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Molecular and Cellular Pathobiology

Identification of Alternative Splicing Events Regulated by theOncogenic Factor SRSF1 in Lung Cancer

Fernando J. de Miguel1, Ravi D. Sharma4, María J. Pajares1,2, Luis M. Montuenga1,2,Angel Rubio4, and Ruben Pio1,3

AbstractAbnormal alternative splicing has been associated with cancer. Genome-wide microarrays can be used to

detect differential splicing events. In this study, we have developed ExonPointer, an algorithm that uses data fromexon and junction probes to identify annotated cassette exons. We used the algorithm to profile differentialsplicing events in lung adenocarcinoma A549 cells after downregulation of the oncogenic serine/arginine-richsplicing factor 1 (SRSF1). Data were generated using two different microarray platforms. The PCR-basedvalidation rate of the top 20 ranked genes was 60% and 100%. Functional enrichment analyses found a sub-stantial number of splicing events in genes related to RNA metabolism. These analyses also identified genesassociated with cancer and developmental and hereditary disorders, as well as biologic processes such as celldivision, apoptosis, and proliferation. Most of the top 20 ranked genes were validated in other adenocarcinomaand squamous cell lung cancer cells, with validation rates of 80% to 95% and 70% to 75%, respectively. Moreover,the analysis allowed us to identify four genes, ATP11C, IQCB1, TUBD1, and proline-rich coiled-coil 2C (PRRC2C),with a significantly different pattern of alternative splicing in primary non–small cell lung tumors compared withnormal lung tissue. In the case of PRRC2C, SRSF1 downregulation led to the skipping of an exon overexpressed inprimary lung tumors. Specific siRNA downregulation of the exon-containing variant significantly reduced cellgrowth. In conclusion, using a novel analytical tool, we have identified new splicing events regulated by theoncogenic splicing factor SRSF1 in lung cancer. Cancer Res; 74(4); 1105–15. �2013 AACR.

IntroductionAlternative splicing is the process by which different mRNA

isoforms are processed from a single primary transcript. Inhumans, estimates indicate that asmany as 95% ofmultiexonicgenes are alternatively spliced (1). Cassette exon, in which anexon is either retained or spliced out of the transcript, is themost common alternative splicing event in mammals, andaccounts for approximately half of all events (2–4). Alternativesplicing has an essential role in the regulation of cell homeo-stasis. When this process becomes deregulated it can lead topathologic conditions such as cancer (5–8). Deregulation ofsplicing is often associated with an abnormal expression

pattern of splicing factors (9). One of these factors is serine/arginine-rich splicing factor 1 (SRSF1). This molecule is amember of the serine/arginine-rich protein family, which isinvolved in constitutive and alternative splicing (10). TheSRSF1 protein contains 2 RNA recognition motives (RRM) atthe N-terminal and one arginine/serine rich domain at the C-terminal. The arginine/serine rich domain mediates protein–protein interactions in the spliceosome assembly (11). TheSRSF1 protein is also implicated in other functions, such asnonsense-mediated RNA decay, translation and RNA trans-port, genome stability, and senescence (12, 13). The expressionof SRSF1 is upregulated in several human neoplasias andparticipates in the establishment and maintenance of a trans-formed phenotype (14, 15). Consequently, the study of thealternative splicing events regulated by this factor is of par-ticular interest.

A number of genome-wide array approaches have beendeveloped to study changes in alternative splicing (16). Thesearrays contain probes in exons (exon arrays), probes along thewhole genome (tiling arrays), or probes in exons and junctions(junction arrays). The characteristics of the splicing arraysdeveloped to date are summarized in Supplementary Table S1.The analytical process for the evaluation of differential splicingevents is a key aspect to consider when using these arrays. Aserious limitation of exon arrays is that the exclusion of an exonmust be inferred by the absence of a signal. In contrast, amicroarray design that includes junction probes indicates theexclusion of an exon by the hybridization of probes to the

Authors' Affiliations: 1Division of Oncology, Center for Applied MedicalResearch (CIMA); Departments of 2Histology and Pathology and 3Bio-chemistry and Genetics, Schools of Science and Medicine, University ofNavarra, Pamplona; and 4CEIT and TECNUN, University of Navarra, SanSebastian, Spain

Note: Supplementary data for this article are available at Cancer ResearchOnline (http://cancerres.aacrjournals.org/).

F.J. de Miguel and R.D. Sharma contributed equally to this work.

Corresponding Authors: Luis M. Montuenga, CIMA Building, Pio XIIAvenue 55, 31008 Pamplona, Spain. Phone: 34-948-194700; Fax: 34-948-194714; E-mail: [email protected], or Angel Rubio, CEIT, PaseoMikeletegi 48, San Sebastian, Spain. Phone: 34-943-212800; Fax: 34-943-213076; E-mail: [email protected]

doi: 10.1158/0008-5472.CAN-13-1481

�2013 American Association for Cancer Research.

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skipping junction. Consequently, the current trend is to workwith arrays that include probes in annotated junctions. Unfor-tunately, there are very few algorithms that take advantage ofthe redundancy provided by junction probes (17–25).

In this study we have developed an algorithm, namedExonPointer, which combines data from exon and junctionprobes and was optimized to identify annotated cassetteexons.Weused the algorithm to identify differential alternativecassette events in lung adenocarcinoma A549 cells after thedownregulation of SRSF1. Expression data were generatedusing 2 platforms: Oryzon custom arrays (Agilent technology)and GeneSplice arrays (Affymetrix technology). Validationrates were 60% and 100% for Agilent and Affymetrix platforms,respectively, which demonstrate the effectiveness of this pro-cedure in finding alternative splicing events. In addition, mostof these events were validated in other lung cancer cell linesand, more importantly, allowed us to identify genes withsignificant differences in splicing between primary lungtumors and normal lung tissue. Finally, functional studiesdemonstrated the implication of one splicing isoform of pro-line-rich coiled-coil 2C (PRRC2C), one of the SRSF1-regulatedgenes, in cell proliferation. In conclusion, we have developed auseful tool for the analysis of alternative splicing and haveidentified new splicing events regulated by the oncogenicsplicing factor SRSF1.

Materials and MethodsBiological material

Primary lung tumorswere obtained frompatients with non–small cell lung cancer who had been treated with resectionalsurgery at the Clinica Universidad de Navarra (Pamplona,Spain). Clinicopathologic characteristics of the patients areshown in Supplementary Table S2. Nontumor lung specimenswere sampled at a distance from the tumor to guarantee thatthe tissues were free from cancerous cells. Tissue samples wereimmediately frozen in liquid nitrogen and stored at �80�Cuntil extraction of RNA. Samples were included in the study ifgreater than 70% of their cells were tumor cells. The studyprotocol was approved by the Institutional Ethical Committeeand all patients gavewritten informed consent. RNA extractionwas performed as previously described (26).

Lung adenocarcinoma cell lines A549, HCC827, and H2087were obtained from the American Type Culture Collection.Squamous cell carcinoma cell lines HCC95 and LUDLU-1 wereobtained from the Korean Cell Line Bank and the EuropeanCollection of Cell Cultures, respectively. Cell lines were authen-ticated by analysis of their genetic alterations. Cellswere grownin RPMI supplemented with 2 mmol/L glutamine, 10% Fetal-clone (Thermo), 100 U/mL of penicillin and 100 mg/mL ofstreptomycin (Invitrogen).

Downregulation of SRSF1 and expression analysisCells were transfected with a 30 nmol/L solution of a

nontargeting scramble siRNA (50-ACGACACGCAGGTCGT-CATtt-30) or an SRSF1 siRNA (50-TGAAGCAGGTGATGTA-TGTtt-30) using lipofectamine as described by the manufac-turer (Invitrogen). Cells were incubated for 48 hours aftertransfection. Total RNA was extracted using the RNA/DNA

Mini Kit (Qiagen). The efficiency of the SRSF1 siRNA knock-down was determined by real-time PCR, using SYBR GreenPCR Master Mix and the following primers: 50-GGAACAAC-GATTGCCGCATCTA-30 (forward); 50-CTTGAGGTCGGATG-TCGCGGATA-30 (reverse).

Microarray hybridizationSamples from 3 independent experiments using lipofecta-

mine alone, scramble siRNA, or SRSF1 siRNA (i.e., a total of ninesamples) were labeled and hybridized in 2 different platforms:Agilent custom-built microarrays from Oryzon Genomics andAffymetrix GeneSplice arrays. The former were developed byOryzon Genomics in collaboration with our group, and havebeen previously described (27). These arrays contain exonprobes and thermodynamically balanced junction probes. Thenoncommercial Human GeneSplice Arrays were obtainedthrough a Technology Access program with Affymetrix. Thesehigh-density arrays contain an average of 8 probes per probe-set. RNA labeling and hybridization on the Agilent microarrayswere performed by Oryzon Genomics, as previously described(27). Labeling and hybridization on the Affymetrix GeneSplicemicroarrays were performed by the Proteomics, Genomics andBioinformatics Core Facility of the Center for Applied MedicalResearch (CIMA) following manufacturer's instructions. Themicroarray data from this study have been submitted to GeneExpression Omnibus (http://www.ncbi.nlm.nih.gov/geo) underaccession number GSE53410.

ExonPointer algorithmThe ExonPointer algorithmwas used to process and analyze

the microarray data. The aim in the design of ExonPointer wasto identify differential alternative splicing, i.e. genes that showdifferent relative concentrations of their isoforms, under dif-ferent conditions. Fig. 1 illustrates the summarized steps ofExonPointer. The mathematical definition of differential alter-native splicing and the analytical steps are described in detailin Supplementary Methods. Probes that did not indicateexpression levels greater than the background noise wereexcluded from the analysis. Short genes with no more than2 exons (i.e., no more than 3 probe sets) were also excluded.Supplementary Fig. S1 shows a representative example of thereport generated by the algorithm, which points to the differ-ential splicing event in a given gene.

Downregulation, expression analysis, and sequencing ofPRRC2C

Cells were cultured and transfected as described in section"Down-regulation of SRSF1 and expression analysis." ThesiRNA sequences were as follows: PRRC2C 50-CGAACAGCCA-GUCCAGCAA[dA][dA]-30; PRRC2C-L (long) 50-GGAAGAGGA-CUCAGCCACA[dA][dA]-30; PRRC2C-S (short) 50-GCACCCCA-GGCAAAGCAGA[dA][dA]-30. Primers used to quantify andsequence the PRRC2C-L variant were: 50-GTCCAGCAAAAT-GAACAGCA-30 (forward) and 50-GAGTCCTCTTCCCACAGC-TCTCT-30 (reverse). Primers used to quantify and sequence thePRRC2C-S variant were: 50-ACCCCAGGCAAAGCAGAG-30 (for-ward) and 50-GATCGCCTGAGGTTTGATTG-30 (reverse). The

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PCR products were sequenced in the Proteomics, Genomics,and Bioinformatics Core Facility of CIMA.

MTT and clonogenic assaysCells were cultured and transfected as described in sec-

tion "Downregulation of SRSF1 and expression analysis."Six hours after transfection, cells were trypsinized andseeded in 96-well plates at 1,500 cells/well for MTT assays.Thiazolyl Blue Tetrazolium Bromide (MTT; Sigma) wasadded at 0, 24, 48, 72, 96, and 120 hours, at a final concen-tration of 0.5 mg/mL. After 4 hours of incubation, 100 mLof solubilization solution (10% sodium dodecyl sulfate, 50%N,N-dimethylformamide, pH 4.7) was added. Absorbancewas measured at 540 nm using 690 nm as a reference. Forclonogenic assays, 6 hours after transfection, cells weretrypsinized and seeded in 6-well plates at 300 cells/well.Culture medium was replaced after 1 week of incubation andremoved after 2 weeks. Clones were fixed with 1 mL offormaldehyde/well for 30 minutes, dyed with few dropsof crystal violet for 10 minutes, and counted.

Cell-cycle and apoptosis analysesCells were cultured and transfected as described in section

"Downregulation of SRSF1 and expression analysis," and themedium was replaced after 6 hours. At the indicated times,suspended and attached cells were harvested. For apoptosisanalysis, cells were washed and stained with 10 mg/mL ofpropidium iodide (Sigma) and 5 mL of Annexin V (BD Bio-sciences). For cell-cycle analysis, cells were fixed with 70%ethanol for at least 1 hour at 4�C and treated with 0.2 mg/mLRNaseA (Sigma) for 1 hour at 37�C. Cells were then stainedwith10 mg/mL of propidium iodide (Sigma). All samples wereanalyzedon a FACSCalibur Flow cytometer (BectonDickinson).Percentages of cells in sub-G0–G1, G0–G1, S, and G2–M, for cell-cycle analysis, or in quadrants, for apoptosis analysis, weredetermined with FlowJo 9.3 software (Tree Star).

Enrichment analysesThe DAVID Functional Annotation Clustering applica-

tion was used to create clusters in accordance with annota-tions from several databases (28). The DAVID Functional

Figure 1. A, representation of thedifferent steps of the ExonPointeralgorithm. In the case of Affymetrix,cel files are processed at a low levelusing standardAffymetrixmethods(background removal and quantilenormalization). The Oryzon arraysare preprocessed (backgroundcorrection andnormalization) usingOryzon's proprietary methods.Once all the probe signals areknown, the residuals of the medianpolish algorithm are used as theinput into a linear model. The linearmodel provides a P value for eachprobe. B, ExonPointer analysisof a particular gene. In the lowerpart of the figure, it can beobserved that the residuals of theprobes that hit an exon and itsflanking junctions are positive andthe residuals of the skippingjunction are negative. The linearmodel computes the P values thatcorrespond to these residuals. Thevaluesare close to zero for theexonand flanking probes and close toone for the skipping junction. Theoverall P value is computed usingthe Irwin–Hall distribution. These Pvalues are summarized taking intoaccount the possible inclusion orretention of the analyzed exon.

Identification of SRSF1-Regulated Splicing Events

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Annotation tool was run to obtain P values for enrichedannotation terms. Ingenuity Pathway Analysis from IngenuitySystems (http://www.ingenuity.com/products/ipa) was usedto perform enrichment analyses of networks and functions.Whole genome databases of the respective tools were used asa reference for the analyses.

Statistical analysesThe statistical analysis for the selection of significant alter-

native splicing events is detailed in the description of theExonPointer algorithm (Supplementary Methods). Differencesin the expression of splice variants in cell lines were assessed bythe Student t test. Ratios of expression in primary tumors andnormal tissues were tested for normality with the Shapiro–Wilk test. TheWilcoxon signed rank testwas used to determinethe statistical significance of the differences between thesetwo groups. For MTT assays, the natural logarithm was cal-culated for each growth curve to obtain a straight line. Linearregression was performed for each condition and the slopeswere compared with the Student t test. Clonogenic assays wereanalyzed with the Student t test. All tests were performed inSTATA/IC 12.1 software (StataCorp).

ResultsWe developed the ExonPointer algorithm to identify

genome-wide differential alternative splicing events in A549cells in which the levels of the oncogenic SRSF1 were down-regulated with a specific siRNA. The efficiency of downregu-lation was assessed by real-time PCR (Supplementary Fig.S2A). The splicing pattern of caspase-9 (CASP9) was used asa positive control to confirm the validity of our model.Caspase-9 has two splice variants that differ in four conse-cutive exons, proapoptotic caspase-9a, and anti-apoptoticcaspase-9b. It has been reported that, in A549 cells, SRSF1regulates the inclusion (CASP9a) or exclusion (CASP9b) ofthis exon cassette (29). In accordance with the literature,downregulation of SRSF1 induced a marked increase of theCASP9b variant (Supplementary Fig. S2B).

Microarray data were obtained from three independentexperiments using two different platforms: Agilent custom-built microarrays from Oryzon Genomics and Affymetrix Gen-eSplice arrays. The ExonPointer algorithmwas used to processand analyze the results. The number of cassette events, afterthe application of the selection criteria, was 24,205 for Oryzonarrays and 18,583 for GeneSplice arrays. The total number ofgenes was 4,062 and 7,326 for Oryzon and GeneSplicearrays, respectively. Of these, 2,300 were common to bothplatforms. For each platform, events were ranked in accor-dance with their P values. For validation, we selected the top 20events ranked by ExonPointer in the twomicroarray platforms.Only one gene, PPIP5K2, was common to both shortlists. Foreach splicing event, a PCRwas runwith primers designed in theexons that flank the exon of interest, and the ratio between thehigher (exon inclusion) and lower band (exon exclusion) wascalculated. Twelve of the 20 genes (60% validation rate) select-ed by ExonPointer from the Oryzon platform data showeddifferential alternative splicing (Supplementary Fig. S3 andTable S3). The other 8 genes showed differences in expression

or no observable changes (data not shown). Results wereconsistent in all genes, that is the validated events were foundin all the triplicates, and, conversely, the events that were notvalidated did not appear in any of the samples. Among thevalidated genes, SRSF1 downregulation resulted in exon exclu-sion in 9 genes (NPRL3, PQBP1, DHPS, CHD3,MYOB1, RABL2B,TMPO, MATR3, and CXorf26) and exon inclusion in 3 genes(FN1, WNK1, and PP1P5K2). These changes were confirmedusing samples from two independent experiments with A549cells transfected under the same experimental conditions asthose used in the microarray studies (data not shown).

The same validation strategy was followed for the genesselected by ExonPointer from the GeneSplice platform data.Remarkably, 100% of the top 20 events were validated (Table 1and Fig. 2) and the results were fully consistent among tripli-cates. Exclusion was again the predominant effect of SRSF1downregulation, with 15 genes that showed an increase in exonexclusion (ATP11C, SSH1, ABCD4, FIP1L1, TFDP1, IQCB1,MPV17L, NAGK, RAD51C, PARPBP, ASAP1, EWSR1, TUBD1,PRRC2C, and USP8), and only 5 genes that showed an increasein exon retention (MYCBP2, MORF4L2, PPIP5K2, MAPT, andSETD5). These changeswere confirmed using samples from the2 independent experiments (data not shown). To establish areliable limit for the validity of the ranked list generated byExonPointer using GeneSplice microarray data, we validated20 genes placed at lower positions in the list. We chose thegenes listed at positions 96–100, 146–150, 196–200, and 246–250. We were able to validate 12 of these 20 genes (Supple-mentary Fig. S4). Exon exclusion was again the most frequentevent caused by SRSF1 downregulation (11 of the 12 validatedevents). The top 250 events selected by ExonPointer were usedto perform an enrichment analysis of the cellular networks andfunctions affected by the downregulation of SRSF1 in A549cells. In the DAVID Functional Annotation Chart, the 2 mostcommon and enriched annotations were "alternative splicing"and "splice variant" (Fig. 3). Within the top annotations, "RNAbinding," "mRNA processing," "Nucleotide binding," and "RNArecognitionmotives"were present. These results indicated thatSRSF1 regulates splicing in pathways that are intrinsicallyimplicated in RNA metabolism. "Phosphoprotein" was also onthe top of the enriched annotations. The ingenuity analysis ofthe differentially spliced genes regulated by SRSF1 built a totalof 11 networks. The first contained 22 genes and included thefollowing annotations: "Cell Morphology, Developmental Dis-order, and Hereditary Disorder" (Supplementary Fig. S5). Thenext twonetworks contained 23 and 21 genes highly involved intumorigenesis and cancer development. The annotationsincluded in these two networks were: "Cell Cycle, CellularAssembly, and Organization and Cellular Movement" for thesecond network (Supplementary Fig. S6); and "OrganismalDevelopment, Cancer, Cell Death, and Survival" for the thirdnetwork (Supplementary Fig. S7). Many genes involved in thesenetworks have functions related to RNAmetabolism (Table 2).Functional annotations related to infectionwere also at the topof the rank, although its biologic relevance needs to beestablished.

We further assessed the reliability of the list generated fromthe GeneSplice data by searching for events described in the

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literature as targets of SRSF1. We first analyzed the genesreported by Karni and colleagues in their seminal paperdescribing the oncogenic potential of SRSF1 (14). Interestingly,even though the cellmodels were different (A549 vs. IMR90 andHeLa cells), most of the events described in their study werecorrectly identified by ExonPointer in our model (Supplemen-tary Table S4). Other genes described in the literature as targetsof SRSF1 were also detected in our analysis: MAPT, P ¼1.55e�66 (30); CD44, P ¼ 2.04e�22 (31); EPB41, P ¼ 2.22e�22(32); SRSF3, P ¼ 1.91e�21 (33); CASP9, P ¼ 4.79e�21 (29);BCL2L1, P¼ 5.03e�03 (34);MCL1, P¼ 1.13e�17 (34); and FN1,P ¼ 3.32e�15 (35). These data reveal the notable capacity ofour procedure to identify alternative splicing events regulatedby SRSF1.To confirm the relevance of the information obtained from

lung adenocarcinoma A549 cells, we validated the results infour additional lung cancer cell lines: two adenocarcinomas(HCC827 and H2087) and two squamous cell lung carcinomas(HCC95 and LUDLU-1). Results of the validation after SRSF1downregulation are shown in Table 1. Most of the genes werevalidated in both histologic subtypes, although, as expected,validation rates were higher in adenocarcinoma cell lines (95%and 80%) than in squamous cell lines (75% and 70%).Human cell lines retainmany properties of the cells of origin,

but they also show clear differences and are not fully repre-sentative of the in vivo tumors (36). Therefore, we evaluated theclinical relevance of our findings by determining the presenceof the identified splicing events in a cohort of primary lungtumors and their corresponding normal lung tissues. After

confirming the overexpression of SRSF1 in these tumors (Fig.4A), we found a significant deregulation in the splicing of fourgenes: ATP11C, IQCB1, TUBD1, and PRRC2C (Fig. 4B–E).

Finally, we analyzed the role of one of these new splicingevents on the proliferative capacity of lung cancer cells. Theselected event was a cassette exon in PRRC2C. Based on theExonPointer report and the validation experiments, SRSF1downregulation resulted in a significant increase in exonskipping. This result was further validated by PCR and real-time PCR in samples from both A549 cells (Fig. 5A and B) andan extended cohort of human primary tumors (Fig. 4F).Sequencing of the variants revealed that the alternative exoncontains 53 nucleotides and is located between exons 33and 34. The presence of this exon results in a frameshift thatintroduces a unique sequence of 41 amino acids in the C-terminal domain of the protein (Fig. 5C). Hereafter, the variantcontaining the alternative exon will be referred as PRRC2C-L(long) and the variant without this exon as PRRC2C-S (short).To test the functional significance of each variant on lungcancer growth, we knocked down PRRC2C-L and PRRC2C-S inA549 cells using specific siRNAs. Cell proliferation wasdecreased after downregulation of the two spliced isoforms.However, the effect of PRRC2C-L downregulation was signif-icantly more pronounced (Fig. 5D). The differences were morenoticeable in clonogenic assays. Downregulation of PRRC2C-Lsignificantly inhibited the formation of clones, whereas down-regulation of PRRC2C-S had no effect (Fig. 5E). Cell-cycleanalysis, evaluated by flow cytometry, showed an accumula-tion of cells in G2–M after the specific downregulation of

Table 1. List of the top 20 ranked genes selected by ExonPointer from the expression data obtained usingGeneSplice arrays

Validated

Rank Gene P value ExonEnsembltranscript ID Event A549 HCC827 H2087 HCC95 LUDLU-1

1 MYCBP2 1.78e�91 57 201 Inclusion Yes Yes Yes Yes Yes2 ATP11C 4.78e�87 20 007 Exclusion Yes Yes No No No3 SSH1 2.10e�82 11 001 Exclusion Yes Yes Yes Yes Yes4 ABCD4 9.01e�79 8 001 Exclusion Yes Yes Yes Yes Yes5 FIP1L1 9.35e�73 2 005 Exclusion Yes Yes Yes Yes Yes6 TFDP1 7.90e�72 10 202 Exclusion Yes Yes Yes Yes Yes7 MORF4L2 1.07e�71 3 006 Inclusion Yes Yes No Yes Yes8 PPIP5K2 1.73e�71 24 201 Inclusion Yes Yes Yes No Yes9 IQCB1 2.70e�70 12 001 Exclusion Yes Yes Yes Yes No10 MPV17L 3.20e�70 2 002 Exclusion Yes Yes Yes No No11 NAGK 1.38e�69 3 201 Exclusion Yes Yes Yes Yes No12 RAD51C 2.56e�69 4 001 Exclusion Yes No No No Yes13 MAPT 1.55e�66 8 201 Inclusion Yes Yes No Yes No14 PARPBP 1.40e�65 5 201 Exclusion Yes Yes Yes Yes Yes15 ASAP1 1.11e�60 2 002 Exclusion Yes Yes Yes Yes Yes16 EWSR1 5.74e�60 9 002 Exclusion Yes Yes Yes Yes Yes17 TUBD1 1.48e�59 4 201 Exclusion Yes Yes Yes Yes Yes18 SETD5 1.48e�58 4 020 Inclusion Yes Yes Yes No No19 PRRC2C 6.32e�58 34 201 Exclusion Yes Yes Yes Yes Yes20 USP8 1.40e�55 12 003 Exclusion Yes Yes Yes Yes Yes






PRRC2C-L (Fig. 5F). An increase in the apoptotic sub-G0–G1

peak was also detected (Fig. 5F). This increase in the rate ofapoptosis was confirmed using Annexin V (Fig. 5G). Compa-rable results were obtained with lung adenocarcinomaHCC827 and H2087 cells (Supplementary Fig. S8A–S8F). Inlung squamous carcinoma HCC95 cells, downregulation ofPRRC2C resulted in a reduction of in vitro tumor growth,mostly detected in clonogenic assays, although no differenceswere observed between the 2 splice variants (Supplementary

Fig. S8G and S8H). No differences were observed in lungsquamous carcinoma LUDLU-1 cells (data not shown). Takentogether, these data suggest that the splicing of PRRC2C isinvolved in the oncogenic activity of SRSF1.

DiscussionWe have developed the ExonPointer algorithm to identify

differentially spliced genes in data obtained from genome-wide

Figure 2. Validation of differential alternative splicing events from the top 20 genes selected by the ExonPointer algorithm using microarray data from theGeneSplice platform. Densitometry values of the isoform ratios are shown as mean � SEM from three independent experiments. A, genes undergoingexon exclusion after SRSF1 downregulation. In the case of RAD51C, the absence of the lower band under one condition precluded the statistical analysis. B,genes undergoing exon inclusion after SRSF1 downregulation. �, P < 0.05; ��, P < 0.01; ��, P < 0.001.

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exon-junction microarray technologies. We applied this algo-rithm to analyze differential alternative cassette events regu-lated by SRSF1 in the context of lung cancer. The experimentalvalidation rates using data from both Oryzon (Agilent) andGeneSplice (Affymetrix) arrays demonstrated the efficacy ofExonPointer, although GeneSplice performed noticeably bet-ter. Agilent probes are longer and more sensitive than Affyme-trix probes (37). However, the number of probes per array, andconsequently the number of probes used to interrogate eachsplicing event, was considerably smaller in the Oryzon arraysthan in the GeneSplice arrays (40,000 vs. 6,000,000, respective-ly). This characteristic counterbalances the drawback ofshorter and less-specific probes. During the implementationof ExonPointer we also found that the use of junction probes(particularly those that skip the exon) was fundamental for thesuccess of the algorithm. Other aspects of ExonPointer deserveto bementioned. Themain source of false positives was weaklyexpressed sequences (i.e., probes with low signal) and thefiltering of these probes drastically improved the results. Thecombination of P values of several probes (as opposed tothe combination of signals of the probes) was proven to bean effective approach to the quantification of splicing events.The algorithm exploits the redundancy of the junctions, andeffectively combines them with the exon probes. ExonPointeris an extension of a linear model and, therefore, can be appliedto any experiment where a linear model holds (most of thepresent algorithms are focused on case–control studies).Finally, the characteristics of ExonPointer allow for a straight-

forward implementation to other microarray platforms, pro-vided that they contain exon and junction probes (e.g., theGeneChip Human Transcriptome 2.0 array, recently launchedby Affymetrix).

RNA sequencing (RNA-seq) is an alternative genome-wideapproach used to characterize alternative splicing. TheRNA-seq technology is able to generate millions of readsthat can be mapped to the whole genome using specificRNA aligners (38). This process requires a laborious tuningof numerous parameters involved in each of the steps.Using this approach, approximately 300 million reads wouldbe required to accurately quantify 90% of the transcriptomein a single sample (39). With the decrease in cost and theimprovement in computing resources, read length and algo-rithms, RNA-seq will become a major player in the identi-fication of alternative splicing events, but, at present, storageand computational requirements can be unaffordable ifthe number of samples is high. Alternatively, if the focusis on the detection of annotated splicing events in a reliableand cost effective manner, a microarray platform with anadequate design is a very viable option (16). Despite thelimitations of microarrays (e.g., the results are tied to theprobes that are included in the array design, based onthe existing annotations of gene structures), we have dem-onstrated that this technology performs well, requires onlymodest computing power and is simple to interpret. In lessthan 1 hour of analysis, ExonPointer obtained a remarkablyreliable list of differential splicing events.

Figure 3. DAVID FunctionalAnnotation Chart derived from theanalysis of differential alternativesplicing events after SRSF1downregulation. Annotations aresorted by significance. A, eachcolumn depicts �log10 of P valuesfor the corresponding annotation.B, number of analyzed genespresent in each annotation. Thedatabase for each annotation isshown in parenthesis: SP,SwissProt; UP, UniProt; GO,Gene Ontology; SM, SMART; IP,InterPro.






The use of ExonPointer allowed us to study the alterations incassette exons caused by the downregulation of SRSF1 in lungcancer cells. Upregulation of SRSF1 is frequent in cancer andcontributes to the oncogenic potential of H-Ras and Myc(14, 40). Overexpression of SRSF1 in human non–small celllung cancers leads to a more invasive phenotype (15, 41).Through the control of caspase-9 alternative splicing, SRSF1also regulates the sensitivity of lung cancer cells to chemo-therapy (42). The serine/arginine-rich proteins are best knownfor their ability to promote exon inclusion (43). In accordancewith this observation, our results show that SRSF1 mainlyfunctions as a splicing repressor in A549 cells. This is alsoconsistent with the exon-skipping phenotypes of disease-asso-ciated mutations that affect the SRSF1-specific splicing motifs(44). The splicing analysis described here also provides newinformation on genes and functions regulated by SRSF1, whichhelps to understand the transforming capacity of SRSF1. In ourstudy we identified a substantial number of splicing events ingenes related to RNA metabolism. This supports the datareported by Sanford and colleagues, which showed an enrich-ment for genes that encode RNA binding proteins within agroup of alternative exons targeted by SRSF1 (45). In agree-ment with our functional analysis, the authors also foundenrichment for genes associated with biologic processes suchas cell division, apoptosis, and proliferation. To our knowledge,the implication of SRSF1 in the regulation of alternativesplicing in genes related to developmental and hereditarydisorders is a novel observation. The association with func-tional annotations related to phosphoproteins and infectionalso merit further attention.

In support of the validity of our approach, the ExonPointeralgorithm found statistically significant differences in many

Figure 4. Differential alternative splicing events in a cohort of primary lungtumors and their corresponding normal lung tissues (n ¼ 6). A, SRSF1expression measured by real-time PCR using IPO8 as housekeepinggene. B–E, PCR validation of differential alternative splicing events inATP11C, IQBC1, TUBD1, and PRRC2C, respectively. The ratio of thedensitometry values is shown. F, ratios of PRRC2C-L/PRRC2C-Sexpression, measured by real-time PCR, in an extended cohort ofpatients (n ¼ 16). �, P < 0.05.

Table 2. Ingenuity function enrichment analysis

Rank Function annotation Category P value No. genesa

1 Accumulation of RNA Molecular transport 1.16e�07 5/16RNA trafficking

2 Infection of embryonic cell lines Infectious disease 1.01e�04 12/325Organismal injury and abnormalities

3 Infection of epithelial cell lines Dermatological diseases and conditions 1.01e�04 12/325Infectious disease

4 Infection of kidney cell lines Infectious disease 1.42e�04 12/357Renal and urological disease

5 Accumulation of mRNA Molecular transport 5.94e�04 2/5RNA trafficking

6 Processing of RNA RNA Posttranscriptional modification 8.27e�04 10/3107 Tetramerization of protein Posttranslational modification 8.86e�04 5/78

Protein synthesis8 Formation of cervical cancer cell lines Cellular growth and proliferation 9.84e�04 2/69 Formation of nuclear pores Cellular assembly and organization 9.84e�04 2/5

DNA replication, recombination, and repair10 Transport of axons Cellular assembly and organization 9.84e�04 2/6

Cellular movementNervous system development and function

11 Delay in initiation of prometaphase Cell cycle 1.47e�03 2/6

aNumber of genes in the list that belong to the functional annotation/total number of genes in the functional annotation.

de Miguel et al.





genes previously reported to be regulated by SRSF1 (14, 29–35).Nevertheless, most of the splicing events that we found werenovel. Moreover, the analysis allowed us to identify 4 genes,ATP11C, IQCB1, TUBD1, and PRRC2C, with a significantlydifferent pattern of splicing in primary non–small cell lungtumors. The association of these molecules with lung cancerhad not been previously reported, although, in the case ofPRRC2C, there was some information suggesting its role incancer. PRRC2C is amplified and overexpressed in bladder

cancer (46), and it has been related to proliferation and cell-cycle regulation through an interaction with histone H4 tran-scription factor (47). We have now been able to identify a newsplice variant of PRRC2C, which is regulated by SRSF1 and isoverexpressed in primary lung tumors. We have also shownthat PRRC2C is able to regulate lung cancer growth in vitro.Interestingly, in lung adenocarcinoma cells this activity ismostly restricted to the splice variant overexpressed in primarylung tumors. The SRSF1 protein has been reported tomodulate

Figure 5. PRRC2C splice variants in A549 lung cancer cells. A, validation of the modified PRRC2C splicing pattern after SRSF1 downregulation. PRRC2C-Lstands for PRRC2C-long and PRRC2C-S for PRRC2C-short. The analysis was performed by PCR with the same RNA samples used for the microarrayanalysis. The densitometry value of each variant was standardizedwith GAPDH and is shown asmean�SEM. B, validation of the splicing change using a setof independent samples andmeasured by real-time PCR.Mean� SEMof triplicates is shown. C, nucleotide sequence of the alternative exon and C-terminalprotein sequences of the 2 variants. The presence of the alternative exon results in a frameshift that introduces a new sequence of 41 amino acids(underlined). D,MTT assay of A549 cells treatedwith scramble siRNA, PRRC2C-L siRNA, or PRRC2C-S siRNA. Absorbance valueswere standardized to time0 to make all curves comparable. Data points in the left panel represent mean values from four independent experiments. Natural logarithms of the valueswere used to statistically compare growth slopes (right). Mean � SEM of triplicates is shown. E, clonogenic assay. Mean � SEM of triplicates andrepresentative images are shown. F, cell-cycle analysis after 48 hours from transfection. Percentages of cells in each phase (sub-G0–G1, G0–G1, S, andG2–M)are indicated. One representative experiment is shown from three independent repetitions. G, analysis of apoptosis after 48 hours from transfection. Onerepresentative experiment is shown from three independent repetitions. n.s., no significant; �, P < 0.05; ��, P < 0.01; ��, P < 0.001.






p53 by interfering with its murine double minute 2 (MDM2)-dependent proteasomal degradation (13), and there is evidencefor an interaction between PRRC2C and MDM2 (48). The roleof the PRRC2C splice variants in the regulation of p53 byMDM2 merits further investigation.

In conclusion, exon–junctionmicroarrays, together with theappropriate algorithm, provide a reliable analytical tool forgenome-wide examination of differential alternative splicing ina time and cost-effective manner. This technology has allowedus to identify new splicing events regulated by the oncogenicsplicing factor SRSF1, and some biologic pathways in whichthis factor may have a significant role. The use of this tech-nology in other experimental and biologic conditions wouldprovide valuable information on the implication of alternativesplicing in normal and pathologic human biology.

Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.

Authors' ContributionsConception and design: F.J. deMiguel, R.D. Sharma, L.M.Montuenga, A. Rubio,R. PioDevelopment of methodology: F.J. de Miguel, R.D. Sharma, L.M. Montuenga,A. Rubio, R. Pio

Acquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): F.J. de Miguel, L.M. MontuengaAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): F.J. de Miguel, R.D. Sharma, L.M. Montuenga,A. Rubio, R. PioWriting, review, and/or revision of the manuscript: F.J. de Miguel,M.J. Pajares, L.M. Montuenga, A. Rubio, R. PioAdministrative, technical, or material support (i.e., reporting or orga-nizing data, constructing databases): F.J. de Miguel, R.D. Sharma, A. RubioStudy supervision: L.M. Montuenga, A. Rubio, R. Pio

AcknowledgmentsThe authors thank A. Lavin and C. Sainz for technical support, andDr. L. Ortiz

for performing the Affymetrix array hybridizations.

Grant SupportThis work has been supported by "UTE project CIMA," the Spanish

Government (ISCIII-RTICC RD12/0036/0040, PI10/00166, and PI1100618),and the European Community's Seventh Framework Programme(HEALTH-F2-2010-258677-CURELUNG). F.J. de Miguel is supported by apredoctoral fellowship from the Instituto de Salud Carlos III and Ministeriode Economía y Competitividad.

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicate thisfact.

Received May 22, 2013; revised December 4, 2013; accepted December 9, 2013;published OnlineFirst December 26, 2013.

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2014;74:1105-1115. Published OnlineFirst December 26, 2013.Cancer Res Fernando J. de Miguel, Ravi D. Sharma, María J. Pajares, et al. Oncogenic Factor SRSF1 in Lung CancerIdentification of Alternative Splicing Events Regulated by the

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