Skip to main content

Human basonuclin 2 up-regulates a cascade set of interferon-stimulated genes with anti-cancerous properties in a lung cancer model



Human basonuclin 2 (BNC2) acts as a tumor suppressor in multiple cancers in an as yet unidentified manner. The role and expression of the BNC2 gene in lung cancer has not yet been investigated.


BNC2 expression was studied in the A549 and BEAS-2B cell lines, as well as in lung cancer tissue. Illumina array analysis and a viability assay were used to study the effects of transient transfection of BNC2 in A549 cells. Ingenuity pathway analysis and g:Profiler were applied to identify affected pathways and networks. RT-qPCR was used to validate the array results.


We showed the reduced mRNA expression of BNC2 in non-small cell lung cancer tissue and lung cancer cell line A549 compared to non-cancerous lung tissue and BEAS-2B cells, respectively. Further array analysis demonstrated that the transfection of BNC2 into A549 cells resulted in the increased expression of 139 genes and the down-regulation of 13 genes. Pathway analysis revealed that half of the up-regulated genes were from the interferon/signal transducer and activator of transcription signaling pathways. The differential expression of selected sets of genes, including interferon-stimulated and tumor suppressor genes of the XAF1 and OAS families, was confirmed by RT-qPCR. In addition, we showed that the over-expression of BNC2 inhibited the proliferation of A549 cells.


Our data suggest that human BNC2 is an activator of a subset of IFN-regulated genes and might thereby act as a tumor suppressor.


Lung cancer is the most malignant tumor and the leading cause of cancer deaths worldwide, with 1.8 million new cases in 2012 [1]. In Estonia, the incidence rate for lung cancer per 100,000 was 71 for men and 14 for women in 2012 [2]. Non-small cell lung cancer (NSCLC) accounts for 80–85% of all lung malignancies. In contrast to the steady increase in survival for most cancers, advances have been slow for lung cancer, with a corresponding 5-year relative survival of 18% [3]. Depending on the stage of cancer, treatment options for people with NSCLC include surgical resection, chemotherapy, radiation therapy, targeted therapy and immunotherapy [4, 5]. Increasing focus has been placed on the development of immunotherapies, including the directed targeting of specific immune suppressors, such as cytotoxic T-lymphocyte antigen-4 protein (CTLA-4) and programmed cell death-1 protein receptor (PD-1) [5,6,7]. Another important group of immunotherapeutics have been developed based on interferons (IFNs). IFNs are naturally occurring cellular cytokines that activate immune responses and have been shown to have anti-proliferative, anti-angiogenic and pro-apoptotic effects [8, 9].

IFN receptor signaling induces the up-regulation of many ISG-s (interferon stimulated genes), including genes with antiviral properties, such as protein kinase R (PKR), 2,5-oligoadenylate synthetase (OAS) and myxovirus resistance protein (MX) family genes [10,11,12,13,14]. In addition to the ISG-s implicated in anti-viral, anti-angiogenic, immunomodulatory and cell cycle inhibitory effects, oligonucleotide microarray studies have identified ISG-s with apoptotic functions, such as XIAP associated factor-1 (XAF1), caspase-4, caspase-8, death activating protein kinases (DAPKs) and IRFs [15,16,17,18].

Human BNC2 is an evolutionarily conserved C2H2 zinc finger protein, which has been suggested to be involved in the regulation of mRNA splicing, processing [19, 20] or transcription [19,20,21]. BNC2 has been detected in a wide range of tissues: it is abundantly expressed in the ovary, skin, uterus, and kidneys, and its expression has also been detected in the testis, prostate, and lung [19, 20, 22]. BNC2 expression has been detected in cell lines, including primary human keratinocytes, the keratinocyte cell line HaCaT, and HeLa and HEK293 cells [19].

Little is known about the expression and function of BNC2 in tumor progression. Genetic variations in the BNC2 gene have been associated with skin cancer risk [23,24,25], susceptibility to ovarian cancer [26,27,28] and prostate cancer development [29, 30]. The deletion of the BNC2 gene and the corresponding decreased expression of BNC2 mRNA have been detected in Barrett’s esophagus [31], hepatocellular carcinoma [32] and high-grade serous ovarian carcinoma [33]. In esophageal adenocarcinoma cells, the stable expression of BNC2 caused the growth arrest of tumor cells [31], suggesting that BNC2 might also be a tumor suppressor gene. Thus far, there is no evidence of the role of BNC2 in lung cancer.

In this study, the mRNA expression of BNC2 was analyzed in lung squamous cell carcinoma tissue samples and a lung cancer cell line. In addition, the effect of transfected BNC2 on gene expression and cell viability was investigated in the human lung carcinoma cell line A549.


Tumor samples

Lung squamous cell carcinoma (SCC) and corresponding adjacent non-tumor tissue samples were collected from 8 patients who had undergone curative resection and been histologically characterized by a clinical pathologist in Tartu University Lung Hospital (Tartu, Estonia). The study was approved by the Research Ethics Committee of the University of Tartu, and written informed consent was obtained from all patients. Tissue specimens of appropriate sizes (1–2 cm3) were cut from tumorous and morphologically tumor-free lung tissue. One half of each sample was fixed in formalin and used for pathological examination. The other half of each specimen was snap frozen and stored at −80 °C until use.

Cell culture

The adenocarcinomic human alveolar basal epithelial cell line A549 and human normal lung epithelial cell line BEAS-2B were purchased from the American Type Culture Collection (Manassas, VA, USA). A549 cells were grown in RPMI-1640 medium (PAA Laboratories, Linz, Austria) supplemented with 10% fetal bovine serum (FBS) (Biochrom AG, Berlin, Germany) and penicillin/streptomycin (PAA Laboratories, Linz, Austria). BEAS-2B cells were grown in DMEM (Lonza, Cologne, Germany) medium supplemented with 3% FBS (Biochrom AG, Berlin, Germany) and penicillin/streptomycin (PAA Laboratories, Linz, Austria). Both cell lines were cultured in a humidified tissue culture incubator with 5% CO2 at 37 °C.

Plasmids and transfections

The expression plasmid containing full-length human BNC2 coding sequence and corresponding empty plasmid pCMV-HA ( were kindly provided by Dr. Satrajit Sinha (State University of New York, NY, USA). For transient transfection, 106 A549 cells were electroporated with 5 µg plasmid DNA in 250 µl Ingenio electroporation solution (Mirus Bio LLC, Madison, WI, USA) using the Gene Pulser Xcell Electroporation System (Bio-Rad, Stockholm, Sweden) under the following conditions: 280 V, 950 µF and ∞ Ω. After electroporation, cells were plated and harvested every 24 h for 3 days.

Cell viability assay

For the viability assay, 2 × 104 A549 cells per well were seeded in a 24-well plate. The next day, cells were transfected with expression plasmid containing a full-length human BNC2 coding sequence and corresponding empty plasmid pCMV-HA using Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) according to manufacturer’s instructions. Cell proliferation was measured 48 h after transfection using CellTiter-Glo® Luminescent Cell Viability Assay (Promega, Madison, WI, USA), where the Luciferase activity was proportional with the quantity of cellular adenosine triphosphate (ATP).

RNA extraction and RT-qPCR

Total RNA was isolated using the Ambion RNA extraction kit (Ambion Inc., Austin, TX, USA) according to the manufacturer’s instructions. One microgram of total RNA was converted to cDNA using the First Strand cDNA Synthesis kit (Fermentas, Vilnius, Lithuania). Real-time PCR was performed using SYBR Green ROX mix (Fermentas, Vilnius, Lithuania) and ABI 7900HT Sequence Detection System (Applied Biosystems, Foster City, CA, USA). Data were analyzed using SDS 2.2.2 software (Applied Biosystems, Foster City, CA, USA). The primer sequences for RT-qPCR amplifications are shown in Table 1. Gene expression levels were determined by the 2−ΔΔCT method [34] after normalization to ESD (Esterase D) [35]. Relative gene expression was calculated as a fold change compared to the control transfection.

Table 1 List of oligonucleotide primers

Array analysis

The Illumina® TotalPrep™ RNA Amplification Kit (Ambion Inc., Austin, TX, USA) was used to generate biotinylated amplified RNA for hybridization with Illumina HumanHT-12 v4 Expression BeadChip (Illumina Inc., San Diego, CA, USA) and the Illumina BeadChip platform (Illumina Inc., San Diego, CA, USA). Experiments were performed according to the manufacturer’s instructions. Raw expression data were collected and background subtracted by Illumina GenomeStudio Gene Expression Module v1.8.0 (Illumina, Inc., San Diego, CA, USA). Data were transformed by variance-stabilizing transformation and quantile normalization using the Lumi package (v 2.14.0) [36] from Bioconductor ( Differentially expressed genes were identified using the Limma package (v 3.18.1) [37]. A p value of 0.05 was used as threshold for differential expression after multiple testing correction by the Benjamini-Hochberg method [38].

Gene enrichment analysis

Pathway and gene ontology (GO) enrichment analyses were performed with ingenuity pathway analysis (IPA) Ingenuity Systems ( (Qiagen, Redwood City, CA, USA) and g:Profiler ( [39] using the default settings and the g:SCS method for statistical analysis. The g:SCS method computes multiple testing corrections for p values from GO and pathway enrichment analysis using a threshold of 0.05. All reported pathways and biological processes are listed according to their GO enrichment score provided by the two software packages as −log (p values) and with a false discovery rate (FDR) <0.05%.

Statistical analysis

Statistical significance between the different groups and conditions was assessed with Student’s t-test, the Wilcoxon matched pair test was used to analyze the relative mRNA expression in tumor and matched adjacent non-tumor tissues. Results were considered significant at p < 0.05 (*) and highly significant at p < 0.01 (**). Statistical analysis was performed using GraphPad Prism5 (GraphPad Software, San Diego, CA, USA).


Decreased expression of BNC2 in the lung carcinoma cell line and in lung cancer tissue

To study whether the expression of BNC2 is altered in lung cancer, similarly to other tumors [26, 31,32,33], first, RT-qPCR was used to analyze the mRNA expression level of BNC2 in the human adenocarcinomic alveolar epithelial cell line A549 and the normal lung epithelial cell line BEAS-2B. As shown in Fig. 1a, significantly lower expression levels of BNC2 were detected in the A549 cell line compared to BEAS-2B cells. Next, we tested the expression of BNC2 in 8 pairs of matched SCC and adjacent non-tumor tissues. Consistent with the results in the A549 cell line, the BNC2 expression level in cancerous SCC tissues were lower than in corresponding non-tumor tissues (Fig. 1b).

Fig. 1

The expression of BNC2 is reduced in the lung carcinoma cell line A549 and lung cancer tissue. a Relative BNC2 mRNA expression in A549 and BEAS-2B cell lines. The mean expression of BNC2 in BEAS-2B cells was normalized to 1. Data represent the mean ± SEM from four transfections. Student’s t-test, ***p value < 0.001. b Expression of BNC2 in 8 pairs of squamous cell carcinoma (SCC) and adjacent non tumor tissues (NL). Wilcoxon matched pair test, *p value <0.05

Transient transfection of BNC2 affects cancer cell proliferation and global gene expression patterns

Although the reduced expression of BNC2 has been detected in several tumors [26, 31, 32], the role of BNC2 in the suppression of the cancerous processes in the lung has not been studied before. Thus, we analyzed whether BNC2 over-expression has an effect on the cell proliferation rate and global gene expression pattern in the A549 cell line. A greater than 20-fold increase in BNC2 mRNA expression was detected in A549 cells transfected with BNC2-coding plasmid (Fig. 2a), which led to the reduced proliferation rate of A549 cells compared to the control after 48 h of transfection (Fig. 2b).

Fig. 2

The effect of BNC2 transfection in A549 cells. a Relative BNC2 mRNA expression was measured 48 h after the transfection. Human A549 cells were transfected with either BNC2 expression vector or the control (empty vector). Data represent the mean ± SEM of four transfections. Student’s t-test, **p value <0.01. b The proliferation rate of A549 cells was measured 48 h after transfection. A549 cells were transfected with either BNC2 expression vector or the control (empty vector) followed by Luminescent Cell Viability Assay. Student’s t-test, **p value <0.01. c Heatmap of the top 30 of the most differentially expressed genes from array data chosen based on fold changes. Red represents the lower and yellow the higher expression of each gene in all six samples. Data represents the quantile normalized expression values across all of the samples in heatmap. The column-side dendrogram represents the hierarchical clustering of control and BNC2-transfected samples using the complete linkage method with Euclidean distance measures. The samples were collected from two sets of transfections performed in triplicate both times. d Comparison of microarray and RT-qPCR results. Data are normalized to the control-transfected cells and are shown as a log2-transformed mRNA fold change. The RT-qPCR results represent four independent transfections with the error bar indicating SEM

Next, total RNA samples from both conditions were harvested and subjected to Illumina Expression BeadChip containing 47,000 probes for more than 31,000 annotated genes. Out of the more than 24 000 expressed genes (detection p < 0.05), 152 genes (195 transcripts) were altered in response to BNC2 over-expression (139 genes (181 transcripts) up-regulated, 13 genes (14 transcripts) down-regulated). The heatmap in Fig. 2c contains the top 30 genes with the largest fold change. The full list of all significantly changed genes (p < 0.05) is provided in Additional file 1. A set of the differentially expressed genes was then validated by means of RT-qPCR. For the validation, we chose two strongly up-regulated genes, OAS2 and IFITM1, and randomly selected three other up-regulated and five down-regulated genes. Although some variation in the extent of fold changes was observed, the RT-qPCR analysis substantially confirmed the results of microarray analysis (Fig. 2d).

Pathway and network analysis of BNC2-influenced genes

Subsequently, we analyzed which gene networks and functional pathways are influenced by BNC2 in A549 cells using 2 different analysis programs, IPA and g:Profiler. IPA pathway analysis software identified 148 statistically significant canonical pathways, of which the top 15 enriched signaling pathways with a p value <10−5 are shown in Additional file 2. The top three signaling pathways by IPA were interferon signaling, antigen presentation and the activation of IRF by cytosolic pattern recognition receptors. The signaling pathway that was affected the most, interferon signaling, and associated genes are shown in Fig. 3. Among gene regulatory networks, three networks, each consisting at least 40% of the affected genes, were identified by IPA. These three networks were associated with the inflammatory response, infectious diseases, immunological diseases and dermatological diseases and conditions (Additional file 3).

Fig. 3

The functional network of the interferon signaling pathway by IPA. Genes that were significantly up-regulated in BNC2-transfected A549 cells are shown in red. The intensity of red corresponds to an increase in fold change

G:Profiler analysis revealed 188 statistically significant functional groups, of which over 100 had a p value less than 10−5. The top 20 BNC2-influenced functional groups are listed in Additional file 4. The most significant overlap was determined for the type I interferon signaling pathway, the cellular response to type I interferon and the cytokine-mediated signaling pathway.

BNC2 induces the expression of ISGs with anti-cancerous properties

Several ISGs, such as OAS family members and the tumor suppressors XAF1 and IRF7, have been shown to play crucial roles in counteracting cancer progression, and their increased expression is associated with the inhibition of cell growth and the promotion of the apoptosis of cancer cell lines [40,41,42,43]. Concordantly, the reduced expression of XAF1 and OAS family members has been observed in several cancer cell lines [44,45,46,47]. Our microarray data analysis revealed (Additional file 1) and RT-qPCR confirmed (Fig. 4) the increased mRNA expression of all of the OAS gene family members (OAS1, OAS2, OAS3, OASL) and XAF1 and IRF7 in the lung cancer cell line A549 after transfection with BNC2. Notably, the increased expression of the studied ISGs was persistent and could be detected 72 h after the transfection of the A549 cells with BNC2.

Fig. 4

The over-expression of BNC2 induces the expression of ISGs associated with the repression of cancer development. Human A549 cells were transfected with either BNC2 or the control. Data represent the mean ± SEM of four transfections


Lung cancer is a leading cause of cancer-related death worldwide [48]. Although improvements in molecular diagnostics and targeted therapies have been achieved in recent decades, the average 5-year survival rate for lung cancer is still below 20% [3]. New therapeutic targets are eagerly needed for this disease. In the current study, we demonstrate that human BNC2 is down-regulated in the adenocarcinomic alveolar epithelial cell line A549 and in SCC tissue compared to non-cancerous cells and tissue, respectively. The transfection of BNC2 to A549 cells led to the up-regulation of numerous ISGs, of which a subset (XAF1, IRF7, OAS family) is known to inhibit cancer growth and promote the apoptosis of cancer cells.

BNC2 was discovered as a gene with a similar domain structure as basonuclin 1, with a serine-rich region, nuclear localization signal (NLS) and three pairs of distinct C2H2 zinc fingers [20]. BNC2 is evolutionarily conserved in vertebrates: there is a remarkable conservation of the amino acid sequence of BNC2 across species as distant as the zebrafish, chicken, and mammals. The level of similarity of amino acids between human and mouse BNC2 is 97% [20, 21].

Early studies suggested that BNC2 might act as a transcription regulator [19, 20]. Later, it was proposed that BNC2 has a function in RNA processing [21] and may regulate the expression of genes essential for the development of craniofacial bones [49]. Multiple studies have demonstrated the down-regulation of BNC2 in numerous cancers [31,32,33]. Akagi and colleagues detected the decreased expression of BNC2 mRNA in esophageal adenocarcinoma cells and showed that the stable expression of BNC2 caused the growth arrest of tumor cells, which suggests that BNC2 is a tumor suppressor [31, 32]. Our results show that BNC2 was significantly down-regulated in the lung adenocarcinoma cell line A549 compared to the human normal bronchial epithelial cell line BEAS-2B, as well as in lung tumor tissue compared to non-tumor tissue. In addition, we also show that the over-expression of BNC2 inhibits the proliferation of A549 cells. Thus, our data are in line with previous studies that report the down-regulation of the BNC2 gene in cancers of epithelial origin and indicate that BNC2 has a tumor-suppressive function.

Microarray technologies have been intensively used in cancer research [50,51,52,53] and are useful to profile gene expression patterns to facilitate diagnosis, predict the response to therapy, find new biomarkers and examine the development of drug resistance in cancer [54,55,56].

Microarray data from A549 cells transfected with BNC2 show the relationship of BNC2 with the modulation of immune system. Increased BNC2 expression in Th22 cells compared to other T cell subsets [57] and the suppression of NF-κB basal activity in HEK293 cells [58] have been reported previously. We determined the relationship of BNC2 with immune regulation with two different pathway analysis programs: IPA and G:profiler, which both revealed that the increased expression of BNC2 primarily affects genes associated with the interferon signaling pathway. Several ISGs with increased expression in BNC2-transfected cells have been associated with the restriction of tumor growth and development. For example, XAF1 has been shown to inhibit proliferation and to induce the apoptosis of cancer cells as it negatively regulates the caspase-inhibiting activity of XIAP [42, 47]. Along with XAF1, we discovered the up-regulation of a subset of genes with the capacity to inhibit cell proliferation and to stimulate cancer cells to undergo apoptosis (IRFs, IFIT1-3, ISG12a, IFITM and the OAS family members) [59,60,61].

The use of interferons (IFNs) could be a potential strategy in the treatment of lung cancer [8]. Type I IFNs (the IFN-α family and IFN-β) have been used with some success for the treatment of different cancers, including hematological malignancies and solid tumors [62,63,64,65]. Type II IFN, IFN-γ, also has antitumor effects in various types of cancers [66, 67]. In addition to in vitro studies, several pre-clinical and clinical in vivo studies demonstrate the efficacy of type I IFNs alone or in combination with other treatments in cancer therapy [68,69,70,71,72,73,74].

Thus, our results suggest that BNC2 has the capacity to increase the expression of IFN-regulated genes and thereby act as a tumor suppressor gene in lung epithelial cells.


Our results suggest that BNC2 is a tumor suppressor gene with reduced expression in lung cancer cells and with the capacity to inhibit cell proliferation and to up-regulate IFN-regulated genes.



basonuclin 2


adenocarcinomic human alveolar basal epithelial cell line


human normal lung epithelial cell line


non-small cell lung cancer




XIAP associated factor-1


2,5-oligoadenylate synthetase


cytotoxic T-lymphocyte antigen-4


programmed cell death-1 protein receptor


interferon regulatory factor


interferon stimulated gene


myxovirus resistance protein


death activating protein kinase


aneuploid immortal keratinocyte cell line from adult human skin


cervical cancer cell line


human embryonic kidney cell line


squamous cell carcinoma


esterase D


false discovery rate


interferon-induced transmembrane protein


nuclear localization signal


nuclear factor kappa-light-chain-enhancer of activated B cells


interferon Induced Protein with tetratricopeptide repeats


X-linked inhibitor of apoptosis protein activator


adenosine triphosphate


protein kinase R


  1. 1.

    Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, Parkin DM, Forman D, Bray F. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN. Int J Cancer. 2012;136:E359–86.

    Article  Google Scholar 

  2. 2.

    Ferlay J, Steliarova-Foucher E, Lortet-Tieulent J, Rosso S, Coebergh JW, Comber H, Forman D, Bray F. Cancer incidence and mortality patterns in Europe: estimates for 40 countries in 2012. Eur J Cancer. 2012;49:1374–403.

    Article  Google Scholar 

  3. 3.

    Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2015. Ca-a Cancer J Clinicians. 2015;65:5–29.

    Article  Google Scholar 

  4. 4.

    Siegel R, DeSantis C, Virgo K, Stein K, Mariotto A, Smith T, Cooper D, Gansler T, Lerro C, Fedewa S, et al. Cancer treatment and survivorship statistics. CA Cancer J Clin. 2012;62:220–41.

    Article  PubMed  Google Scholar 

  5. 5.

    Chae YK, Pan A, Davis AA, Raparia K, Mohindra NA, Matsangou M, Giles FJ. Biomarkers for PD-1/PD-L1 blockade therapy in non-small-cell lung cancer: is PD-L1 expression a good marker for patient selection? Clin Lung Cancer. 2016;17:350–61.

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Fehrenbacher L, Spira A, Ballinger M, Kowanetz M, Vansteenkiste J, Mazieres J, Park K, Smith D, Artal-Cortes A, Lewanski C, et al. Atezolizumab versus docetaxel for patients with previously treated non-small-cell lung cancer (POPLAR): a multicentre, open-label, phase 2 randomised controlled trial. Lancet. 2016;387:1837–46.

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 2012;12:252–64.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Wilderman MJ, Sun J, Jassar AS, Kapoor V, Khan M, Vachani A, Suzuki E, Kinniry PA, Sterman DH, Kaiser LR, Albelda SM. Intrapulmonary IFN-beta gene therapy using an adenoviral vector is highly effective in a murine orthotopic model of bronchogenic adenocarcinoma of the lung. Cancer Res. 2005;65:8379–87.

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Antoniou KM, Ferdoutsis E, Bouros D. Interferons and their application in the diseases of the lung. Chest. 2003;123:209–16.

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Chua PK, McCown MF, Rajyaguru S, Kular S, Varma R, Symons J, Chiu SS, Cammack N, Najera I. Modulation of alpha interferon anti-hepatitis C virus activity by ISG15. J Gen Virol. 2009;90:2929–39.

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Samuel CE. Antiviral actions of interferons. Clin Microbiol Rev. 2001;14:778–809.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Taniguchi T, Ogasawara K, Takaoka A, Tanaka N. IRF family of transcription factors as regulators of host defense. Annu Rev Immunol. 2001;19:623–55.

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Taniguchi T, Takaoka A. The interferon-alpha/beta system in antiviral responses: a multimodal machinery of gene regulation by the IRF family of transcription factors. Curr Opin Immunol. 2002;14:111–6.

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Khodarev NN, Roizman B, Weichselbaum RR. Molecular pathways: interferon/Stat1 pathway: role in the tumor resistance to genotoxic stress and aggressive growth. Clin Cancer Res. 2012;18:3015–21.

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Itsui Y, Sakamoto N, Kurosaki M, Kanazawa N, Tanabe Y, Koyama T, Takeda Y, Nakagawa M, Kakinuma S, Sekine Y, et al. Expressional screening of interferon-stimulated genes for antiviral activity against hepatitis C virus replication. J Viral Hepatitis. 2006;13:690–700.

    CAS  Article  Google Scholar 

  16. 16.

    Leaman DW, Chawla-Sarkar M, Jacobs B, Vyas K, Sun YP, Ozdemir A, Yi TL, Williams BR, Borden EC. Novel growth and death related interferon-stimulated genes (ISGs) in melanoma: greater potency of IFN-beta compared with IFN-alpha 2. J Interferon Cytokine Res. 2003;23:745–56.

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Der SD, Zhou AM, Williams BRG, Silverman RH. Identification of genes differentially regulated by interferon alpha, beta, or gamma using oligonucleotide arrays. Proc Natl Acad Sci USA. 1998;95:15623–8.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. 18.

    de Veer MJ, Holko M, Frevel M, Walker E, Der S, Paranjape JM, Silverman RH, Williams BRG. Functional classification of interferon-stimulated genes identified using microarrays. J Leukoc Biol. 2001;69:912–20.

    CAS  PubMed  Google Scholar 

  19. 19.

    Romano RA, Li H, Tummala R, Maul R, Sinha S. Identification of basonuclin2, a DNA-binding zinc-finger protein expressed in germ tissues and skin keratinocytes. Genomics. 2004;83:821–33.

    CAS  Article  PubMed  Google Scholar 

  20. 20.

    Vanhoutteghem A, Djian P. Basonuclin 2: an extremely conserved homolog of the zinc finger protein basonuclin. Proc Natl Acad Sci USA. 2004;101:3468–73.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Vanhoutteghem A, Djian P. Basonuclins 1 and 2, whose genes share a common origin, are proteins with widely different properties and functions. Proc Natl Acad Sci USA. 2006;103:12423–8.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Bhoj EJ, Ramos P, Baker LA, Garg V, Cost N, Nordenskjold A, Elder FF, Bleyl SB, Bowles NE, Arrington CB, et al. Human balanced translocation and mouse gene inactivation implicate basonuclin 2 in distal urethral development. Eur J Hum Genet. 2011;19:540–6.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Jacobs LC, Hamer MA, Gunn DA, Deelen J, Lall JS, van Heemst D, Uh HW, Hofman A, Uitterlinden AG, Griffiths CE, et al. A Genome-wide association study identifies the skin color genes IRF4, MC1R, ASIP, and BNC2 influencing facial pigmented spots. J Invest Dermatol. 2015;135:1735–42.

    CAS  Article  PubMed  Google Scholar 

  24. 24.

    Asgari MM, Wang W, Ioannidis NM, Itnyre J, Hoffmann T, Jorgenson E, Whittemore AS. Identification of susceptibility loci for cutaneous squamous cell carcinoma. J Invest Dermatol. 136:930–37.

  25. 25.

    Chahal HS, Lin Y, Ransohoff KJ, Hinds DA, Wu W, Dai HJ, Qureshi AA, Li WQ, Kraft P, Tang JY, et al. Genome-wide association study identifies novel susceptibility loci for cutaneous squamous cell carcinoma. Nat Commun. 2016;7:12048.

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Goode EL, Chenevix-Trench G, Song H, Ramus SJ, Notaridou M, Lawrenson K, Widschwendter M, Vierkant RA, Larson MC, Kjaer SK, et al. A genome-wide association study identifies susceptibility loci for ovarian cancer at 2q31 and 8q24. Nat Genet. 2010;42:874–9.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Song H, Ramus SJ, Tyrer J, Bolton KL, Gentry-Maharaj A, Wozniak E, Anton-Culver H, Chang-Claude J, Cramer DW, DiCioccio R, et al. A genome-wide association study identifies a new ovarian cancer susceptibility locus on 9p22.2. Nat Genet. 2009;41:996–1000.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Winham SJ, Armasu SM, Cicek MS, Larson MC, Cunningham JM, Kalli KR, Fridley BL, Goode EL. Genome-wide investigation of regional blood-based DNA methylation adjusted for complete blood counts implicates BNC2 in ovarian cancer. Genet Epidemiol. 2014;38:457–66.

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Huang CN, Huang SP, Pao JB, Chang TY, Lan YH, Lu TL, Lee HZ, Juang SH, Wu PP, Pu YS, et al. Genetic polymorphisms in androgen receptor-binding sites predict survival in prostate cancer patients receiving androgen-deprivation therapy. Ann Oncol. 2011;23:707–13.

    Article  PubMed  Google Scholar 

  30. 30.

    Sun Y, Jia X, Hou L, Liu X. Screening of differently expressed miRNA and mRNA in prostate cancer by integrated analysis of transcription data. Urology. 2019;94:313.

    Google Scholar 

  31. 31.

    Akagi T, Ito T, Kato M, Jin Z, Cheng Y, Kan T, Yamamoto G, Olaru A, Kawamata N, Boult J, et al. Chromosomal abnormalities and novel disease-related regions in progression from Barrett’s esophagus to esophageal adenocarcinoma. Int J Cancer. 2009;125:2349–59.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Wu Y, Zhang X, Liu Y, Lu F, Chen X. Decreased expression of BNC1 and BNC2 is associated with genetic or epigenetic regulation in hepatocellular carcinoma. Int J Mol Sci. 2016;17:153.

    Article  PubMed Central  Google Scholar 

  33. 33.

    Cesaratto L, Grisard E, Coan M, ZandonĆ L, De Mattia E, Poletto E, Cecchin E, Puglisi F, Canzonieri V, Mucignat MT, et al. BNC2 is a putative tumor suppressor gene in high-grade serous ovarian carcinoma and impacts cell survival after oxidative stress. Cell Death Dis. 2016;7:e2374.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) method. Methods. 2001;25:402–8.

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Saviozzi S, Cordero F, Iacono M, Novello S, Giorgio S, Calogero R. Selection of suitable reference genes for accurate normalization of gene expression profile studies in non-small cell lung cancer. BMC Cancer. 2000;6:200.

    Article  Google Scholar 

  36. 36.

    Du P, Kibbe WA, Lin SM. Lumi: a pipeline for processing Illumina microarray. Bioinformatics. 2008;24:1547–8.

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.

    Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc: Ser B (Methodol). 1995;57:289–300.

    Google Scholar 

  39. 39.

    Reimand J, Arak T, Adler P, Kolberg L, Reisberg S, Peterson H, Vilo J. g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 2016;44:W83–9.

    Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Xiang Y, Wang Z, Murakami J, Plummer S, Klein EA, Carpten JD, Trent JM, Isaacs WB, Casey G, Silverman RH. Effects of RNase L mutations associated with prostate cancer on apoptosis induced by 2′,5′-oligoadenylates. Cancer Res. 2003;63:6795–801.

    CAS  PubMed  Google Scholar 

  41. 41.

    Malathi K, Paranjape JM, Ganapathi R, Silverman RH. HPC1/RNASEL mediates apoptosis of prostate cancer cells treated with 2′,5′-oligoadenylates, topoisomerase I inhibitors, and tumor necrosis factor-related apoptosis-inducing ligand. Cancer Res. 2004;64:9144–51.

    CAS  Article  PubMed  Google Scholar 

  42. 42.

    Zhu LM, Shi DM, Dai Q, Cheng XJ, Yao WY, Sun PH, Ding Y, Qiao MM, Wu YL, Jiang SH, Tu SP. Tumor suppressor XAF1 induces apoptosis, inhibits angiogenesis and inhibits tumor growth in hepatocellular carcinoma. Oncotarget. 2014;5:5403–15.

    Article  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Andrews HN, Mullan PB, McWilliams S, Sebelova S, Quinn JE, Gilmore PM, McCabe N, Pace A, Koller B, Johnston PG, et al. BRCA1 regulates the interferon gamma-mediated apoptotic response. J Biol Chem. 2002;277:26225–32.

    CAS  Article  PubMed  Google Scholar 

  44. 44.

    Liston P, Fong WG, Kelly NL, Toji S, Miyazaki T, Conte D, Tamai K, Craig CG, McBurney MW, Korneluk RG. Identification of XAF1 as an antagonist of XIAP anti-Caspase activity. Nat Cell Biol. 2001;3:128–33.

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Maia CJ, Rocha SM, Socorro S, Schmitt F, Santos CR. Oligoadenylate synthetase 1 (OAS1) expression in human breast and prostate cancer cases, and its regulation by sex steroid hormones. 2016; 2.

  46. 46.

    Nagahata T, Sato T, Tomura A, Onda M, Nishikawa K, Emi M. Identification of RAI3 as a therapeutic target for breast cancer. Endocr Relat Cancer. 2005;12:65–73.

    CAS  Article  PubMed  Google Scholar 

  47. 47.

    Fong WG, Liston P, Rajcan-Separovic E, St Jean M, Craig C, Korneluk RG. Expression and genetic analysis of XIAP-associated factor 1 (XAF1) in cancer cell lines. Genomics. 2000;70:113–22.

    CAS  Article  PubMed  Google Scholar 

  48. 48.

    Bunn PA. Worldwide overview of the current status of lung cancer diagnosis and treatment. Arch Pathol Lab Med. 2012;136:1478–81.

    Article  PubMed  Google Scholar 

  49. 49.

    Vanhoutteghem A, Maciejewski-Duval A, Bouche C, Delhomme B, Herve F, Daubigney F, Soubigou G, Araki M, Araki K, Yamamura K, Djian P. Basonuclin 2 has a function in the multiplication of embryonic craniofacial mesenchymal cells and is orthologous to disco proteins. Proc Natl Acad Sci USA. 2009;106:14432–7.

    Article  PubMed  PubMed Central  Google Scholar 

  50. 50.

    Sanchez-Palencia A, Gomez-Morales M, Gomez-Capilla JA, Pedraza V, Boyero L, Rosell R, Farez-Vidal ME. Gene expression profiling reveals novel biomarkers in nonsmall cell lung cancer. Int J Cancer. 2011;129:355–64.

    CAS  Article  PubMed  Google Scholar 

  51. 51.

    Einav U, Tabach Y, Getz G, Yitzhaky A, Ozbek U, Amariglio N, Izraeli S, Rechavi G, Domany E. Gene expression analysis reveals a strong signature of an interferon-induced pathway in childhood lymphoblastic leukemia as well as in breast and ovarian cancer. Oncogene. 2005;24:6367–75.

    CAS  PubMed  Google Scholar 

  52. 52.

    Mengual L, Ars E, Lozano JJ, Burset M, Izquierdo L, Ingelmo M, Gaya JM, Algaba F, Villavicencio H, Ribal MJ, Alcaraz A. Gene expression profiles in prostate cancer identification of candidate non-invasive diagnostic markers. Actas UrolĆ3gicas EspaĆ ± olas (English Edition). 2014;38:143–9.

    CAS  Article  Google Scholar 

  53. 53.

    Long J, Liu Z, Wu X, Xu Y, Ge C. Gene expression profile analysis of pancreatic cancer based on microarray data. Mol Med Rep. 2016;13:3919.

    Google Scholar 

  54. 54.

    Chepovetsky J, Kalir T, Weiderpass E. Clinical applicability of microarray technology in the diagnosis, prognostic stratification, treatment and clinical surveillance of cervical adenocarcinoma. Curr Pharm Des. 2013;19:1425–9.

    CAS  PubMed  Google Scholar 

  55. 55.

    Tseng GC, Cheng C, Yu YP, Nelson J, Michalopoulos G, Luo JH. Investigating multi-cancer biomarkers and their cross-predictability in the expression profiles of multiple cancer types. Biomark Insights. 2009;4:57–79.

    PubMed  PubMed Central  Google Scholar 

  56. 56.

    Singhal S, Miller D, Ramalingam S, Sun SY. Gene expression profiling of non-small cell lung cancer. Lung Cancer. 2008;60:313–24.

    Article  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Eyerich S, Eyerich K, Pennino D, Carbone T, Nasorri F, Pallotta S, Cianfarani F, Odorisio T, Traidl-Hoffmann C, Behrendt H, et al. Th22 cells represent a distinct human T cell subset involved in epidermal immunity and remodeling. J Clin Invest. 2009;119:3573–85.

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58.

    Li S, Wang L, Berman M, Kong YY, Dorf ME. Mapping a dynamic innate immunity protein interaction network regulating type I interferon production. Immunity. 2011;35:426–40.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Rosebeck S, Leaman DW. Mitochondrial localization and pro-apoptotic effects of the interferon-inducible protein ISG12a. Apoptosis. 2008;13:562–72.

    CAS  Article  PubMed  Google Scholar 

  60. 60.

    Stawowczyk M, Van Scoy S, Kumar KP, Reich NC. The interferon stimulated gene 54 promotes apoptosis. J Biol Chem. 2011;286:7257–66.

    CAS  Article  PubMed  Google Scholar 

  61. 61.

    Mullan PB, Hosey AM, Buckley NE, Quinn JE, Kennedy RD, Johnston PG, Harkin DP. The 2,5 oligoadenylate synthetase/RNaseL pathway is a novel effector of BRCA1- and interferon-gamma-mediated apoptosis. Oncogene. 2005;24:5492–501.

    CAS  Article  PubMed  Google Scholar 

  62. 62.

    Ferrantini M, Capone I, Belardelli F. Interferon-alpha and cancer: mechanisms of action and new perspectives of clinical use. Biochimie. 2007;89:884–93.

    CAS  Article  PubMed  Google Scholar 

  63. 63.

    Krejcova D, Prochazkova J, Kubala L, Pachernik J. Modulation of cell proliferation and differentiation of human lung carcinoma cells by the interferon-alpha. Gen Physiol Biophys. 2009;28:294–301.

    CAS  Article  PubMed  Google Scholar 

  64. 64.

    Booy S, van Eijck CH, Dogan F, van Koetsveld PM, Hofland LJ. Influence of type-I Interferon receptor expression level on the response to type-I interferons in human pancreatic cancer cells. J Cell Mol Med. 2014;18:492–502.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Moschos S, Kirkwood JM. Present role and future potential of type I interferons in adjuvant therapy of high-risk operable melanoma. Cytokine Growth Factor Rev. 2007;18:451–8.

    CAS  Article  PubMed  Google Scholar 

  66. 66.

    Tate DJ, Patterson JR, Velasco-Gonzalez C, Carroll EN, Trinh J, Edwards D, Aiyar A, Finkel-Jimenez B, Zea AH. Interferon-gamma-induced nitric oxide inhibits the proliferation of murine renal cell carcinoma cells. Int J Biol Sci. 2012;8:1109–20.

    CAS  Article  PubMed  Google Scholar 

  67. 67.

    Mizokami MM, Hu P, Khawli LA, Li J, Epstein AL. Chimeric TNT-3 antibody/murine interferon-gamma fusion protein for the immunotherapy of solid malignancies. Hybrid Hybridomics. 2003;22:197–207.

    CAS  Article  PubMed  Google Scholar 

  68. 68.

    Akman T, Oztop I, Unek IT, Koca D, Unal OU, Salman T, Yavuzsen T, Yilmaz AU, Somali I, Demir N, Ellidokuz H. Long-term outcomes and prognostic factors of high-risk malignant melanoma patients after surgery and adjuvant high-dose interferon treatment: a single-center experience. Chemotherapy. 2015;60:228–38.

    Article  Google Scholar 

  69. 69.

    Caraglia M, Dicitore A, Marra M, Castiglioni S, Persani L, Sperlongano P, Tagliaferri P, Abbruzzese A, Vitale G. Type I interferons: ancient peptides with still under-discovered anti-cancer properties. Protein Pept Lett. 2013;20:412–23.

    CAS  PubMed  Google Scholar 

  70. 70.

    Suarez-Kelly LP, Kemper GM, Duggan MC, Stiff A, Nole TC, Markowitz J, Luedke EA, Yildiz VO, Yu L, Jaime-Ramirez AC, et al. The combination of MLN2238 (ixazomib) with interferon-alpha results in enhanced cell death in melanoma. Oncotarget. 2016;7:81172–6.

    PubMed  Google Scholar 

  71. 71.

    Parker BS, Rautela J, Hertzog PJ. Antitumour actions of interferons: implications for cancer therapy. Nat Rev Cancer. 2016;16:131–44.

    Article  PubMed  Google Scholar 

  72. 72.

    Vitale G, Zappavigna S, Marra M, Dicitore A, Meschini S, Condello M, Arancia G, Castiglioni S, Maroni P, Bendinelli P, et al. The PPAR-gamma agonist troglitazone antagonizes survival pathways induced by STAT-3 in recombinant interferon-beta treated pancreatic cancer cells. Biotechnol Adv. 2012;30:169–84.

    CAS  Article  PubMed  Google Scholar 

  73. 73.

    Caraglia M, Marra M, Viscomi C, D’Alessandro AM, Budillon A, Meo G, Arra C, Barbieri A, Rapp UR, Baldi A, et al. The farnesyltransferase inhibitor R115777 (ZARNESTRA) enhances the pro-apoptotic activity of interferon-alpha through the inhibition of multiple survival pathways. Int J Cancer. 2007;121:2317–30.

    CAS  Article  PubMed  Google Scholar 

  74. 74.

    Zhang K, Yin XF, Yang YQ, Li HL, Xu YN, Chen LY, Liu XJ, Yuan SJ, Fang XL, Xiao J, et al. A potent in vivo anti-tumor efficacy of novel recombinant type I interferon. Clin Cancer Res. 2016.

Download references

Authors’ contributions

EU and AR conceived the study, EU performed the experiments and prepared the manuscript. ER analyzed the array results. All the authors participated in the design of study, data analysis, interpretation of results, writing the manuscript and approved the final version of the manuscript. All authors read and approved the final manuscript.


We thank Viljo Soo and Kelli Grand for their contributions in the array experiments.

Competing interests

The authors declare that they have no competing interests.

Availability of data and material

Please contact author for data requests.

Ethics approval and consent to participate

The study was approved by the Ethics Committee on Human Research of Tartu University (ethic license numbers 197/M-19, 181/T-9, 161/49) and written informed consent was obtained from all patients.


This work was supported by Estonian Science Foundation grant ETF7859, by Grant from the Estonian Research Council IUT 20-60 (A.M.), by Centre of Excellence for Genomics and Translational Medicine (GENTRANSMED), by European Union through the European Regional Development Fund (Project No. 2014-2020.4.01.15-0012) and by Estonian Research Council personal research grant PUT214.

Author information



Corresponding author

Correspondence to Egon Urgard.

Additional files

Additional file 1.

Differentially expressed genes by microarray analysis.

Additional file 2.

Top 15 BNC2-influenced IPA pathways.

Additional file 3.

BNC2-influenced genes in immune system-related gene networks. Genes that were significantly up-regulated are shown in red and genes that were down-regulated in green. The intensity of the color corresponds to an increase in fold change. A. BNC2-influenced genes in the inflammatory response gene network. B. BNC2-influenced genes in dermatological diseases and conditions, infectious disease and endocrine system disorder-associated gene networks. C. BNC2-influenced genes in antimicrobial response-associated gene networks.

Additional file 4.

Top 20 canonical pathways identified by g:Profiler analysis.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Urgard, E., Reigo, A., Reinmaa, E. et al. Human basonuclin 2 up-regulates a cascade set of interferon-stimulated genes with anti-cancerous properties in a lung cancer model. Cancer Cell Int 17, 18 (2017).

Download citation


  • BNC2
  • Type I IFN
  • XAF1
  • OAS family
  • Lung cancer