Open Access

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

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


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.

Introduction

Cytokines are glycosylated proteins that allow communication among various cell types involved in immune response. Interleukins (ILs) are cytokines mainly produced by T-cells, as well by monocytes, macrophages and endothelial cells (1,2). The different ILs share special biochemical or functional characteristics and are numbered in order of their identification. The emergence of new technologies is translating into a steady increase in the number of known molecules (3). In 2008, Lin et al (4) produced 3,400 recombinant secreted proteins that encode secreted proteins and extracellular domains of transmembrane proteins in 293T cells and examined their activities based on human monocyte screening assays. Subsequently, the authors (4) discovered a novel cytokine, IL-34. The human IL-34 protein is composed of 222 amino acids, has a molecular mass of 39 kDa and forms homodimers. It binds to the macrophage colony-stimulating factor (M-CSF) receptor, c-FMS (also known as CSF-1 receptor), expressed on the cell surface of human monocytes and has a stronger, although short-lived effect compared to M-CSF. IL-34 has been shown to be involved in the process of osteoclastogenesis and rheumatoid arthritis (RA) (58). IL-34 has been shown to promote the proliferation, survival and differentiation of monocytes and macrophages, the release of pro-inflammatory chemokines, and thereby plays an important role in innate immunity and inflammatory processes. It also plays an important role in the regulation of osteoclast proliferation and differentiation, and in the regulation of bone resorption (58).

IL-34 and M-CSF both signal via the same receptor, the M-CSF receptor. Although IL-34 and M-CSF show no appreciable similarity in their primary structure, they are evolutionally distant ligands, but are structurally related (9). There is evidence indicating that the M-CSF-IL-34-c-FMS axis is involved in the initiation, growth and metastasis of tumors (10,11). M-CSF levels may constitute a useful biomarker for a number of types of cancer, as it is expressed at high levels in a number of types of cancer, including breast cancer, ovarian cancer and colorectal carcinoma and its expression correlates with a poor prognosis (12). The direct inhibition of M-CSF or the inhibition of c-FMS kinase activity can lead to significant changes in the growth of grafted tumors (13.14). Tumor-associated macrophages are the most abundant component of the leukocyte infiltrate of solid tumors. In M-CSF-deficient mice (M-CSFop/op or M-CSF−/−), the growth of the primary tumor and the metastatic spread of tumor cells has been shown to be significantly reduced due to the inability of angiogenesis to feed the tumors (1215).

However, studies on the role of IL-34 in tumorigenesis. In the present study, we identified the IL-34 gene in various mammalian genomes by comparative genomic analyses. The conserved transcription factor-binding sites within the promoter region of the human IL-34 gene were then searched. Analyses of the expression data, functional relevant single nucleotide polymorphisms (SNPs) and comparative proteomic analysis were also conducted. Furthermore, a meta-analysis of the prognostic value of the IL-34 gene in various types of cancer was performed.

Materials and methods

Identification of the novel IL-34 gene in vertebrate genomes and integrative genomic analyses

All the IL-34 gene and amino acid sequences were obtained from the Ensembl database (http://www.ensembl.org/index.html), based on orthologous and paralogous relationships. The gained IL-34 sequences were applied as queries to search the IL-34 gene using BLAST at the National Center for Biotechnology Information (NCBI), in order to confirm whether their best hit was an IL-34 gene (1618). The number and length of IL-34 exons and introns in all competent sequences were investigated for exon-intron conservation analyses. The number, length and structures of the exons and introns in IL-34 in all species were also collected from the Ensembl database (http://www.ensembl.org/index.html). Conserved transcription factor-binding sites within the promoter region of the human IL-34 gene were obtained from SABiosciences’ proprietary database which combines Text Mining Application and data from the UCSC Genome Browser (1921).

Comparative proteomic analysis of IL-34 protein

The protein coding sequences of IL-34 were aligned using ClustalW software implemented in MEGA 5.05. We constructed a maximum likelihood (ML) tree of IL-34 amino acid sequences using MEGA 5.05 with the optimal model (Kimura 2-parameter model). For the relative support of the internal node, bootstrap analysis was performed with 1,000 replications for ML reconstructions (22). The program CodeML implemented in the PAML 4.7 software package was used to investigate whether the IL-34 protein is under positive selection (23). The site-specific model was exerted using likelihood ratio tests (LRTs) to compare the M7 (null model) with the M8 model. M7 is a null model that does not allow for any codons with ω >1, whereas the M8 model allows for positively selective sites (ω >1). When the M8 model was fitted to the data more efficiently (P-value <0.05) than the null model (M7), the presence of sites with ω >1 was suggested. On the contrary, the results of P-value >0.05 proved the absence of sites with ω >1. Twice the log likelihood difference between the two compared models (2Δl) was compared against χ2 with critical values of 5.99 and 9.21 at 0.05 and 0.01 significance levels, respectively, as previously described (24).

Functionally relevant SNP evaluation of the human IL-34 gene and identification of somatic mutations in human cancer

Functionally relevant SNPs of the human IL-34 gene were identified as previously described (1621). The SNPs were extracted from the Ensembl (http://www.ensembl.org) and the NCBI SNPdb (http://www.ncbi.nlm.nih.gov) databases. The SNPs that disrupted exonic splicing enhancer/exonic splicing silencer (ESE/ESS) motifs and cause missence mutations were also identified. The identification of somatic mutations of the human IL-34 gene in human cancer was conducted in the Catalogue of Somatic Mutations in Cancer (COSMIC), a database for mining complete cancer genomes in the catalogue of somatic mutations in cancer (25).

In silico expression analyses of the human IL-34 gene

Expressed sequence tags (ESTs) derived from the human IL-34 gene were searched for using the BLAST programs as previously described (2629). The human IL-34 gene (NM_152456) was used as query sequences for the BLAST programs. The expression profiles for normal human tissues were obtained from GeneAnnot (30) and ArrayExpress (31) databases. Northern analysis of the NCBI uniGene dataset was also performed (1921).

Meta-analysis of the prognostic value of the IL-34 gene in cancer

A database termed ‘PrognoScan’ has been previously developed (32). This database includes a large collection of publicly available cancer microarray datasets with clinical annotation, as well as a tool for assessing the biological association between gene expression and prognosis. PrognoScan employs the minimum P-value approach for grouping patients for survival analysis. PrognoScan provides a powerful platform for evaluating potential tumor markers and therapeutic targets and is publicly accessible at http://www.sabiosciences.com. The human IL-34 gene was used as an input source as a query and the data were collected for analysis.

Results

Comparative proteomic analysis of IL-34 protein identified in vertebrate genomes

All the IL-34 gene and protein sequences were collected from the Ensembl database and confirmed by BLAST at NCBI. The complete IL-34 gene was identified in the human, chimpanzee, gibbon, macaque, orangutan, marmoset, bushbaby, pika, squirrel, rat, mouse, kangaroo rat, elephant, cat, dog, panda, ferret, pig, horse, cow, flycatcher, chicken, zebrafish, platyfish and tilapia. The sequence and structural alignment of IL-34 is illustrated in Fig. 1. The phylogenetic tree was constructed according to the protein coding sequences of IL-34 using the ML method (Fig. 2). The IL-34 gene from the primate lineage, rodent lineage and teleost lineage forms a species-specific cluster. The exon-intron information collected from the Ensembl database is presented in Table I and Fig. 3. In the majority of genomes, the IL-34 gene has 6 exons with similar lengths in different species (Table I). In the majority of vertebrates, the IL-34 gene shows exon-intron conservation with 5 introns and similar sizes of each intron. With exception, there are 8 exons and 7 introns in the IL-34 gene in the kangaroo rat. Moreover, the IL-34 gene in the platyfish and tilapia contains 7 exons and 6 introns. Thus, the intron deletions of the IL-34 gene may occur during the evolutionary process in fish. Furthermore, site-specific tests for positive selection were performed for vertebrate, mammalian, primate and mammalian excluding primate, rodent and teleost lineages. Although some positive selection sites were computed, only the 2Δl of M7 and M8 of mammalian IL-34 was >5.99, indicating that the M8 model was more efficient than the M7 model in fitting the data. It seemed that mammalian IL-34 was under positive selecetion pressure with the identified positively selected site, 196Val (Table II).

Table I

Exon and intron lengths of IL-34.

Table I

Exon and intron lengths of IL-34.

Length (bp)

SpeciesExon 1Intron 1Exon 2Intron 2Exon 3Intron 3Exon 4Intron 4Exon 5Intron 5Exon 6Intron 6Exon 7Intron 7Exon 8Total exons
Human287,5621341,930782801622,496136243191----729
Chimpanzee288,1791341,926782801622,495136250191----729
Gibbon286,4721341,930782801622,538136250191----729
Orangutan287,6431342,042782801622,563136258191----729
Macaque287,3691342,064782881622,531136239191----729
Marmoset287,7891424,152271172052,848136236191----729
Bushbaby154871471,461782391621,589136236170----711
Cat286,405134787782851622,902136230173----711
Dog286,254134940782811622,366136238173----711
Panda285,018134797782841622,066136230173----711
Horse285,227134841782841622,162136217176----714
Ferret284,504134771783061622,043136234173----711
Cow285,849134751782611622,879133229170----705
Rat285,4841341,125782691595,154136264170----705
Mouse285,672134684782581625,463136195170----708
Squirrel285,565134789782771623,231136210185----723
Kangaroo rat283,3511341,060782231621,055136202160416215729
Flycatcher289210429378841743,6271399920----543
Chicken2886104623781081741,2251338220----537
Zebrafish404,445107431813,0522011421693,37544----642
Platyfish4026,7381041,34478256784,4791082,3761844,04286--678
Tilapia3711,96410446178154782,1701084,07218479959--648

[i] IL-34, interleukin-34.

Table II

Site-specific tests for positive selection on IL-34.

Table II

Site-specific tests for positive selection on IL-34.

SpeciesModelsEstimates of parameterslnL2ΔlPositively selected sites
VertebrateM7P=1.03903, Q=5.39585−4659.9129702.057682NS
M8P0=0.98039, P=1.20110, Q=7.10944 (P1=0.01961) w=1.00000−4657.855288
MammalianM7P=0.48941, Q=1.45186−4490.1398256.824409196 Va
M8P0=0.96625, P=0.63368, Q=2.34086 (P1=0.03375), w=1.81330−4483.315416
PrimateM7P=0.01895, Q=0.02238−1715.9258240.033788NS
M8P0=0.56073, P=0.00997, Q=0.16501 (P1=0.43927) w=1.00000−1715.959612
Mammalian excluding primateM7P=0.30844, Q=1.06769−3530.1800112.160234200 Qa
M8P0=0.98088, P=0.35332, Q=1.40393 (P1=0.01912) w=2.08098−3528.019777
RodentM7P=0.33256, Q=1.26857−1743.9729970.000104NS
M8P0=0.99999, P=0.33225, Q=1.26708 (P1=0.00001) w=3.17132−1743.973101
TeleostM7P=0.55893, Q=1.64564−1656.0099630.088367NS
M8P0=0.99243, P=0.58684, Q=1.81573 (P1=0.00757) w=8.77551−1655.921596

a The positively selected sites were identified with posterior probability ≥0.95 using the Bayes empirical Bayes (BEB) approach.

{ label (or @symbol) needed for fn[@id='tfn3-ijmm-35-01-0092'] } lnL, the log-likelihood difference between the two models; 2Δl, twice the log-likelihood difference between the two models (in all species, 2Δl <9.21, the P-value is more than the significance level 0.05, indicating that the M8 model was more efficient than the M7 model); NA, not allowed; NS, not shown (sites under positive selection did not reach the significance level of 0.95). IL-34, interleukin-34.

Expression profile of the human IL-34 gene

By EST sequence searching, the human IL-34 gene was found to be expressed in the adult and fetal brain, the hippocampus, spleen, embryonic stem cells, heart, medulla, lung, testes, ovaries, metastatic chondrosarcoma, epidermis, keratinocytes, osteoarthritic cartilage, adipose tissue, choroid, eyes, amygdala, kidneys, thymus, small intestine, hypothalamus, islets of Langerhans, glioblastoma and the retinal pigment epithelium. The investigation of available microarray analyses and ‘virtual northern blot analysis’ revealed a predominant expression of IL-34 in the lymph nodes, brain, heart, skeletal muscle, colon, adipocyte, kidneys, liver, lungs, thyroid, adrenal gland, ovaries, prostate and testes. When performing a search in the PrognoScan database, the human IL-34 gene was also found to be expressed in various types of cancer, such as blood, brain, breast, colorectal, eye, head and neck, lung, ovarian and skin cancer.

Comparative genomic anlaysis of the human IL-34 gene

The upstream transcription factor 1 (USF1), regulatory factor X-1 (RFX1), the Sp1 transcription factor 1, POU class 3 homeobox 2 (POU3F2) and the forkhead box L1 (FOXL1) regulatory transcription factor binding sites were identified in the IL-34 gene upstream (promoter) region.

Functionally relevant SNP evaluation of the human IL-34 gene and identification of somatic mutations in human cancer

A total of 2,141 available SNPs were identified in the human IL-34 gene. Among these SNPs, a total of 55 SNPs were functionally relevant; these included 32 SNPs causing missense mutations, 3 exonic splicing enhancer SNPs and 20 SNPs causing nonsense mutations (Table III). As presented in Table IV, by performing a search of the COSMIC database, we identified 18 somatic mutations of the IL-34 gene in cancer.

Table III

Functionally relevant SNP evaluation of the human IL-34 gene.

Table III

Functionally relevant SNP evaluation of the human IL-34 gene.

SNP IDChr 16 position sequenceSequenceTypeAmino acid change
rs20015870170680854(+)CCATGC/TCCCGGmisPS
rs19233700170680866(+)GCTTCA/CCCTGGmisTP
rs13913347670688459(+)CCTTGG/CCGTGGmisAG
rs14289068270688461(+)TTGGCG/ATGGCCmisMV
rs11806233370690511(+)AACACT/CACTTCmisHY
rs20059797970690960(+)GGGCCA/GCCCATmisHR
rs804642470690989(+)AGGTGC/GAGACGmisQE
rs18716656370693576(+)CCCAGA/GGCCAAmisEG
rs14221490470693626(+)GCTTCC/TGGGTCmisRW
rs720650970693945(+)GCCAAG/CTCCTCmisTS
rs20127764070693984(+)GTATGC/TGGCCAmisAV
rs20212298270694001(+)TGTACC/TCTCCGmisPS
rs14828633970694011(+)GCCCCC/TGTGGTmisPL
rs14151363870694056(+)GAGGCC/TGGTCAmisPL
rs11263936970694073(+)AGGGCG/AAGGGCmisKE
rs144464320170694076(+)GCGAGG/AGCCTCmisSG
rs36785133870693627(+)CTTCCA/GGGTCAmisQR
rs36814341870690933(+)TGAGTC/TGGTGCmisSL
rs37466533970690963(+)CCACCC/TATCCTmisPL
rs36892365570691023(+)CCTCAC/TGGTGAmisTM
rs36836727470693597(+)GGTGCA/GGCCCAmisQR
rs20089192470693560(+)TGTCCC/ATCTTGmisIL
rs37299891770694041(+)CTCCAC/TGGGCTmisTM
rs37043638670690927(+)TGCCAC/TTGAGTmisTL
rs14442748270690571(+)CCAACG/ATCACCmisIV
rs20110846470693569(+)TGAATG/ACCCCAmisTA
rs14414442670690541(+)GTGTGC/TCTTACmisPS
rs37741143170690885(+)CGAGCG/TGGAGCmisRL
rs36901117770680875(+)GGCTGC/TGCTGTmisRC
rs14578276870693979(+)TTGCAG/CTATGCmisHQ
rs20178445970694005(+)CCCTCC/TGCCCCmisPL
rs20048883570693655(+)TCCTGC/GTGTAAmisCW
rs381390470680744(+)TGACTG/CAGTGAese
rs381390570680850(+)ACCACC/GATGCCese
rs498555670694000(+)CTGTAC/ACCTCCese

[i] A total of 55 SNPs were functionally relevant; including 32 SNPs causing missense mutations, 3 exonic splicing enhancer SNPs and 20 SNPs causing nonsense mutations. SNP, single nucleotide polymorphism; IL-34, interleukin-34; mis, missense; ese, exonic splicing enhancer.

Table IV

Somatic mutations of IL-34 in cancer tissue.

Table IV

Somatic mutations of IL-34 in cancer tissue.

Position (AA)Mutation (CDS)Mutation (amino acid)Mutation ID (COSM)CountMutation type
4c.11G>Ap.G4DCOSM9730551Substitution - missense
9c.25C>Tp.R9CCOSM36911331Substitution - missense
33c.99G>Ap.E33ECOSM7043111Substitution - coding silent
38c.114G>Ap.T38TCOSM9730571Substitution - coding silent
42 c.125_126GG>AAp.R42QCOSM1435551Substitution - missense
59c.176C>Tp.P59LCOSM1080321Substitution - missense
61c.182A>Gp.N61SCOSM33875731Substitution - missense
104c.311C>Tp.S104LCOSM4356671Substitution - missense
155c.465C>Tp.N155NCOSM3285949Substitution - coding silent
170c.508C>Tp.R170WCOSM1948701Substitution - missense
183c.549C>Ap.S183RCOSM34024481Substitution - missense
197c.589C>Tp.Q197*COSM13793211Substitution - nonsense
197c.590A>Gp.Q197RCOSM13793221Substitution - missense
197c.591G>Ap.Q197QCOSM403241Substitution - coding silent
208c.623C>Tp.A208VCOSM4172922Substitution - missense
208c.624G>Ap.A208ACOSM11774121Substitution - coding silent
217c.651G>Ap.P217PCOSM13793231Substitution - coding silent
229c.686C>Tp.S229LCOSM4172913Substitution - missense

[i] IL-34, interleukin-34.

Meta-analysis of the prognostic value of the human IL-34 gene in cancer

When the name of a gene is submitted, PrognoScan displays a summary in table format of tests for the gene with columns for dataset, cancer type, subtype, endpoint, cohort, contributor, array type, probe ID, number of patients, optimal cutpoint, Pmin and Pcor. Among the databases which detected the expression of the IL-34 gene, 5 out of 40 tests revealed an association between the expression of the IL-34 gene and cancer prognosis (blood cancer, 1/4; brain cancer, 1/4; breast cancer, 0/11; colorectal cancer, 1/7; eye cancer, 0/1; head and neck cancer, 0/3; lung cancer, 2/6; ovarian cancer, 0/3; and skin cancer, 0/1) with a 5% significance level (Table V). Among the two types of lung cancer, the lower expression of the IL-34 gene was related to poor survival and was found in non-small cell lung cancer (NSCLC) case (GSE8894). However, a higher expression of the IL-34 gene was related to poor survival in a case of adenocarcinoma (GSE31210). As for blood cancer cases and colorectal cancer, we found that a lower expression of the IL-34 gene was associated with poor survival. However, in the brain cancer cases, a higher expression of the IL-34 gene was related to poor survival.

Table V

Dataset content from the PrognoScan database demonstrating an association between microarray analyses in IL-34 and cancer prognosis.

Table V

Dataset content from the PrognoScan database demonstrating an association between microarray analyses in IL-34 and cancer prognosis.

DatabaseCase typeSubsyteNo. of patientsEndpointCutpointP-valuePrognosis(Refs.)
GSE12417-GPL570Blood cancerAML79Overall survival0.180.0281(45)
GSE4412-GPL97Brain cancerGlioma74Overall survival0.720.0032(46)
GSE17537Colorectal cancer55Overall survival0.380.041(47)
GSE31210Lung cancerAdenocarcinoma204Relapse-free survival0.890.032(48)
GSE8894Lung cancerNSCLC138Relapse-free survival0.40.00021(49)

[i] A total of 5 out of 40 tests showed an association between the expression of the IL-34 gene in microarray analysis and cancer prognosis (blood cancer, 1/4; brain cancer, 1/4; breast cancer, 0/11; colorectal cancer, 1/7; eye cancer, 0/1; head and neck cancer, 0/3; lung cancer, 2/6; ovarian cancer, 0/3; and skin cancer, 0/1) with a 5% significance level. IL-34, interleukin-34; NSCLC, non-small cell lung cancer.

Discussion

IL-34 was identified by functional screening of a library of secreted proteins, based on its ability to support human monocyte survival and to promote, with the same efficiency as M-CSF, the formation of the colony forming unit-macrophage (CFU-M) in human bone marrow cell cultures (4).

In the present study, we identified the complete IL-34 gene in 25 various mammalian genomes, including the human, chimpanzee, gibbon, macaque, orangutan, marmoset, bushbaby, pika, squirrel, rat, mouse, kangaroo rat, elephant, cat, dog, panda, ferret, pig, horse, cow, flycatcher, chicken, zebrafish, platyfish and tilapia genomes. In addition, we found that IL-34 existed in all types of vertebrates, including fish, amphibians, birds and mammals. The IL-34 gene has a similar 7 exon/6 intron gene organization in various species, and genes in the IL-34 loci were syntenically conserved (33,34). The phylogenetic tree demonstrated that the IL-34 gene from the primate lineage, rodent lineage and teleost lineage formed a species-specific cluster. From the alignment and phylogenetic tree, mammalian IL-34 was conversed among vertebrate genomes, suggesting that the function of the IL-34 gene plays an important physiological role in all vertebrates in the long evolutionary process. It seemed that the mammalian IL-34 gene was under positive selection pressure with the identified positively selected site, 196Val. This is in accordance the with multiple biological functions of a cytokine, which plays a key role in the immune system.

IL-34 mRNA is widely expressed in various types of tissue, including tissue of the heart, brain, lung, liver, kidneys, thymus and spleen (4). Accordingly, by EST sequence searching, the IL-34 gene was also found to be expressed in various other types of tissues and cells, including the hippocampus, embryonic stem cells, medulla, testes, ovaries, metastatic chondrosarcoma, epidermis, keratinocytes, osteoarthritic cartilage, adipose tissue, choroid, eyes, amygdala thymus, small intestine, hypothalamus, islets of Langerhans, glioblastoma and the retinal pigment epithelium. This suggests that the IL-34 gene is widely expressed in many types of tissues and organs. The investigation of available microarray analyses and ‘virtual northern blot analysis’ confirmed the predominant expression of IL-34 in the lymph nodes, brain, heart, skeletal muscle, colon, adipocyte, kidneys, liver, lung, thyroid, adrenal gland, ovaries, prostate and testes. A total of 55 functionally relevant 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, which may affect the multiple functions of IL-34. However, the effects of these SNPs on the physiological and pathological function of IL-34 require further investigation.

IL-34 and M-CSF both signal via the same receptor, the M-CSF receptor, c-FMS. It has been shown that M-CSF is expressed at high levels in many types of tumor, including breast cancer, ovarian cancer and colorectal carcinoma and correlates with a poor prognosis (1015). However, studies on the role of IL-34 in tumor development are limited. In the present study, we firstly found that IL-34 was indeed expressed in various types of cancer, such as 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. The mechanisms responsible for the involvement of IL-34 in the progression of these tumors require further investigation. It should be noted that the association between the expression of IL-34 and prognosis varies in different types of cancer, even in the same type of cancer from different databases. This suggests that the function of IL-34 in these tumors may be multidimensional, not only functioning as a tumor inhibitor or promoter. Moreover, we identified 18 somatic mutations of IL-34 in cancer tissue in the present study. The mechanisms through which these mutations affect tumor formation require further investigation. These data suggest that IL-34, similar to M-CSF, is involved in tumor formation.

USF1, RFX1, Sp1, POU3F2 and FOXL1 regulatory transcription factor binding sites were identified in the IL-34 gene upstream (promoter) region. USF-1 is an important transcription factor that participates in glucose metabolism and tumorigenesis. It has a negative effect on cell proliferation in some cell types and stabilizes the p53 protein and promotes a transient cell cycle arrest, in the presence of DNA damage (34,35). RFX1 is unique transcription factor that contains a highly conserved 76-amino-acid DNA binding domain. RFX1 can directly regulate CD44 expression (36,37). This mechanism may contribute to the effects of RFX1 on the proliferation, survival and invasion of glioblastoma cells. Sp1 is a member of the Sp/Krüppel-like factor (KLF) family of transcription factors that play a critical role in embryonic and early postnatal development, differentiation, cell cycle regulation and in multiple diseases, including cancer (3841). POU domain transcription factors are present in a number of cell lineages where they perform various functions, either as ubiquitous regulators of ‘housekeeping’ genes, or as developmental- and lineage-specific coordinators of cell fate decisions (42). POU3F2 has been shown to be responsive to MAPK pathway activation and to modulate the levels of microphthalmia-associated transcription factor (MITF) so as to suppress the differentiated melanocytic phenotype and to enhance tumor metastasis (29). FOXL1 is located at the junction of multiple signaling pathways and plays critical roles in a variety of physiological and pathological processes, including cancer development. These tumor-related transcriptional factors may be involved in the effects of IL-34 in tumors (28,43,44).

Acknowledgements

This study was sponsored by grants from the Chinese National Natural Science Foundation (81202077 and 810001329), the National Major Scientific and Technological Special Project for ‘Significant New Drugs Development’ (2011ZX09302-003-02), the Jiangsu Province Major Scientific and Technological Special Project (BM2011017) and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

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

Related Articles

Journal Cover

January-2015
Volume 35 Issue 1

Print ISSN: 1107-3756
Online ISSN:1791-244X

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Wang B, Xu W, Tan M, Xiao Y, Yang H and Xia T: Integrative genomic analyses of a novel cytokine, interleukin-34 and its potential role in cancer prediction. Int J Mol Med 35: 92-102, 2015
APA
Wang, B., Xu, W., Tan, M., Xiao, Y., Yang, H., & Xia, T. (2015). Integrative genomic analyses of a novel cytokine, interleukin-34 and its potential role in cancer prediction. International Journal of Molecular Medicine, 35, 92-102. https://doi.org/10.3892/ijmm.2014.2001
MLA
Wang, B., Xu, W., Tan, M., Xiao, Y., Yang, H., Xia, T."Integrative genomic analyses of a novel cytokine, interleukin-34 and its potential role in cancer prediction". International Journal of Molecular Medicine 35.1 (2015): 92-102.
Chicago
Wang, B., Xu, W., Tan, M., Xiao, Y., Yang, H., Xia, T."Integrative genomic analyses of a novel cytokine, interleukin-34 and its potential role in cancer prediction". International Journal of Molecular Medicine 35, no. 1 (2015): 92-102. https://doi.org/10.3892/ijmm.2014.2001