Abstract
Background
KPNA7 modulated cellular functions via RNA interaction and had a crucial part in post-transcriptional gene expression regulation. However, the potential function played by KPNA7 in pan-cancer remained unclear.
Methods
The Cancer Genome Atlas (TCGA) was performed to study the expression of KPNA7 in normal and cancer tissues. Kaplan-Meier survival analysis and spearman correlation analysis were used to evaluate the associations of KPNA7 expression with prognosis, immune cell infiltration, tumor mutational burden (TMB) and microsatellite instability (MSI). We further investigated the relationship between KPNA7 expression levels and sensitivity to anticancer drugs. Finally, the effects of KPNA7 on gastric cancer cell proliferation, clonogenicity, migration, invasion, apoptosis, cell cycle, and in vivo tumor growth were evaluated using CCK-8, colony formation, scratch, transwell, flow cytometry, and subcutaneous xenograft assays.
Results
Bioinformatic analyses showed that KPNA7 was overexpressed in multiple tumors and was associated with unfavorable survival in selected cancer types. KPNA7 expression was significantly correlated with immune-related features, including immune cell infiltration, immunomodulatory factors, TMB, and MSI in several cancers. In addition, KPNA7 expression was associated with anticancer drug sensitivity patterns. Functional experiments further showed that KPNA7 knockdown inhibited the proliferation, migration, and invasion of gastric cancer cells while promoting apoptosis.
Conclusion
These findings suggested that KPNA7 may serve as a potential biomarker with tumor-type-specific prognostic and immune-related relevance in cancer, providing new insights into its possible value in cancer biology and therapeutic stratification.
Supplementary Information
The online version contains supplementary material available at https://doi.org/10.1186/s12885-026-16456-7.
Keywords: KPNA7, Tumor immunity, Prognosis, Biomarker, Pan-cancer
Introduction
Malignant transformation represented a complex multistep process involving genomic instability, dysregulated signaling cascades, and microenvironmental reprogramming [1]. The immune system was closely related to tumors during disease progression and metastasis [2]. Cancer had specific tumor immune microenvironment (TIME) that demanded better targeted therapies [3, 4]. TIME had a significant connection with the clinical prognosis of tumor patients [5]. Notably, the state of the immune response was determined by the TIME in the tumor microenvironment (TME), which was critically dependent on the activity and composition of the infiltrating immune cells, as well as several related influences, including alterations in immune checkpoint molecules and cell surface expression. Tumor cells developed multiple strategies to avoid recognition and clearance by host immune cells, thereby evading immune attack and continuing cancer progression [6]. The TIME contained various immunosuppressive components, including regulatory T cells, tumor-associated macrophages, myeloid-derived suppressor cells, and immunosuppressive cytokines. This immunological landscape significantly influenced disease progression and therapeutic response, with immune checkpoint molecules like PD-1/PD-L1 playing pivotal roles in immune evasion mechanisms [7]. Among currently available biomarkers, tumor mutational burden (TMB) has emerged as one of the most extensively studied predictors of immunotherapy response, although its clinical utility remained constrained by methodological and biological challenges [8]. More recently, integrative predictive models such as THOR and LORIS had further improved the assessment of immunotherapy response and patient outcomes by incorporating clonal genomic, clinical, pathologic, and other genomic features [9, 10].
It was well known that nucleoplasmic transport served a crucial role in mediating cellular functions. In recent years, researchers have increasingly focused on the effects of nuclear translocation in malignant tumors in terms of diagnosis, treatment, and prognosis. In this context, the KPNA family stood out as a group of soluble transporter receptors [11]. KPNA consisted of a group of soluble transporter receptors with seven isoforms that recognized proteins containing nuclear localization signals with their own specificity [12]. KPNA subtypes correlated with cancer-specific changes and cancer biology [13, 14]. Altered nuclear translocation was an important player in cancer pathogenesis. KPNA7, which was highly expressed during human embryonic development, has been implicated in the regulation of mitosis by contributing to proper mitotic spindle organization and the maintenance of nuclear envelope integrity [15, 16]. Previous studies have shown that KPNA7 was associated with malignant phenotypes in pancreatic cancer and breast cancer, however, studies on KPNA7 in cancer were lacking [15, 17].
In this study, we examined the expression and prognosis of KPNA7 and further analyzed the immune infiltration and drug sensitivity of KPNA7 in pan-cancer based on bioinformatics techniques. In addition, we investigated the relationship between KPNA7 and immune checkpoint inhibitors (ICIs), TMB, and microsatellite instability (MSI). We further clarified the effects of KPNA7 on the proliferation, migration, invasion and apoptosis of gastric cancer cells by CCK8, colony formation, scratch, transwell, flow cytometry and subcutaneous tumor formation assay. It was intended to better dissect the value of KPNA7 on tumors and provided new insights for precision medicine.
Materials and methods
KPNA7 expression analysis
RNA-seq data for tumor and non-tumor tissues were retrieved from The Cancer Genome Atlas (TCGA) database (http://cancergenome.nih.gov) [18]. The expression levels of KPNA7 in different cancer tissues were acquired utilizing the “Gene_DE” module within TIMER 2.0 (http://timer.comp-genomics.org/timer/) [19]. The association between KPNA7 expression and tumor staging was assessed through the Gene Expression Profiling Interactive Analysis (GEPIA) database (http://gepia.cancer-pku.cn/) [20].
Survival analysis
The correlation between KPNA7 expression and the overall survival (OS) of cancer patients was investigated using data from the TCGA database. Forest plots and Kaplan-Meier survival curves were generated using the ggplot2 package.
Immune infiltration and immunotherapy analysis
We downloaded the standardized data set from the UCSC (https://xenabrowser.net/) database and used the deconvo_quantiseq method, econvo_epic method, timer method and deconvo_CIBERSOR method of the IOBR package to evaluate the infiltration score of tumor patients. In addition, we studied the link between KPNA7 and immune checkpoints in tumors through spearman analysis.
In our study, we utilized the “Biomarker Evaluation” module to make a comparison of the biomarker value of KPNA7 and other previously reported biomarkers using the TIDE database (http://tide.dfci.harvard.edu/) [21]. Additionally, we utilized the “Regulator Prioritization” module of the TIDE database to analyze the relationship between KPNA7 and T cell dysfunction value as well as immunotherapy in diverse cohorts.
Mutation analysis, MSI analysis and drug sensitivity analysis
TMB data originated from Vesteinn Thorsson et al.‘s study [22], while MSI data were extracted from Russell Bonneville et al.‘s research [23]. We used the Clinical Bioinformatics Home online platform (https://www.aclbi.com/static/index.html) to analyze the relationship between TMB/MSI and KPNA7 expression. Sensitivity and resistance data for anticancer drugs targeting KPNA7 were retrieved from the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org/) [24]. Three-dimensional conformations of compounds were obtained from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) [25].
Protein-protein interaction and co-expression analysis
The proteins that interacting with KPNA7 were retrieved from the GeneMANIA database (http://genemania.org/) [26], and further analyzed the interaction via the String database (https://cn.string-db.org/) [27]. Co-expression patterns were visualized by using the ggplot2 package to create a co-expression heatmap.
Enrichment analysis
The ID conversion for PPI molecules was performed using the org.Hs.eg.db package. GO and KEGG joint logFC enrichment analysis was conducted using the clusterProfiler package. The z-score values for each enrichment entry were computed using the GOplot package. Differential analysis was conducted on the raw counts matrix using the DESeq2 package and the edgeR package, with a filtering criterion of Padj < 0.05. The msigdbr package was utilized to extract gene sets from the MSigDB Collections database (https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp) [28], specifically c2.cp.all.v2022.1.Hs.symbols.gmt. Gene Set Enrichment Analysis (GSEA) was performed using the clusterProfiler package. The CancerSEA database (http://biocc.hrbmu.edu.cn/CancerSEA/home.jsp) was employed to probe into the biological procedures that KPNA7 participated in regulating.
Cell culture
All cell lines (CellCook, China) were cultured using complete medium containing 10% fetal bovine serum (FBS). High-glucose culture media included DMEM (GES and MGC-803), F12K medium (AGS), and RPMI-1640 medium (HGC-27, MKN-45, BGC-823, and MKN-28).
RNA extraction and RT-PCR
Total RNA from cells and tissues was extracted using TriQuick Reagent (Solarbio, China) following the provided instructions. Subsequent reverse transcription (RT) and RT-qPCR were conducted. The primer sequences used were as follows: GAPDH: Forward Primer: GGTCGGAGTCAACGGATTTG; Reverse Primer: GGAAGATGGTGATGGGATTTC; KPNA7: Forward Primer: AAGTGGTTAACACGGGGGTC; Reverse Primer: GAGGGCTTGTTGTGTTGCAG.
Cell transfection
The transfection of LV-NC and LV-KPNA7 (LV-1 and LV-2, Genechem, China) into gastric cancer cells (AGS and HGC-27) was performed. Puromycin was used to select stable transfected cell lines. The LV-RNA sequences were shown in Supplementary Table 3.
CCK-8 and colony formation assay
Transfected cells were seeded into a 96-well plate, with 3 replicate wells for each group. The plate was kept away from light, and 10 µl of CCK-8 solution was added to each well. Following a 1-hour incubation in a cell culture incubator, the absorbance at 450 nm was determined.
Gastric cancer cells in the logarithmic growth phase were seeded into six-well plates at a density of approximately 500 cells per well according to the experimental groups. The cells were cultured at 37 °C in a humidified incubator with 5% CO₂ for 10–14 days until visible colonies had formed. The colonies were then fixed with 4% paraformaldehyde and stained with 0.1% crystal violet. After staining, the plates were air-dried at room temperature and photographed.
Cell scratch and transwell assays
For the scratch assay, cells were transfected for 24 h and then treated with Opti-MEM (BasalMedia, China). Images were captured with a digital microscope (Olympus, Japan) at 0 h, 12 h, and 24 h, and the wound healing rate was calculated. The transwell chamber (Corning, USA) was used to assess cell migration and invasion. Transfected cells suspended in 100 µl serum-free medium were seeded into the upper chamber (uncoated membrane for migration, pre-coated with matrix gel for invasion). 500 µl of DMEM containing 20% FBS was introduced in the lower chamber. Following a 24-hour incubation period, non-invading cells on the membrane’s upper surface were eliminated using a cotton swab. The residual cells were fixed with 4% paraformaldehyde, stained with a 0.1% crystal violet solution, and visualized using a digital microscope (Olympus, Japan) for the analysis of migration or invasion.
Flow cytometry
Flow cytometry was performed using the apoptosis kit (KGA1107-100) from KETGEN BioTECH, China. Incubate Annexin V-APC and PI for 5 min away from light according to the instructions. Cell cycle distribution was analyzed using a cell cycle detection kit (CCS012) from MULTI SCIENCES Biotech, China. Cells were then incubated with PI for 30 min. On-line analysis was conducted using an Agilent flow cytometer.
Subcutaneous tumor formation assay
Four-week-old SPF-grade BALB/c-nu mice were purchased from Chengdu Yaokang Biotechnology Co., Ltd (SCXK (Chuan) 2025-0034) and housed under SPF conditions at the Laboratory Animal Center of the Lanzhou University. Experiments were performed after one week of acclimatization. All procedures were approved by the Institutional Animal Care and Use Committee of the First Hospital of Lanzhou University (LDYYLL2025-470). BALB/c nude mice were utilized to establish a subcutaneous xenograft model of gastric cancer. Cells from each experimental group (3 × 10^6 cells per injection) were subcutaneously inoculated into the mice. After 3–4 weeks, the mice were sacrificed, and tumor size and tumor weight were measured. Before tissue collection, mice were anesthetized with 4% isoflurane in oxygen and euthanized by intraperitoneal injection of sodium pentobarbital at 150 mg/kg. Death was confirmed by the absence of spontaneous respiration, corneal reflex, and heartbeat. The 3R principles were followed throughout the study to minimize animal suffering.
Statistical analysis
Kaplan-Meier survival curves were compared using the log-rank test. Cox regression analysis was used to estimate hazard ratios (HRs). Spearman correlation analysis was used to evaluate the relationships between KPNA7 and immune cell infiltration, TMB, MSI, drug sensitivity, chemokines, and immune checkpoint-related genes. Quantitative results were shown as mean ± SD based on at least three independent biological replicates. Differences between two groups were analyzed using the unpaired Student’s t-test, whereas comparisons among multiple groups were assessed by one-way ANOVA followed by Tukey’s multiple-comparison test. All statistical analyses were carried out with GraphPad Prism 8.0 (GraphPad Software). Statistical significance was assigned as follows: *for 0.01 < p < 0.05, ** for 0.001 < p < 0.01, and *** for p < 0.001.
Results
Expression and prognosis of KPNA7 in pan-Cancers
We utilized the TCGA database to investigate the expression of KPNA7 across various cancers (11/33), revealing elevated expression in multiple cancer types (Fig. 1A). Subsequently, the TIMER database was employed to assess KPNA7 expression in cancers, demonstrating significant upregulation in LUAD, LIHC, ESCA, and STAD, among others (Fig. 1B). In addition, the GEPIA database revealed that KPNA7 expression was associated with clinicopathologic staging of KIRC, COAD, ESCA, BLCA, KIRP, PAAD, and THCA (Fig. 1C-I). Based on single-cell sequencing, the T-SNE plot showed that KPNA7 was highly expressed in GBM, BRCA and OV (Fig. 1J).
Fig. 1.
Differential expression of KPNA7 in pan-cancer. A Expression of KPNA7 in in TCGA database. B Expression of KPNA7 in timer database. C-I Relationship between expression of KPNA7 and tumor stage in GEPIA database. J The expression of KPNA7 in BRCA, GBM, and OV in CancerSEA database. *p < 0.05, **p < 0.01, ***p < 0.001
The relationship between KPNA7 expression and the prognosis of cancer patients was explored by constructing OS forest plots and Kaplan-Meier survival curves (Fig. 2A). The results demonstrated that a high expression level of KPNA7 was related to a less favorable prognosis in patients diagnosed with GBM (Fig. 2B), KIRC (Fig. 2C), KIRP (Fig. 2D), PAAD (Fig. 2E), and STAD (Fig. 2F).
Fig. 2.
Survival analysis of KPNA7 in pan-cancer. A. Relationship between KPNA7 expression and OS. B-F. KM curves of OS with significance in 5 cancer types (GBM, KIRC, KIRP, PAAD, STAD). Overall survival: OS, Kaplan-Meier: KM
Correlation between KPNA7 and immune cell infiltration, chemokines, chemokine receptors and immune checkpoints
We examined the relationship between KPNA7 expression and immune cell infiltration using four independent algorithms, including QUANTISEQ (Fig. 3A), EPIC (Fig. 3B), TIMER (Fig. 3C), and CIBERSORT (Fig. 3D). Across multiple cancer types, KPNA7 expression showed relatively prominent positive correlations with several immune cell populations, particularly neutrophils, macrophages, CD4 + T cells, and CD8 + T cells. These findings suggested that KPNA7 may be associated with remodeling of the tumor immune microenvironment, involving both myeloid cell infiltration and T-cell-related immune responses. To further explore the potential immunomodulatory relevance of KPNA7, we analyzed its correlations with chemokines, chemokine receptors, and immune checkpoint-related genes across 33 cancer types. Several chemokines, including CXCL5, CXCL6, CXCL8, and CXCL16 (Fig. 4A), as well as chemokine receptors, including CCR3, CCR6, CXCR1, and CXCR2 (Fig. 4B), were positively correlated with KPNA7 expression in various cancers. These associations suggested that KPNA7 may be linked to chemokine-mediated immune cell recruitment and immune microenvironment remodeling. In addition, KPNA7 expression was inversely correlated with CTLA4 and PDCD1 in most cancer types, whereas positive correlations were observed in STAD, HNSC, and TGCT (Fig. 4C), indicating that the relationship between KPNA7 and immune checkpoint-related features may be tumor context-dependent.
Fig. 3.
Immune infiltration analysis. A QUANTISEQ infiltration score. B EPIC infiltration score. C TIMER infiltration score. D CIBERSORT infiltration score. *p < 0.05, **p < 0.01, ***p < 0.001
Fig. 4.
Correlation of KPNA7 expression with chemokines, chemokine receptors, immune checkpoints, TMB and MSI in pan-cancer. A chemokines. B chemokine receptors. C immune checkpoints. D TMB. (E) MSI. *p < 0.05, **p < 0.01, ***p < 0.001
Correlation between KPNA7 and TMB and MSI
TMB and MSI served as promising biological markers for forecasting the efficacy of cancer immunotherapy [29, 30]. Our results showed that there was a negative association between KPNA7 expression and TMB in COAD, READ, and STAD, conversely, a positive correlation was detected in ESCA, HNSC, LGG, LUAD, and PAAD (Fig. 4D and Supplementary Table 1). Additionally, KPNA7 expression showed a negative correlation with MSI in STAD and UCS (Fig. 4E and Supplementary Table 2). These findings suggested that the relationship between KPNA7 and TMB/MSI was clearly cancer-type-specific rather than showing a uniform directional pattern across different tumors.
KPNA7 mutation analysis
Figure 5A-B showed the genetic alteration status of KPNA7 in patient samples. The main alteration type of KPNA7 in tumors was “amplification”, referring to copy number alteration (CNA). Figure 5C showed the numerous mutation sites of KPNA7 in the TCGA cohort, highlighting the sites with the highest alteration frequency of KPNA7 (D167G/Y). We analyzed the main types of somatic mutations of KPNA7 through the COSMIC database. The results suggested that the main type of somatic mutation in KPNA7 was missense substitution (38.21%), among which the main bases of missense substitution were G> A (28.83%), C> T (26.64%), and C> A (10.95%) (Fig. 5D). The cbioportal database also proved that KPNA7 mainly underwent missense mutations (Fig. 5E). In addition, the alteration of the gene copy number of KPNA7 mainly occurred in diploid (Fig. 5F).
Fig. 5.
Analysis of mutation feature of KPNA7 in pan-cancer. A Genetic alteration in KPNA7 in pan-cancer tissues, accounting for 1.1% of alterations. The alteration frequency of KPNA7 with mutation types in pan – cancer in cbioportal database (B) and COMSIC database (D). C The mutation site of KPNA7. E The mutation count of KPNA7. F The copy number alterations of KPNA7
Immunotherapy response and sensitivity analysis of KPNA7 to anti-tumor drugs
A comparative analysis of the biomarker value of KPNA7 and other published biomarkers was carried out, concentrating on their predictive capabilities for response efficacy and OS. KPNA7 had an AUC value > 0.5 in 8 studies among 25 studies, which was similar to TMB and preferable to B.clonality and T.clonality (Fig. 6A). In addition, we investigated the correlation between KPNA7 and immunotherapy. KPNA7 was found to be highly expressed in the ICB_Mariathasan2018_PD-L1 cohort, whereas KPNA7 exhibited low expression in the TCGA Endometrial cohort as determined by normalized Z score calling from Cox-PH regression in the immunotherapy dataset (Fig. 6B). Besides, we conducted an analysis of the sensitivity of KPNA7 to anti-tumor drugs using the GSCA online database. We selected the top 10 ranked anti-tumor drugs to illustrate their correlation with KPNA7 expression. As depicted in the Fig. 6C, the results indicated a positive correlation between CHIR-99,021, Cisplatin, Talazoparib, Olaparib, Bleomycin (50 µM), SN-38, and KPNA7. Conversely, KIN001-055, AICAR, Afatinib, and Gefitinib showed a negative correlation with KPNA7. The three-dimensional conformations of these six small-molecule compounds were then visualized using the PubChem website (Fig. 6D).
Fig. 6.
Screening drugs for cancer therapy. (A) The biomarker relevance of KPNA7 in comparison with standardized biomarkers regarding tumor immune evasion within ICB therapy cohorts. (B) Analyses of the associations between KPNA7 and T cell dysfunction as well as outcomes when using ICB treatment. (C) Drug sensitivity analysis. (D-I) The corresponding three-dimensional structures were shown in (D) Afatinib, (E) Talazoparib, (F) 2-6-((2-((4-(2-4-Dichlorophenyl)-5-(4-methyl-1 H-imidazol, (G) Olaparib, (H) Gefitinib, and (I) Acadesine, respectively
Protein-protein interaction and enrichment analysis of KPNA7
We explored the interaction between KPNA7 and proteins using the GEPIA and STRING online databases. The results from both databases indicated a close relationship between KPNA7 and nine proteins (KPNA4, KPNA1, RANBP6, IPO5, IPO4, CSE1L, KPNB1, KPNA5, and KPNA3) (Supplementary Fig. 1A-B). Subsequently, we performed co-expression analysis of KPNA7 and these nine proteins in gastric cancer. The results revealed a positive correlation between KPNA7 and KPNA4, KPNA1, IPO4, and CSE1L (Fig. 7A). Furthermore, GO and KEGG joint logFC enrichment analysis indicated that KPNA7 and its interacting proteins primarily regulate nucleocytoplasmic transport processes (Fig. 7B-C; Table 1). GSEA enrichment analysis of differentially expressed genes regulated by KPNA7 (Padj<0.05) showed significant enrichment in immune tumor migration and invasion (Fig. 8A-E). Intriguingly, we found that KPNA7 was positively correlated with STAT3 expression in 17 cancers, including ACC, BLCA, BRCA, and CESC, and the opposite in DLBC (Fig. 8F). In addition, it was found that KPNA7 was related to inflammatory responses in BRCA (Fig. 8G), related to migration and invasion in GBM (Fig. 8H), and related to invasion in OV based on single-cell sequencing data (Fig. 8I).
Fig. 7.
Co-expression analysis and enrichment analysis. (A) Co-expression analysis of KPNA7 and its reciprocal proteins. (B-C) Enrichment analysis of GO-KEGG combined with logFC
Table 1.
Detailed information of GO and KEGG joint logFC enrichment analysis
| Ontology | ID | Description | GeneRatio | BgRatio | pvalue | p.adjust | zscore |
|---|---|---|---|---|---|---|---|
| BP | GO:0006606 | protein import into nucleus | 10/10 | 159/18,800 | 1.41e-21 | 1.1e-19 | 1.2649 |
| BP | GO:0051170 | import into nucleus | 10/10 | 163/18,800 | 1.82e-21 | 1.1e-19 | 1.2649 |
| BP | GO:0017038 | protein import | 10/10 | 210/18,800 | 2.44e-20 | 9.84e-19 | 1.2649 |
| BP | GO:0006607 | NLS-bearing protein import into nucleus | 7/10 | 20/18,800 | 5.64e-20 | 1.71e-18 | 1.1339 |
| BP | GO:0034504 | protein localization to nucleus | 10/10 | 294/18,800 | 7.51e-19 | 1.82e-17 | 1.2649 |
| CC | GO:0031074 | nucleocytoplasmic transport complex | 5/10 | 11/19,594 | 4.83e-15 | 1.11e-13 | 2.2361 |
| CC | GO:0005635 | nuclear envelope | 6/10 | 479/19,594 | 4e-08 | 4.6e-07 | 1.633 |
| CC | GO:0005643 | nuclear pore | 4/10 | 93/19,594 | 9.77e-08 | 7.49e-07 | 1 |
| CC | GO:0018995 | host cellular component | 1/10 | 10/19,594 | 0.0051 | 0.0234 | 1 |
| CC | GO:0043657 | host cell | 1/10 | 10/19,594 | 0.0051 | 0.0234 | 1 |
| MF | GO:0140142 | nucleocytoplasmic carrier activity | 10/10 | 30/18,410 | 2.44e-29 | 4.15e-28 | 1.2649 |
| MF | GO:0061608 | nuclear import signal receptor activity | 9/10 | 19/18,410 | 1.38e-27 | 1.17e-26 | 1 |
| MF | GO:0008139 | nuclear localization sequence binding | 9/10 | 27/18,410 | 7.01e-26 | 3.97e-25 | 1 |
| MF | GO:0140104 | molecular carrier activity | 10/10 | 73/18,410 | 5.05e-25 | 2.15e-24 | 1.2649 |
| MF | GO:0005048 | signal sequence binding | 9/10 | 50/18,410 | 3.74e-23 | 1.27e-22 | 1 |
| KEGG | hsa03013 | Nucleocytoplasmic transport | 9/9 | 108/8164 | 8.85e-18 | 3.54e-17 | 1.6667 |
| KEGG | hsa05207 | Chemical carcinogenesis - receptor activation | 6/9 | 212/8164 | 2.25e-08 | 4.5e-08 | 1.633 |
| KEGG | hsa05132 | Salmonella infection | 4/9 | 249/8164 | 9.43e-05 | 0.0001 | 2 |
| KEGG | hsa05164 | Influenza A | 3/9 | 171/8164 | 0.0007 | 0.0007 | 0.57735 |
Fig. 8.
Pathways involved in the regulation of KPNA7. A GO-KEGG enrichment analysis. B-E GSEA enrichment analysis. F Correlation analysis of KPNA7 and STAT3 in pan-cancer. G-I Biological processes regulated by KPNA7 in BRCA, GBM, and OV
Experimental validation of the impact of KPNA7 on the malignant phenotype in gastric cancer cells
Through RT-PCR experiments, we observed elevated expression of KPNA7 in gastric cancer cells (Fig. 9A). The downregulation of KPNA7 in gastric cancer cell lines (AGS and HGC-27) was confirmed (Fig. 9B). Scratch assays indicated that the downregulation of KPNA7 inhibited the migration of gastric cancer cells (AGS and HGC-27) (Fig. 9C), and transwell experiments demonstrated that the downregulation of KPNA7 suppressed the migration and invasion of gastric cancer cells (AGS and HGC-27) (Fig. 9D-E). Flow cytometry revealed that down-regulation of KPNA7 promoted apoptosis in gastric cancer cells. (Fig. 9F). Besides, flow cytometry revealed that down-regulation of KPNA7 reduced S phase and up-regulated G2 phase (Fig. 10A). CCK8 experiments and colony formation assay revealed that the suppression of KPNA7 inhibiting the proliferation of gastric cancer cells (AGS and HGC-27) (Fig. 10B-D). In vivo experiments demonstrated that down-regulation of KPNA7 inhibited the growth of subcutaneous tumors in nude mice (Fig. 10E).
Fig. 9.
KPNA7 knockdown suppressed metastasis and promotes apoptosis in gastric cancer cells. A KPNA7 was highly expressed in gastric cancer cells. B Down-regulation of KPNA7 in gastric cancer cells. C Scratch assays showed that down-regulation of KPNA7 inhibited migration in gastric cancer cells. D-E Transwell assay showed that down-regulation of KPNA7 inhibited migration and invasion in gastric cancer cells D migration, E invasion. F Downregulation of KPNA7 promotes apoptosis in gastric cancer cells. The scale bar indicated 50 μm
Fig. 10.
KPNA7 knockdown regulates cell cycle progression and inhibits proliferation in gastric cancer cells. A Flow cytometry for cell cycle detection. B-D CCK8 assay and colony formation assay showed that down-regulation of KPNA7 inhibited proliferation of gastric cancer cells. E Subcutaneous tumor formation in nude mice. The scale bar indicated 1 cm
Discussion
In this study, we performed an extensive bioinformatics analysis to systematically evaluate the clinical significance of KPNA7 across cancers. Our analyses showed that high KPNA7 expression was associated with unfavorable survival in several cancer types, including GBM, KIRC, KIRP, PAAD, and STAD. However, the prognostic value of KPNA7 should be interpreted with caution, as OS in cancer patients is influenced by multiple clinical and biological factors, and gene expression alone was unlikely to directly determine survival outcomes. Consistent with this, our pan-cancer analysis showed that KPNA7 expression was not significantly associated with OS in most cancer types. Therefore, KPNA7 should not be regarded as a universal prognostic biomarker across cancers, but rather as a potential biomarker with tumor-type-specific prognostic relevance.
Nucleocytoplasmic transport proteins play important roles in cellular function and cancer biology, including the regulation of PD-L1 expression and tumor immune evasion [31, 32]. Immune cell infiltration is a major determinant of the tumor microenvironment and plays an important role in tumor initiation and progression [33]. Immune evasion is closely linked to tumor development and is shaped by complex interactions among inflammatory signals, stromal components, immunosuppressive cells, and tumor–immune crosstalk [6, 34, 35]. At present, the role of KPNA7 in tumor immunity remains insufficiently understood. In our study, KPNA7 expression was associated with immune checkpoint-related features in multiple tumors, suggesting a potential link between KPNA7 and tumor immune regulation. Among the immune cell populations analyzed, KPNA7 showed the strongest positive correlations with neutrophils, macrophages, CD4 T cells, and CD8 T cells across multiple cancer types. In addition, KPNA7 expression was positively associated with several chemokines and chemokine receptors, including CXCL5, CXCL6, CXCL8, CXCL16, CCR3, CCR6, CXCR1, and CXCR2, suggesting that KPNA7 may be involved in immune cell recruitment and remodeling of the tumor immune microenvironment. Together, these findings supported a potential role of KPNA7 in shaping an immune-regulatory and possibly immunosuppressive microenvironment.
Immunotherapy has become an essential component of modern cancer treatment [36, 37]. However, not all patients derive durable clinical benefit. Our immunotherapy-related analyses suggested that KPNA7 may be associated with response to immune checkpoint blockade. Specifically, TIDE-based biomarker evaluation and immunotherapy cohort analysis indicated that KPNA7 expression was linked to immunotherapy-related features, supporting its potential relevance in the immunotherapeutic setting. However, the current study does not provide direct evidence that KPNA7 independently determines the response to any specific immune checkpoint inhibitor. Therefore, KPNA7 should currently be regarded as a potential immunotherapy-related biomarker rather than a validated predictor of response to a particular agent. Future studies using agent-specific clinical cohorts and mechanistic experiments will be necessary to determine whether KPNA7 directly influences sensitivity or resistance to specific immunotherapeutic drugs.
Afatinib and gefitinib were EGFR-targeted tyrosine kinase inhibitors with established clinical significance in EGFR-mutant non-small cell lung cancer [38]. In this study, we found that the sensitivity to both drugs was negatively correlated with KPNA7 expression, suggesting that KPNA7 may be involved in regulating the therapeutic response of tumor cells to afatinib and gefitinib. However, this finding is currently correlative and does not demonstrate a direct regulatory effect of KPNA7 on drug sensitivity. Future studies using drug-specific functional experiments and treatment cohorts with comprehensive clinical annotations will be required to further validate the predictive value and potential therapeutic relevance of KPNA7.
Our analyses further suggested that KPNA7 may have biological and clinical relevance in cancer, particularly in relation to immune regulation and therapeutic response. However, the associations between KPNA7 expression and TMB were not directionally consistent across cancer types, with positive correlations observed in some tumors and negative correlations in others. This heterogeneity indicated that the relationship between KPNA7 and tumor immunogenicity was context-dependent and may reflect differences in genomic background, immune microenvironment, and tumor evolution among cancer types.
According to the GSEA results, KPNA7 may participate in immune-related signaling pathways. Many processes involved in tumor immune regulation are mediated by the STAT signaling pathway, including tumor immune evasion. STAT1 and STAT2 act as key mediators of anti-tumor immunity by inducing type I and type II interferon responses [39]. STAT3 predominantly promotes cancer cell survival and contributes to persistent inflammation and immunosuppression in the tumor microenvironment [40]. In our study, KPNA7 showed a significant correlation trend with STAT3-related signaling, raising the possibility that KPNA7 may participate in tumor immune regulation through a STAT3-associated mechanism. However, this interpretation remains preliminary and requires further direct validation.
Given that KPNA7 knockdown significantly inhibited migration and invasion in gastric cancer cells, KPNA7 may also be functionally linked to key metastasis-related pathways. MMP1 and MMP2 are major mediators of extracellular matrix degradation and invasive behavior [41–44]. TGF-β is a central regulator of epithelial–mesenchymal transition, invasive plasticity, and pro-metastatic microenvironment remodeling [45, 46]. Metastatic progression is closely associated with cytoskeletal remodeling, biomechanical adaptation, extracellular matrix remodeling, and microenvironmental interactions [47, 48]. In this context, KPNA7, as a nucleocytoplasmic transport-related factor, may promote malignant progression by influencing signaling or transcriptional programs associated with protease expression, TGF-β-driven invasive processes, and metastatic microenvironment remodeling. Moreover, the subcutaneous xenograft assays further support the tumor-promoting role of KPNA7 at the phenotypic level. Further studies are required to define the downstream pathways through which KPNA7 contributes to tumor progression and metastasis.
Because the present pan-cancer analyses were primarily exploratory and based on publicly available datasets, potential confounding factors, including patient age, treatment history, and tumor stage, were not uniformly adjusted for across all cancer types. These unaccounted variables may have influenced the observed correlations. Overall, our study highlighted the multifaceted role of KPNA7 in cancer, including its potential prognostic relevance, immune modulatory effects, and therapeutic implications. These findings provided a basis for future studies aimed at elucidating the underlying mechanisms and further evaluating KPNA7 as a candidate target for cancer diagnosis and treatment.
Supplementary Information
Acknowledgements
We gratefully acknowledge the public databases used in this study for providing access to the data.
Authors' contributions
Conceptualization and writing, X.C. and A.Y.; Data analysis, X.C., H.W.; Literature search and extracted data, X.C., A.Y. H. Z. and W.S.; Supervision, Y.W. All authors contributed to the article and approved the submitted version.
Funding
This work was supported by the Natural Science Foundation of Gansu Province (26JRRA351), Gansu Provincial Department of Education: 2026 Graduate Innovation Star Project (2026CXZX-111), Lanzhou Talent Innovation and Entrepreneurship Program (2023-RC-29), the Foundation of The First Hospital of Lanzhou University, China (ZX-62000002-2025-555), Open Research Fund of the National Key Laboratory of Neural and Tumor Drug Development (SKLSIM-20251451).
Data availability
All data generated or analysed during this study are included in this published article. And the relevant data sources and analytical methods are described in the Materials and Methods section.
Declarations
Ethics approval and consent to participate
All animal experiments involved in this study were approved by the Ethics Committee of The First Hospital of Lanzhou University (LDYYLL2025-470).
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Weiming Sun, Email: ldyy_weimingsun@lzu.edu.cn.
Yuping Wang, Email: wangyuping@lzu.edu.cn.
References
- 1.Hanahan D. Hallmarks of cancer-Then and now, and beyond. Cell. 2026;189(8):2254–77. [DOI] [PubMed] [Google Scholar]
- 2.McGinnis CS, Miao Z, Superville D, Yao W, Goga A, Reticker-Flynn NE, Winkler J, Satpathy AT. The temporal progression of lung immune remodeling during breast cancer metastasis. Cancer Cell. 2024;42(6):1018–e10311016. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Glaviano A, Lau HS, Carter LM, Lee EHC, Lam HY, Okina E, Tan DJJ, Tan W, Ang HL, Carbone D, et al. Harnessing the tumor microenvironment: targeted cancer therapies through modulation of epithelial-mesenchymal transition. J Hematol Oncol. 2025;18(1):6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Wang Y, Zhou H, Ju S, Dong X, Zheng C. The solid tumor microenvironment and related targeting strategies: a concise review. Front Immunol. 2025;16:1563858. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Cords L, Engler S, Haberecker M, Rüschoff JH, Moch H, de Souza N, Bodenmiller B. Cancer-associated fibroblast phenotypes are associated with patient outcome in non-small cell lung cancer. Cancer Cell. 2024;42(3):396–e412395. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Tufail M, Jiang CH, Li N. Immune evasion in cancer: mechanisms and cutting-edge therapeutic approaches. Signal Transduct Target therapy. 2025;10(1):227. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Zhao X, Zhu Y, He Y, Gu W, Zhou Q, Jin B, Chen S, Lin H. Unraveling the immune evasion mechanisms in the tumor microenvironment of head and neck squamous cell carcinoma. Front Immunol. 2025;16:1597202. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Budczies J, Kazdal D, Menzel M, Beck S, Kluck K, Altbürger C, Schwab C, Allgäuer M, Ahadova A, Kloor M, et al. Tumour mutational burden: clinical utility, challenges and emerging improvements. Nat reviews Clin Oncol. 2024;21(10):725–42. [DOI] [PubMed] [Google Scholar]
- 9.Chang TG, Cao Y, Sfreddo HJ, Dhruba SR, Lee SH, Valero C, Yoo SK, Chowell D, Morris LGT, Ruppin E. LORIS robustly predicts patient outcomes with immune checkpoint blockade therapy using common clinical, pathologic and genomic features. Nat cancer. 2024;5(8):1158–75. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Wang Y, Guan Y, Lai X, Liu Y, Chang Z, Wang X, Wang Q, Liu J, Zhao J, Yang S, et al. THOR: a TMB heterogeneity-adaptive optimization model predicts immunotherapy response using clonal genomic features in group-structured data. Brief Bioinform. 2024;26(1):bbae648. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Mizuno K, Sugahara M, Kutomi O, Kato R, Itoh T, Fujita S, Yamada M. Direct observation of importin α family member KPNA1 in axonal transport with or without a schizophrenia-related mutation. J Biol Chem. 2025;301(4):108343. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Liang SH, Clarke MF. The nuclear import of p53 is determined by the presence of a basic domain and its relative position to the nuclear localization signal. Oncogene. 1999;18(12):2163–6. [DOI] [PubMed] [Google Scholar]
- 13.Hu Y, Zhou J, Ling X, Zhao K, Huang P, Gao M, Li X, Sun M, Zou Y, Feng G. KPNA5 Suppresses Malignant Progression of Ovarian Cancer Through Importing the PTPN4 Into the Nucleus. Cancer Med. 2025;14(7):e70731. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Chen X, Wei H, Yue A, Zhang H, Zheng Y, Sun W, Zhou Y, Wang Y. KPNA2 promotes the progression of gastric cancer by regulating the alternative splicing of related genes. Sci Rep. 2024;14(1):17140. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Vuorinen EM, Rajala NK, Ihalainen TO, Kallioniemi A. Depletion of nuclear import protein karyopherin alpha 7 (KPNA7) induces mitotic defects and deformation of nuclei in cancer cells. BMC Cancer. 2018;18(1):325. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Oostdyk LT, McConnell MJ, Paschal BM. Characterization of the Importin-β binding domain in nuclear import receptor KPNA7. Biochem J. 2019;476(21):3413–34. [DOI] [PubMed] [Google Scholar]
- 17.Laurila E, Vuorinen E, Savinainen K, Rauhala H, Kallioniemi A. KPNA7, a nuclear transport receptor, promotes malignant properties of pancreatic cancer cells in vitro. Exp Cell Res. 2014;322(1):159–67. [DOI] [PubMed] [Google Scholar]
- 18.Blum A, Wang P, Zenklusen JC. SnapShot: TCGA-Analyzed Tumors. Cell. 2018;173(2):530. [DOI] [PubMed] [Google Scholar]
- 19.Li T, Fan J, Wang B, Traugh N, Chen Q, Liu JS, Li B, Liu XS. TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells. Cancer Res. 2017;77(21):e108–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Tang Z, Li C, Kang B, Gao G, Li C, Zhang Z. GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses. Nucleic Acids Res. 2017;45(W1):W98–102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Yao Y, Yang K, Wang Q, Zhu Z, Li S, Li B, Feng B, Tang C. Prediction of CAF-related genes in immunotherapy and drug sensitivity in hepatocellular carcinoma: a multi-database analysis. Genes Immun. 2024;25(1):55–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, Porta-Pardo E, Gao GF, Plaisier CL, Eddy JA, et al. The Immune Landscape of Cancer. Immunity. 2018;48(4):812–e830814. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Bonneville R, Krook MA, Kautto EA, Miya J, Wing MR, Chen HZ, Reeser JW, Yu L, Roychowdhury S. Landscape of Microsatellite Instability Across 39 Cancer Types. JCO Precis Oncol. 2017;2017:PO.17.00073. [DOI] [PMC free article] [PubMed]
- 24.Yang W, Soares J, Greninger P, Edelman EJ, Lightfoot H, Forbes S, Bindal N, Beare D, Smith JA, Thompson IR, et al. Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 2013;41(Database issue):D955–961. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, Li Q, Shoemaker BA, Thiessen PA, Yu B, et al. PubChem 2023 update. Nucleic Acids Res. 2023;51(D1):D1373–80. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Franz M, Rodriguez H, Lopes C, Zuberi K, Montojo J, Bader GD, Morris Q. GeneMANIA update 2018. Nucleic Acids Res. 2018;46(W1):W60–4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Szklarczyk D, Kirsch R, Koutrouli M, Nastou K, Mehryary F, Hachilif R, Gable AL, Fang T, Doncheva NT, Pyysalo S, et al. The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic Acids Res. 2023;51(D1):D638–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015;1(6):417–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Jardim DL, Goodman A, de Melo Gagliato D, Kurzrock R. The Challenges of Tumor Mutational Burden as an Immunotherapy Biomarker. Cancer Cell. 2021;39(2):154–73. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Zhao P, Li L, Jiang X, Li Q. Mismatch repair deficiency/microsatellite instability-high as a predictor for anti-PD-1/PD-L1 immunotherapy efficacy. J Hematol Oncol. 2019;12(1):54. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Yang Y, Guo L, Chen L, Gong B, Jia D, Sun Q. Nuclear transport proteins: structure, function, and disease relevance. Signal Transduct Target therapy. 2023;8(1):425. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Ying H, Ran L, Huang Y, Yang K, Li X. Beyond the membrane: The emerging landscape of nuclear PD-L1. Pharmacol Res. 2026;228:108208. [DOI] [PubMed] [Google Scholar]
- 33.Racacho KJ, Shiau YP, Villa R, Mahri S, Tang M, Lin TY, Li Y. The tumor immune microenvironment: implications for cancer immunotherapy, treatment strategies, and monitoring approaches. Front Immunol. 2025;16:1621812. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Yu J, Fu L, Wu R, Che L, Liu G, Ran Q, Xia Z, Liang X, Zhao G. Immunocytes in the tumor microenvironment: recent updates and interconnections. Front Immunol. 2025;16:1517959. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Goto N, Agudo J, Yilmaz ÖH. Early immune evasion in colorectal cancer: interplay between stem cells and the tumor microenvironment. Trends cancer. 2025;11(9):850–61. [DOI] [PubMed] [Google Scholar]
- 36.Atkins MB, Ascierto PA, Feltquate D, Gulley JL, Johnson DB, Khushalani NI, Sosman J, Yap TA, Kluger H, Sullivan RJ, et al. Society for Immunotherapy of Cancer (SITC) consensus definitions for resistance to combinations of immune checkpoint inhibitors with targeted therapies. J Immunother Cancer. 2023;11(3):e005923. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Zhang M, Liu C, Tu J, Tang M, Ashrafizadeh M, Nabavi N, Sethi G, Zhao P, Liu S. Advances in cancer immunotherapy: historical perspectives, current developments, and future directions. Mol Cancer. 2025;24(1):136. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Park K, Tan EH, O’Byrne K, Zhang L, Boyer M, Mok T, Hirsh V, Yang JC, Lee KH, Lu S, et al. Afatinib versus gefitinib as first-line treatment of patients with EGFR mutation-positive non-small-cell lung cancer (LUX-Lung 7): a phase 2B, open-label, randomised controlled trial. Lancet Oncol. 2016;17(5):577–89. [DOI] [PubMed] [Google Scholar]
- 39.Groner B, von Manstein V. Jak Stat signaling and cancer: Opportunities, benefits and side effects of targeted inhibition. Mol Cell Endocrinol. 2017;451:1–14. [DOI] [PubMed] [Google Scholar]
- 40.Hu Y, Dong Z, Liu K. Unraveling the complexity of STAT3 in cancer: molecular understanding and drug discovery. J experimental Clin cancer research: CR. 2024;43(1):23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Xu DM, Chen LX, Xue T, Zhuang XY, Wei LC, Han H, Mo M. Decoding the impact of MMP1 + malignant subsets on tumor-immune interactions: insights from single-cell and spatial transcriptomics. Cell death discovery. 2025;11(1):244. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Wang Q, Feng W, Tan Y, Qiao J, Liu H, Liu Q, Wang H, Zhang Q, Tao J, Li Z, et al. Antiphospholipid antibodies inhibit the migration and invasion of trophoblast cells by suppressing the JNK/C-Jun/MMP1 signaling pathway. J translational Med. 2025;23(1):581. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
- 43.Tian S, Chen X, Li J. Zinc finger transcription factor ZNF24 inhibits colorectal cancer growth and metastasis by suppressing MMP2 transcription. Genes Dis. 2025;12(5):101529. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Xing P, Liu H, Xiao W, Zhang G, Zhang C, Liao Z, Li T, Yang J. The fusion gene LRP1-SNRNP25 drives invasion and migration by activating the pJNK/37LRP/MMP2 signaling pathway in osteosarcoma. Cell death discovery. 2024;10(1):198. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Wang Z, Sun H, Zhu S, Wang F, Li Q, Zhou J. RGS3 acts as a tumor promoter by facilitating the regulation of the TGF-β signaling pathway and promoting EMT in ovarian cancer. Cell death discovery. 2025;11(1):262. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Liu C, Wang S, Zhang X, Han Y, Tan M, Fan J, Du J, Fan Y, Zhao X. The biomechanical signature of tumor invasion. Genes Dis. 2026;13(1):101771. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Hao Z, Zhang M, Du Y, Liu J, Zeng G, Li H, Peng X. Invadopodia in cancer metastasis: dynamics, regulation, and targeted therapies. J translational Med. 2025;23(1):548. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Li Y, Wang H, Mao D, Che X, Chen Y, Liu Y. Understanding pre-metastatic niche formation: implications for colorectal cancer liver metastasis. J translational Med. 2025;23(1):340. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
All data generated or analysed during this study are included in this published article. And the relevant data sources and analytical methods are described in the Materials and Methods section.