Integrative genomic analyses of a novel cytokine, interleukin-34 and its potential role in cancer prediction

Wang,Bo; Xu,Wenming; Tan,Miaolian; Xiao,Yan; Yang,Haiwei; Xia,Tian-Song

doi:10.3892/ijmm.2014.2001

Integrative genomic analyses of a novel cytokine, interleukin-34 and its potential role in cancer prediction

Authors:
- Bo Wang
- Wenming Xu
- Miaolian Tan
- Yan Xiao
- Haiwei Yang
- Tian-Song Xia
View Affiliations

Affiliations: Department of Medical Oncology, The Eastern Hospital of The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong 510700, P.R. China, Department of Endocrinology, The Eastern Hospital of The First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, Guangdong 510700, P.R. China, Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, P.R. China, Department of Breast Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu 210029, P.R. China
Published online on: November 12, 2014 https://doi.org/10.3892/ijmm.2014.2001
Pages: 92-102
Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY_NC 3.0].

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )

Metrics: Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

Interleukin-34 (IL-34) is a novel cytokine, which is composed of 222 amino acids and forms homodimers. It binds to the macrophage colony-stimulating factor (M-CSF) receptor and plays an important role in innate immunity and inflammatory processes. In the present study, we identified the completed IL-34 gene in 25 various mammalian genomes and found that IL-34 existed in all types of vertebrates, including fish, amphibians, birds and mammals. These species have a similar 7 exon/6 intron gene organization. The phylogenetic tree indicated that the IL-34 gene from the primate lineage, rodent lineage and teleost lineage form a species-specific cluster. It was found mammalian that IL-34 was under positive selection pressure with the identified positively selected site, 196Val. Fifty-five functionally relevant single nucleotide polymorphisms (SNPs), including 32 SNPs causing missense mutations, 3 exonic splicing enhancer SNPs and 20 SNPs causing nonsense mutations were identified from 2,141 available SNPs in the human IL-34 gene. IL-34 was expressed in various types of cancer, including blood, brain, breast, colorectal, eye, head and neck, lung, ovarian and skin cancer. A total of 5 out of 40 tests (1 blood cancer, 1 brain cancer, 1 colorectal cancer and 2 lung cancer) revealed an association between IL-34 gene expression and cancer prognosis. It was found that the association between the expression of IL-34 and cancer prognosis varied in different types of cancer, even in the same types of cancer from different databases. This suggests that the function of IL-34 in these tumors may be multidimensional. The upstream transcription factor 1 (USF1), regulatory factor X-1 (RFX1), the Sp1 transcription factor 1 , POU class 3 homeobox 2 (POU3F2) and forkhead box L1 (FOXL1) regulatory transcription factor binding sites were identified in the IL-34 gene upstream (promoter) region, which may be involved in the effects of IL-34 in tumors.

View Figures

View References

1	Wakabayashi S, Yamaguchi K, Kumakura S, et al: Effects of anesthesia with sevoflurane and propofol on the cytokine/chemokine production at the airway epithelium during esophagectomy. Int J Mol Med. 34:137–144. 2014.PubMed/NCBI
2	Signorelli SS, Fiore V and Malaponte G: Inflammation and peripheral arterial disease: the value of circulating biomarkers (Review). Int J Mol Med. 33:777–783. 2014.PubMed/NCBI
3	Clavel G, Thiolat A and Boissier MC: Interleukin newcomers creating new numbers in rheumatology: IL-34 to IL-38. Joint Bone Spine. 80:449–453. 2013. View Article : Google Scholar : PubMed/NCBI
4	Lin H, Lee E, Hestir K, Leo C, et al: Discovery of a cytokine and its receptor by functional screening of the extracellular proteome. Science. 320:807–811. 2008. View Article : Google Scholar : PubMed/NCBI
5	Boström EA and Lundberg P: The newly discovered cytokine IL-34 is expressed in gingival fibroblasts, shows enhanced expression by pro-inflammatory cytokines, and stimulates osteoclast differentiation. PLoS One. 8:e816652013. View Article : Google Scholar : PubMed/NCBI
6	Moon SJ, Hong YS, Ju JH, Kwok SK, Park SH and Min JK: Increased levels of interleukin 34 in serum and synovial fluid are associated with rheumatoid factor and anticyclic citrullinated peptide antibody titers in patients with rheumatoid arthritis. J Rheumatol. 40:1842–1849. 2013. View Article : Google Scholar : PubMed/NCBI
7	Hwang SJ, Choi B, Kang SS, et al: Interleukin-34 produced by human fibroblast-like synovial cells in rheumatoid arthritis supports osteoclastogenesis. Arthritis Res Ther. 14:R142012. View Article : Google Scholar : PubMed/NCBI
8	Chen Z, Buki K, Vääräniemi J, Gu G and Väänänen HK: The critical role of IL-34 in osteoclastogenesis. PLoS One. 6:e186892011. View Article : Google Scholar : PubMed/NCBI
9	El-Gamal MI, Anbar HS, Yoo KH and Oh CH: FMS kinase inhibitors: Current status and future prospects. Med Res Rev. 33:599–636. 2013. View Article : Google Scholar
10	Burns CJ and Wilks AF: c-FMS inhibitors: a patent review. Expert Opin Ther Pat. 21:147–165. 2011. View Article : Google Scholar : PubMed/NCBI
11	Baud’huin M, Renault R, Charrier C, et al: Interleukin-34 is expressed by giant cell tumours of bone and plays a key role in RANKL-induced osteoclastogenesis. J Pathol. 221:77–86. 2010. View Article : Google Scholar
12	Petráčková M, Staněk L, Mandys V, Dundr P and Vonka V: Properties of bcr-abl-transformed mouse 12B1 cells secreting interleukin-2 and granulocyte-macrophage colony stimulating factor (GM-CSF): II. Adverse effects of GM-CSF. Int J Oncol. 40:1915–1922. 2012.
13	Fournier P, Aigner M and Schirrmacher V: Targeting of IL-2 and GM-CSF immunocytokines to a tumor vaccine leads to increased anti-tumor activity. Int J Oncol. 38:1719–1729. 2011.PubMed/NCBI
14	Kitoh Y, Saio M, Gotoh N, et al: Combined GM-CSF treatment and M-CSF inhibition of tumor-associated macrophages induces dendritic cell-like signaling in vitro. Int J Oncol. 38:1409–1419. 2011. View Article : Google Scholar : PubMed/NCBI
15	Tada F, Abe M, Hirooka M, et al: Phase I/II study of immunotherapy using tumor antigen-pulsed dendritic cells in patients with hepatocellular carcinoma. Int J Oncol. 41:1601–1609. 2012.PubMed/NCBI
16	Yang L, Luo Y and Wei J: Integrative genomic analyses on Ikaros and its expression related to solid cancer prognosis. Oncol Rep. 24:571–577. 2010. View Article : Google Scholar : PubMed/NCBI
17	Yang L, Luo Y, Wei J and He S: Integrative genomic analyses on IL28RA, the common receptor of interferon-λ1, -λ2 and -λ3. Int J Mol Med. 25:807–812. 2010.PubMed/NCBI
18	Yang L, Wei J and He S: Integrative genomic analyses on interferon-λs and their roles in cancer prediction. Int J Mol Med. 25:299–304. 2010.PubMed/NCBI
19	Yu H, Yuan J, Xiao C and Qin Y: Integrative genomic analyses of recepteur d’origine nantais and its prognostic value in cancer. Int J Mol Med. 31:1248–1254. 2013.PubMed/NCBI
20	Wang M, Wei X, Shi L, Chen B, Zhao G and Yang H: Integrative genomic analyses of the histamine H1 receptor and its role in cancer prediction. Int J Mol Med. 33:1019–1026. 2014.PubMed/NCBI
21	Wang B, Chen K, Xu W, Chen D, Tang W and Xia TS: Integrative genomic analyses of secreted protein acidic and rich in cysteine and its role in cancer prediction. Mol Med Rep. 10:1461–1468. 2014.PubMed/NCBI
22	Kumar S, Nei M, Dudley J and Tamura K: MEGA: a biologist-centric software for evolutionary analysis of DNA and protein sequences. Brief Bioinform. 9:299–306. 2008. View Article : Google Scholar : PubMed/NCBI
23	Yang Z: PAML: a program package for phylogenetic analysis by maximum likelihood. Comput Appl Biosci. 13:555–556. 1997.PubMed/NCBI
24	Yang Z, Nielsen R, Goldman N and Pedersen AM: Codon-substitution models for heterogeneous selection pressure at amino acid sites. Genetics. 155:431–449. 2000.PubMed/NCBI
25	Forbes SA, Bindal N, Bamford S, et al: COSMIC: mining complete cancer genomes in the Catalogue of Somatic Mutations in Cancer. Nucleic Acids Res. 39:D945–D950. 2011. View Article : Google Scholar :
26	Katoh Y and Katoh M: Integrative genomic analyses on GLI1: positive regulation of GLI1 by Hedgehog-GLI, TGFβ-Smads, and RTK-PI3K-AKT signals, and negative regulation of GLI1 by Notch-CSL-HES/HEY, and GPCR-Gs-PKA signals. Int J Oncol. 35:187–192. 2009. View Article : Google Scholar : PubMed/NCBI
27	Katoh M and Katoh M: Integrative genomic analyses of WNT11: transcriptional mechanisms based on canonical WNT signals and GATA transcription factors signaling. Int J Mol Med. 24:247–251. 2009. View Article : Google Scholar : PubMed/NCBI
28	Katoh M and Katoh M: Transcriptional mechanisms of WNT5A based on NF-κB, Hedgehog, TGFβ, and Notch signaling cascades. Int J Mol Med. 23:763–769. 2009. View Article : Google Scholar : PubMed/NCBI
29	Katoh M and Katoh M: Integrative genomic analyses of ZEB2: Transcriptional regulation of ZEB2 based on SMADs, ETS1, HIF1α, POU/OCT, and NF-κB. Int J Oncol. 34:1737–1742. 2009. View Article : Google Scholar : PubMed/NCBI
30	Chalifa-Caspi V, Yanai I, Ophir R, et al: GeneAnnot: comprehensive two-way linking between oligonucleotide array probesets and GeneCards genes. Bioinformatics. 20:1457–1458. 2004. View Article : Google Scholar : PubMed/NCBI
31	Parkinson H, Sarkans U, Shojatalab M, et al: ArrayExpress - a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 33:D553–D555. 2005. View Article : Google Scholar
32	Mizuno H, Kitada K, Nakai K and Sarai A: PrognoScan: a new database for meta-analysis of the prognostic value of genes. BMC Med Genomics. 2:182009. View Article : Google Scholar : PubMed/NCBI
33	Wang T, Kono T, Monte MM, et al: Identification of IL-34 in teleost fish: differential expression of rainbow trout IL-34, MCSF1 and MCSF2, ligands of the MCSF receptor. Mol Immunol. 53:398–409. 2013. View Article : Google Scholar
34	Bouafia A, Corre S, Gilot D, Mouchet N, Prince S and Galibert MD: p53 requires the stress sensor USF1 to direct appropriate cell fate decision. PLoS Genet. 10:e10043092014. View Article : Google Scholar : PubMed/NCBI
35	Ikeda R, Nishizawa Y, Tajitsu Y, et al: Regulation of major vault protein expression by upstream stimulating factor 1 in SW620 human colon cancer cells. Oncol Rep. 31:197–201. 2014.
36	Katoh Y and Katoh M: Comparative genomics on Vangl1 and Vangl2 genes. Int J Oncol. 26:1435–1440. 2005.PubMed/NCBI
37	Feng C, Zhang Y, Yin J, Li J, Abounader R and Zuo Z: Regulatory factor X1 is a new tumor suppressive transcription factor that acts via direct downregulation of CD44 in glioblastoma. Neuro Oncol. 16:1078–1085. 2014. View Article : Google Scholar : PubMed/NCBI
38	Xu F, Zhu X, Han T, et al: The oncoprotein hepatitis B X-interacting protein promotes the migration of ovarian cancer cells through the upregulation of S-phase kinase-associated protein 2 by Sp1. Int J Oncol. 45:255–263. 2014.PubMed/NCBI
39	Liang X, Li ZL, Jiang LL, Guo QQ, Liu MJ and Nan KJ: Suppression of lung cancer cell invasion by LKB1 is due to the downregulation of tissue factor and vascular endothelial growth factor, partly dependent on SP1. Int J Oncol. 44:1989–1997. 2014.PubMed/NCBI
40	Chae JI, Cho JH, Lee KA, et al: Role of transcription factor Sp1 in the quercetin-mediated inhibitory effect on human malignant pleural mesothelioma. Int J Mol Med. 30:835–841. 2012.PubMed/NCBI
41	Lee KA, Lee YJ, Ban JO, et al: The flavonoid resveratrol suppresses growth of human malignant pleural mesothelioma cells through direct inhibition of specificity protein 1. Int J Mol Med. 30:21–27. 2012.PubMed/NCBI
42	Moritsugu R, Tamai K, Nakano H, et al: Functional analysis of the nuclear localization signal of the POU transcription factor Skn-1a in epidermal keratinocytes. Int J Mol Med. 34:539–544. 2014.PubMed/NCBI
43	Katoh M and Katoh M: Human FOX gene family (Review). Int J Oncol. 25:1495–1500. 2004.PubMed/NCBI
44	Katoh M and Katoh M: Transcriptional regulation of WNT2B based on the balance of Hedgehog, Notch, BMP and WNT signals. Int J Oncol. 34:1411–1415. 2009.PubMed/NCBI
45	Metzeler KH, Hummel M, Bloomfield CD, et al: An 86-probe-set gene-expression signature predicts survival in cytogenetically normal acute myeloid leukemia. Blood. 112:4193–4201. 2008. View Article : Google Scholar : PubMed/NCBI
46	Freije WA, Castro-Vargas FE, Fang Z, et al: Gene expression profiling of gliomas strongly predicts survival. Cancer Res. 64:6503–6510. 2004. View Article : Google Scholar : PubMed/NCBI
47	Smith JJ, Deane NG, Wu F, et al: Experimentally derived metastasis gene expression profile predicts recurrence and death in patients with colon cancer. Gastroenterology. 138:958–968. 2010. View Article : Google Scholar
48	Okayama H, Kohno T, Ishii Y, et al: Identification of genes upregulated in ALK-positive and EGFR/KRAS/ALK-negative lung adenocarcinomas. Cancer Res. 72:100–111. 2012. View Article : Google Scholar
49	Lee ES, Son DS, Kim SH, et al: Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression. Clin Cancer Res. 14:7397–7404. 2008. View Article : Google Scholar : PubMed/NCBI