Identifying LRRC16B as an oncofetal gene with transforming enhancing capability using a combined bioinformatics and experimental approach

Abstract

Oncofetal genes are expressed in embryos or fetuses, are downregulated or undetectable in adult tissues, and then re-expressed in tumors. Known oncofetal genes, such as AFP, GCB, FGF18, IMP-1 and SOX1, often have important clinical applications or pivotal biological functions. To find new oncofetal-like genes, we used the public information of expressed sequence tags to systematically analyze gene expression patterns and identified a novel oncofetal-like gene, LRRC16B. It increased the proliferation, anchorage-independent growth and tumorigenesis of transformed cells in xenografts, possibly through its effects on cyclin B1 protein levels. These findings exemplify the feasibility of using bioinformatics to find new oncofetal-like genes and suggest that more genes with important functional roles will be uncovered in the candidate gene list.

Introduction

Tumorigenesis is caused by various factors that upset the balance between cell proliferation and cell death, which results in uncontrolled cell division and tumor formation (Gilboa, 1999; Vogelstein and Kinzler, 2004). A tumor may be described as chaotic organ genesis, and tumor-related genes, like genes in the Wnt (Giles et al., 2003; Katoh, 2007) or Sonic hedgehog pathways (Ding and Schultz, 2004; Morton et al., 2007), may be important in human embryonic development.

Oncofetal genes are defined as genes expressed in the embryos or fetuses, which are turned off or suppressed in adult tissue, but re-expressed in tumor cells (Cleynen et al., 2007; Sarandakou et al., 2007). Most are clinically useful markers or functionally interesting genes. The prototype oncofetal gene/protein is α-fetoprotein (AFP), discovered in human fetal serum in 1960 (Bergstrand and Czar, 1956) and now widely used as a serum marker for hepatocellular carcinoma (HCC) and yolk sac tumor (Trojan et al., 1995). Other oncofetal genes/proteins include β-human chorionic gonadotropin (a placental glycoprotein) (Farghaly, 1992; McNamee, 1995), glypican 3 (Grozdanov et al., 2006; Hishinuma et al., 2006), H19 (Gabory et al., 2006; Matouk et al., 2007), the IMP family (IGF2 mRNA-binding protein) (Nielsen et al., 1999; Kato et al., 2007) and high-mobility group A 2 (Cleynen et al., 2007).

Most oncofetal genes were discovered as targets for some other research purposes (that is, embryogenesis or metabolism). There is only one previous report (Monk and Holding, 2001) of a specific search for oncofetal genes; it found the OCT4 gene. However, for a more comprehensive search, it is necessary to collect embryonic tissue at various developmental stages, adult tissue that represent most tissue types and tissue from many different kinds of human tumors. These tissue samples can then be subjected to high-throughput technologies for biomarker discovery (Aouacheria et al., 2006; Campagne and Skrabanek, 2006; Niemann et al., 2007, Xu et al., 2007). Fortunately, this gigantic research project has been largely accomplished through the expressed sequence tags (EST). So far, some 1.5 million human EST are stored in the National Center for Biotechnology Information (NCBI) dbEST database, which contains at least 7000 human EST cDNA libraries (Boguski and Schuler, 1995; Colantuoni et al., 2000).

In this study, to show that systemic analysis of the oncofetal patterns in the dbEST can lead to the discovery of genes with interesting functions and potential clinical use, we used the number of AFP, the prototype of oncofetal genes/proteins, as the threshold for discovering genes with potential oncofetal expression patterns.

Results

Bioinformatic analysis of EST database for oncofetal genes and literature review of the resulting gene list

The bioinformatic analysis was carried out as described in the Materials and methods section and summarized in Figure 1. The final gene list included 29 genes with known functions and 44 genes with unknown functions. The frequencies of the 29 known genes in the mature, tumor and immature groups are listed in Supplementary Table 1. Three known oncofetal genes (AFP, GCB and IMP-1) were rediscovered. Two other genes (FGF18 and SOX1) had been shown (Alexander, 1970; Shimokawa et al., 2003) to have oncofetal expression patterns, but were not clearly designated as oncofetal genes/proteins in the literature. Three (DPYSL5, MDFI and TITF1) were tissue-specific genes or genes with limited tissue distribution (Chen et al., 1996; Fukada et al., 2000; Garcia-Barcelo et al., 2007). Twelve genes (AXIN2, ASCL1, BMP4, DKK2, EPHB3, GDF11, KISS1, MYB, MYCN, PGF, POLS and SOX2) were related to oncogenesis or embryogenesis (Boiani and Scholer, 2005; Esteve and Bovolenta, 2006; Dao et al., 2007; Katoh and Katoh, 2007; Sugimori et al., 2008). Only nine genes did not have interesting expression patterns or functions (Table 1).

Figure 1
figure1

Flow chart of bioinformatic analysis of oncofetal-like genes.

Table 1 Literature and review and functional classification of candidate genes

Reverse transcription–polymerase chain reaction screening of the unknown genes for potential oncofetal expression patterns

As the known genes have a high percentage of interesting functions, we studied the unknown genes. Five unknown genes were evaluated for their expression patterns in various tumor cell lines, as well as in a panel of normal tissue cDNAs (BD Clontech, Mountain View, CA, USA) with three known oncofetal genes. The tumor cell lines were used as a more inclusive first-line screening. AFP was expressed only in Huh-7, HepG2 and SW480 cells. IMP1 (known control), FGF18 (known control), LOC129607 (unknown gene) and LRRC16B (unknown gene) were detected in the majority of tumor cell lines (Figure 2a). In normal tissue RNA, RT–PCR analysis showed that AFP was expressed only in fetal liver tissue. The unknown LOC129607 gene was ubiquitously expressed in normal tissue. The three known oncofetal genes, AFP, IMP-1 and FGF18 (Shimokawa et al., 2003; Kato et al., 2007), showed expected expression patterns, with higher expression in tumor cell lines and fetal tissue, and lower expression in normal tissue. The unknown LRRC16B gene showed an expression pattern very similar to that of IMP-1, with the exception in adult brain tissue (Figures 2b and c). Notably, both IMP-1 and LRRC16B were expressed in testis tissue. The remaining three unknown genes, FLJ10156, GPR153 and ZNF500, were neither oncofetal nor tissue specific (data not shown). FLJ10156, like LOC129607, was ubiquitously expressed, and GPR153 and ZNF500 were expressed in various normal tissue types.

Figure 2
figure2

Expression profiles of selected genes in various tumor cell lines, normal tissues and fetal tissues. RT–PCR was performed on (a) various tumor cell lines, (b) a panel of normal total RNA (Master Panel II, BD Clontech) and (c) Human Fetal Normal Tissue Total RNA (BioChain). (d) The expression profiles of LRRC16B in (b) and (c) were further verified by quantitative real-time RT–PCR. The calculated copy numbers of LRRC16B and GAPDH were documented in Supplementary Table 2. The normalized LRRC16B expression levels in adult (open bar) and fetal (closed bar) tissues were depicted pairwise in panel d. Downregulation of LRRC16B from fetal to adult tissues is evident. H2O, negative control; P, positive control.

Downregulation of LRRC16B in adult tissue

Real-time quantitative RT–PCR confirmed the expression pattern of LRRC16B. LRRC16B was highly expressed in fetal brain and lung tissue. It was also expressed in fetal spinal cord, kidney, stomach, liver and colon (4.4–0.2% of fetal brain expression) tissue. Skeletal muscle and heart tissue showed the lowest expression (0.0026–0.0014% of fetal brain expression) (Supplementary Table 2A). Most adult tissue samples also showed very low expression of LRRC16B (Supplementary Table 2B). Adult brain tissue had a LRRC16B/GAPDH ratio of 5.94 × 10−7, which was not much higher than that for fetal liver tissue (3.22 × 10−7).

Adult testis, liver, spinal cord, thyroid gland and thymus tissue had expression levels comparable to the lowest ones in fetal tissue. LRRC16B expression was downregulated 207-fold from fetal to adult brains. The lungs showed much more significant downregulation from fetus to adult (32 651-fold, −log 4.5), whereas the liver showed the least downregulation (21-folds, −log 1.3). Skeletal muscle, kidney, spinal cord, colon and heart tissue showed downregulation of 96 099- (−log 4.9), 7204- (−log 3.8), 2181- (−log 3.3), 507- (−log 2.7) and 266-fold (−log 2.4), respectively (Figure 2d).

RT–PCR using rat embryos and adult rats also showed downregulation of rat LRRC16B in the adult organs (Supplementary Figure 1). Similar to humans, the adult rat brain also expresses rat LRRC16B.

Expression of LRRC16B in ovarian, hepatic and colorectal cancers

To investigate LRRC16B expression in cancerous tissue, RT–PCR (upper) and real-time PCR (bottom) were used in tumor and non-tumor tissue pairs, respectively, from 10 ovarian, hepatic and colorectal cancers (Figure 3). The non-tumor tissue of the ovarian cancer came from the contralateral uninvolved ovary. Fifty percent of ovarian cancers (Figure 3a; cases 2, 4, 7, 8 and 9) and 60% of colorectal cancers (Figure 3b; cases 1 and 3–7) showed significant LRRC16B upregulation in cancerous tissue. The result of hepatoma was mixed, with both up- and downregulation observed in the tumors (Figure 3c). These results showed that LRRC16B was upregulated in certain cancerous tissue, such as ovarian and colorectal cancers.

Figure 3
figure3

Expression of LRRC16B in ovarian, hepatic and colorectal cancer and normal tissues. RT–PCR (top) and real-time PCR (bottom) analysis of LRRC16B in each type of cancer tissue (T) and their corresponding non-tumor tissue (N), including (a) ovarian, (b) hepatic and (c) colorectal cancers. PLA and GAPDH were internal controls. *, not detectable.

Overall, LRRC16B was expressed in many fetal tissue types, downregulated in adults and upregulated in certain tumors, which conforms to the general definition of an oncofetal gene.

LRRC16B gene cloning, sequence analysis and in vitro expression

The LRRC16B gene was predicted to produce a 4.7-kb mRNA and encode a 1372-aa protein with a predicted molecular weight of 150 kDa (Figure 4a). Northern blotting on five different tumor cell lines using a 3′ probe showed a 4.7-kb transcript (Figure 4b). RT–PCR of fetal brain total RNA produced four overlapping fragments that pieced together into a 4770-bp cDNA, including the 4119-bp coding region, and 323-bp 5′- and 328-bp 3′-untranslated regions. The full-length LRRC16B was fused to enhanced green fluorescent protein (EGFP) and myc tag, cloned into pMSCVpuro vectors and expressed in human embryonic kidney 293T and baby hamster kidney (BHK) cells as stable pools.

Figure 4
figure4

(a) Gene structure of human LRRC16B. There are three predicted motifs/domains: leucine-rich repeats domain (251 to 632), nuclear localization signal (1049 to 1063, and 1345 to 1362) and actin-interacting domain (114 to 419). (b) Total RNA 30 μg from each of the indicated cell lines was analyzed using northern blot against a LRRC16B cDNA probe. The size of the LRRC16B transcript was indicated (arrow). (c) Western blot of 293T stable pool against EGFP antibody. The size of the LRRC16B translation was indicated (arrow).

Western blotting showed that LRRC16B-EGFP and LRRC16B-myc had molecular weights of 177 and 150 kDa, respectively (Figures 4c and 6a). Stable pools expressing LRRC16B-EGFP were created to determine the subcellular localization of LRRC16B protein in 293T cells. These showed that the majority of LRRC16B-EGFP was detected in the cytoplasm, whereas some were observed as small nucleus aggregates (Supplementary Figure 2).

Figure 6
figure6

LRRC16B-myc fusion protein also promoted 293T cells growth. (a) Western blot of the 293T stable pool against myc antibody. The size of the LRRC16B translation was indicated (arrow). (b) Growth curves revealed that 293T-LRRC16B-myc cells proliferated (triangles) more than the control cells (squares) (*P<0.05). (c) Western blotting showed that LRRC16B overexpression significantly induced cyclin B1 expression in 293T-LRRC16B-myc stable cell lines.

LRRC16B promoted cell proliferation in 293T cells

XTT (2,3-bis (2-methoxy-4-nitro-5-sulfophenyl)-5-[(phenylamino) carbonyl]-2H-tetrazolium hydroxide) tests were carried out on 293T and BHK stable pools expressing LRRC16B-EGFP fusion proteins. Within 72 h, the growth curve had significantly increased by 90% in 293T stable pool cells compared with control cells expressing EGFP only (Figure 5a). The growth curve was slightly increased by 15% in BHK cells overexpressing LRRC16B-EGFP. The effect on growth regulation was further evaluated using bromodeoxyuridine (BrdU) incorporation. Both 293T and BHK cells overexpressing LRRC16B-EGFP showed significant increases of BrdU incorporation (Figure 5b).

Figure 5
figure5figure5

The LRRC16B-EGFP fusion protein promoted 293T cells growth. (a) Growth curves revealed that 293T-LRRC16B-EGFP (triangles) and BHK-LRRC16B-EGFP (squares) cells proliferated more than the control cells (P<0.05). (b) BrdU incorporation of 293T and BHK stable cell lines was analyzed by flow cytometry. 293T-LRRC16B-EGFP (closed bar) and BHK-LRRC16B-EGFP (closed bar) were evaluated (P<0.05). (c) Flow cytometry for cell cycle profiles. The G/2 M was retained in 293T-LRRC16B stable cell lines (closed bar), but not in BHK-LRRC16B-EGFP stable cell lines (striped bar). (d) Western blot showed that LRRC16B overexpression significantly induced c-myc and cyclin B1 expression in 293T-LRRC16B-EGFP stable cell lines. *P<0.05.

Cell cycle distribution of LRRC16B-EGFP stable pools was analyzed using a flow cytometer. After 72 h of incubation, only 20% of 293T cells overexpressing LRRC16B-EGFP were retained in the G2/M phase, compared with 50% in control cells (Figure 5c). The BHK stable pools, however, did not show significant changes in cell cycle distribution (Figure 5c).

In terms of LRRC16B-EGFP effect, there were no significant changes in cyclin D1 or cyclin A levels in either 293T or BHK stable pools. Both stable pools showed elevated c-Myc protein levels. Only 293T cells overexpressing LRRC16B-EGFP showed a significant increase of cyclin B1 protein levels (Figure 5d). These results indicated that cyclin B1 protein levels might correlate with changes in cell cycle distribution.

The effect of LRRC16B-EGFP on 293T cells was reconfirmed using a different fusion tag. The 293T stable pool overexpressing LRRC16B-myc was created and subjected to XTT assays and western blotting. The molecular weight of the expressed fusion protein was as predicted, and the results were essentially the same as for LRRC16B-EGFP (Figures 6a and b), with one exception in western blot analysis. Only cyclin B1 increased; there was no significant change in the c-Myc protein level (Figure 6c).

Knocking down endogenous LRRC16B inhibited proliferation

To explore the role of LRRC16B in cancer cells, short hairpin RNAs (shRNAs) were used to knock down LRRC16B expression in BG1 and Huh-7 cells. Four LRRC16B shRNAs were designed, and two, LRRC16B-RNAi-1 and LRRC16B-RNAi-2, significantly reduced the amount of protein product of co-transfected LRRC16B-EGFP in 293T cells after 48 h (Supplementary Figure 3). BG1 and Huh-7 stable pools expressing LRRC16B-RNAi-1 and LRRC16B-RNAi-2 were established and subjected to XTT assays and western blotting. The proliferation rates of BG1-LRRC16B-RNAi-1 and BG1-LRRC16B-RNAi-2 stable pools were significantly reduced (Figure 7a left panel). There were no significant changes in Huh-7 stable pools (Figure 7b right panel). Western blotting showed that cyclin B1 was reduced in BG1, but not in Huh-7 stable pools (Figure 7b). On the other hand, there was no increased cleavage of caspase-3 or LC3-I in BG1 stable pools (Figure 7c), which indicated that LRRC16B knockdown did not affect apoptosis or autophagy while decreasing the proliferation of BG1 cells. The inhibition of endogenous LRRC16B against BG1 cell proliferation correlated with the decreased cyclin B1 protein level, which was consistent with the results of LRRC16B overexpression in 293T cells.

Figure 7
figure7

shRNA induced LRRC16B knockdown and decreased cell proliferation in BG1 ovarian cancer cells. (a) Growth curves revealed that BG1-LRRC16B-RNAi-1 (circles) and BG1-LRRC16B-RNAi-2 (squares) cells proliferated slower than the control BG1-RNAi-NC cells (triangles) (a). Huh-7-LRRC16B-RNAi stable cell lines were not affected (b). The growth rate of BG1 and Huh-7 cells was shown as means±s.d. of three independent experiments (*P<0.05, **P<0.001). (b) Western blot showed that LRRC16B knockdown significantly reduced cyclinB1 expression in BG1-LRRC16B-RNAi stable cell lines, but not Huh-7-LRRC16B-RNAi stable cell lines. (c) Western blot showed that LRRC16B-RNAi did not induce apoptosis or autophagy, as shown by the lack of caspase-3 or LC3-I cleavage.

The transforming potential of LRRC16B in vitro and in vivo

The transforming potential of LRRC16B on 293T was gauged using a soft agar assay. The number of 293T cell colonies overexpressing LRRC16B-EGFP or LRRC16B-myc significantly increased compared with 293T cells overexpressing EGFP or treated with mock transduction or vector alone (Figure 8a). However, the numbers were much less than that produced by 7-4 cells, a ras-overexpressing 3T3 cell line that was a positive control for the assay. The sizes of the colonies increased with LRRC16B fusion gene expression (Figure 8b).

Figure 8
figure8figure8

LRRC16B promoted stable cell lines transformation in vitro and in vivo. (a) Colony number and size of stable cell lines 7-4, 293T, 293T-pMSCV, 293T-pMSCV-EGFP, 293T-pMSCV-LRRC16B-EGFP were observed after 10 days in soft agar (*P<0.05). (b) Representatives of colony size and morphology with different stable cell lines. (c, d) After 14 days in soft agar, LRRC16B shRNA stable cell lines of BG1 and Huh-7 were analyzed as (a) and (b). (e) The average of tumor growth curve (*P<0.05) and average of tumor growth mass (**P<0.001) of tumors dissected from NOD/LtSZ Prkdc mice that were injected with 293T-pMSCV-EGFP and 293T-pMSCV-LRRC16B-EGFP (1 × 106/200 μl). Bar was 50 μm.

Using LRRC16B-RNAi-1 and -2 to inhibit endogenous LRRC16B in BG1 cells significantly decreased the number and sizes of the colonies in the soft agar assay. There were no changes in the number and size of the colonies for LRRC16B knockdown Huh-7 cells (Figures 8c and d).

To test whether LRRC16B promoted tumor growth in vivo, control and LRRC16B 293T stable pool cells were subcutaneously injected into NOD/LtSZ Prkdc mice. Mice given LRRC16B 293T stable pool cells grew local tumors significantly larger and heavier than mice given control cells during the 35-day observation. Routine histological staining showed no morphological changes in these mice (Figure 8e).

Discussion

By systematically analyzing the EST database for oncofetal expression patterns, a previously unknown gene, LRRC16B, was shown to have transforming enhancing capabilities. This exemplifies the feasibility of a bioinformatics approach for finding functionally interesting genes. There may be more oncofetal genes, tissue-specific genes or genes related to oncogenesis, embryogenesis or both to be uncovered in the gene list, including 39 unknown genes that have not yet been examined.

The bioinformatics algorithm used in this study is uncomplicated and works well enough as a screening tool for further experimental confirmation. More sophisticated analysis may result in a more comprehensive gene list. For example, a combined profile of several known oncofetal genes, instead of AFP alone, may be used as the cutoff criterion. It is also possible to reiterate the cyclic process of bioinformatics analysis, experimental confirmation, discovering new oncofetal genes, using a new set of oncofetal genes to set a new cutoff, and then repeating the analysis until no new gene can be found.

Two of the five genes tested in this study were ubiquitously expressed, even though the bioinformatics analysis predicted them to be oncofetal. Most likely, they are under-represented in the selected mature group libraries.

The choice of cDNA libraries in this study was based on their size and the magnitude of the EST projects; we presumed that bigger EST projects have better annotation quality for their cDNA libraries. Unfortunately, this may not always be true. It will be helpful if someone with better knowledge of the annotation qualities of cDNA libraries chooses the cDNA libraries and has them classified. A more accurate classification of the cDNA libraries may result in a better gene list. Knowledge of the libraries will be particularly helpful in the mature group. In this study, libraries without any descriptions for disease status or age have been classified as mature (normal adult) because of the sheer lack of other helpful information.

Cell lines were used in the first step of experimental screening; however, a gene expressed in the cell lines may not be expressed in the tumor tissues. We used the cell lines as a more inclusive screening test. If a gene was not expressed in any of the cell lines tested, it would be less likely to be expressed in tumor tissues. By excluding such genes, we might have missed some highly tissue-specific oncofetal genes, but the genes we tested happened to be expressed in the majority of the cell lines; therefore, we believe that this did not occur.

An oncofetal gene is not strictly defined in the literature. It is generally defined as embryonic genes downregulated in adults and re-expressed in tumors. However, the magnitude of downregulation is not specified. LRRC16B is downregulated from 21- to 96 099-fold among various organs and, as such, it is proposed as an oncofetal-like gene. Even though it is expressed in the adult central nervous system, there is a 207-fold reduction of its expression from the fetal to the adult brain. LRRC16B is expressed in purified mouse neurons, but not in glia cells (unpublished data). In practice, it can be regarded as an oncofetal marker in organs or body fluids outside the central nervous system owing to the blood–brain barrier.

The expression of LRRC16B in the central nervous system is intriguing. Its promotion of cell proliferation and transforming activity should play no role in adult neurons. More studies are warranted to unravel its function in neurons.

It would be interesting to know which cell types express LRRC16B in which organs. Supplementary Figure 4 shows preliminary immunohistochemical results using a not-fully-characterized antibody obtained through the Human Protein Atlas portal (www.proteinatlas.org). The antibody highlighted the neurons, but not glial cells in the human fetal brain. There were signals in the epithelial cells of fetal colon, kidney and lung tissue. There were also immunohistochemical signals in the mesenchymal cells in the lamina propria of fetal colonic mucosa.

From a protein structural modeling analysis (MotifScan; http://myhits.isb-sib.ch/cgi-bin/motif_scan), the novel protein LRRC16B shows some sequences that belong to the leucine-rich repeats domain. Leucine-rich repeats domains are involved in protein–protein interaction, such as ribonuclease-inhibitor, GTPase-activating protein (Kobe and Kajava, 2001), SDS22-like subfamily and cysteine-containing subfamily (Kutay and Guttinger, 2005). They not only take part in many important bio-functions, such as signal transduction, cell metastasis, immune responses and tumorigenesis (Qiao et al., 2007; Barker et al., 2009), but are also involved in cell polarization (Imamura et al., 2006), apoptosis, cell cycle control (Adachi-Yamada et al., 2005; Park et al., 2009), cell migration, proliferation, development (Liu et al., 2005; Cowin et al., 2007; Hossain et al., 2008; Wang et al., 2009) and actin polymerization (Eisenmann et al., 2007; Kopecki and Cowin, 2008). The bio-function of the LRRC16B protein influences cell proliferation, cell cycle control and transformation.

Sequence analysis predicts that LRRC16B has actin-binding domains and nuclear localization signals. Confocal microscopy shows that some overexpressed LRRC16B-EGFP fusion proteins localize in the nucleus as small dots. Phalloidin staining indicates that LRRC16B-EGFP fusion proteins colocalize with actin (unpublished data). Whether LRRC16B binds directly with actin, and whether it interacts with importin, the key player for nuclear transportation, remains to be investigated.

In this study, 293T and BHK cells were used for soft agar assays. The more common cell lines for such an experiment are NIH3T3 and 293 cells. However, NIH3T3 and 293 cells expressing LRRC16B fusion genes quickly die (data not shown); therefore, stable pools were never established. LRRC16B appears to be more of promoter rather than gatekeepers in oncogenesis.

The transformation-enhancing capability of LRRC16B may be tumor-type specific and may be more important in ovarian cancer rather than in HCC. We found a trend of LRRC16B overexpression in ovarian cancer, but not in HCC. The knockdown experiment showed reduced proliferation and colony formation in BG1 cells, an ovarian cancer cell line, with a concomitant reduction of cyclin B1 protein levels. LRRC16B RNA interference (RNAi), however, had no effect on Huh-7, an HCC cell line. More clinical samples are needed to verify this initial observation.

In summary, we presented an effective method of finding interesting genes through a systematic bioinformatics analysis of the EST database for embryonic or fetal genes that are downregulated in adult tissue and re-expressed in tumors. LRRC16B is one such gene that has transformation-enhancing capability, possibly mediated by cyclin B1. Future investigations should focus on the other unexamined genes in the gene list and on LRRC16B functions.

Materials and methods

Bioinformatic analysis

All EST were collected from the Cancer Genome Anatomy Project (http://cgap.nci.nih.gov/). To classify each library, a certified pathologist read each library's description, which contained its tissue origin, developmental stage (which included ‘fetus’, ‘infant’, ‘adult’ and ‘not specified’), and its histology or disease status (‘normal’, ‘tumor’, ‘other diseases’ and ‘not specified’). Excluding SAGE libraries, there were 7066 EST libraries in the file ‘library.report’ downloaded in 2005. From these, 6021 were initially chosen. To expand the coverage of fetal and normal mature groups, additional 162 normal mature and 63 fetal libraries were added; 128 of the 6246 libraries were later removed for various reasons. All subtraction libraries and all non-tumor libraries from tissue with inflammatory diseases were also excluded.

The 6118 EST libraries finally used were classified into three groups. The immature group had 77 libraries from fetal tissue, 10 libraries from infant tissue, 394 libraries from the placenta and two libraries from cord blood, for a total of 483 libraries. The tumor group contained 3911 libraries from benign and malignant tumors. The remaining 1724 libraries formed the mature group, including those without information about age or disease status.

Information for gene expression profiles was obtained from the file Hs_ExprData.dat. This contained three columns: UniGene cluster ID, library ID and frequency. There were data for the frequency of each unigene in a library. For each group (mature, immature and tumor), IDs were evaluated for their constituent libraries and unigenes. The frequency of the unigene expressed for a specific group of libraries was calculated.

The calculated frequencies of the AFP gene in each group of the libraries (immature group: 175; mature group: 10; and tumor group: 108) were used as references to set the thresholds of bioinformatics analysis. To exclude genes with higher expression than AFP in the adult tissues, the first step of the bioinformatics analysis was to eliminate any gene with a frequency >10 in the mature group (Figure 1). Subsequent steps were less stringent. Any gene with frequencies >5 in both of the immature and tumor groups was retained. For genes with frequencies 2 (and 10) in the mature group, the ratio between tumor and mature groups (tumor/mature) was then calculated, and only those with a ratio >5 were collected. The 73 genes collected at the end of analysis were subjected to a literature review.

Cell cultures

The various human cancer cell lines included those for ovarian cancer (PA-1, NIH:OVCAR-3, SK-OV-3, BG1, TOV-21G and ES-2), renal carcinoma (786-O and ACHN), liver cancer (Huh-7 and HepG2), bladder cancer (T24, TSGH-8301), breast cancer (MCF-7), lung cancer (A549), colon cancer (SW480), clone 7-4 (NIH3T3 overexpressed in Ha-ras), 293T and BHK-21. These were maintained in various culture media supplemented with 10% fetal bovine serum, 100 U/ml penicillin and 100 mg/ml streptomycin (Gibco, Grand Island, NY, USA), under 5% CO2 at 37 °C.

Tissue specimens

Cancer samples were from the Department of Pathology, NCKU Hospital. Their use had been approved by the Institutional Review Board of NCKU Hospital with signed informed consent.

RNA analysis

RNA preparation

Normal and fetal cDNAs were screened for gene expression using panels of multiple normal total RNAs (Master Panel II; BD Clontech) and multiple fetal total RNAs (Human Fetal Normal Tissue Total RNA; BioChain, Hayward, CA, USA). Various tumor cell lines, embryos, P1 rat tissue and adult rat tissue were titrated using a homogenizer. Ten frozen 20-μm tissue slices were collected using TRIzol RNA isolation reagent (Invitrogen, Carlsbad, CA, USA). The quality of RNA was confirmed on agarose gel. The concentration was determined using a spectrophotometer.

RT–PCR

Five micrograms of total RNA from each sample was used for reverse-transcription using oligo(dT)12−18 primer and Superscript II reverse transcriptase (Invitrogen), according to the manufacturer’s protocol. The total RT product was diluted 10 times in TE buffer and stored at −20 °C. Quality was checked using two housekeeping genes, β-actin and GAPDH, and PLA in the PCR system, with loading amounts adjusted according to different cDNA, with 5 μl of each cDNA per 25 μl of PCR mixture (BV buffer, 2.5 U of Platinum Taq, 0.4 mM dNTP and 1 μM of each primer). Conditions for touchdown PCR reactions involved the touchdown protocol (one cycle of initial denaturation at 95 °C for 2 min, three cycles of 94 °C for 30 s, 63 °C for 30 s, 70 °C for 30 s, three cycles of 94 °C for 30 s, 61 °C for 30 s, 70 °C for 30 s, three cycles of 94 °C for 30 s, 59 °C for 30 s, 70 °C for 30 s, and 25 cycles of 94 °C for 30 s, 58 °C for 30 s, 70 °C for 30 s and 70 °C for 10 min). Supplementary Table 3 shows primer sequences for the examined genes.

Real-time PCR

Quantitative RT–PCR was carried out using the Light-Cycler TaqMAn Master (Roche, Mannheim, Germany). The number of transcript copies per microgram of total RNA from each tissue was calculated from standard curves of amplified cDNA clone. The expression level of LRRC16B was normalized as a relative ratio of its signal to that of GAPDH or PLA.

Northern blotting

Thirty micrograms of total RNA from each sample was electrophoretically separated on 1.2% formaldehyde gel, and then transferred to Hybond-N+ nylon membrane and fixed by baking it at 80 °C for 2 h. The membrane was pre-hybridized with hybridization buffer 1 h at 42 °C, and then hybridized with random-primed 32P-labeled cDNA probe (106 c.p.m./ml) at 42 °C for 16 h. After it had been hybridized, the membrane was washed at room temperature with 2 × standard saline citrate plus 0.1% sodium dodecyl sulfate for 20 min; 0.5 × SSC plus 0.1% sodium dodecyl sulfate for 20 min; and twice with 0.2 × SSC plus 0.1% sodium dodecyl sulfate at 42 °C for 10 min. The blots were auto-radiographed at −80 °C for one month.

Full-length and small interfering RNA plasmid construction and generation of stable clones

RT–PCR was used to clone full-length human LRRC16B from normal brain total RNA (BD Clontech). Their products were cloned into pGEM-T Easy vector (Promega, Madison, WI, USA), and the insert was confirmed using DNA sequencing. The pGEM-T-LRRC16B was subcloned, using restriction enzyme digestion, into pEGFP-C1, pEGFP-N1 and pMYC to generate the LRRC16B-EGFP and LRRC16B-myc fusion genes. The plasmid expressing LRRC16B RNAi was constructed by ligating an shRNA sequence containing both sense and antisense strands.

Retroviral plasmid: pMSCVpuro (BD Clontech), pMSCV-EGFP, pMSCV-LRRC16B-EGFP, pMSCV-LRRC16B-myc and pSUPERretro vectors were co-transfected into GP2-293T package cells with VSV-G plasmids using the calcium phosphate method for 48 h. The 293T, BHK, BG1 and Huh-7 were seeded in 1 × 105 cells per well in a 6-cm dish and incubated overnight in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum, 100 U/ml of penicillin and 100 mg/ml of streptomycin under 5% CO2 at 37 °C. Retroviral supernatant was added with 5 μg/ml of polybrene (Sigma, St Louis, MO, USA), and then the cells were incubated overnight at 37 °C in 5% CO2 with 5 μg/ml of puromycin selection.

Western blotting and fluorescent microscopy

Lysis buffer (100 μl per well) (Complete Lysis M, EDTA free, Roche) was added to each stable pool of cells, incubated for 5 min at room temperature and centrifuged at 14 000 g for 15 min. Protein concentration was determined using Bradford reagent (BioRad, Hercules, CA, USA). Fifty micrograms of protein from each sample was subjected to sodium dodecyl sulfate–polyacrylamide gel electrophoresis with 10% polyacrylamide gels. Polyacrylamide gel electrophoresis-separated proteins were electroblotted onto polyvinylidene fluoride membrane (Millipore, Billerica, MA, USA) and incubated with mouse monoclonal anti-GFP, anti-Myc (Millipore), anti-cyclin A and anti-cyclin B1 (Santa Cruz, Santa Cruz, CA, USA) rabbit polyclonal anti-cyclin D1 antibody (Millipore), anti-caspase-3 and anti-LC3 (Cell Signaling, Boston, MA, USA). Goat anti-mouse and anti-rabbit IgG-HRP antibodies (Calbiochem, Darmstadt, Germany) were the secondary antibodies.

Cells were seeded onto glass slides, grown for 2 days, fixed at 4 °C with paraformaldehyde, permeabilized, counterstained with 4′,6-diamidino-2-phenylindole and fluorescent images taken using a confocal microscope (Olympus FV1000, Tokyo, Japan).

In vitro and in vivo growth property analysis

XTT proliferation and BrdU incorporation assays

pMSCV-EGFP, pMSCV-LRRC16B-EGFP, pMSCV-LRRC16B-myc, BG1 RNAi stable pools and Huh-7 RNAi stable pools were seeded in 12-well plates for 96 h. Triplicate wells were plated for each time point and taken at 24-h intervals for 4 days. The number of viable cells for each time point was determined using the XTT reagent standard protocol (Roche).

Cell proliferation was measured using an ErdU incorporation assay (Invitrogen). Results are expressed as the percentage of BrdU-positive cells.

Fluorescence-activated cell sorting analysis

The 293T and BHK stable pool cells (106 per dish) were first seeded and trypsinized for flow cytometric analysis on the third day. The remaining cells were trypsinized and placed together with growth media and phosphate-buffered saline. Cells were pelleted, resuspended in 75% ethanol and then stored overnight at −20 °C. They were centrifuged, washed with phosphate-buffered saline, resuspended in phosphate-buffered saline containing 10 mg/ml of DNase-free RNase, and then incubated in 37 °C for 1 h. Finally, propidium iodide at a concentration of 0.05 mg/ml was added and the cells were incubated at room temperature for 20 min. Cell clumps were filtered and the DNA content was measured on FACscan flow cytometer (BD Clontech). The percentage of cells in each phase of the cell cycle was determined using WinMDI2.9.

Soft agar assay

The six-well plates (lower layer) were added 1 ml of 0.7% agar in culture medium and incubated for 30 min at 4 °C. Stable pool cells, 293T-pMSCV, 293T-EGFP, 293T-LRRC16B-EGFP, 293T-LRRC16B-myc, 293T, 7-4, BG1 RNAi stable pools and Huh-7 RNAi stable pools (1 × 104 cells per ml) were suspended in the upper layer of a six-well plate containing 0.35% agar in the medium. After 10 days (14 days for BG1 and Huh-7 stable pools), colony formation (>100 μm diameter) was microscopically examined after the samples had been stained with 0.005% crystal violet.

Induction of tumor xenografts in mice

For each experimental group, ten 5-week-old female NOD/LtSZ Prkdc mice were injected subcutaneously with 0.2 ml Dulbecco's modified Eagle's medium containing 1 × 106 293T clones. The appearance of tumors (tumor incidence) was monitored, and when they developed, their size was determined twice a week using the following formula: D × d2 × π/6, where D was the tumor diameter at its widest, and d at its smallest. The mice were treated in accordance with guidelines approved by the Institutional Animal Care and Use Committee of NCKU.

Statistical analysis

Statistical significance was evaluated using Student’s t-test for single comparisons and one-way analysis of variance for multiple comparisons. Significance was set at P<0.05.

References

  1. Adachi-Yamada T, Harumoto T, Sakurai K, Ueda R, Saigo K, O'Connor MB et al. (2005). Wing-to-leg homeosis by spineless causes apoptosis regulated by Fish-lips, a novel leucine-rich repeat transmembrane protein. Mol Cell Biol 25: 3140–3150.

  2. Alexander P . (1970). Mechanism of growth and dissemination of antigenic tumors in normal immunological carriers. Medicina (B Aires) 30: 176–183.

  3. Aouacheria A, Navratil V, Barthelaix A, Mouchiroud D, Gautier C . (2006). Bioinformatic screening of human ESTs for differentially expressed genes in normal and tumor tissues. BMC Genom 7: 94.

  4. Barker N, Ridgway RA, van Es JH, van de Wetering M, Begthel H, van den Born M et al. (2009). Crypt stem cells as the cells-of-origin of intestinal cancer. Nature 457: 608–611.

  5. Bergstrand CG, Czar B . (1956). Demonstration of a new protein fraction in serum from the human fetus. Scand J Clin Lab Invest 8: 174.

  6. Boguski MS, Schuler GD . (1995). Establishing a human transcript map. Nat Genet 10: 369–371.

  7. Boiani M, Scholer HR . (2005). Regulatory networks in embryo-derived pluripotent stem cells. Nat Rev Mol Cell Biol 6: 872–884.

  8. Campagne F, Skrabanek L . (2006). Mining expressed sequence tags identifies cancer markers of clinical interest. BMC Bioinform 7: 481.

  9. Chen CM, Kraut N, Groudine M, Weintraub H . (1996). I-mf, a novel myogenic repressor, interacts with members of the MyoD family. Cell 86: 731–741.

  10. Cleynen I, Brants JR, Peeters K, Deckers R, Debiec-Rychter M, Sciot R et al. (2007). HMGA2 regulates transcription of the Imp2 gene via an intronic regulatory element in cooperation with nuclear factor-kappaB. Mol Cancer Res 5: 363–372.

  11. Colantuoni C, Purcell AE, Bouton CM, Pevsner J . (2000). High throughput analysis of gene expression in the human brain. J Neurosci Res 59: 1–10.

  12. Cowin AJ, Adams DH, Strudwick XL, Chan H, Hooper JA, Sander GR et al. (2007). Flightless I deficiency enhances wound repair by increasing cell migration and proliferation. J Pathol 211: 572–581.

  13. Dao DY, Yang X, Chen D, Zuscik M, O'Keefe RJ . (2007). Axin1 and Axin2 are regulated by TGF- and mediate cross-talk between TGF- and Wnt signaling pathways. Ann N Y Acad Sci 1116: 82–99.

  14. Ding S, Schultz PG . (2004). A role for chemistry in stem cell biology. Nat Biotechnol 22: 833–840.

  15. Eisenmann KM, Harris ES, Kitchen SM, Holman HA, Higgs HN, Alberts AS . (2007). Dia-interacting protein modulates formin-mediated actin assembly at the cell cortex. Curr Biol 17: 579–591.

  16. Esteve P, Bovolenta P . (2006). Secreted inducers in vertebrate eye development: more functions for old morphogens. Curr Opin Neurobiol 16: 13–19.

  17. Farghaly SA . (1992). Tumor markers in gynecologic cancer. Gynecol Obstet Invest 34: 65–72.

  18. Fukada M, Watakabe I, Yuasa-Kawada J, Kawachi H, Kuroiwa A, Matsuda Y et al. (2000). Molecular characterization of CRMP5, a novel member of the collapsin response mediator rotein family. J Biol Chem 275: 37957–37965.

  19. Gabory A, Ripoche MA, Yoshimizu T, Dandolo L . (2006). The H19 gene: regulation and function of a non-coding RNA. Cytogenet Genome Res 113: 188–193.

  20. Garcia-Barcelo MM, Lau DK, Ngan ES, Leon TY, Liu TT, So MT et al. (2007). Evaluation of the thyroid transcription factor-1 gene (TITF1) as a Hirschsprung's disease locus. Ann Hum Genet 71: 746–754.

  21. Gilboa E . (1999). How tumors escape immune destruction and what we can do about it. Cancer Immunol Immunother 48: 382–385.

  22. Giles RH, van Es JH, Clevers H . (2003). Caught up in a Wnt storm: Wnt signaling in cancer. Biochim Biophys Acta 1653: 1–24.

  23. Grozdanov PN, Yovchev MI, Dabeva MD . (2006). The oncofetal protein glypican-3 is a novel marker of hepatic progenitor/oval cells. Lab Invest 86: 1272–1284.

  24. Hishinuma M, Ohashi KI, Yamauchi N, Kashima T, Uozaki H, Ota S et al. (2006). Hepatocellular oncofetal protein, glypican 3 is a sensitive marker for alpha-fetoprotein-producing gastric carcinoma. Histopathology 49: 479–486.

  25. Hossain MS, Ozaki T, Wang H, Nakagawa A, Takenobu H, Ohira M et al. (2008). N-MYC promotes cell proliferation through a direct transactivation of neuronal leucine-rich repeat protein-1 (NLRR1) gene in neuroblastoma. Oncogene 27: 6075–6082.

  26. Imamura F, Nagao H, Naritsuka H, Murata Y, Taniguchi H, Mori K . (2006). A leucine-rich repeat membrane protein, 5T4, is expressed by a subtype of granule cells with dendritic arbors in specific strata of the mouse olfactory bulb. J Comp Neurol 495: 754–768.

  27. Kato T, Hayama S, Yamabuki T, Ishikawa N, Miyamoto M, Ito T et al. (2007). Increased expression of insulin-like growth factor-II messenger RNA-binding protein 1 is associated with tumor progression in patients with lung cancer. Clin Cancer Res 13: 434–442.

  28. Katoh M . (2007). Networking of WNT, FGF, Notch, BMP, and Hedgehog signaling pathways during carcinogenesis. Stem Cell Rev 3: 30–38.

  29. Katoh M, Katoh M . (2007). WNT antagonist, DKK2, is a Notch signaling target in intestinal stem cells: augmentation of a negative regulation system for canonical WNT signaling pathway by the Notch-DKK2 signaling loop in primates. Int J Mol Med 19: 197–201.

  30. Kobe B, Kajava AV . (2001). The leucine-rich repeat as a protein recognition motif. Curr Opin Struct Biol 11: 725–732.

  31. Kopecki Z, Cowin AJ . (2008). Flightless I: an actin-remodelling protein and an important negative regulator of wound repair. Int J Biochem Cell Biol 40: 1415–1419.

  32. Kutay U, Guttinger S . (2005). Leucine-rich nuclear-export signals: born to be weak. Trends Cell Biol 15: 121–124.

  33. Liu CI, Cheng TL, Chen SZ, Huang YC, Chang WT . (2005). LrrA, a novel leucine-rich repeat protein involved in cytoskeleton remodeling, is required for multicellular morphogenesis in Dictyostelium discoideum. Dev Biol 285: 238–251.

  34. Matouk IJ, DeGroot N, Mezan S, Ayesh S, Abu-lail R, Hochberg A et al. (2007). The H19 non-coding RNA is essential for human tumor growth. PLoS ONE 2: e845.

  35. McNamee D . (1995). Beta-hCG inhibits Kaposi's sarcoma. Lancet 345: 1169.

  36. Monk M, Holding C . (2001). Human embryonic genes re-expressed in cancer cells. Oncogene 20: 8085–8091.

  37. Morton JP, Mongeau ME, Klimstra DS, Morris JP, Lee YC, Kawaguchi Y et al. (2007). Sonic hedgehog acts at multiple stages during pancreatic tumorigenesis. Proc Natl Acad Sci USA 104: 5103–5108.

  38. Nielsen J, Christiansen J, Lykke-Andersen J, Johnsen AH, Wewer UM, Nielsen FC . (1999). A family of insulin-like growth factor II mRNA-binding proteins represses translation in late development. Mol Cell Biol 19: 1262–1270.

  39. Niemann H, Carnwath JW, Kues W . (2007). Application of DNA array technology to mammalian embryos. Theriogenology 68 (Suppl 1): S165–S177.

  40. Park TJ, Kim JY, Park SH, Kim HS, Lim IK . (2009). Skp2 enhances polyubiquitination and degradation of TIS21/BTG2/PC3, tumor suppressor protein, at the downstream of FoxM1. Exp Cell Res 315: 3152–3162.

  41. Qiao M, Iglehart JD, Pardee AB . (2007). Metastatic potential of 21T human breast cancer cells depends on Akt/protein kinase B activation. Cancer Res 67: 5293–5299.

  42. Sarandakou A, Protonotariou E, Rizos D . (2007). Tumor markers in biological fluids associated with pregnancy. Crit Rev Clin Lab Sci 44: 151–178.

  43. Shimokawa T, Furukawa Y, Sakai M, Li M, Miwa N, Lin YM et al (2003). Involvement of the FGF18 gene in colorectal carcinogenesis, as a novel downstream target of the beta-catenin/T-cell factor complex. Cancer Res 63: 6116–6120.

  44. Sugimori M, Nagao M, Parras CM, Nakatani H, Lebel M, Guillemot F et al. (2008). Ascl1 is required for oligodendrocyte development in the spinal cord. Development 135: 1271–1281.

  45. Trojan J, Naval X, Johnson T, Lafarge-Frayssinet C, Hajeri-Germond M, Farges O et al. (1995). Expression of serum albumin and of alphafetoprotein in murine normal and neoplastic primitive embryonic structures. Mol Reprod Dev 42: 369–378.

  46. Vogelstein B, Kinzler KW . (2004). Cancer genes and the pathways they control. Nat Med 10: 789–799.

  47. Wang R, Kaul A, Sperry AO . (2009). TLRR (lrrc67) interacts with PP1 and is associated with a cytoskeletal complex in the testis. Biol Cell 102: 173–189.

  48. Xu L, Geman D, Winslow RL . (2007). Large-scale integration of cancer microarray data identifies a robust common cancer signature. BMC Bioinform 8: 275.

Download references

Acknowledgements

This was supported by Grant NSC-95-2320-B-006-057-MY2 from the National Science Council, Taiwan, and Grant DOH99-TD-C-111-003 from the Department of Health, Taiwan. There is no competing financial interest in relation to this work.

Author information

Correspondence to C-L Ho.

Ethics declarations

Competing interests

The authors declare no conflict of interest.

Additional information

Supplementary Information accompanies the paper on the Oncogene website

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Hsu, C., Chiang, C., Cheng, H. et al. Identifying LRRC16B as an oncofetal gene with transforming enhancing capability using a combined bioinformatics and experimental approach. Oncogene 30, 654–667 (2011) doi:10.1038/onc.2010.451

Download citation

Keywords

  • oncofetal genes
  • expressed sequence tags
  • LRRC16B

Further reading