Inhibition of Importin 7-mediated IPO7/c-Myc/IPO7 positive feedback loop suppresses DNA damage repair and improves sorafenib sensitivity in hepatocellular carcinoma

doi:10.21203/rs.3.rs-3578022/v1

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Inhibition of Importin 7-mediated IPO7/c-Myc/IPO7 positive feedback loop suppresses DNA damage repair and improves sorafenib sensitivity in hepatocellular carcinoma

https://doi.org/10.21203/rs.3.rs-3578022/v1

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Background：Hepatocellular carcinoma (HCC) is a common malignancy worldwide with an increasing incidence. Sorafenib is currently the first-line treatment for HCC. However, drug resistance resulting from patients' insensitivity to the medication remains a major cause of high mortality. The specific mechanisms underlying sorafenib resistance in HCC have not yet been fully elucidated.

Methods：We have established the function of Importin7 (IPO7) in enhancing sorafenib sensitivity through DNA damage repair pathway through biological information. The expression of IPO7 in hepatocellular carcinoma and normal tissue and cell lines was detected by immunohistochemistry and western blot.Wound healing assay and Transwell assay were employed to examine the function of IPO7. The effect of γ-h2ax on the sensitivity of sorafenib was investigated by down-regulating IPO7. The mechanism between IPO7 and c-Myc was verified by bioinformatics prediction, western blotting, immunoprecipitation (IP), and immunofluorescence assay.

Results：Upregulation of IPO7 in liver cancer tissues correlates with an unfavorable prognosis for individuals with liver cancer. The suppression of IPO7 demonstrates a substantial inhibitory effect on the migratory and invasive capabilities of tumor cells. At the same time, inhibition of IPO7 can significantly up-regulate the expression of DNA damage repair factor γH2AX, and improve the sensitivity of sorafenib. Additionally, our findings demonstrate that IPO7 promotes the nuclear translocation of c-Myc, leading to the activation of the IPO7-driven DNA damage repair response to sorafenib. Conclusion: Our results provide new insights into the sensitivity of IPO7 to sorafenib and provide new potential targets for further investigation.

IPO7

Hepatocellular carcinoma (HCC)

Differentially expressed genes (DEGs)

Hub gene

DNA damage repair

Sorafenib

Liver cancer exhibits a rising global prevalence, with hepatocellular carcinoma (HCC) being the predominant subtype, accounting for over 90% of all primary liver cancers [1]. HCC ranks as the third leading cause of global mortality, as reported by GLOBOCAN 2020, and is projected to raise by 60.9% by 2040. Therefore, it is crucial to acknowledge the current insufficiency of effective treatment strategies for HCC. The challenge of early diagnosis often results in liver cancer patients being diagnosed at advanced stages, limiting their treatment options. Therefore, systemic therapy stands as the primary treatment option [2]. Sorafenib, a multi-target tyrosine kinase inhibitor, has obtained approval from the U.S. Food and Drug Administration as the first-line treatment for advanced liver cancer [3].Although sorafenib extended the median overall survival of patients with advanced HCC by approximately 3 months, the use of sorafenib in the clinical setting was constrained by the emergence of resistance within a six-month period [4]. The therapeutic sensitivity of sorafenib is significantly hindered by both inherent and acquired resistance. Indeed, the urgent priority lies in comprehending the underlying mechanisms responsible for sorafenib resistance and investigating novel targets to enhance sorafenib sensitivity. This research is important to advance treatment options for liver cancer and improve patient outcomes.

Known nuclear transporters (including input and output proteins) are identified by nuclear localization signals and combined with goods. Subsequently, RanGTP promotes active transport in the nuclear membrane[5]. The cargo includes a lot of protein[6–8]. In addition, emerging evidence supports that the overexpression of nuclear transporters leads to localization errors of crucial mediators and are associated with tumorigenesis and drug resistance in various types of tumors [9–12].

In this study, we conducted a simultaneous comparison of genes between patients who achieved incomplete and complete responses to sorafenib treatment using the GSE109211 dataset. By utilizing the Genecards database for DNA damage repair and nuclear mass transfer gene sets, as well as the HTFtarget database, we identified Importing 7 (IPO7) as a promising candidate. Our functional analysis shows that down-regulation of IPO7 enhances sensitivity to sorafenib by disrupting DNA damage repair pathways. Furthermore, we found that IPO7 is upregulated by the transcription factor c-Myc. Interestingly, we discovered that IPO7 protein promotes nuclear translocation in MYC.

In conclution, Our provide new insights into the sensitivity of IPO7 to sorafenib and provide new potential targets for further investigation.

Microarray data acquisition and identification of differentially expressed genes (DEGs)

We obtained the microarray datasets GSE109211 [13] from the Gene Expression Omnibus (GEO) database. Raw data (information on samples, platforms, and GSE records,) were extracted from the MINiML files, and log transformed for standardization. The datasets excluded probes that matched multiple genes. We calculated the average value from several probes used to detect each expression.To identify DEGs, we conducted an analysis of all genes in normal and cancer tissues samples using the GEO2R website .

Enrichment Analysis

In order to explore the potential function of up-regulated differential genes, we performed gene enrichment analysis. Gene Ontology (GO), a well-established tool for functional gene annotation, was employed. The GO database served as a reference, and we utilized the "clusterProfiler" R package for enrichment analysis.

Acquisition of eligible genes

We used the website GeneCards to search for genes associated with "DNA-damage-repair" and "Nucleoplasmic-transport" keywords. The corresponding gene sets were downloaded, and eligible genes were extracted. We then used the keyword "c-Myc" into the website htftarget to obtain the corresponding gene set. Creating a Venn diagram by overlapping the unregulated deg and three sets of genes allowed us to identify shared genes between them.

Co-expression network construction

The minimum gene dendrogram size was set to 30. Sensitivity was set to 2. To further analyze the modules, we calculated the dissimilarity of module eigen genes and then determined a cut line for the module dendrogram. This allowed us to merge certain modules. Specifically, we merged modules with a distance of less than 0.25, leading to the identification of a final set of 10 co-expression modules. Grey modules are groups of genes that cannot be assigned to any specific module.

Screening of Hub Genes

We utilized the STRING to obtain a protein-protein interaction (PPI) network [14]. To screen the top 10 Hub genes, we used Cytoscape software[15].

Construction of prognostic signatures

We utilized least absolute shrinkage and selection operator (LASSO)-penalized Cox regression analysis to explore the potential diagnostic value of the identified hub genes (16). A LASSO regression was performed, accompanied by a 10-fold cross-validation approach to determine the optimal value for the penalty parameter (λ). The outcomes obtained from the LASSO regression were employed to compute risk scores. Overall Survival(OS)analysis was performed to evaluate the difference in prognosis of patients [16–18].

Survival analysis

It was performed using the UALCAN database.

Immunohistochemical

Streptavidin-peroxidase (SP) staining was used in this study. The tissue slices had a cross-sectional thickness of approximately 130 µm, which was later reduced to approximately 4 µm. To prepare the slices for staining, they were baked at 85°C for 30 minutes, dewaxed using xylene, and dehydrated with ethanol. For antigen retrieval, an alkaline repair solution was subjected to high pressure heating and then cooled to room temperature. Subsequently, the slices were blocked with a peroxidase blocker for 20 minutes. T The primary antibody IPO7 (67525-1-Ig, proteintech) was applied at a dilution concentration of 1:50 and left overnight at 4°C. The next day, coated with antibody (PV-6001, Zhongshan Biotechnology) and incubated for 20 minutes. After each step, the slices were rinsed with PBS buffer for 5 minutes, repeated three times. To visualize the staining, the slices were treated with DAB (Dako, DK) for 30 seconds, followed by hematoxylin staining for another 30 seconds. Before sealing the slides with neutral resin, ethanol dehydration and xylene dewaxing steps were performed.

Cell culture and transfection

The cell lines used in this study and their respective culture media are as follows: HepG2 (ATCC, Manassas, VA, USA)、Huh7 (KCLB, Korean Cell Line Bank, Seoul, Korea) cultured in DMEM; Cells in the logarithmic growth phase were seeded in 6-well plates at a density of 70%-80%. Transfection experiments were performed using shNC, shIPO7, shNC and shc-Myc (Jikai gene, Shanghai, China).

Western blotting

Protein isolation from cells with RIPA buffer (NCM Biotech, China). The extracted proteins are then transferred to the PVDF membrane. Block with 5% milk powder and incubate at 4℃ overnight. The relevant secondary antibody is then used. The primary antibodies included rabbit anti-c-Myc (ab32072, Abcam, UK) and mouse anti-IPO7 (Proteintech, China).

Transwell invasion experiment

A polycarbonate filter with a pore size of 8µm was used to detect the invasion force of the cells after intervention. 5×10⁴ cells were added to the basic medium and inoculated in the culture chamber for 48 hoursAnhydrous ethanol fixation, dyeing. Cells are viewed and imaged under a microscope.

Scratch wound healing assay

Cells were transfected with shRNA in 6-well plates at a density of 5×10⁵ cells per well and incubated for 24 hours. Upon reaching 90% confluence, the attached cells are lightly scratched with the tip of a sterile pipette gun, creating a scratch. Let the scratched monolayer heal for 48 hours. Images of the healing process were captured at 0 and 48 hours using a 2x10 optical microscope. The entire experiment was replicated three times, and each sample was evaluated in triplicate, ensuring rigorous assessment of the wound healing process.

Immunofluorescence staining

The cells were cultured for 24 to 48 hours before fixed with 4% paraformaldehyde. Subsequently, The cells were then infiltrated with 0.1% Triton.Primary antibodies for c-Myc, IPO7, or FLAG were diluted in 1% bovine serum albumin (1:200). After PBS washing, the cells were incubated with the second antibody. The nuclei were stained with DAPI(1:10,000).The localization of the target proteins was observed using a confocal fluorescence microscope (Leica DM 14000B).

Huh7 cells were treated with 6 µm/ml sorafenib for 1 h. Sorafenib is purchased from MedChemExpress. Cells were immobilized with 4% paraformaldehyde and infiltrated with 0.1% Triton X-100. γH2AX was detected using a DNA damage kit (Biyantian).Quantification of gamma-H2AX fluorescence was performed using Image J (NIH, Bethesda, MD).

Statistical analyses

The between-group comparisons were assessed using the independent sample t-test, while the multi-sample comparisons were analyzed using ANOVA. For two-pair comparisons, the LSD-t method was employed.

Functional enrichment analysis

The gene expression data from the sorafenib complete and incomplete response group in GSE109211 were subjected to analysis using the GEO2R online tool. A total of 13518 DEGs were screened, as shown in Fig. 1A. The GO enrichment analysis conducted on these DEGs revealed that they primarily enriched in biological processes related to transport, DNA damage, and DNA repair (Fig. 1B).

Weighted co-expression network construction and identification of key modules

Following data preprocessing, a gene expression matrix consisting of 20,696 genes was obtained from GSE109211. Co-expression network was then constructed using WGCNA, as described in Material and Methods. Sample clustering was performed using the Pearson correlation coefficient, resulting in the generation of a sample clustering tree (Fig. 2A). To construct the scale-free network, a soft threshold of 14 (R2 = 0.86) was set. The adjacency matrix was transformed into the TOM matrix(Figs. 2B and C). Through average hierarchical clustering and dynamic tree pruning, a total of 10 modules were identified (Fig. 2D ). For further clinical analysis, three modules were selected: black, dark red and midnight blue (Fig. 2D).

Identification and analysis of key gene IPO7

Based on the analysis of the three selected modules and the DNA damage repair gene, a total of 1,979 DEGs were obtained from the Venn plot (Fig. 2E). These DEGs were further analyzed using STRING and Cytoscape, resulting in the identification of the top 10 pivotal genes: MYC, ACTB, AKT1, HSP90AA1, RPS27A, HSPA4, UBA52, CTNNB1, HSPA8, and EGFR (Fig. 2F). To investigate the mechanism of DNA damage repair in relation to sorafenib resistance in HCC, a Venn diagram was generated using DEGs, MYC-Target genes, nucleo-plasmic transport genes, and DNA damage repair genes upregulated by GSE109211. 32 DEGs were obtained (Fig. 3A). The network analysis of these 32 genes revealed 32 nodes and 38 edges (Fig. 3B). Using Cytoscape, the top 10 hub genes were determined: PARD3, POM121, HSPA1L, NUP133, NUP155, STX1A, NUP188, IPO7, KPNA5, and RANBP2 (Fig. 3C). Next, The prediction genes were screened by LASSO-Cox regression model. By evaluating the sum of absolute values of the forced regression coefficients, certain coefficients were reduced to zero, signifying their insignificance. The most powerful prognostic gene was determined based on its relative regression coefficient. In this model,the relationship between partial likelihood bias and log(λ) was depicted in the plot, showcasing the impact of regularization on the model's performance and variable selection (Fig. 3D-E).the median time indicated the time corresponding to the 50% survival rate of the high-risk group and the low-risk group (Fig. 3F). The higher the AUC, the stronger the prediction ability of the model (Fig. 3F). Furthermore, the correlation between PARD3, POM121, HSPA1L, NUP133, NUP155, STX1A, NUP188, IPO7, KPNA5, RANBP2, and MYC was investigated in the TCGA database, revealing the highest positive correlation between IPO7 and MYC (Fig. 3G). Therefore, IPO7 was identified as a key gene in this analysis.

The expression and prognostic value of IPO7

As shown in Fig. 4A, the expression of nuclear transporter IPO7 in liver cancer cell lines has been little studied in liver cancer. In addition,a statistically significant overexpression of IPO7 in HCC relative to normal liver tissues was identified (Fig. 4B). Immunohistochemical staining of fresh tissue samples obtained from patients with liver cancer demonstrated a marked increase in IPO7 expression in tumor tissues when compared to adjacent normal tissues (Fig. 4C).The expression of IPO7 was lower in normal tissues and higher in tumor tissues (Fig. 4D).The expression of IPO7 in HCC cell lines(Fig. 4E).

Knockdown of IPO7 inhibits HCC cell proliferation

The effectiveness of IPO7 knockdown was confirmed through western blotting analysis (Fig. 5A and D). Through transwell experiments, we found that the silencing of IPO7 in HuH7 and HepG2 cells led to decreased invasion ability (Fig. 5B and E). Additionally, revealed that IPO7-silenced cells exhibited a considerable decrease in migration at the edge of the cell exposure area compared to wildtype cells (Fig. 5C and F).

The knockout of IPO7 prevented nuclear translocation of c-Myc, and the downregulation of IPO7 was controlled through the c-Myc downgrade

IPO7 belongs to the nucleoglobulin-β family. Based on the observation that c-Myc is localized within the nucleus of hepatoma cells, that IPO7 may be involved in the nuclear transport of c-Myc. To confirm whether c-Myc is an IPO7 cargo protein, co-ip and western blotting analysis were conducted, confirming the co-ip of c-Myc and IPO7 in hepatoma cell lines (Fig. 6A). We also assessed the distribution of c-Myc and IPO7 in cell through immunostaining (Fig. 6B). The evaluation of c-Myc subcellular localization after knocking down IPO7 expression in two HCC cell lines, HuH7 and HepG2, also showed consistent results regarding c-Myc subcellular localization (Fig. 6C). These results establish IPO7 as a nuclear-imported protein for c-Myc. As IPO7 was identified as a potential transcription factor for c-Myc, the binding information between the c-Myc promoter region and the transcription factor IPO7 gene sequence was predicted (Fig. 6D). Knocking down c-Myc resulted in a decrease in IPO7 expression, indicating that c-Myc can transcriptionally regulate IPO7 expression (Fig. 6E). The results revealed that in HuH7 cells, the knockout of c-Myc enhanced the effectiveness of sorafenib in inducing cellular DNA damage (Fig. 6F).

IPO7 attenuates sorafenib sensitivity

In the GSE109211 dataset, the expression of IPO7 was found to be upregulated (Fig. 7A). To investigate whether IPO7 plays a role in modulating the sensitivity of HCC to sorafenib, we knocked down IPO7 in HCC cells, and found that the knockdown group exhibited significantly lower IC50 values compared to the control group, indicating increased sensitivity to sorafenib (Fig. 7B). Additionally, the cellular DNA damage induced by sorafenib was enhanced in HuH7 cells with IPO7 knockdown (Fig. 7C).

The effectiveness of sorafenib, the standard treatment for advanced unresectable liver cancer, has been predominantly hindered by the development of sorafenib resistance [3, 19, 20]. Therefore, in order to improve the sensitivity of sorafenib, it is imperative to further study the potential mechanism of improving the sensitivity. Simultaneously, efforts should focus on improving the sensitivity of sorafenib to better respond to treatment and identifying novel targets. In this study, we demonstrate that inhibition of IPO7-mediated IPO7 / c-Myc/IPO7 positive feedback loop inhibits IPO7-driven DNA damage and increases sorafenib sensitivity in HCC (Fig. 8).

In this study, the GSE109211 dataset was analyzed to identify DEGs, and the upregulated DEGs were further subjected to GO analysis. We found that DEGs were associated with DNA damage repair. Three modules related to DNA damage repair were found by WGCNA method. The intersection of genes within these modules and DNA damage repair-related genes resulted in the identification of 1,979 common genes. The top 10 hub genes were extracted from the STRING database and Cytoscape, ultimately identifying c-Myc as a significant gene. Further analysis involved intersecting the upregulated DEGs with c-Myc targets and genes associated with nucleoplasmic transport and DNA damage repair, resulting in the identification of 32 genes. Finally, using LASSO regression and assessing its correlation with MYC, IPO7 emerged as a prominent candidate. To investigate the clinical relevance of IPO7 in liver cancer,the Kaplan-Meier database was utilized to analyze its association with prognosis.Additionally, we investigated the impact of IPO7 on the progression of HuH7 and HepG2 hepatoma cells through wound healing and transwell experiments. These findings indicate a potential link between IPO7 and the onset and progression of liver cancer, highlighting IPO7 as a potential prognostic marker for this disease.

There is already evidence that IPO7 and its family of molecules are associated with the acquisition of drug resistance in a variety of cancers[21–23]. Disrupting DNA damage repair through the silencing of IPO4 can enhance drug sensitivity [22]. In our study, we established that IPO7 can augment the sensitivity of HCC cells to sorafenib treatment.

Mechanistically, we found that IPO7 is activated by c-Myc transcription, and the silencing of c-Myc leads to increased sensitivity to sorafenib. However, directly targeting transcription factors can be challenging. Therefore, instead of directly targeting c-Myc, we focused on targeting its nuclear delivery. Growing evidence suggests that elevated expression of nuclear transporters plays a role in tumorigenesis [24]. Recent research has unveiled the role of IPO3/KPNA4 in head and neck squamous cell carcinoma [25], while KPNA2 has been identified as a target in gallbladder carcinoma [26]. Moreover, recent evidence have indicated that exportin-1 (XPO1) is implicated in chemotherapeutic resistance across various types of cancer. The XPO1 inhibitor selinexor has emerged as a promising therapeutic option to overcome chemotherapeutic resistance [11, 27, 28]. In our study, we have discovered a novel association between IPO7 and c-Myc, highlighting the involvement of IPO7 in c-Myc nuclear transport.This finding sheds light on the involvement of IPO7 in HCC tumorigenesis and sorafenib sensitivity, which has not been previously reported. Our findings provide unique insights into the mechanisms underlying HCC development and sensitivity to sorafenib, and the implications of IPO7 may extend beyond HCC to other cancer types. Furthermore, the heightened proliferative and metabolic demands of cancer cells compared to nomal cells contribute to their increased vulnerability to karyopherin inhibitors [29, 30]. This represents that specific nucleoprotein inhibitors can be a promising cancer treatment.

It is important to acknowledge that although our study demonstrates the role of IPO7 in increasing the nuclear import of c-Myc, it can not be ruled out that other nuclear transporters may also be involved in nuclear transport of c-Myc, as nuclear transporters often share common cargoes [31]. It should be noted, however, that among all 25 nuclear transporters, only IPO7 was found in our comprehensive analysis based on multiple bioinformatics tools. Most notably, IPO7 demonstrated a stronger association with c-Myc compared to other molecules. Therefore, we focused on IPO7 as a key nuclear transporter. However, our team will further explore the potential interactions of other nuclear transporters with c-Myc in HCC.

In conclusion, our study suggests a new approach to addressing sorafenib resistance in HCC and reveals the mechanism of IPO7 in sorafenib resistance.Our findings suggest that blocking the IPO7/c-Myc/IPO7 feedback loop by targeting IPO7 holds potential in overcoming sorafenib resistance and improving treatment outcomes in HCC.

Conflict of Interest

The authors declare no competing interests.

Authorship

Song Xue: Writing - Original Draft. Qing Hu: Investigation, Data Curation. Yumeng Wu: Software. Tianyu Chen: Validation. Jian Xu: Supervision. Xiubing Zhang: Writing - Review and Editing. Hongyu Cai: Conceptualization, Resources, Project administration.

Funding statement

This study was supported by the Health Committee of Nantong (grant no. MA2021023), and the Health Committee of Nantong (grant no. QA2021030), and the Health Committee of Nantong (grant no.MS2022058) ,and the Health Committee of Nantong (grant no.JC22022084).

Acknowledgments

This is a short text to acknowledge the contributions of specific colleagues, institutions, or agencies that aided the efforts of the authors.

Institutional Review Board Statement

The studies involving human participants were reviewed and approved by the Ethics Committee of Nantong Tumour Hospital. The patients/participants provided their written informed consent to participate in this study.

Availability of data and materials

The datasets obtained from TCGA database (https://portal.gdc.cancer.gov/) and the GEO repository (https://www.ncbi.nlm.nih.gov/geo/).

and UALCAN database(http://ualcan.path.uab.edu/analysis.html ), partial analysis by Cbioportal for Cancer Genomics website (http://www.cbioportal.org ) .

All data generated or analysed during this study are included in this published article.

Consent for publication

Not applicable.

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Inhibition of Importin 7-mediated IPO7/c-Myc/IPO7 positive feedback loop suppresses DNA damage repair and improves sorafenib sensitivity in hepatocellular carcinoma

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Abstract

Figures

Introduction

Materials and Methods

Microarray data acquisition and identification of differentially expressed genes (DEGs)

Enrichment Analysis

Acquisition of eligible genes

Co-expression network construction

Screening of Hub Genes

Construction of prognostic signatures

Survival analysis

Immunohistochemical

Cell culture and transfection

Western blotting

Transwell invasion experiment

Scratch wound healing assay

Immunofluorescence staining

Statistical analyses

Results

Functional enrichment analysis

Weighted co-expression network construction and identification of key modules

Identification and analysis of key gene IPO7

The expression and prognostic value of IPO7

Knockdown of IPO7 inhibits HCC cell proliferation

IPO7 attenuates sorafenib sensitivity

Discussion

Declarations

References

Additional Declarations

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