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IRF2 Destabilizes Oncogenic KPNA2 to Modulate the Development of Osteosarcoma

IRF2 Destabilizes Oncogenic KPNA2 to Modulate the Development of Osteosarcoma Hindawi Journal of Oncology Volume 2022, Article ID 9973519, 14 pages https://doi.org/10.1155/2022/9973519 Research Article IRF2 Destabilizes Oncogenic KPNA2 to Modulate the Development of Osteosarcoma 1 2 Shuchi Xia and Yiqun Ma Department of Dentistry, Zhongshan Hospital, Fudan University, Shanghai 200032, China Department of Orthopedics, Zhongshan Hospital, Fudan University, Shanghai 200032, China Correspondence should be addressed to Yiqun Ma; mayiqun090@163.com Received 29 June 2022; Accepted 24 August 2022; Published 26 September 2022 Academic Editor: Shinji Miwa Copyright © 2022 Shuchi Xia and Yiqun Ma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Osteosarcomas (OS) are the most common primary malignant bone tumor. Emerging evidence revealed that karyopherin alpha 2 (KPNA2) was strongly associated with the tumorigenesis and development of numerous human cancers. The aim of the present study was to investigate the expression pattern, biological functions, and underlying mechanism of KPNA2 in OS. Bioinformatics TFBIND online was applied to forecast transcription factor (TF) binding sites in the promoter region of KPNA2. The expression profile of KPNA2 in OS tissues were firstly assessed. CCK8, colony formation, wound healing, and Transwell assays were used to assess cell viability, proliferation, and migration in vitro, and in vivo experiments were performed to explore the effects of KPNA2 and interferon regulatory factor-2 (IRF2) on tumor growth. Furthermore, the correlation between IRF2 and KPNA2 was investigated using chromatin immunoprecipitation (ChIP), RT-qPCR, western blot, and dual-luciferase assays. KPNA2 was obviously upregulated, while IRF2 decreased significantly in OS tissues and cell lines, as well as negatively correlated with each other. KPNA2 removal remarkably suppressed OS cell growth, migration, invasion in vitro, and tumor growth in vivo, while IRF2 knockdown exerts an opposing effect. IRF2 binds to the KPNA2 promoter to modulate the malignant phenotypes of OS cells by regulating epithelial-to-mesenchymal transition (EMT). The present study demonstrated that KPNA2 performed the oncogenic function, possibly regulating tumor development through EMT. Importantly, it was confirmed that IRF2 serves as a potential upstream TF of KPNA2 involved in the regulation of EMT progress in OS. 1. Introduction Karyopherin alpha 2 (KPNA2, 58 kDa) is one of seven members of the karyopherin α-family [6, 7]; Dysregulation of KPNA2 had been reported serving as a potential bio- Osteosarcoma (OS) is the frequent primary malignant bone tumor, affecting mainly pediatric and adolescents [1], which marker in several malignancies, including breast cancer [8], is composed of malignant mesenchymal cells producing gastric cancer [9], lung cancer [10], and glioma [11]. KPNA2 osteoid and/or immature bone [2]. It typically forms in served as the adaptor to transfer p65 to the nucleus to iden- the metaphysis of long bones, specifically the proximal tibia, tify the classic nuclear localization signal [7, 12]. Emerging the distal femur, and the proximal humerus, accompanied data suggest a role for the epithelial-mesenchymal transition by swelling and pain [3]. It is common that metastasis to (EMT) in the regulation of cellular plasticity in normal adult the lungs of OS [4]. Before the use of neoadjuvant and adju- tissues and tumors, where they can generate multiple and vant chemotherapy, approximately 90% of OS patients died distinct cellular subpopulations that contribute to intratu- from lung metastases [5]. OS is characterized by high levels moural heterogeneity [13]. EMT is important for invasion, of genomic instability. However, the molecular basis metastasis, and drug-resistance of cancer cells. A previous involved in OS remains unclear and continuing to seek study showed that KPNA2 silence may reduce ovarian carci- new treatments is urgently needed to continue looking for noma migration and invasion by inhibiting Akt/GSK-3β/ new treatments. Snail pathway and suppressing EMT [14]. In addition, 2 Journal of Oncology patients. No patients had undergone chemotherapy, radia- KPNA2 was involved in the regulation of autophagy and the EMT of glioblastoma cells [15]. However, the molecular tion therapy, or other related targeted therapy before sur- pathways regulated by KPNA2-mediated EMT in OS are gery. The diagnosis of OS was confirmed by at least two yet to be elucidated. pathologists. All surgical tissue samples used in our study Transcription factors (TFs), could specifically recognize were immediately placed in liquid nitrogen and then stored DNA through consensus sequences, thus to control chroma- at -80 C until use. tin and transcription, guiding expression of the genome [16]. 2.3. RT-qPCR. Total RNA was isolated from OS cell lines To identify the TF of KPNA2 transcription, the online soft- and tissue samples with the TRIzol™ Reagent (Invitrogen) ware of TFBIND (http://tfbind.hgc.jp/) was employed to according to the manufacturer’s protocol. Complementary identify putative binding sites in the promoter region of DNA (cDNA) synthesis was performed using the Prime- KPNA2. Specifically, several canonical IRF2-binding sites Script RT reagent kit (Takara) for mRNA expression analy- in the promoter region of KPNA2 were observed, and sis. The cDNA was applied to perform qRT-PCR assay with IRF2 was then extracted from 147 candidate genes. IRF2, SYBR Premix Ex Taq kit (TaKaRa) following the protocols. belonging to the IRF family, was widely expressed in various ΔΔCt The 2 − method was used to analyze the difference in tissues [17]. Among several types of cancer, belong to the the level of mRNA between different groups. Primers used IRF family, IRF1 signaling pathways may directly induce p21-dependent G0/G1 cell cycle and p21-independent mod- in this study were listed as follows: KPNA2, 5 -ATTGCA ulation of survival [18]. IRF2 was shown to serve as an ′ ′ GGTGATGGCTCAGT-3 (forward) and 5 -CTGCTCAAC important regulator in acute myeloid leukemia by targeting ′ ′ AGCATCTATCG-3 (reverse); IRF2, 5 -CATGCGGCTAG INPP4B [19]. A recent study has indicated that IRF2 was ′ ′ ACATGGGTG-3 (forward) and: 5 -GCTTTCCTGTATGG able to suppress the strengthening of cell migration and ATTGCCC-3 (reverse); The GAPDH was used as an inter- invasion in OS, which were mediated by miR-18a-5p [20]. nal control and was detected using the following primers: 5 - Similarly, IRF2 was not expressed or at a low level in OS tis- ′ ′ AATCCCATCACCATCTTCC-3 (forward) and 5 -AGTC sues [21]. Intriguingly, IRF was already identified as a func- CTTCCACGACCAA-3 (reverse). tional TF in nonsmall cell lung cancer (NSCLC) that suppressed KPNA2 expression [22]. Therefore, we speculate 2.4. Lentiviral Vector Encoding shRNA Plasmids. KPNA2 that IRF2 may negatively regulated KPNA2 as its upstream cDNA was cloned into the Lenti-OE vector (Genepharma, TF to modulate OS progression. Shanghai, China) to generate KPNA2-overexpressing lenti- This study aimed to investigate the role of IRF2 in mod- viral vectors. Short hairpin RNAs (shRNAs) were designed ulating KPNA2 expression, which may serve an important by the RiboBio Co., Ltd. (Guangzhou, China). Lentiviral vec- role in p65 nuclear importation in the progression of OS. tors encoding KPNA2 and IRF2 were synthesized and pack- Here, we found that KPNA2 deficiency suppressed the aged by Genepharma company. malignant behaviors of OS cells, and that the underlying mechanisms involved were regulated by IRF2 and associated 2.5. Cell Proliferation Assay. The cell proliferation assay was with EMT progress. Taken together, our findings demon- performed using a Cell Counting Kit (CCK-8, Dojindo, strated that KPNA2 may serve as a new potential prognostic Japan) following the manufacturer’s instructions. Cells at a indicator and therapeutic target for OS. density of 5×10 were added into the 96-well plate and 10 microliters of CCK-8 solution was added to each well at 1, 2. Materials and Methods 2, 3, 4 and 5 days at 37 C. An additional 1 h later, the absor- bance at wavelength of 450 nm was measured under a 2.1. Cell Culture. Four human OS cell lines (Saos-2, HOS, microplate reader. U2OS, and MG-63) and human fetal osteoblasts cell line (hFOB 1.19) were obtained from the Cell Bank of Type 2.6. Colony Formation Assay. For the colony formation Culture Collection of the Chinese Academy of Sciences assay, 500 cells were plated into each well of a 6-well culture (Shanghai, China). Cells were grown in Dulbecco’s modified plate. The plates containing DMEM were incubated at 37 C Eagle medium (DMEM; Gibco) harboring with 10% fetal for 2 weeks. After being washed with PBS for three times, bovine serum (FBS) (Gibco). All OS cell lines were cultured cells were fixed with 4% paraformaldehyde for 10 min at in a humidified incubator under 5% CO at 37 C, while 2 room temperature, followed by staining with 0.5% crystal hFOB1.19 cells were grown at 34 C. Mycoplasma-microbial violet solution for another 20 min. Lastly, the visible colonies contamination examination and STR profiling were checked of more than 50 cells were manually counted and imaged to confirm the genotypes. under a microscope. 2.2. Patients and Tissue Samples. Twenty-five paired tumor 2.7. Transwell Invasion Assay. Cells at the density of 1×10 samples and their adjacent nontumor tissues from patients were seeded into a diameter Transwell plate with 8-μm who had undergone surgery were obtained from Zhongshan pores (Sigma-Aldrich). The upper chamber of the plate Hospital, Fudan University. This study was approved by the was added with 50 μl of Matrigel collagen and 600 μLof Ethics Committee of Zhongshan Hospital, Fudan University complete DMEM was added to the lower chambers, and (Y2014-185) according to the Declaration of Helsinki, and then the cells were incubated for 24 h. The cells on the written informed consents were obtained from all the upper layer were removed and the invasive cells were fixed Journal of Oncology 3 formed and the mice were anesthetized and sacrificed 24 with 4% formaldehyde for 20 min, and then stained with crystal violet for 15 min. Cells that had invaded the bottom days after tumor inoculation. After being removed, the surface of the filter were counted to assess the invasive tumors were imaged and weighed, and the volume of tumors capacity. Invaded cells were quantified by at least five fields was monitored every 3 or 5 days and calculated as follows: of view under light microscopy (Leica) to obtain the repre- Volume ðmm Þ = ðlength × ðwidth Þ/2Þ. sentative images. 2.13. Histological Analysis and Immunohistochemistry. 2.8. Wound Healing Assay. Cells were cultured in six-well Xenotransplant tumor samples were isolated and fixed in 4% plates. After reaching 90% confluence, a 200 μl pipette tip paraformaldehyde. Paraffin was used to embed tumor tissues was used to create scratch wounds in the cell monolayer. and then sectioned at a thickness of 5-μm. The paraffin Representative images of cell migration were photographed sections were dewaxed, hydrated, and then stained with hema- under light microscopy (Leica) at 0 and 24 h after wounding. toxylin and counterstained with eosin. Antigens were recov- Migration ability was assessed by measuring changes in ered with citrate buffer and blocked with 3% H2O2, wound width or area with ImageJ software. immunohistochemistry (IHC) was performed with diluted primary KI67 antibody overnight at 4 C, followed by incuba- 2.9. Western Blot Assay. The protein was lysed from tissues tion with secondary antibody at room temperature. The slides and cells with RIPA buffer (Thermo Fisher Scientific). Pro- were developed by diaminobenzidine (DAB) and stained with tein concentration was assessed using the bicinchoninic acid hematoxylin. IHC staining pictures were obtained under a (BCA) assay kit (Thermo Fisher Scientific). Equal amounts of light microscope. All results were determined by two patholo- protein samples were separated by 12% SDS-PAGE gels and gists who were completely blinded to the grouping. transferred to polyvinylidene difluoride (PVDF) membranes. After incubation with 5% nonfat milk for 2 h at room tem- 2.14. Function Enrichment Analysis Based on the TARGET- perature, to hatch the blots with the primary antibodies OS Dataset. Gene Ontology (GO) and Kyoto Encyclopedia including anti-KPNA2 (ab6036), IRF2 (ab124744), E- of Genes and Genomes (KEGG) enrichment analysis were cadherin (ab1416), N-cadherin (ab18203), Vimentin performed on the upregulated genes in the high- and low- (ab92547), and GAPDH (ab9484) overnight at 4 C, which IRF2 or KPNA2 subgroups based on the TARGET-OS data- were purchased from Abcam. Hatching of horseradish set, and deduced their functions by analyzing the gene set. In peroxidase-conjugated secondary antibodies at room tem- our work, we explored whether differentially expressed genes perature, the endogenous GAPDH is the internal reference between these subgroups were enriched among OS-related protein. The protein band signals were visualized on an biological functions or pathways. Significant GO biological ECL detector (Pierce) and quantified by scanning the densi- process terms and KEGG pathways with p <0:05 were col- tometry using ImageJ software. lected and visualized using the R package “ggplot2” (version 4.0.3). 2.10. Chromatin Immunoprecipitation (ChIP) Assay. ChIP assays were performed using a kit (Sigma-Aldrich) following 2.15. Correlation Analysis of 22 Immune Cell Infiltration with the protocol provided by the manufacturer. To hatch the IRF2 and KPNA2. 22 tumor-infiltrating immune cells diluted DNA-protein complex, the antibodies of anti-IRF2 (TIICs) in OS samples from the TARGET dataset were and mouse IgG (Sigma-Aldrich) were added in the presence assessed by applying the deconvolution algorithm (referred of protein A/G beads and incubated at 4 C overnight. The to as CIBERSORT) in the osteosarcoma microenvironment. RT-qPCR assay was applied to examine the ChIP DNA sam- Samples with p <0:05 in CIBERSORT analysis result were ples. IgG was the negative control. used in further analysis. The matrix of gene expression sig- natures of 22 TIICs was obtained from the CIBERSORT 2.11. Dual Luciferase Test. Wild (WT) and mutant (MUT) of platform (https://cibersortx.stanford.edu) [23]. The matrix KPNA2 were inserted into the pGL3 promoter vector, which data of IRF2 and KPNA2 levels were compared with those was transfected into U2OS and MG-63 cells using Lipofecta- of the signature matrix of 22 TIICs from the CIBERSORT mine 2000 (Invitrogen) together with plasmid of IRF2 over- platform to generate a proportion matrix for the 22 TIICs expression or empty plasmid (NC). 48 h later, luciferase in OS tissues. activity was measured using a dual-luciferase reporter assay system (Promega) following the manufacture’s protocols. 2.16. Statistical Analysis. All data obtained are expressed as the mean ± standard deviation (SD). Differences Student’s t 2.12. Tumor Xenograft Assay. All animal experiments were -test or one-way ANOVA followed by a Tukey post hoc test in accordance with the Institutional Animal Care and Use were used to compare data between two groups or among Committee Guide (IACUC) of Zhongshan Hospital, Fudan multiple groups. Statistical difference was analyzed using University (2018-014). The mice were placed in an environ- the GraphPad Prism 8.0 software. Followed by a Tukey’s mentally controlled pathogen-free isolation facility under a post hoc test. 12 h light-dark cycle and food and water were freely avail- able. Subsequently, mice were randomly divided into four 3. Results groups (n =3/per group). Equal number of indicated U2OS cells (5 × 105) were subcutaneously implanted into the right 3.1. Overexpression of KPNA2 in the Osteosarcoma Tissues. flank of 6-week-old female athymic nude mice. Tumors were Based on the GSE36001 database, we discovered KPNA2 4 Journal of Oncology tently, the mRNA level of IRF2 was significantly downregu- expression was markedly upregulated in OS tissues when compared to nontumor tissues (Figure 1(a)). To examine lated in 25 cases of clinical OS tissue samples when whether KPNA2 altered clinical OS, qRT-PCR assay was compared with adjacent normal tissues (Figure 2(f)). Besides, a ChIP test was conducted in two OS cell lines performed to examine KPNA2 levels in 25 pairs of cancer- ous OS samples and their adjacent normal samples. and hFOB1.19 to evaluate the binding relationship of IRF2 Figure 1(b) showed that the mRNA levels of KPNA2 were with KPNA2, and we found that the enrichment of IRF2 obviously upregulated in OS samples compared to normal binding was prominently decreased in two OS cell lines samples. Representative IHC images showed similar results comparing to the hFOB1.19 cells (Figure 2(g)). The present findings showed that IRF2 might be one of the major regu- (Figure 1(c)). Furthermore, the protein levels of KPNA2 in 8 cases of OS samples were obviously elevated as well lators to regulate KPNA2 expression in OS. To further deter- (Figure 1(d)). Furthermore, compared to hFOB1.19 cells, mine whether IRF2 bound to the KPNA2 promoter, a dual a higher level of KPNA2 in four OS cell lines, including luciferase assay revealed that IRF2 was able to dramatically U2OS, HOS, Saos2, and MG-63, was observed (Figure 1(e)). reduce the luciferase activity of KPNA2-WT, but not in KPNA2-MUT (Figure 2(h)). Collectively, IRF2, as a func- KPNA2 expression was the highest in U2OS and MG-63, which were chosen to use for subsequent analysis. These tional TF, could bind to KPNA2 in OS cells. results suggest that KPNA2 might play a vital role in the pro- gression of OS. 3.3. IRF2 Deficiency May Cooperate with KPNA2 to Regulate Cell Proliferation and Tumor Growth of OS Cells In Vivo and In Vitro. Since a negative correlation existed among KPNA2 3.2. The Transcription Factor IRF2 Specifically Regulates the Expression of KPNA2 in Osteosarcomas. To better under- and IRF2, we investigate their effects on cell proliferation, stand the molecular mechanism of KPNA2 in OS, we first migration, invasion, and cell cycle. Firstly, in four OS cell lines, applied the online databases to seek KPNA2-related factors. IRF2 protein and mRNA levels were lower than that in Considering that the regulation of KPNA2 in OS was hFOB1.19 cells (Figure 3(a)). Downregulation of IRF2 or KPNA2 in U2OS cells was achieved by lentiviral transductions focused on the transcriptional level, we performed a bioin- formatic analysis to find a transcription factor (TF) in the of IRF2 or KPNA2 knockdown vectors (shKPNA2 and KPNA2 promoter region. Then, we extracted 147 KPNA2 shIRF2), as confirmed by western blot (Figure 3(b)). Regard- TFs from online TFBIND dataset. Data from TARGET data- ing the malignant phenotypes of OS cells, KPNA2 knockdown sets showed that a total of 9792 genes were negatively asso- inhibited cell viability, proliferation while IRF2 knockdown had the opposite effects and partially rescued above- ciated with KPNA2 in OS (R >0:2, FDR < 0:05). We further found that 5950 genes overlapped in downregulated mentioned malignant phenotypes suppressed by KPNA2 gens from the GSE157322 dataset, and 31 genes were over- knockdown in vitro (Figures 3(c)–3(d)). In vivo, KPNA2 knockdown markedly reduced tumor weight and tumor vol- lapped in 147 KPNA2 TFs. Therefore, a total of 26 genes were at the intersection of three (Figure 2(a)). Under the umes while IRF2 knockdown promoted tumor growth, and IRF2 knockdown could weaken KPNA2 knockdown- same conditions, a total of 382 genes were positively corre- lated with KPNA2 in OS. After the intersection of three gene medicated tumor growth inhibition (Figures 3(e) and 3(f)). sets, only one gene, MYB, was located in the center circle The KI67 expression was reduced by KPNA2 knockdown while elevated by IRF2 knockdown (Figure 3(g)). These find- (Figure S1(a)). However, subsequent experiments showed that MYB was not differentially expressed in OS and was ings demonstrated that IRF2 silence partially attenuates the impact of KPNA2 knockdown on OS growth. not regulated by KPNA2 (Figure S1(b) and 1(c)). According to the correlation index of these 26 genes with KPNA2, we have chosen the top 5 TFs of KPNA2, including 3.4. IRF2/KPNA2 Might Regulate Migration and Invasion of Osteosarcoma Cells, as Well as Regulate EMT Process. RFX1, STAT3, IRF2, PPARA, and MZF1. To assess whether these five TFs could alter KPNA2 expression, we overex- Regarding the malignant phenotypes of migration and inva- pressed these TFs in two OS cell lines. As shown in sion, KPNA2 knockdown inhibited the abilities of migration Figure 2(b), these results demonstrated that only IRF2 over- and invasion while IRF2 knockdown had the opposite effects expression significantly suppressed KPNA2 expression in and partially rescued above-mentioned malignant pheno- types suppressed by KPNA2 knockdown OS cells two OS cell lines, while the other four TFs had no significant effect on KPNA2 expression. Meanwhile, due to the fact that (Figure 4(a) and 4(b)). To investigate whether IRF2/KPNA2 KPNA2 was overexpressed in four OS cell lines, we also expression is correlated with other molecular alterations in determined the change of these five TFs after KPNA2 knock- OS, we evaluated several molecules that are associated with down using RT-qPCR. The silence of KPNA2 markedly EMT in tumor progression [24]. Then, the EMT-related proteins (E-cadherin, N-cadherin, and Vimentin) were upregulated the mRNA levels of IRF2, while the other four TFs did not obviously changed response to the elimination detected by Western blotting. As shown in Figure 4(c), of KPNA2 (Figure 2(c)). Therefore, we decided to use IRF2 shKPNA2 significantly elevated E-cadherin while reduced for subsequent experiments. N-cadherin and Vimentin expression in both U2OS and Through the GSE36001 dataset, IRF2 expression was MG-63 cells. In contrast, shIRF2 had the opposite effect on these EMT-related proteins. These findings indicated that mildly downregulated in OS tissues without significant dif- ferences (Figure 2(d)), while IRF2 expression was strongly knockdown of KPNA2 may inhibite EMT progress while negatively correlated with KPNA2 (Figure 2(e)). Consis- IRF2 silence promoted EMT progress of OS cells. Journal of Oncology 5 KPNA2 expression ⁎⁎⁎⁎ ⁎⁎⁎ p = 0.000 8 0 –100 Non-tumor OS Normal OS (a) (b) KPNA2 Normal OS (c) Normal OS Normal OS Normal OS Normal OS ⁎⁎⁎ 58 kDa ⁎⁎⁎ KPNA2 1# 2# 3# 4# 4 ⁎⁎⁎ GAPDH 36 kDa 3 ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ Normal OS Normal OS Normal OS Normal OS 58 kDa KPNA2 5# 6# 7# 8# 1# 2# 3# 4# 5# 6# 7# 8# GAPDH 36 kDa Normal OS (d) KPNA2 58 kDa GAPDH 36 kDa ⁎⁎ ⁎⁎ ⁎⁎ (e) Figure 1: High expression of KPNA2 in osteosarcomas samples. (a) KPNA2 mRNA expression in OS and nontumor tissues based on data from GSE36001 dataset. (b) KPNA2 mRNA levels were detected in 25 pairs of OS samples and their adjacent normal samples by RT-qPCR. (c) Representative immunohistochemical (IHC) images of KPNA2 expression in OS tissues and normal tissues. (d) KPNA2 protein expression in 8 pairs of clinical OS samples were examined by western blotting. (e) KPNA2 expression in four OS cell lines and one normal hFOB1.19 cells using RT-qPCR and western blot assays. The data was presented as the mean ± SD from three independent ∗ ∗∗ ∗∗∗ experiments. p <0:05, p <0:01 and p <0:001. KPNA2 Relative mRNA level of KPNA2 hFOB1.19 U-2OS HOS Relative mRNA level of KPNA2 Saos2 MG-63 Relative mRNA level of KPNA2 6 Journal of Oncology 1.5 1.5 1.5 1.0 1.0 1.0 ⁎⁎ ⁎⁎ 0.5 0.5 0.5 DEGs Negative-correlation 0.0 0.0 0.0 U2OS MG-63 U2OS MG-63 U2OS MG-63 7417 5924 3837 (42.8%) (34.2%) (22.2%) NC NC NC RFX1 STAT3 IRF2 (0.2%) 6 5 (0%) (0%) 1.5 1.5 (0.6%) 1.0 1.0 0.5 TF factors 0.5 0.0 0.0 U2OS MG-63 U2OS MG-63 NC shNC MZF1 shPPARA (a) (b) IRF2 expression ⁎⁎⁎ ⁎⁎ Nontumor OS RFX1 STAT3 IRF2 PPARA MZF1 TISSUE MG-63-shNC U2OS-shNC Nontumor MG-63-shKPNA2 U2OS-shKPNA2 OS (c) (d) 15 ⁎⁎⁎ R = –0.22, p = 0.039 p = 0.000 11 0 –2 68 10 12 Normal OS IRF2 (e) (f) Figure 2: Continued. Relative gene expression KPNA2 Relative expression of KPNA2 Relative expression of KPNA2 Relative expression of KPNA2 Relative mRNA of KPNA2 Relative expression of KPNA2 IRF2 Relative expression of KPNA2 Journal of Oncology 7 1.5 U2OS MG-63 1.5 1.5 ⁎⁎⁎ ⁎⁎⁎ 1.0 1.0 1.0 0.5 0.5 0.5 0.0 0.0 0.0 KPNA2-WT KPNA2-MUT KPNA2-WT KPNA2-MUT NC NC IRF2 IRFC hFOB1.19 MG-63 U2OS (g) (h) Figure 2: Identification of KPNA2 transcription factor (TF) (a) Venn diagram displayed the overlap among differentially expressed protein- coding genes (DEGs) from GSE157322, KPNA2 negative-related genes from TARGET dataset and TF factors of KPNA2 from online TFBIND dataset. (b) The mRNA expression of KPNA2 in response to overexpression of five candidate genes (RFX1, STAT3, IRF2, PPARA, and MZF1) in U2OS and MG-63 cells. (c) The mRNA expression of five candidate genes (RFX1, STAT3, IRF2, PPARA, and MZF1) in response to KPNA2 silence in U2OS and MG-63 cells. (d) Expression of IRF2 mRNA in OS and nontumor tissues based on data from GSE36001 dataset. (e) The negatively correlation between KPNA2 and IRF2 was analyzed under a Pearson correlation analysis according to TARGET database. (f) IRF2 mRNA expression in 25 pairs of OS and normal samples. (g) The enrichment of IRF2 bind on the KPNA2 promoter was significantly reduced in two OS cell lines when compared to hFOB1.19 cells from ChIP and qRT-PCR assays. (h) Luciferase assays were performed to detect the luciferase activity of KPNA2-WT and KPNA2-MUT after IRF2 overexpression in ∗ ∗∗ ∗∗∗ U2OS and MG-63 cells. The data was presented as the mean ± SD from three independent experiments p <0:05, p <0:01, and p < 0:001. 3.5. Functions and Pathway Enrichments Related to KPNA2 itive correlations with KPNA2 expression (Figure 6(a)). Fur- and IRF2. GO and KEGG enrichment analysis of the upreg- thermore, the Pearson correlation scatter plot further also ulated genes in the high- and low-IRF2 subgroups were per- presented these findings (Figure 6(b)). These data suggested formed. The results of the GO enrichment analysis showed that plasma cells might play a key role in the IRF2/KPNA2- that the upregulated genes in the high-IRF2 subgroup mediated osteosarcoma-immune interaction. mainly played a major role in several biological processes, such as the positive regulation of cell adhesion and the reg- 4. Discussion ulation of the immune effector process (Figure 5(a)). The results of the KEGG enrichment analysis showed that these Osteosarcomas are relatively rare but devastating [5]. Unfor- upregulated genes were involved in osteoclast differentiation tunately, the introduction of new adjuvant chemotherapy and the NF-kappa B signaling pathway and others. after aggressive surgical resection has temporarily improved (Figure 5(b)). Similarly, GO analysis revealed that several overall 10-year survival but has not significantly improved hallmarks of the tumor were enriched in the high-KPNA2 patient survival since the 1990s [25]. Therefore, it is of great subgroup, such as DNA replication and cell cycle G2/M significance to identify new molecules, which further helps phase transition, while genes in the low KPNA2 subgroup to develop effective methods to diagnose and treat this was associated with immune response, bone resorption and malignant bone tumor. Here, we propose a mechanism for bone remodeling (Figure 5(c)). KEGG showed that KPNA2 the role of KPNA2 in OS pathogenesis: KPNA2 may trans- was related to cell cycle, DNA replication, and osteoclast dif- port IRF2 into the nucleus where it regulates transitivity, ferentiation (Figure 5(d)). These results are derived from the triggering EMT and subsequent malignant biological prop- enrichment of upregulated genes related to IRF2 and erties of OS cells (Supplementary material, Graphical KPNA2 based on the TARGET dataset, which is helpful Abstract). This evidence may provide new ideas for the diag- for researchers to find possible research directions when nosis and treatment of osteosarcoma. studying the functions of IRF2/KPMA2 in OS progression. Recently, several studies have linked KPNA2 to various cancers, such as lung, breast, colon, and pulmonary cancer. 3.6. The Key Infiltrating Immune Cell Related to IRF2/ High KPNA2 was positively related to cancer invasiveness KPMA2 in the Osteosarcoma Microenvironment. Based on and poor prognosis, thus regarded KPNA2 as a potentially the TARGET dataset, all OS samples with low and high relevant therapeutic target for patients with different cancers immune score, respectively, were eligible for CIBERSORT [26]. KPNA2 was involved in several cellular biological pro- (p <0:05). The correlations among the 22 TIICs ranged cesses, including cell differentiation, development, viral from weak to moderate. Obviously, plasma cells showed infection, immune response, and transcriptional regulation highly negative correlations with IRF2 expression, while pos- [6]. Similarly, our study illustrated that KPNA2 was IRF2-ChIP IgG-ChIP IRF2-ChIP IgG-ChIP IRF2-ChIP IgG-ChIP Relative enrichment fold Relative luciferase activity (Fire/Renilla value) Relative luciferase activity (Fire/Renilla value) 8 Journal of Oncology IRF2 50 kDa GAPDH 36 kDa 1.5 2.0 ⁎⁎⁎ ⁎⁎⁎ 1.0 1.5 KPNA2 ⁎⁎ 58 kDa ⁎⁎ 1.0 ⁎⁎ ⁎⁎ 0.5 ⁎⁎⁎ IRF2 50 kDa 0.5 ⁎⁎⁎ GAPDH 36 kDa 0.0 KPNA2 IRF2 shCtrl shKPNA2 shIRF2 (a) (b) ⁎⁎⁎ shCtrl shKPNA2 shIRF2 shKPNA2 + shIRF2 4 300 ⁎⁎⁎ ⁎⁎ 3 ⁎⁎ U2OS ⁎⁎ 2 ⁎⁎⁎ 100 ⁎⁎⁎ ⁎⁎⁎ MG-63 U2OS MG-63 shCtrl shKPNA2 Time (day) shIRF2 shKPNA2 + shIRF2 shCtrl shKPNA2 shIRF2 shKPNA2 + shIRF2 (c) (d) ⁎⁎⁎ ⁎ ⁎⁎ 1.0 ⁎⁎ 0.8 shCtrl 0.6 shKPNA2 ⁎⁎ 0.4 shIRF2 0.2 shKPNA2 + shIRF2 10 15 20 25 Days post-tumor inoculation shCtrl shIRF2 shKPNA2 shKPNA2 + shIRF2 (e) (f) Figure 3: Continued. OD450 (fold) Relative mRNA level of IRF2 hFOB1.19 U-2OS HOS Saos2 MG-63 Tumor weight (g) shCtrl shKPNA2 shIRF2 shCtrl shKPNA2 + shIRF2 shKPNA2 shIRF2 Tumor volume (mm ) Relative protein level Colony numbers Journal of Oncology 9 shCN shKPNA2 shIRF2 shKPNA2 + shIRF2 HE 100 um KI67 100 um (g) Figure 3: Effects of IRF2 and KPNA2 on cell proliferation, colony formation, and tumor growth of OS cells in vitro and in vivo. (a) Western blot and RT-qPCR analyses were performed to measure the relative mRNA and protein levels of IRF2 in four OS cell lines including U2OS, HOS, Saos2, and MG-63 control to the hFOB1.19 cells. (b) U2OS cells were cotransfected with shKPNA2 or shIRF2 plasmids, and the protein expression of KPNA2 and IRF2 was assessed using western blotting assay. (c) Cell viability was determined using an CCK-8 kit at different time points. (d) Cells were seeded in plates and grown for 14 days. (Left) Cell colonies were stained with 0.1% crystal violet. (Right) Colony numbers were quantified. (e–f) In vivo tumor growth. U2OS cells were injected into mice and tumor weight (e) and tumor volumes (f) were measured every 5 or 3 days for 24 days posttumor inoculation. n =3. (g) Tumor tissues were separated from mice for HE and IHC staining for detecting KI67 expression. Scale bar = 100um. The data was presented as the mean ± SD from three ∗ ∗∗ ∗∗∗ independent experiments p <0:05, p <0:01, and p <0:001. dramatically elevated in OS samples compared to normal EMT is regulated by various signaling pathways, includ- samples. Although KPNA2 has been shown to be frequently ing NF-κB, Wnt, and transforming growth factor-β [13]. In expressed in OS as a new marker for the diagnosis, as well as our study, the key outcome is that migration and invasion of in chondrosarcoma and Ewing sarcomas [27], the functions OS cells were significantly inhibited by deletion of KPNA2, of KPNA2 in osteosarcoma are unclear. In the present study, which also resulted in a decrease in the EMT characteristics data mining and bioinformatics analysis indicated that of OS cells; epithelial cell markers were increased, and mes- KPNA2 was overexpressed in OS patients from GSE36001 enchymal markers were decreased. However, the opposite results were found after IRF2 was knocked down. KPNA2 dataset, and the experiments verified high KPNA2 level in clinical OS samples and OS cell lines. Furthermore, KPNA2 and IRF2 have opposite regulatory effects on the activation knockdown inhibited the proliferation, migration, and inva- of EMT progress of OS cells. Thus, KPNA2 might contribute sion in two OS cell lines, and remarkably reduced tumor to the progression of OS by negatively regulating IRF2 via weight and tumor volumes in vivo. These findings revealed promoting EMT. KPNA2 is involved in the nucleocytoplasmic transport that KPNA2 might play a crucial role in the biological prog- ress of OS. pathway of multiple tumor-associated proteins and is Interferon regulatory factor-2 (IRF2) exerted antitumor overexpressed in various cancers thereby being suggested effects in several human cancers. For example, IRF2 could as a prospect in the diagnosis and treatment of cancer suppress cell proliferation and migration ability and promote [6]. Given that IRF2 has the ability to exert antioncogenic activities, IRF2 overexpression led to a dramatic cell death cell apoptosis in nonsmall cell lung cancer cells [28]. IRF2 might play as a tumor suppressor by regulating P53 signaling response by apoptosis in hepatocellular carcinoma [31]. in gastric cancer [29]. Furthermore, IRF2 was shown to serve These are consistent with our results of functional enrich- as a tumor suppressor in patients with hepatocellular carci- ment, showing these genes are related to DNA replication noma, whose inactivation led to impaired TP53 function and cell cycle processes. Furthermore, it could be observed that KPNA2 was positively correlated with plasma cell [30]. The current study highlighted that KPNA2 could nega- tively alter IRF2 expression in OS cells. Meanwhile, by data level, while IRF2 had a negative relationship with plasma mining in the GSE157322 and TARGET datasets, we discov- cell level. Infiltrating immune cell subsets detected by ered that IRF2 could bind to the KPNA2 promoter and acti- CIBERSORT analysis can reflect the time course of innate vate KPNA2 expression by bioinformatic analysis for TF and adaptive immune responses in OS. CIBERSORT may have the potential to characterize the detail of infiltrating prediction. This underlying mechanism was consistent with a previous report that IRF2 could bind to the miR-1227 immune cells in OS tissues and provide novel insights into promoter, thus inhibiting tumor growth [21]. Furthermore, the pathogenesis of OS. To our knowledge, these two IRF2 was obviously downregulated, which was negatively genes are the first to link immune cell levels in the osteo- associated with KPNA2 in OS. More importantly, IRF2 sarcoma microenvironment in this study, with the hope of providing guidance for the next study of relevant molecu- knockdown promoted malignant behaviors, which were seemingly suppressed by KPNA2 knockdown. These rescu- lar mechanisms. ing effects of IRF2 on KPNA2 were also reflected in tumor However, most of the experiments in our study are per- growth in vivo. These findings demonstrated that IRF2 formed using isolated tumor cell lines cultured in vitro or silence might partially attenuate the impact of KPNA2 immunodeficient nude mice with human U2OS xenografts, which do not account for any tumor-extrinsic effects that knock-down on osteosarcoma progressions. 10 Journal of Oncology shKPNA2 shCtrl shKPNA2 shIRF2 + shIRF2 1.5 0 h ⁎⁎ 1.0 24 h ⁎⁎ ⁎⁎⁎ 0.5 0.0 0 h U2OS MG-63 shCtrl shKPNA2 shlRF2 24 h shKPNA2 + shlRF2 (a) 2.5 ⁎⁎ shKPNA2 ⁎⁎ 2.0 shCtrl shKPNA2 shIRF2 + shIRF2 1.5 U2OS ⁎⁎ 1.0 0.5 ⁎⁎ ⁎⁎ 0.0 MG-63 U2OS MG-63 shCtrl shKPNA2 shlRF2 shKPNA2 + shlRF2 (b) U2OS MG-63 shCtrl shKPNA2 shIRF2 shCtrl shKPNA2 shIRF2 135 kDa E-cadherin 140 kDa N-cadherin 57 kDa Vimentin GAPDH 36 kDa (c) Figure 4: KPNA2 promoted the activation of EMT through IRF2. (a) The cell migration capacity of OS cells was examined using wound healing assays at 0 and 24 h. (b) Cells were stained with 0.1% crystal violet (left) Bars = 50 μm. Cell numbers were quantified to value the invasive capacity of OS cells after different transfection (right). (c) Protein levels of EMT-related proteins (E-cadherin, N-cadherin, and ∗ ∗∗ Vimentin) in two OS cells (MG-63 and U2OS). Data were presented as mean ± SD from three independent experiments p <0:05, p < ∗∗∗ 0:01, and p <0:001. MG-63 U2OS Cell migration fold distance Invasion cells Journal of Oncology 11 KPNA2 IRF2 Upregulated GO Upregulated GO P.adjust P.adjust 1.703207e–28 2.829501–38 Positive regulation of cell adhesion Organelle fission Regulation of immune effector process Nuclear division 1.234542e–25 Positive regulation of cell activation Chromosome segregation 6.716633e–25 Positive regulation of leukocyte activation Mitotic nuclear division Positive regulation of lymphocyte activation Nuclear chromosome segregation 2.467381e–25 1.343367e–24 Leukocyte proliferation DNA replication Mononuclear cell proliferation Cell cycle G2/M phase transition Regulation of leukocyte proliferation 3.700220e–25 G2/M transition of mitotic cell cycle 2.015060e–24 Regulation of mononuclear cell proliferation Sister chromatid segregation Regulation of lymphocyte proliferation Mitotic sister chromatid segregation 4.933059e–25 2.686733e–24 0.06 0.07 0.08 0.06 0.07 0.08 0.09 0.10 Generation Generation Count Count 70 90 70 100 80 100 80 110 Downregulated GO Downregulated GO P.adjust P.adjust 4e–04 Cartilage development Neutrophil activation Pyruvate metabolic process Neutrophil degranulation 6e–04 0.003 Purine nucleoside diphosphate metabolic process Neutrophil activation involved in immune response Purine ribonucleoside diphosphate metabolic process Regulation of peptidase activity Ribonucleoside diphosphate metabolic process Endothelial cell migration 0.006 8e–04 ADP metabolic proc Tissue remodelling NADH regeneration Response to interferon–gamma Interferon–gamma–mediated Canonical glycolysis 6e–04 0.009 Signaling pathway Glucose catabolic process to pyruvate Bone resorption Glycolytic process through glucose –6–phosphate Bone remodelling 1e–03 0.015 0.020 0.025 0.030 0.035 0.03 0.04 0.05 0.02 Generation Generation Count Count 8 16 15 35 12 20 20 40 25 45 (a) (c) Upregulated KEGG P.adjust Upregulated KEGG P.adjust Epstein–Barr virus infection Herpes simplex virus 1 infection NOD–like receptor signaling pathway Cell cycle Cell adhesion molecules 1e–08 Nucleocytoplasmic transport 0.0004 Phagosome Spliceosome Toxoplasmosis Oocyte meiosis Osteoclast differentiation Fanconi anemia pathway 0.0008 2e–08 NF–kappa B signaling pathway Progesterone–mediated oocyte maturation Hematopoietic cell lineage DNA replication Pertussis 3e–09 Homologous recombination 0.0012 Viral myocarditis Mismatch repair 0.03 0.06 0.09 0.04 0.05 0.06 0.07 Generation Generation Count Count 10 40 25 35 20 50 30 40 Downregulated KEGG P.adjust Downregulated KEGG P.adjust Lysosome 0.01 0.02 Oxidative phosphorylation Glycolysis/ gluconeogenesis 0.014798 0.03 Osteoclast differentiation 0.04 0.02 0.04 0.06 0.08 0.00 0.039 0.042 0.045 0.048 0.051 Generation Generation Count Count 17 20 18 21 19 22 (b) (d) Figure 5: Functions and Pathway enrichments related to KPNA2 and IRF2. (a) Dotplot of the top 10 enriched terms across the upregulated- genes in the high-IRF2 and low-IRF2 subgroups using GO cluster analysis, colored according to p-value. (b) Dotplot of enriched pathways of upregulated genes in the high-IRF2 and low-IRF2 subgroups using KEGG analysis, colored according to p-value. (c) Dotplot of the top 10 enriched terms across the upregulated-genes in the high-KPNA2 and low-KPNA2 subgroups using GO cluster analysis, colored according to p-value. (d) Dotplot of enriched pathways of the upregulated genes in the high-KPNA2 and low-KPNA2 subgroups using KEGG analysis, colored according to p-value. 12 Journal of Oncology ⁎⁎⁎ IRF2 ⁎ ⁎⁎⁎ KPNA2 ⁎ ⁎⁎⁎ B_cells_naive_CIBERSORT ⁎⁎⁎ B_cells_memory_CIBERSORT ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ Plasma_cells_CIBERSORT ⁎⁎⁎ T_cells_CD8_CIBERSORT ⁎⁎⁎ T_cells_CD4_naive_CIBERSORT ⁎ ⁎⁎⁎ T_cells_CD4_memory_resting_CIBERSORT ⁎ ⁎⁎⁎ T_cells_CD4_memory_activated_CIBERSORT ⁎⁎⁎ ⁎⁎⁎ T_cells_follicular_helper_CIBERSORT ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ T_cells_regulatory_tregs_CIBERSORT ⁎⁎⁎ T_cells_gamma_delta_CIBERSORT ⁎⁎⁎ ⁎⁎ ⁎⁎⁎ NK_cells_resting_CIBERSORT ⁎⁎⁎ ⁎⁎⁎ NK_cells_activated_CIBERSORT ⁎ ⁎⁎⁎ Monocytes_CIBERSORT ⁎⁎ ⁎⁎⁎ ⁎ ⁎⁎ ⁎⁎⁎ Macrophages_M0_CIBERSORT ⁎⁎⁎ ⁎ ⁎⁎ ⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ Macrophages_M1_CIBERSORT ⁎ ⁎⁎⁎ ⁎⁎⁎ Macrophages_M2_CIBERSORT ⁎ ⁎⁎⁎ Dendritic_cells_ resting_CIBERSORT ⁎⁎⁎ Dendritic_cells_ activated_CIBERSORT ⁎⁎⁎ Mast_cells_resting_CIBERSORT ⁎⁎ ⁎ ⁎⁎⁎ ⁎⁎ ⁎⁎⁎ Mast_cells_activated_CIBERSORT ⁎⁎⁎ Eosinophils_CIBERSORT ⁎ ⁎⁎⁎ Neutrophils_CIBERSORT –1 –0.8 –0.6 –0.4 –0.2 0 0.2 0.4 0.6 0.8 1 (a) TRAGET–OS TRAGET–OS 0.20 0.20 R = –0.33, p = 0.016 R = –0.26, p = 0.014 0.15 0.15 0.10 0.10 0.05 0.05 0.00 0.00 6 7 8 9 2 3 4 5 6 KPNA2 IRF2 (b) Figure 6: The key infiltrating immune cell related to IRF2/KPMA2 in the osteosarcoma microenvironment. (a) Correlation matrix of all 22 TIICs proportions with IRF2 and KPNA2. (b) Correlation scatter plots of plasma cell level with IRF2 and KPNA2 based on the TARGET-OS dataset using Pearson’s correlation analysis. the candidate signaling pathway may have on immune cell treatment of OS with restraint of KPNA2 or IRF2 overex- interactions with tumor cells. This limitation suggests that pression may be extra to other therapeutic interventions studying tumor-immune interactions will be a potential for the development of this disease. future direction that will extend from this study. Taken together, our results illustrated that KPNA2 regu- Data Availability lated OS development, as well as IRF2 play a potential upstream TF of KPNA2 in regulating EMT progress. This All data in the results of this study can be obtained on a rea- may provide a novel target for OS therapy. Therefore, the sonable request from the corresponding authors. Public data Plasma_cells_CIBERSORT IRF2 KPNA2 B_cells_naive_CIBERSORT B_cells_memory_CIBERSORT Plasma_cells_CIBERSORT Plasma_cells_CIBERSORT T_cells_CD8_CIBERSORT T_cells_CD4_naive_CIBERSORT T_cells_CD4_memory_resting_CIBERSORT T_cells_CD4_memory_activated_CIBERSORT T_cells_CD4_memory_activated_CIBERSORT T_cells_follicular_helper_CIBERSORT T_cells_gamma_delta_CIBERSORT NK_cells_resting_CIBERSORT NK_cells_activated_CIBERSORT Monocytes_CIBERSORT Macrophages_M0_CIBERSORT Macrophages_M1_CIBERSORT Macrophages_M2_CIBERSORT Dendritic_cells_resting_CIBERSORT Dendritic_cells_activated_CIBERSORT Mast_cells_resting_CIBERSORT Mast_cells_activated_CIBERSORT Eosinophils_CIBERSORT Neutophils_CIBERSORT Journal of Oncology 13 sources include GSE36001, GSE157322, and TARGET data- [3] S. J. Taran, R. Taran, and N. B. Malipatil, “Pediatric osteosar- coma: an updated review,” Indian Journal of Medical and Pae- base online. diatric Oncology, vol. 38, no. 1, pp. 33–43, 2017. [4] J. L. Ferguson and S. P. 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IRF2 Destabilizes Oncogenic KPNA2 to Modulate the Development of Osteosarcoma

Journal of Oncology , Volume 2022 – Sep 26, 2022

IRF2 Destabilizes Oncogenic KPNA2 to Modulate the Development of Osteosarcoma

Abstract

Osteosarcomas (OS) are the most common primary malignant bone tumor. Emerging evidence revealed that karyopherin alpha 2 (KPNA2) was strongly associated with the tumorigenesis and development of numerous human cancers. The aim of the present study was to investigate the expression pattern, biological functions, and underlying mechanism of KPNA2 in OS. Bioinformatics TFBIND online was applied to forecast transcription factor (TF) binding sites in the promoter region of KPNA2. The expression profile of KPNA2 in OS tissues were firstly assessed. CCK8, colony formation, wound healing, and Transwell assays were used to assess cell viability, proliferation, and migration in vitro, and in vivo experiments were performed to explore the effects of KPNA2 and interferon regulatory factor-2 (IRF2) on tumor growth. Furthermore, the correlation between IRF2 and KPNA2 was investigated using chromatin immunoprecipitation (ChIP), RT-qPCR, western blot, and dual-luciferase assays. KPNA2 was obviously upregulated, while IRF2 decreased significantly in OS tissues and cell lines, as well as negatively correlated with each other. KPNA2 removal remarkably suppressed OS cell growth, migration, invasion in vitro, and tumor growth in vivo, while IRF2 knockdown exerts an opposing effect. IRF2 binds to the KPNA2 promoter to modulate the malignant phenotypes of OS cells by regulating epithelial-to-mesenchymal transition (EMT). The present study demonstrated that KPNA2 performed the oncogenic function, possibly regulating tumor development through EMT. Importantly, it was confirmed that IRF2 serves as a potential upstream TF of KPNA2 involved in the regulation of EMT progress in OS.

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Abstract

Hindawi Journal of Oncology Volume 2022, Article ID 9973519, 14 pages https://doi.org/10.1155/2022/9973519 Research Article IRF2 Destabilizes Oncogenic KPNA2 to Modulate the Development of Osteosarcoma 1 2 Shuchi Xia and Yiqun Ma Department of Dentistry, Zhongshan Hospital, Fudan University, Shanghai 200032, China Department of Orthopedics, Zhongshan Hospital, Fudan University, Shanghai 200032, China Correspondence should be addressed to Yiqun Ma; mayiqun090@163.com Received 29 June 2022; Accepted 24 August 2022; Published 26 September 2022 Academic Editor: Shinji Miwa Copyright © 2022 Shuchi Xia and Yiqun Ma. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Osteosarcomas (OS) are the most common primary malignant bone tumor. Emerging evidence revealed that karyopherin alpha 2 (KPNA2) was strongly associated with the tumorigenesis and development of numerous human cancers. The aim of the present study was to investigate the expression pattern, biological functions, and underlying mechanism of KPNA2 in OS. Bioinformatics TFBIND online was applied to forecast transcription factor (TF) binding sites in the promoter region of KPNA2. The expression profile of KPNA2 in OS tissues were firstly assessed. CCK8, colony formation, wound healing, and Transwell assays were used to assess cell viability, proliferation, and migration in vitro, and in vivo experiments were performed to explore the effects of KPNA2 and interferon regulatory factor-2 (IRF2) on tumor growth. Furthermore, the correlation between IRF2 and KPNA2 was investigated using chromatin immunoprecipitation (ChIP), RT-qPCR, western blot, and dual-luciferase assays. KPNA2 was obviously upregulated, while IRF2 decreased significantly in OS tissues and cell lines, as well as negatively correlated with each other. KPNA2 removal remarkably suppressed OS cell growth, migration, invasion in vitro, and tumor growth in vivo, while IRF2 knockdown exerts an opposing effect. IRF2 binds to the KPNA2 promoter to modulate the malignant phenotypes of OS cells by regulating epithelial-to-mesenchymal transition (EMT). The present study demonstrated that KPNA2 performed the oncogenic function, possibly regulating tumor development through EMT. Importantly, it was confirmed that IRF2 serves as a potential upstream TF of KPNA2 involved in the regulation of EMT progress in OS. 1. Introduction Karyopherin alpha 2 (KPNA2, 58 kDa) is one of seven members of the karyopherin α-family [6, 7]; Dysregulation of KPNA2 had been reported serving as a potential bio- Osteosarcoma (OS) is the frequent primary malignant bone tumor, affecting mainly pediatric and adolescents [1], which marker in several malignancies, including breast cancer [8], is composed of malignant mesenchymal cells producing gastric cancer [9], lung cancer [10], and glioma [11]. KPNA2 osteoid and/or immature bone [2]. It typically forms in served as the adaptor to transfer p65 to the nucleus to iden- the metaphysis of long bones, specifically the proximal tibia, tify the classic nuclear localization signal [7, 12]. Emerging the distal femur, and the proximal humerus, accompanied data suggest a role for the epithelial-mesenchymal transition by swelling and pain [3]. It is common that metastasis to (EMT) in the regulation of cellular plasticity in normal adult the lungs of OS [4]. Before the use of neoadjuvant and adju- tissues and tumors, where they can generate multiple and vant chemotherapy, approximately 90% of OS patients died distinct cellular subpopulations that contribute to intratu- from lung metastases [5]. OS is characterized by high levels moural heterogeneity [13]. EMT is important for invasion, of genomic instability. However, the molecular basis metastasis, and drug-resistance of cancer cells. A previous involved in OS remains unclear and continuing to seek study showed that KPNA2 silence may reduce ovarian carci- new treatments is urgently needed to continue looking for noma migration and invasion by inhibiting Akt/GSK-3β/ new treatments. Snail pathway and suppressing EMT [14]. In addition, 2 Journal of Oncology patients. No patients had undergone chemotherapy, radia- KPNA2 was involved in the regulation of autophagy and the EMT of glioblastoma cells [15]. However, the molecular tion therapy, or other related targeted therapy before sur- pathways regulated by KPNA2-mediated EMT in OS are gery. The diagnosis of OS was confirmed by at least two yet to be elucidated. pathologists. All surgical tissue samples used in our study Transcription factors (TFs), could specifically recognize were immediately placed in liquid nitrogen and then stored DNA through consensus sequences, thus to control chroma- at -80 C until use. tin and transcription, guiding expression of the genome [16]. 2.3. RT-qPCR. Total RNA was isolated from OS cell lines To identify the TF of KPNA2 transcription, the online soft- and tissue samples with the TRIzol™ Reagent (Invitrogen) ware of TFBIND (http://tfbind.hgc.jp/) was employed to according to the manufacturer’s protocol. Complementary identify putative binding sites in the promoter region of DNA (cDNA) synthesis was performed using the Prime- KPNA2. Specifically, several canonical IRF2-binding sites Script RT reagent kit (Takara) for mRNA expression analy- in the promoter region of KPNA2 were observed, and sis. The cDNA was applied to perform qRT-PCR assay with IRF2 was then extracted from 147 candidate genes. IRF2, SYBR Premix Ex Taq kit (TaKaRa) following the protocols. belonging to the IRF family, was widely expressed in various ΔΔCt The 2 − method was used to analyze the difference in tissues [17]. Among several types of cancer, belong to the the level of mRNA between different groups. Primers used IRF family, IRF1 signaling pathways may directly induce p21-dependent G0/G1 cell cycle and p21-independent mod- in this study were listed as follows: KPNA2, 5 -ATTGCA ulation of survival [18]. IRF2 was shown to serve as an ′ ′ GGTGATGGCTCAGT-3 (forward) and 5 -CTGCTCAAC important regulator in acute myeloid leukemia by targeting ′ ′ AGCATCTATCG-3 (reverse); IRF2, 5 -CATGCGGCTAG INPP4B [19]. A recent study has indicated that IRF2 was ′ ′ ACATGGGTG-3 (forward) and: 5 -GCTTTCCTGTATGG able to suppress the strengthening of cell migration and ATTGCCC-3 (reverse); The GAPDH was used as an inter- invasion in OS, which were mediated by miR-18a-5p [20]. nal control and was detected using the following primers: 5 - Similarly, IRF2 was not expressed or at a low level in OS tis- ′ ′ AATCCCATCACCATCTTCC-3 (forward) and 5 -AGTC sues [21]. Intriguingly, IRF was already identified as a func- CTTCCACGACCAA-3 (reverse). tional TF in nonsmall cell lung cancer (NSCLC) that suppressed KPNA2 expression [22]. Therefore, we speculate 2.4. Lentiviral Vector Encoding shRNA Plasmids. KPNA2 that IRF2 may negatively regulated KPNA2 as its upstream cDNA was cloned into the Lenti-OE vector (Genepharma, TF to modulate OS progression. Shanghai, China) to generate KPNA2-overexpressing lenti- This study aimed to investigate the role of IRF2 in mod- viral vectors. Short hairpin RNAs (shRNAs) were designed ulating KPNA2 expression, which may serve an important by the RiboBio Co., Ltd. (Guangzhou, China). Lentiviral vec- role in p65 nuclear importation in the progression of OS. tors encoding KPNA2 and IRF2 were synthesized and pack- Here, we found that KPNA2 deficiency suppressed the aged by Genepharma company. malignant behaviors of OS cells, and that the underlying mechanisms involved were regulated by IRF2 and associated 2.5. Cell Proliferation Assay. The cell proliferation assay was with EMT progress. Taken together, our findings demon- performed using a Cell Counting Kit (CCK-8, Dojindo, strated that KPNA2 may serve as a new potential prognostic Japan) following the manufacturer’s instructions. Cells at a indicator and therapeutic target for OS. density of 5×10 were added into the 96-well plate and 10 microliters of CCK-8 solution was added to each well at 1, 2. Materials and Methods 2, 3, 4 and 5 days at 37 C. An additional 1 h later, the absor- bance at wavelength of 450 nm was measured under a 2.1. Cell Culture. Four human OS cell lines (Saos-2, HOS, microplate reader. U2OS, and MG-63) and human fetal osteoblasts cell line (hFOB 1.19) were obtained from the Cell Bank of Type 2.6. Colony Formation Assay. For the colony formation Culture Collection of the Chinese Academy of Sciences assay, 500 cells were plated into each well of a 6-well culture (Shanghai, China). Cells were grown in Dulbecco’s modified plate. The plates containing DMEM were incubated at 37 C Eagle medium (DMEM; Gibco) harboring with 10% fetal for 2 weeks. After being washed with PBS for three times, bovine serum (FBS) (Gibco). All OS cell lines were cultured cells were fixed with 4% paraformaldehyde for 10 min at in a humidified incubator under 5% CO at 37 C, while 2 room temperature, followed by staining with 0.5% crystal hFOB1.19 cells were grown at 34 C. Mycoplasma-microbial violet solution for another 20 min. Lastly, the visible colonies contamination examination and STR profiling were checked of more than 50 cells were manually counted and imaged to confirm the genotypes. under a microscope. 2.2. Patients and Tissue Samples. Twenty-five paired tumor 2.7. Transwell Invasion Assay. Cells at the density of 1×10 samples and their adjacent nontumor tissues from patients were seeded into a diameter Transwell plate with 8-μm who had undergone surgery were obtained from Zhongshan pores (Sigma-Aldrich). The upper chamber of the plate Hospital, Fudan University. This study was approved by the was added with 50 μl of Matrigel collagen and 600 μLof Ethics Committee of Zhongshan Hospital, Fudan University complete DMEM was added to the lower chambers, and (Y2014-185) according to the Declaration of Helsinki, and then the cells were incubated for 24 h. The cells on the written informed consents were obtained from all the upper layer were removed and the invasive cells were fixed Journal of Oncology 3 formed and the mice were anesthetized and sacrificed 24 with 4% formaldehyde for 20 min, and then stained with crystal violet for 15 min. Cells that had invaded the bottom days after tumor inoculation. After being removed, the surface of the filter were counted to assess the invasive tumors were imaged and weighed, and the volume of tumors capacity. Invaded cells were quantified by at least five fields was monitored every 3 or 5 days and calculated as follows: of view under light microscopy (Leica) to obtain the repre- Volume ðmm Þ = ðlength × ðwidth Þ/2Þ. sentative images. 2.13. Histological Analysis and Immunohistochemistry. 2.8. Wound Healing Assay. Cells were cultured in six-well Xenotransplant tumor samples were isolated and fixed in 4% plates. After reaching 90% confluence, a 200 μl pipette tip paraformaldehyde. Paraffin was used to embed tumor tissues was used to create scratch wounds in the cell monolayer. and then sectioned at a thickness of 5-μm. The paraffin Representative images of cell migration were photographed sections were dewaxed, hydrated, and then stained with hema- under light microscopy (Leica) at 0 and 24 h after wounding. toxylin and counterstained with eosin. Antigens were recov- Migration ability was assessed by measuring changes in ered with citrate buffer and blocked with 3% H2O2, wound width or area with ImageJ software. immunohistochemistry (IHC) was performed with diluted primary KI67 antibody overnight at 4 C, followed by incuba- 2.9. Western Blot Assay. The protein was lysed from tissues tion with secondary antibody at room temperature. The slides and cells with RIPA buffer (Thermo Fisher Scientific). Pro- were developed by diaminobenzidine (DAB) and stained with tein concentration was assessed using the bicinchoninic acid hematoxylin. IHC staining pictures were obtained under a (BCA) assay kit (Thermo Fisher Scientific). Equal amounts of light microscope. All results were determined by two patholo- protein samples were separated by 12% SDS-PAGE gels and gists who were completely blinded to the grouping. transferred to polyvinylidene difluoride (PVDF) membranes. After incubation with 5% nonfat milk for 2 h at room tem- 2.14. Function Enrichment Analysis Based on the TARGET- perature, to hatch the blots with the primary antibodies OS Dataset. Gene Ontology (GO) and Kyoto Encyclopedia including anti-KPNA2 (ab6036), IRF2 (ab124744), E- of Genes and Genomes (KEGG) enrichment analysis were cadherin (ab1416), N-cadherin (ab18203), Vimentin performed on the upregulated genes in the high- and low- (ab92547), and GAPDH (ab9484) overnight at 4 C, which IRF2 or KPNA2 subgroups based on the TARGET-OS data- were purchased from Abcam. Hatching of horseradish set, and deduced their functions by analyzing the gene set. In peroxidase-conjugated secondary antibodies at room tem- our work, we explored whether differentially expressed genes perature, the endogenous GAPDH is the internal reference between these subgroups were enriched among OS-related protein. The protein band signals were visualized on an biological functions or pathways. Significant GO biological ECL detector (Pierce) and quantified by scanning the densi- process terms and KEGG pathways with p <0:05 were col- tometry using ImageJ software. lected and visualized using the R package “ggplot2” (version 4.0.3). 2.10. Chromatin Immunoprecipitation (ChIP) Assay. ChIP assays were performed using a kit (Sigma-Aldrich) following 2.15. Correlation Analysis of 22 Immune Cell Infiltration with the protocol provided by the manufacturer. To hatch the IRF2 and KPNA2. 22 tumor-infiltrating immune cells diluted DNA-protein complex, the antibodies of anti-IRF2 (TIICs) in OS samples from the TARGET dataset were and mouse IgG (Sigma-Aldrich) were added in the presence assessed by applying the deconvolution algorithm (referred of protein A/G beads and incubated at 4 C overnight. The to as CIBERSORT) in the osteosarcoma microenvironment. RT-qPCR assay was applied to examine the ChIP DNA sam- Samples with p <0:05 in CIBERSORT analysis result were ples. IgG was the negative control. used in further analysis. The matrix of gene expression sig- natures of 22 TIICs was obtained from the CIBERSORT 2.11. Dual Luciferase Test. Wild (WT) and mutant (MUT) of platform (https://cibersortx.stanford.edu) [23]. The matrix KPNA2 were inserted into the pGL3 promoter vector, which data of IRF2 and KPNA2 levels were compared with those was transfected into U2OS and MG-63 cells using Lipofecta- of the signature matrix of 22 TIICs from the CIBERSORT mine 2000 (Invitrogen) together with plasmid of IRF2 over- platform to generate a proportion matrix for the 22 TIICs expression or empty plasmid (NC). 48 h later, luciferase in OS tissues. activity was measured using a dual-luciferase reporter assay system (Promega) following the manufacture’s protocols. 2.16. Statistical Analysis. All data obtained are expressed as the mean ± standard deviation (SD). Differences Student’s t 2.12. Tumor Xenograft Assay. All animal experiments were -test or one-way ANOVA followed by a Tukey post hoc test in accordance with the Institutional Animal Care and Use were used to compare data between two groups or among Committee Guide (IACUC) of Zhongshan Hospital, Fudan multiple groups. Statistical difference was analyzed using University (2018-014). The mice were placed in an environ- the GraphPad Prism 8.0 software. Followed by a Tukey’s mentally controlled pathogen-free isolation facility under a post hoc test. 12 h light-dark cycle and food and water were freely avail- able. Subsequently, mice were randomly divided into four 3. Results groups (n =3/per group). Equal number of indicated U2OS cells (5 × 105) were subcutaneously implanted into the right 3.1. Overexpression of KPNA2 in the Osteosarcoma Tissues. flank of 6-week-old female athymic nude mice. Tumors were Based on the GSE36001 database, we discovered KPNA2 4 Journal of Oncology tently, the mRNA level of IRF2 was significantly downregu- expression was markedly upregulated in OS tissues when compared to nontumor tissues (Figure 1(a)). To examine lated in 25 cases of clinical OS tissue samples when whether KPNA2 altered clinical OS, qRT-PCR assay was compared with adjacent normal tissues (Figure 2(f)). Besides, a ChIP test was conducted in two OS cell lines performed to examine KPNA2 levels in 25 pairs of cancer- ous OS samples and their adjacent normal samples. and hFOB1.19 to evaluate the binding relationship of IRF2 Figure 1(b) showed that the mRNA levels of KPNA2 were with KPNA2, and we found that the enrichment of IRF2 obviously upregulated in OS samples compared to normal binding was prominently decreased in two OS cell lines samples. Representative IHC images showed similar results comparing to the hFOB1.19 cells (Figure 2(g)). The present findings showed that IRF2 might be one of the major regu- (Figure 1(c)). Furthermore, the protein levels of KPNA2 in 8 cases of OS samples were obviously elevated as well lators to regulate KPNA2 expression in OS. To further deter- (Figure 1(d)). Furthermore, compared to hFOB1.19 cells, mine whether IRF2 bound to the KPNA2 promoter, a dual a higher level of KPNA2 in four OS cell lines, including luciferase assay revealed that IRF2 was able to dramatically U2OS, HOS, Saos2, and MG-63, was observed (Figure 1(e)). reduce the luciferase activity of KPNA2-WT, but not in KPNA2-MUT (Figure 2(h)). Collectively, IRF2, as a func- KPNA2 expression was the highest in U2OS and MG-63, which were chosen to use for subsequent analysis. These tional TF, could bind to KPNA2 in OS cells. results suggest that KPNA2 might play a vital role in the pro- gression of OS. 3.3. IRF2 Deficiency May Cooperate with KPNA2 to Regulate Cell Proliferation and Tumor Growth of OS Cells In Vivo and In Vitro. Since a negative correlation existed among KPNA2 3.2. The Transcription Factor IRF2 Specifically Regulates the Expression of KPNA2 in Osteosarcomas. To better under- and IRF2, we investigate their effects on cell proliferation, stand the molecular mechanism of KPNA2 in OS, we first migration, invasion, and cell cycle. Firstly, in four OS cell lines, applied the online databases to seek KPNA2-related factors. IRF2 protein and mRNA levels were lower than that in Considering that the regulation of KPNA2 in OS was hFOB1.19 cells (Figure 3(a)). Downregulation of IRF2 or KPNA2 in U2OS cells was achieved by lentiviral transductions focused on the transcriptional level, we performed a bioin- formatic analysis to find a transcription factor (TF) in the of IRF2 or KPNA2 knockdown vectors (shKPNA2 and KPNA2 promoter region. Then, we extracted 147 KPNA2 shIRF2), as confirmed by western blot (Figure 3(b)). Regard- TFs from online TFBIND dataset. Data from TARGET data- ing the malignant phenotypes of OS cells, KPNA2 knockdown sets showed that a total of 9792 genes were negatively asso- inhibited cell viability, proliferation while IRF2 knockdown had the opposite effects and partially rescued above- ciated with KPNA2 in OS (R >0:2, FDR < 0:05). We further found that 5950 genes overlapped in downregulated mentioned malignant phenotypes suppressed by KPNA2 gens from the GSE157322 dataset, and 31 genes were over- knockdown in vitro (Figures 3(c)–3(d)). In vivo, KPNA2 knockdown markedly reduced tumor weight and tumor vol- lapped in 147 KPNA2 TFs. Therefore, a total of 26 genes were at the intersection of three (Figure 2(a)). Under the umes while IRF2 knockdown promoted tumor growth, and IRF2 knockdown could weaken KPNA2 knockdown- same conditions, a total of 382 genes were positively corre- lated with KPNA2 in OS. After the intersection of three gene medicated tumor growth inhibition (Figures 3(e) and 3(f)). sets, only one gene, MYB, was located in the center circle The KI67 expression was reduced by KPNA2 knockdown while elevated by IRF2 knockdown (Figure 3(g)). These find- (Figure S1(a)). However, subsequent experiments showed that MYB was not differentially expressed in OS and was ings demonstrated that IRF2 silence partially attenuates the impact of KPNA2 knockdown on OS growth. not regulated by KPNA2 (Figure S1(b) and 1(c)). According to the correlation index of these 26 genes with KPNA2, we have chosen the top 5 TFs of KPNA2, including 3.4. IRF2/KPNA2 Might Regulate Migration and Invasion of Osteosarcoma Cells, as Well as Regulate EMT Process. RFX1, STAT3, IRF2, PPARA, and MZF1. To assess whether these five TFs could alter KPNA2 expression, we overex- Regarding the malignant phenotypes of migration and inva- pressed these TFs in two OS cell lines. As shown in sion, KPNA2 knockdown inhibited the abilities of migration Figure 2(b), these results demonstrated that only IRF2 over- and invasion while IRF2 knockdown had the opposite effects expression significantly suppressed KPNA2 expression in and partially rescued above-mentioned malignant pheno- types suppressed by KPNA2 knockdown OS cells two OS cell lines, while the other four TFs had no significant effect on KPNA2 expression. Meanwhile, due to the fact that (Figure 4(a) and 4(b)). To investigate whether IRF2/KPNA2 KPNA2 was overexpressed in four OS cell lines, we also expression is correlated with other molecular alterations in determined the change of these five TFs after KPNA2 knock- OS, we evaluated several molecules that are associated with down using RT-qPCR. The silence of KPNA2 markedly EMT in tumor progression [24]. Then, the EMT-related proteins (E-cadherin, N-cadherin, and Vimentin) were upregulated the mRNA levels of IRF2, while the other four TFs did not obviously changed response to the elimination detected by Western blotting. As shown in Figure 4(c), of KPNA2 (Figure 2(c)). Therefore, we decided to use IRF2 shKPNA2 significantly elevated E-cadherin while reduced for subsequent experiments. N-cadherin and Vimentin expression in both U2OS and Through the GSE36001 dataset, IRF2 expression was MG-63 cells. In contrast, shIRF2 had the opposite effect on these EMT-related proteins. These findings indicated that mildly downregulated in OS tissues without significant dif- ferences (Figure 2(d)), while IRF2 expression was strongly knockdown of KPNA2 may inhibite EMT progress while negatively correlated with KPNA2 (Figure 2(e)). Consis- IRF2 silence promoted EMT progress of OS cells. Journal of Oncology 5 KPNA2 expression ⁎⁎⁎⁎ ⁎⁎⁎ p = 0.000 8 0 –100 Non-tumor OS Normal OS (a) (b) KPNA2 Normal OS (c) Normal OS Normal OS Normal OS Normal OS ⁎⁎⁎ 58 kDa ⁎⁎⁎ KPNA2 1# 2# 3# 4# 4 ⁎⁎⁎ GAPDH 36 kDa 3 ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ Normal OS Normal OS Normal OS Normal OS 58 kDa KPNA2 5# 6# 7# 8# 1# 2# 3# 4# 5# 6# 7# 8# GAPDH 36 kDa Normal OS (d) KPNA2 58 kDa GAPDH 36 kDa ⁎⁎ ⁎⁎ ⁎⁎ (e) Figure 1: High expression of KPNA2 in osteosarcomas samples. (a) KPNA2 mRNA expression in OS and nontumor tissues based on data from GSE36001 dataset. (b) KPNA2 mRNA levels were detected in 25 pairs of OS samples and their adjacent normal samples by RT-qPCR. (c) Representative immunohistochemical (IHC) images of KPNA2 expression in OS tissues and normal tissues. (d) KPNA2 protein expression in 8 pairs of clinical OS samples were examined by western blotting. (e) KPNA2 expression in four OS cell lines and one normal hFOB1.19 cells using RT-qPCR and western blot assays. The data was presented as the mean ± SD from three independent ∗ ∗∗ ∗∗∗ experiments. p <0:05, p <0:01 and p <0:001. KPNA2 Relative mRNA level of KPNA2 hFOB1.19 U-2OS HOS Relative mRNA level of KPNA2 Saos2 MG-63 Relative mRNA level of KPNA2 6 Journal of Oncology 1.5 1.5 1.5 1.0 1.0 1.0 ⁎⁎ ⁎⁎ 0.5 0.5 0.5 DEGs Negative-correlation 0.0 0.0 0.0 U2OS MG-63 U2OS MG-63 U2OS MG-63 7417 5924 3837 (42.8%) (34.2%) (22.2%) NC NC NC RFX1 STAT3 IRF2 (0.2%) 6 5 (0%) (0%) 1.5 1.5 (0.6%) 1.0 1.0 0.5 TF factors 0.5 0.0 0.0 U2OS MG-63 U2OS MG-63 NC shNC MZF1 shPPARA (a) (b) IRF2 expression ⁎⁎⁎ ⁎⁎ Nontumor OS RFX1 STAT3 IRF2 PPARA MZF1 TISSUE MG-63-shNC U2OS-shNC Nontumor MG-63-shKPNA2 U2OS-shKPNA2 OS (c) (d) 15 ⁎⁎⁎ R = –0.22, p = 0.039 p = 0.000 11 0 –2 68 10 12 Normal OS IRF2 (e) (f) Figure 2: Continued. Relative gene expression KPNA2 Relative expression of KPNA2 Relative expression of KPNA2 Relative expression of KPNA2 Relative mRNA of KPNA2 Relative expression of KPNA2 IRF2 Relative expression of KPNA2 Journal of Oncology 7 1.5 U2OS MG-63 1.5 1.5 ⁎⁎⁎ ⁎⁎⁎ 1.0 1.0 1.0 0.5 0.5 0.5 0.0 0.0 0.0 KPNA2-WT KPNA2-MUT KPNA2-WT KPNA2-MUT NC NC IRF2 IRFC hFOB1.19 MG-63 U2OS (g) (h) Figure 2: Identification of KPNA2 transcription factor (TF) (a) Venn diagram displayed the overlap among differentially expressed protein- coding genes (DEGs) from GSE157322, KPNA2 negative-related genes from TARGET dataset and TF factors of KPNA2 from online TFBIND dataset. (b) The mRNA expression of KPNA2 in response to overexpression of five candidate genes (RFX1, STAT3, IRF2, PPARA, and MZF1) in U2OS and MG-63 cells. (c) The mRNA expression of five candidate genes (RFX1, STAT3, IRF2, PPARA, and MZF1) in response to KPNA2 silence in U2OS and MG-63 cells. (d) Expression of IRF2 mRNA in OS and nontumor tissues based on data from GSE36001 dataset. (e) The negatively correlation between KPNA2 and IRF2 was analyzed under a Pearson correlation analysis according to TARGET database. (f) IRF2 mRNA expression in 25 pairs of OS and normal samples. (g) The enrichment of IRF2 bind on the KPNA2 promoter was significantly reduced in two OS cell lines when compared to hFOB1.19 cells from ChIP and qRT-PCR assays. (h) Luciferase assays were performed to detect the luciferase activity of KPNA2-WT and KPNA2-MUT after IRF2 overexpression in ∗ ∗∗ ∗∗∗ U2OS and MG-63 cells. The data was presented as the mean ± SD from three independent experiments p <0:05, p <0:01, and p < 0:001. 3.5. Functions and Pathway Enrichments Related to KPNA2 itive correlations with KPNA2 expression (Figure 6(a)). Fur- and IRF2. GO and KEGG enrichment analysis of the upreg- thermore, the Pearson correlation scatter plot further also ulated genes in the high- and low-IRF2 subgroups were per- presented these findings (Figure 6(b)). These data suggested formed. The results of the GO enrichment analysis showed that plasma cells might play a key role in the IRF2/KPNA2- that the upregulated genes in the high-IRF2 subgroup mediated osteosarcoma-immune interaction. mainly played a major role in several biological processes, such as the positive regulation of cell adhesion and the reg- 4. Discussion ulation of the immune effector process (Figure 5(a)). The results of the KEGG enrichment analysis showed that these Osteosarcomas are relatively rare but devastating [5]. Unfor- upregulated genes were involved in osteoclast differentiation tunately, the introduction of new adjuvant chemotherapy and the NF-kappa B signaling pathway and others. after aggressive surgical resection has temporarily improved (Figure 5(b)). Similarly, GO analysis revealed that several overall 10-year survival but has not significantly improved hallmarks of the tumor were enriched in the high-KPNA2 patient survival since the 1990s [25]. Therefore, it is of great subgroup, such as DNA replication and cell cycle G2/M significance to identify new molecules, which further helps phase transition, while genes in the low KPNA2 subgroup to develop effective methods to diagnose and treat this was associated with immune response, bone resorption and malignant bone tumor. Here, we propose a mechanism for bone remodeling (Figure 5(c)). KEGG showed that KPNA2 the role of KPNA2 in OS pathogenesis: KPNA2 may trans- was related to cell cycle, DNA replication, and osteoclast dif- port IRF2 into the nucleus where it regulates transitivity, ferentiation (Figure 5(d)). These results are derived from the triggering EMT and subsequent malignant biological prop- enrichment of upregulated genes related to IRF2 and erties of OS cells (Supplementary material, Graphical KPNA2 based on the TARGET dataset, which is helpful Abstract). This evidence may provide new ideas for the diag- for researchers to find possible research directions when nosis and treatment of osteosarcoma. studying the functions of IRF2/KPMA2 in OS progression. Recently, several studies have linked KPNA2 to various cancers, such as lung, breast, colon, and pulmonary cancer. 3.6. The Key Infiltrating Immune Cell Related to IRF2/ High KPNA2 was positively related to cancer invasiveness KPMA2 in the Osteosarcoma Microenvironment. Based on and poor prognosis, thus regarded KPNA2 as a potentially the TARGET dataset, all OS samples with low and high relevant therapeutic target for patients with different cancers immune score, respectively, were eligible for CIBERSORT [26]. KPNA2 was involved in several cellular biological pro- (p <0:05). The correlations among the 22 TIICs ranged cesses, including cell differentiation, development, viral from weak to moderate. Obviously, plasma cells showed infection, immune response, and transcriptional regulation highly negative correlations with IRF2 expression, while pos- [6]. Similarly, our study illustrated that KPNA2 was IRF2-ChIP IgG-ChIP IRF2-ChIP IgG-ChIP IRF2-ChIP IgG-ChIP Relative enrichment fold Relative luciferase activity (Fire/Renilla value) Relative luciferase activity (Fire/Renilla value) 8 Journal of Oncology IRF2 50 kDa GAPDH 36 kDa 1.5 2.0 ⁎⁎⁎ ⁎⁎⁎ 1.0 1.5 KPNA2 ⁎⁎ 58 kDa ⁎⁎ 1.0 ⁎⁎ ⁎⁎ 0.5 ⁎⁎⁎ IRF2 50 kDa 0.5 ⁎⁎⁎ GAPDH 36 kDa 0.0 KPNA2 IRF2 shCtrl shKPNA2 shIRF2 (a) (b) ⁎⁎⁎ shCtrl shKPNA2 shIRF2 shKPNA2 + shIRF2 4 300 ⁎⁎⁎ ⁎⁎ 3 ⁎⁎ U2OS ⁎⁎ 2 ⁎⁎⁎ 100 ⁎⁎⁎ ⁎⁎⁎ MG-63 U2OS MG-63 shCtrl shKPNA2 Time (day) shIRF2 shKPNA2 + shIRF2 shCtrl shKPNA2 shIRF2 shKPNA2 + shIRF2 (c) (d) ⁎⁎⁎ ⁎ ⁎⁎ 1.0 ⁎⁎ 0.8 shCtrl 0.6 shKPNA2 ⁎⁎ 0.4 shIRF2 0.2 shKPNA2 + shIRF2 10 15 20 25 Days post-tumor inoculation shCtrl shIRF2 shKPNA2 shKPNA2 + shIRF2 (e) (f) Figure 3: Continued. OD450 (fold) Relative mRNA level of IRF2 hFOB1.19 U-2OS HOS Saos2 MG-63 Tumor weight (g) shCtrl shKPNA2 shIRF2 shCtrl shKPNA2 + shIRF2 shKPNA2 shIRF2 Tumor volume (mm ) Relative protein level Colony numbers Journal of Oncology 9 shCN shKPNA2 shIRF2 shKPNA2 + shIRF2 HE 100 um KI67 100 um (g) Figure 3: Effects of IRF2 and KPNA2 on cell proliferation, colony formation, and tumor growth of OS cells in vitro and in vivo. (a) Western blot and RT-qPCR analyses were performed to measure the relative mRNA and protein levels of IRF2 in four OS cell lines including U2OS, HOS, Saos2, and MG-63 control to the hFOB1.19 cells. (b) U2OS cells were cotransfected with shKPNA2 or shIRF2 plasmids, and the protein expression of KPNA2 and IRF2 was assessed using western blotting assay. (c) Cell viability was determined using an CCK-8 kit at different time points. (d) Cells were seeded in plates and grown for 14 days. (Left) Cell colonies were stained with 0.1% crystal violet. (Right) Colony numbers were quantified. (e–f) In vivo tumor growth. U2OS cells were injected into mice and tumor weight (e) and tumor volumes (f) were measured every 5 or 3 days for 24 days posttumor inoculation. n =3. (g) Tumor tissues were separated from mice for HE and IHC staining for detecting KI67 expression. Scale bar = 100um. The data was presented as the mean ± SD from three ∗ ∗∗ ∗∗∗ independent experiments p <0:05, p <0:01, and p <0:001. dramatically elevated in OS samples compared to normal EMT is regulated by various signaling pathways, includ- samples. Although KPNA2 has been shown to be frequently ing NF-κB, Wnt, and transforming growth factor-β [13]. In expressed in OS as a new marker for the diagnosis, as well as our study, the key outcome is that migration and invasion of in chondrosarcoma and Ewing sarcomas [27], the functions OS cells were significantly inhibited by deletion of KPNA2, of KPNA2 in osteosarcoma are unclear. In the present study, which also resulted in a decrease in the EMT characteristics data mining and bioinformatics analysis indicated that of OS cells; epithelial cell markers were increased, and mes- KPNA2 was overexpressed in OS patients from GSE36001 enchymal markers were decreased. However, the opposite results were found after IRF2 was knocked down. KPNA2 dataset, and the experiments verified high KPNA2 level in clinical OS samples and OS cell lines. Furthermore, KPNA2 and IRF2 have opposite regulatory effects on the activation knockdown inhibited the proliferation, migration, and inva- of EMT progress of OS cells. Thus, KPNA2 might contribute sion in two OS cell lines, and remarkably reduced tumor to the progression of OS by negatively regulating IRF2 via weight and tumor volumes in vivo. These findings revealed promoting EMT. KPNA2 is involved in the nucleocytoplasmic transport that KPNA2 might play a crucial role in the biological prog- ress of OS. pathway of multiple tumor-associated proteins and is Interferon regulatory factor-2 (IRF2) exerted antitumor overexpressed in various cancers thereby being suggested effects in several human cancers. For example, IRF2 could as a prospect in the diagnosis and treatment of cancer suppress cell proliferation and migration ability and promote [6]. Given that IRF2 has the ability to exert antioncogenic activities, IRF2 overexpression led to a dramatic cell death cell apoptosis in nonsmall cell lung cancer cells [28]. IRF2 might play as a tumor suppressor by regulating P53 signaling response by apoptosis in hepatocellular carcinoma [31]. in gastric cancer [29]. Furthermore, IRF2 was shown to serve These are consistent with our results of functional enrich- as a tumor suppressor in patients with hepatocellular carci- ment, showing these genes are related to DNA replication noma, whose inactivation led to impaired TP53 function and cell cycle processes. Furthermore, it could be observed that KPNA2 was positively correlated with plasma cell [30]. The current study highlighted that KPNA2 could nega- tively alter IRF2 expression in OS cells. Meanwhile, by data level, while IRF2 had a negative relationship with plasma mining in the GSE157322 and TARGET datasets, we discov- cell level. Infiltrating immune cell subsets detected by ered that IRF2 could bind to the KPNA2 promoter and acti- CIBERSORT analysis can reflect the time course of innate vate KPNA2 expression by bioinformatic analysis for TF and adaptive immune responses in OS. CIBERSORT may have the potential to characterize the detail of infiltrating prediction. This underlying mechanism was consistent with a previous report that IRF2 could bind to the miR-1227 immune cells in OS tissues and provide novel insights into promoter, thus inhibiting tumor growth [21]. Furthermore, the pathogenesis of OS. To our knowledge, these two IRF2 was obviously downregulated, which was negatively genes are the first to link immune cell levels in the osteo- associated with KPNA2 in OS. More importantly, IRF2 sarcoma microenvironment in this study, with the hope of providing guidance for the next study of relevant molecu- knockdown promoted malignant behaviors, which were seemingly suppressed by KPNA2 knockdown. These rescu- lar mechanisms. ing effects of IRF2 on KPNA2 were also reflected in tumor However, most of the experiments in our study are per- growth in vivo. These findings demonstrated that IRF2 formed using isolated tumor cell lines cultured in vitro or silence might partially attenuate the impact of KPNA2 immunodeficient nude mice with human U2OS xenografts, which do not account for any tumor-extrinsic effects that knock-down on osteosarcoma progressions. 10 Journal of Oncology shKPNA2 shCtrl shKPNA2 shIRF2 + shIRF2 1.5 0 h ⁎⁎ 1.0 24 h ⁎⁎ ⁎⁎⁎ 0.5 0.0 0 h U2OS MG-63 shCtrl shKPNA2 shlRF2 24 h shKPNA2 + shlRF2 (a) 2.5 ⁎⁎ shKPNA2 ⁎⁎ 2.0 shCtrl shKPNA2 shIRF2 + shIRF2 1.5 U2OS ⁎⁎ 1.0 0.5 ⁎⁎ ⁎⁎ 0.0 MG-63 U2OS MG-63 shCtrl shKPNA2 shlRF2 shKPNA2 + shlRF2 (b) U2OS MG-63 shCtrl shKPNA2 shIRF2 shCtrl shKPNA2 shIRF2 135 kDa E-cadherin 140 kDa N-cadherin 57 kDa Vimentin GAPDH 36 kDa (c) Figure 4: KPNA2 promoted the activation of EMT through IRF2. (a) The cell migration capacity of OS cells was examined using wound healing assays at 0 and 24 h. (b) Cells were stained with 0.1% crystal violet (left) Bars = 50 μm. Cell numbers were quantified to value the invasive capacity of OS cells after different transfection (right). (c) Protein levels of EMT-related proteins (E-cadherin, N-cadherin, and ∗ ∗∗ Vimentin) in two OS cells (MG-63 and U2OS). Data were presented as mean ± SD from three independent experiments p <0:05, p < ∗∗∗ 0:01, and p <0:001. MG-63 U2OS Cell migration fold distance Invasion cells Journal of Oncology 11 KPNA2 IRF2 Upregulated GO Upregulated GO P.adjust P.adjust 1.703207e–28 2.829501–38 Positive regulation of cell adhesion Organelle fission Regulation of immune effector process Nuclear division 1.234542e–25 Positive regulation of cell activation Chromosome segregation 6.716633e–25 Positive regulation of leukocyte activation Mitotic nuclear division Positive regulation of lymphocyte activation Nuclear chromosome segregation 2.467381e–25 1.343367e–24 Leukocyte proliferation DNA replication Mononuclear cell proliferation Cell cycle G2/M phase transition Regulation of leukocyte proliferation 3.700220e–25 G2/M transition of mitotic cell cycle 2.015060e–24 Regulation of mononuclear cell proliferation Sister chromatid segregation Regulation of lymphocyte proliferation Mitotic sister chromatid segregation 4.933059e–25 2.686733e–24 0.06 0.07 0.08 0.06 0.07 0.08 0.09 0.10 Generation Generation Count Count 70 90 70 100 80 100 80 110 Downregulated GO Downregulated GO P.adjust P.adjust 4e–04 Cartilage development Neutrophil activation Pyruvate metabolic process Neutrophil degranulation 6e–04 0.003 Purine nucleoside diphosphate metabolic process Neutrophil activation involved in immune response Purine ribonucleoside diphosphate metabolic process Regulation of peptidase activity Ribonucleoside diphosphate metabolic process Endothelial cell migration 0.006 8e–04 ADP metabolic proc Tissue remodelling NADH regeneration Response to interferon–gamma Interferon–gamma–mediated Canonical glycolysis 6e–04 0.009 Signaling pathway Glucose catabolic process to pyruvate Bone resorption Glycolytic process through glucose –6–phosphate Bone remodelling 1e–03 0.015 0.020 0.025 0.030 0.035 0.03 0.04 0.05 0.02 Generation Generation Count Count 8 16 15 35 12 20 20 40 25 45 (a) (c) Upregulated KEGG P.adjust Upregulated KEGG P.adjust Epstein–Barr virus infection Herpes simplex virus 1 infection NOD–like receptor signaling pathway Cell cycle Cell adhesion molecules 1e–08 Nucleocytoplasmic transport 0.0004 Phagosome Spliceosome Toxoplasmosis Oocyte meiosis Osteoclast differentiation Fanconi anemia pathway 0.0008 2e–08 NF–kappa B signaling pathway Progesterone–mediated oocyte maturation Hematopoietic cell lineage DNA replication Pertussis 3e–09 Homologous recombination 0.0012 Viral myocarditis Mismatch repair 0.03 0.06 0.09 0.04 0.05 0.06 0.07 Generation Generation Count Count 10 40 25 35 20 50 30 40 Downregulated KEGG P.adjust Downregulated KEGG P.adjust Lysosome 0.01 0.02 Oxidative phosphorylation Glycolysis/ gluconeogenesis 0.014798 0.03 Osteoclast differentiation 0.04 0.02 0.04 0.06 0.08 0.00 0.039 0.042 0.045 0.048 0.051 Generation Generation Count Count 17 20 18 21 19 22 (b) (d) Figure 5: Functions and Pathway enrichments related to KPNA2 and IRF2. (a) Dotplot of the top 10 enriched terms across the upregulated- genes in the high-IRF2 and low-IRF2 subgroups using GO cluster analysis, colored according to p-value. (b) Dotplot of enriched pathways of upregulated genes in the high-IRF2 and low-IRF2 subgroups using KEGG analysis, colored according to p-value. (c) Dotplot of the top 10 enriched terms across the upregulated-genes in the high-KPNA2 and low-KPNA2 subgroups using GO cluster analysis, colored according to p-value. (d) Dotplot of enriched pathways of the upregulated genes in the high-KPNA2 and low-KPNA2 subgroups using KEGG analysis, colored according to p-value. 12 Journal of Oncology ⁎⁎⁎ IRF2 ⁎ ⁎⁎⁎ KPNA2 ⁎ ⁎⁎⁎ B_cells_naive_CIBERSORT ⁎⁎⁎ B_cells_memory_CIBERSORT ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ Plasma_cells_CIBERSORT ⁎⁎⁎ T_cells_CD8_CIBERSORT ⁎⁎⁎ T_cells_CD4_naive_CIBERSORT ⁎ ⁎⁎⁎ T_cells_CD4_memory_resting_CIBERSORT ⁎ ⁎⁎⁎ T_cells_CD4_memory_activated_CIBERSORT ⁎⁎⁎ ⁎⁎⁎ T_cells_follicular_helper_CIBERSORT ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ T_cells_regulatory_tregs_CIBERSORT ⁎⁎⁎ T_cells_gamma_delta_CIBERSORT ⁎⁎⁎ ⁎⁎ ⁎⁎⁎ NK_cells_resting_CIBERSORT ⁎⁎⁎ ⁎⁎⁎ NK_cells_activated_CIBERSORT ⁎ ⁎⁎⁎ Monocytes_CIBERSORT ⁎⁎ ⁎⁎⁎ ⁎ ⁎⁎ ⁎⁎⁎ Macrophages_M0_CIBERSORT ⁎⁎⁎ ⁎ ⁎⁎ ⁎ ⁎⁎⁎ ⁎⁎⁎ ⁎⁎⁎ Macrophages_M1_CIBERSORT ⁎ ⁎⁎⁎ ⁎⁎⁎ Macrophages_M2_CIBERSORT ⁎ ⁎⁎⁎ Dendritic_cells_ resting_CIBERSORT ⁎⁎⁎ Dendritic_cells_ activated_CIBERSORT ⁎⁎⁎ Mast_cells_resting_CIBERSORT ⁎⁎ ⁎ ⁎⁎⁎ ⁎⁎ ⁎⁎⁎ Mast_cells_activated_CIBERSORT ⁎⁎⁎ Eosinophils_CIBERSORT ⁎ ⁎⁎⁎ Neutrophils_CIBERSORT –1 –0.8 –0.6 –0.4 –0.2 0 0.2 0.4 0.6 0.8 1 (a) TRAGET–OS TRAGET–OS 0.20 0.20 R = –0.33, p = 0.016 R = –0.26, p = 0.014 0.15 0.15 0.10 0.10 0.05 0.05 0.00 0.00 6 7 8 9 2 3 4 5 6 KPNA2 IRF2 (b) Figure 6: The key infiltrating immune cell related to IRF2/KPMA2 in the osteosarcoma microenvironment. (a) Correlation matrix of all 22 TIICs proportions with IRF2 and KPNA2. (b) Correlation scatter plots of plasma cell level with IRF2 and KPNA2 based on the TARGET-OS dataset using Pearson’s correlation analysis. the candidate signaling pathway may have on immune cell treatment of OS with restraint of KPNA2 or IRF2 overex- interactions with tumor cells. This limitation suggests that pression may be extra to other therapeutic interventions studying tumor-immune interactions will be a potential for the development of this disease. future direction that will extend from this study. Taken together, our results illustrated that KPNA2 regu- Data Availability lated OS development, as well as IRF2 play a potential upstream TF of KPNA2 in regulating EMT progress. This All data in the results of this study can be obtained on a rea- may provide a novel target for OS therapy. Therefore, the sonable request from the corresponding authors. Public data Plasma_cells_CIBERSORT IRF2 KPNA2 B_cells_naive_CIBERSORT B_cells_memory_CIBERSORT Plasma_cells_CIBERSORT Plasma_cells_CIBERSORT T_cells_CD8_CIBERSORT T_cells_CD4_naive_CIBERSORT T_cells_CD4_memory_resting_CIBERSORT T_cells_CD4_memory_activated_CIBERSORT T_cells_CD4_memory_activated_CIBERSORT T_cells_follicular_helper_CIBERSORT T_cells_gamma_delta_CIBERSORT NK_cells_resting_CIBERSORT NK_cells_activated_CIBERSORT Monocytes_CIBERSORT Macrophages_M0_CIBERSORT Macrophages_M1_CIBERSORT Macrophages_M2_CIBERSORT Dendritic_cells_resting_CIBERSORT Dendritic_cells_activated_CIBERSORT Mast_cells_resting_CIBERSORT Mast_cells_activated_CIBERSORT Eosinophils_CIBERSORT Neutophils_CIBERSORT Journal of Oncology 13 sources include GSE36001, GSE157322, and TARGET data- [3] S. J. Taran, R. Taran, and N. B. Malipatil, “Pediatric osteosar- coma: an updated review,” Indian Journal of Medical and Pae- base online. diatric Oncology, vol. 38, no. 1, pp. 33–43, 2017. [4] J. L. Ferguson and S. P. 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Published: Sep 26, 2022

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