC, including GO: 0005654 (nucleoplasm), GO:0005813 (centrosome), GO:0000776 (kinetochore), GO:0005730 (nucleolus) and GO:0000922 (spindle pole) were significantly associated with the exportins alterations (Figure 7B). Furthermore, exportins mutations also prominently affected the MF, such as GO:0005515 (protein binding), GO:0005524 (ATP binding), GO:0003723 (RNA binding), GO:0003677 (RNA binding), and GO:0016887 (ATP hydrolysis activity) (Figure 7B). In KEGG analysis, oocyte meiosis was closely related to the function of exportins in NSCLC, and cell cycle pathway may also be related to exportins, but p > 0.05 (Supplementary Table 2). This result may need to be further verified by experiment. In conclusion, the above results roughly indicated that the functions of exportins mutations and its co-expression genes may be associated with cell proliferation, cell division, and regulation of signal transduction by p53 class mediator, cell cycle and so on.

The Relationship Between Exportins Expression Levels and Immune Infiltration Levels in LUAD and LUSC

Immunity is closely related to the occurrence and development of tumors. Then, the relationship between the transcriptional levels of exportins and the levels of immune infiltration in LUAD and LUSC was evaluated through the TIMER online analysis tool. It was found that exportins were involved in immune cell infiltration. As shown in Figure 8, it was obvious that XPO1 was negatively correlated with B cells and positively correlated with neutrophils. CSE1L was negatively correlated with B cells, CD4+ T cells, macrophages, neutrophils and dendritic cells. Besides, XPO4 and XPO6 were positively correlated with CD4+ T cells, macrophages, neutrophils and dendritic cells. Moreover, XPO4 was positively correlated with CD8+ T cells and XPO6 was also positively correlated with B cells. XPO7 was positively correlated with CD8+ T cells, macrophages and neutrophils. However, it was discovered that XPO5 was only positively correlated with CD8+ T cells. These studies proved that the expression levels of exportins were correlated with the levels of immune infiltration in LUAD and LUSC.

Analysis of Transcription Factor and miRNA Targets of Exportins in Patients with LUAD and LUSC

Then, the transcription factor targets (TF) and mRNA targets of differentially expressed exportins from LinkedOmics database were analyzed. The first three enriched miRNA targets of each exportins were shown in Table 3. It was found that ATCATGA, miR-433 was the common target of XPO1 and CSE1L, and TACTTGA, miR-26A, and miR-26B was the common target of XPOT and XPO5. Similarly, the common targets of XPO4 and XPO7 were TGAATGT, miR-181A, miR-181B, miR-181C, miR-181D, and GAGACTG, and miR-452 was common target of XPO5 and XPO7. Finally, it was confirmed that E2F1 may be the key TF regulated by exportins (Table 4).

Exportins members have been partially shown to be associated with a variety of different tumors, such as liver cancer ¹⁰, breast cancer¹¹, gastric cancer ¹², and lung cancer¹³. Some studies have found that the stable overexpression of CRM1 in human bronchial epithelial cells leads to malignant cellular transformation¹⁵. Nuclear export protein CSE1L interacts with p65 and activates nuclear factor-κB (NF-κB). Mitogen-activated protein kinase (MAPK) signaling pathway promotes NSCLC proliferation and inhibits apoptosis¹⁶. In our study, the expression level, genetic variation, immune infiltration, biological function of different exportins and their relationship with the prognosis of LUAD and LUSC patients were comprehensively analyzed by bioinformatics for the first time.

XPO1 was identified as a gene required to modify chromosome structures or regulate the maintenance of high-order chromosome structures in Schizosaccharomyces pombe, so it was originally named as CRM1 (chromosome region maintenance 1). Subsequently, it was found that it had homology with importin beta-like transporter and could specifically interact with NES motif and Ran GTPase to mediate the nuclear export of protein and mRNA in Saccharomyces cerevisiae, and finally renamed as XPO1 (export 1) ³². As a well-defined nuclear export protein, XPO1 is responsible for exporting a variety of proteins and RNAs (including rRNA, snRNA, mRNA, microRNA and tRNA)³³, and plays an important role in regulating mitosis and chromosome structure³⁴, which indicates that many carcinogenic mechanisms may involve XPO1. In eukaryotic cells, XPO1was considered to be an important exporter of most tumor suppressor proteins, including p53, p21, PI3K / Akt, p27 and BRCA1 / 2, all of which were important targets for tumorigenesis³⁵; ³⁶. XPO1 was highly expressed in many types of malignant tumors and associated with poor prognosis, including ovarian cancer³⁷, cervical cancer³⁸, prostate cancer³⁹, gastric cancer⁴⁰, osteosarcoma⁴¹, leukemia⁴², multiple myeloma⁴³ and glioma⁴⁴. This was the first study showing CRM1 overexpression in lung cancer, the study demonstrated that CRM1 was frequently overexpressed in NSCLC, especially adenocarcinoma and squamous cell carcinoma, which may be caused by the synergistic effect of CRM1 overexpression and p53 phosphorylation in cell malignant transformation. At the same time, inhibiting CRM1 can improve the efficacy of cisplatin in the treatment of lung cancer¹⁵. We found through database analysis that the transcriptional level of XPO1 in LUAD and LUSC patients was higher than that in normal tissues, which was also verified by immunohistochemical staining. This is consistent with the conclusion of the above study. One study found that mutations in STK11/KEAP1 were common in NSCLC patients carrying XPO1 mutations⁴⁵. Therefore, Li⁴⁶ divided NSCLC patients into pure xpo1 mutant, wild-type and XPO1-STK11/KEAP1 mutant groups. The study found that patients with pure XPO1 mutations had longer survival. We found by Kaplan Meier plotter analysis that in NSCLS patients, increased XPO1 transcript level was associated with better OS and FP. This result is similar to the above study. We believe that this may be related to XPO1 mutation, which make NSCLC more sensitive to treatment. Therefore, the prognosis is good. Our study also confirmed that XPO1 has a high mutation rate in LUAD, with a mutation rate of 9%. This finding suggests that XPO1 may become a prognostic marker for NSCLC, but further studies are still needed.

CSE1L, also known as human cellular apoptosis susceptibility and exportin 2 (XPO2), was involved in the regulation of different cellular mechanisms, including mitotic spindle checkpoint, proliferation and apoptosis ⁴⁷. The role of CSE1L in lung cancer has rarely been studied. A previous study shown that ACOT11 can promote the proliferation, migration and invasion of lung cancer cells by binding with CSE1L⁴⁸. CSE1L can regulate the MAPK pathway and promote the development of lung cancer by interacting with p65⁴⁹. Other studies have confirmed that CSE1L silencing impaired the cell proliferation, invasion and migration of lung cancer cells, and the expression of STAT3 and MET protein expression was decreased, then, the apoptosis related marker protein c-PARP was induced, which indicated that the regulation of CSE1L on cell proliferation and anti-apoptosis may be through MET-STAT3 pathway⁵⁰. In our study, ONCOMINE and UALCAN databases revealed that the transcriptional level of CSE1L in LUAD and LUSC was higher than that in normal tissues, which was also confirmed by immunohistochemical staining. Additionally, the high expression level of CSE1L was significantly linked with a worse OS, but not with FP in NSCLC by analyzing the Kaplan-Meier plotter.

XPOT and XPO5 are Kap- β family of tRNA nuclear output receptors. XPOT is a specific nuclear export receptor of tRNA, and XPO5 mainly exports miRNA to the cytoplasm, and thus the role of XPO5 in tRNA export is considered to be secondary⁵¹. Previous studies have reported the key roles of XPOT in the development and progression of various tumors, such as breast cancer⁵² and human promyelocytic leukemia⁵³. The overexpression of XPOT led to more aggressive and chromosomally unstable breast cancers⁵². The elevated expression of XPOT was associated with cell proliferation in human promyelocytic leukemia⁵³. However, the role of XPOT in lung cancer has not been explored before. A previous report shown that the miR-SNP of rs11077 in the miRNA processing gene XPO5 was associated with the recurrence of resected NSCLC, which suggested that the miR-SNP in the miRNA processing mechanism gene of XPO5 was involved in the prognosis of NSCLC⁵⁴. In our study, ONCOMINE database illustrated that the transcriptional levels of XPOT and XPO5 in NSCLC were significantly higher than that in normal lung tissue, and the same results were shown in the UALCAN dataset. Immunohistochemical staining demonstrated that XPO5 was moderately expressed in lung cancer tissues, but not in normal tissues. However, XPOT expression was not found in lung cancer and normal tissues. Furthermore, with the use of the Kaplan-Meier plotter, it was clear that the high expression of XPO5 was associated with worse OS. Therefore, we believe that XPO5 may be an important indicator to predict the prognosis of NSCLC patients.

XPO4 is a bidirectional nuclear transporter involved in nuclear export of translation initiation factor EIF5A and transcriptional regulator Smad3⁵⁵; ⁵⁶. Smad3, an effector of TGF-β signal transduction, can promote or inhibit the growth of cancer cells⁵⁷. EIF-5A was identified as the initiation gene of eukaryotic translation and encoded by two highly related genes (EIF5A1 and EIF5A2). Although the mechanism of EIF5A1 causing cancers has not been fully clarified, some studies have found that EIF5A1 and EIF5A2 proteins were overexpressed in some human tumors ⁵⁸. At present, there are no more reports on XPO4 in various cancers. Our study proved that the ONCOMINE dataset did not record the expression of XPO4 transcription level in lung cancer, and the HPA dataset did not find the expression of XPO4 protein in lung cancer and normal tissues. Due to the lack of relevant studies on XPO4, its role in the occurrence and development of NSCLC cannot be confirmed.

Similar to XPO4, there are relatively few reports on XPO6 / 7 in NSCLC. According to our results, the UALCAN dataset displayed that the transcriptional levels of XPO6 andXPO7 in LUAD and LUSC were higher than that in normal tissues. Immunohistochemical staining revealed that XPO6 and XPO7 were low and moderate expression in lung cancer tissues, but not in normal tissues. Survival analysis exhibited that the high expressions of XPO6 and XPO7 were associated with worse OS and FP in LUAD and LUSC patients. Based on these results, we speculate that XPO6 / 7 may be a potential biomarker for NSCLC patients, but it still needs further study.

Furthermore, it was found that exportins had high mutation rate (50.48%) in LUAD and LUSC patients. The highest mutation was mRNA up-regulation, and a co-occurring relationship was found between differentially expressed exportins, which indicated that these genes play a synergistic role in the occurrence and development of LUAD and LUSC. A network of co-expression genes significantly associated with exportins mutations was constructed, and then their functions were analyzed through GO enrichment analysis and KEGG pathway enrichment. Our studies have found that the functions of these proteins are mainly related to cell proliferation, cell division, regulation of signal transduction by p53 class mediator, cell cycle, and so on. These functions are significantly related to the occurrence and development of tumors. These results provide clues for the rational development of multi-target and exportins mediated targeted therapy.

Recent data suggested that, as an important determinant of prognosis and response to immunotherapy immune cell ⁵⁹, infiltration may affect tumor progression and recurrence⁶⁰. Our study deeply understood the infiltration of six immune cells (B cells, CD8 + T cells, CD4 + T cells, macrophages, neutrophils and dendritic cells) related to exportins. The expression levels of exportins are related to the levels of immune infiltration in LUAD and LUSC, which can reflect the immune status indicators of LUAD and LUSC patients.

We also sought to characterize the transcription miRNA targets of the differentially expressed exportins, and found that several miRNAs (mir-433, mir-26, miR-181 and mir-452) were associated with differentially expressed exportins. These small noncoding RNAs mainly promote tumor growth, invasion, angiogenesis and immune escape by controlling the expression of their target mRNAs. Moreover, tumor miRNA distribution can define related subtypes, patient survival and treatment response, and it plays an important role in cancer biology⁶¹. It was found that the decreased miR433 expression was strongly associated with the tumor node metastasis stage and lymph node metastasis of patients with NSCLC⁶². MiR433 reduces cell proliferation and invasive phenotype in NSCLC patients by mediating Smad2 and Id-1⁶³. Furthermore, miR-26, miR-181 and miR-452 also play an important role in EMT, proliferation, invasion and metastasis of lung cancer cells^64–66. Our analysis suggested that differentially expressed exportins may play a role through these miRNAs to regulate the occurrence and development of LUAD and LUSC.

By mining the TF targets of differentially expressed exportins, it was obvious that E2F1 may be the key TF regulated by exportins. E2F1 is one of the key links in the cell cycle regulation network ⁶⁷. It is a central player involved in cell cycle progression, DNA-damage response, and apoptosis⁶⁸. Some studies have proved that E2F1 promotes EMT by regulating ZEB2 in SCLC⁶⁹. In SCLC cells, ILF2 interacts with E2F1 and regulates the transcriptional activity of E2F1, and it plays a carcinogenic role⁷⁰. Our analysis revealed that E2F1 is a promising regulatory target of exportins. Differentially expressed exportins may regulate cell genesis and development by interacting with E2F1 in LUAD and LUSC.

This study systematically demonstrated the association between exportins and LUAD and LUSC for the first time. Meanwhile, the study proved that the transcriptional and protein expression levels of CSE1L and XPO1 / 5 / 6 / 7 were increased in LUAD and LUSC patients, and the increased transcriptional levels of CSE1L and XPO5 / 6 / 7 were associated with worse prognosis. The increased transcriptional level of XPO1 suggested a better prognosis. These results suggested that CSE1L and XPO1 / 5 / 6 / 7 may be potential prognostic biomarkers for the survival of patients with LUAD and LUSC. Furthermore, it was found that the expression of exportins was significantly correlated with the infiltration of a variety of immune cells. Differentially expressed exportins regulate the occurrence and development of LUAD and LUSC by involving a variety of miRNAs and transcription factor E2F1. Our study may provide novel insights for the selection of prognostic biomarkers of exportins in LUAD and LUSC. However, this study has some limitations. More experimental studies are needed to confirm our results, so as to promote the clinical application of exportins as potential biomarkers of LUAD and LUSC, because all information is obtained from public databases.

NSCLC	Non small cell lung cancer
HPA	Human Protein Atlas
OS	Overall survival
FP	Progression-free survival
SCLC	Small cell Lung cancer
LUAD	Lung adenocarcinoma
LUSC	Lung squamous cell carcinoma
Kap-β	Karyopherin β
NF-κB	Nuclear factor-κB
MAPK	Mitogen-activated protein kinase
TCGA	The Cancer Genome Atlas
GO	Gene Ontology
KEGG	Kyoto Encyclopedia of Genes and Genomes
GSEA	Gene Set Enrichment Analysis
CAN	Copy number alterations
CC	Cellular component
MF	Molecular function
BP	Biological process
PPI	Protein-protein interaction
TF	Factor Targets
EMT	Epithelial mesenchymal transition

Acknowledgements

Not applicable.

Authors' contributions

M. P. and P.H. designed the study. M. P. , P.H. and L.L. collected data. All authors contributed to data analysis, drafting of the paper and manuscript revisions and agree to be accountable for all aspects of the work.

Funging

this work was supported by the Guangxi Medical and health key discipline construction project.

Data availability

The data that support the findings of this study are available from the ONCOMINE (www.ONCOMINE.org), UALCAN (http://ualcan.path.uab.edu/), Human Protein Atlas(https://www.proteinatlas.org/),Kaplan–Meier Plotter (https://kmplot.com/analysis/index.php?p=service&cancer=lung),cBioPortal(https://www.cbioportal.org/), STRING (Version: 11.5) (https://cn.string-db.org/), DAVID (https://david.ncifcrf.gov/tools.jsp),TIMER(https://cistrome.shinyapps.io/timer/),and LinkedOmics(http://www.linkedomics.org/login.php).

Competing interests

The authors declare no competing interests.

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Table 1 Remarkable changes of exportins expression in transcription level between LUAD，LUSC and normal lung tissues (ONCOMINE).

Gene	Type of Lung Cancer versus Normal Lung Tissue	Fold Change	p- Value	t -Test	Source and/or Reference
XPO1	LUAD	1.375	1.09E-09	6.704	Hou Lung[1]
	LUSC	2.828	6.44E-09	7.792	Hou Lung[1]
	LUSC	1.179	3.11E-39	14.842	TCGA Lung 2
	LUAD	1.043	1.78E-12	7.296	TCGA Lung 2
	LUAD	1.35	1.95E-05	4.689	Stearman Lung[2]
CSE1L	LUSC	2.51	1.54E-17	13.493	Hou Lung[1]
	LUAD	1.487	2.01E-08	6.38	Hou Lung[1]
	LUSC	7.468	7.67E-06	4.994	Bhattacharjee Lung[3]
	LUAD	1.566	0.065	1.588	Bhattacharjee Lung[3]
	LUSC	3.497	5.78E-05	5.227	Garber lung[4]
	LUAD	2.29	0.001	4.431	Garber lung[4]
XPOT	LUAD	1.075	0.382	0.304	Bhattacharjee Lung[3]
	LUSC	4.078	9.25E-05	4.166	Bhattacharjee Lung[3]
	LUSC	2.527	5.04E-13	11.371	Hou Lung[1]
	LUAD	1.572	3.86E-11	7.771	Hou Lung[1]
	LUAD	1.768	0.002	3.571	Garber Lung[4]
XPO5	LUSC	2.044	1.72E-11	9.733	Hou Lung[1]
	LUAD	1.684	1.63E-10	7.593	Hou Lung[1]
	LUSC	2.161	0.001	3.977	Garber Lung[4]
	LUAD	2.126	0.002	4.505	Garber Lung[4]

Table 2 The Prognostic values of exportins in LUAD and LUSC patients (Kaplan-Meier plotter).

Exportins	Histology	OS				FP
		Cases	HR	95% CI	p-Value	Cases	HR	95% CI	p-Value
XPO1	Overall	1,925	0.79	0.68–0.92	0.0021	982	0.54	0.43–0.71	1.00E-06
208775_at	LUAD	719	0.27	0.19–0.38	1.50E-15	461	0.55	0.4-0.75	0.00017
	LUSC	524	1.07	0.84-1.35	0.6	141	1.89	1.12-3.2	0.016
CSE1L	Overall	1,925	1.19	1.05–1.35	0.0074	982	1.24	0.99–1.55	0.058
210765_at	LUAD	719	1.35	1.04-1.73	0.021	461	0.7	0.51-0.96	0.026
	LUSC	524	0.58	0.66-1.08	0.19	141	1.67	1-2.78	0.048
XPOT	Overall	1,925	1.18	1.04-1.35	0.014	982	0.74	0.6-0.9	0.0025
212160_at	LUAD	719	0.58	0.46-0.74	4.60E-06	461	0.69	0.5-0.95	0.022
	LUSC	524	0.89	0.67-1.18	0.42	141	1.79	1.05-3.05	0.031
XPO4	Overall	1,925	1.44	1.25-1.68	2.90E-07	982	1.77	1.45-2.18	2.80E-08
218479_s_at	LUAD	719	0.86	0.68-1.08	0.19	461	1.59	1.15-2.2	0.0051
	LUSC	524	1.34	1.04-1.72	0.025	141	2.42	1.34-4.36	0.0025
XPO5	Overall	1,144	1.9	1.6-2.27	3.80E-13	596	1.74	1.32-2.28	5.90E-05
223055_s_at	LUAD	672	1.76	1.38-2.24	3.40E-06	443	1.9	1.38-2.63	7.40E-05
	LUSC	271	1.59	1.09-2.32	0.014	141	1.61	0.97-2.69	0.065
XPO6	Overall	1,925	1.19	1.03-1.37	0.02	982	1.68	1.39-2.04	8.10E-08
214784_x_at	LUAD	719	1.47	1.12-1.93	0.0053	461	1.66	1.22-2.28	0.0013
	LUSC	524	0.82	0.63-1.07	0.14	141	1.47	0.84-2.56	0.17
XPO7	Overall	1925	1.71	1.45-2.02	1.20E-10	982	1.96	1.61-2.38	8.90E-12
208459_s_at	LUAD	719	2.24	1.76-2.84	1.10E-11	461	2.04	1.47-2.83	1.30E-05
	LUSC	524	0.84	0.64-1.1	0.21	141	1.54	0.9-2.63	0.11

Table 3 The miRNAs target networks of exportins in LUAD and LUSC (LinkedOmics).

Gene	Enriched miRNA Target	Leading EdgeNum	p-Value
XPO1	ATCATGA,MIR-433	45	0
	ATTCTTT,MIR-186	75	0
	CATGTAA,MIR-496	47	0
CSE1L	ATAACCT,MIR-154	20	0
	GAGCCTG,MIR-484	37	0
	ATCATGA,MIR-433	35	0
XPOT	AGTCTTA,MIR-499	26	0
	TACTTGA,MIR-26A,MIR-26B	69	0
	TAGCTTT,MIR-9	63	0.003
XPO4	TGAATGT,MIR-181A,MIR-181B,MIR-181C,MIR-181D	156	0
	CAGTGTT,MIR-141,MIR-200A	112	0
	TACTTGA,MIR-26A,MIR-26B	109	0
XPO5	GTTTGTT,MIR-495	82	0
	GTGGTGA,MIR-197	28	0
	GAGACTG,MIR-452	42	0
XPO6	CAGCAGG,MIR-370	43	0
	AGGGCCA,MIR-328	25	0
	CACTGCC,MIR-34A,MIR-34C,MIR-449	94	0
XPO7	CTTGTAT,MIR-381	75	0
	TGAATGT,MIR-181A,MIR-181B,MIR-181C,MIR-181D	180	0
	GAGACTG,MIR-452	27	0

Table 4 The transcription factors target networks of exportins in LUAD and LUSC (LinkedOmics).

Gene	Enriched Transcription Factor Target	Leading EdgeNum	p-Value
XPO1	V$E2F_Q4	86	0
	V$E2F_Q6	86	0
	V$E2F1_Q6	84	0
CSE1L	V$E2F_Q6	99	0
	V$E2F1DP1_01	93	0
	V$E2F1DP2_01	93	0
XPOT	V$E2F_Q4	82	0
	V$E2F_Q6	82	0
	V$E2F1_Q6	85	0
XPO4	V$NFMUE1_Q6	66	0
	V$HTF_01	17	0
	V$STAT5A_04	71	0
XPO5	V$E2F_Q4	107	0
	V$E2F_Q6	108	0
	V$E2F1_Q6	108	0
XPO6	V$E2F_Q6	86	0
	V$E2F_Q4	86	0
	V$E2F1DP1_01	86	0
XPO7	V$HTF_01	17	0
	V$RREB1_01	45	0
	V$E2F_Q6	62	0

No competing interests reported.

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Comprehensive bioinformatics analysis on exportins in lung adenocarcinoma and lung squamous cell carcinoma

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Abstract

Background

Methods

Results

Conclusions

Figures

Introduction

Materials And Methods

ONCOMINE analysis

UALCAN analysis

Human Protein Atlas (HPA) analysis

Kaplan–Meier Plotter analysis

cBioPortal analysis

PPI network construction and module analysis

DAVID analysis

TIMER analysis

LinkedOmics analysis

Results

Transcription Levels and Protein Expression of Exportins in Patients with LUAD and LUSC

Prognostic Characteristics of Exportins Members in Patients with LUAD and LUSC

The Genetic Alteration and Mutation Information of Exportins

GO and KEGG Enrichment Analysis of Exportins and Their Co-expression Genes in LUAD and LUSC Patients

The Relationship Between Exportins Expression Levels and Immune Infiltration Levels in LUAD and LUSC

Analysis of Transcription Factor and miRNA Targets of Exportins in Patients with LUAD and LUSC

Discussion

Conclusions

Abbreviations

Declarations

References

Tables

Additional Declarations

Supplementary Files

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