Clinical samples

RCC tissue samples and signed informed consent were obtained from 20 patients with a computed tomography CT confirmed renal mass following a protocol approved by the ethics committee of Bari Province Hospitals. Patients main demographic and clinical features are summarized in Table 1. At the time of surgery, all the patients showed no evidence of other diseases except for one. Tumor samples were staged using the latest TNM-classification and graded according to Fuhrman, this last information was taken into account for expression analysis [19,20]. Immediately after surgery, tumor and paired adjacent non-tumoral renal parenchyma denominated “non-tumoral renal tissue” (NT) were (separately) stored frozen at -80°C according to a standard procedure. Inclusion criteria were histologically confirmed RCC, of the clear cell subtype, and patients not previously submitted to pre-operative therapy.

Central review of tissue blocks was performed by an experienced uropathologist and only blocks with >95% viable tumor (ccRCC) or NT tissue were included in this study.

In total, 10 RCC tumors and their matched non-tumor kidney tissues were used for exon array analysis, whereas qRT-PCR validations were performed on an extended set of samples for a total of 36 tissues consisting of 18 paired ccRCC and NT kidney tissue. Confocal laser scanning microscopy was performed on 6 ccRCC renal biopsies and compared with their respective normal kidney portion.

RNA extraction and quality assessment

Collected samples were processed for total RNA extraction from 50-100 mg of fresh frozen tissue using the TRIzol reagent (Invitrogen^TM, Life Technologies^TM). RNA samples were further purified using the RNeasy Mini kit (Qiagen^®) according to the manufacturer’s instructions. Purified RNA was then quantified using the NanoDrop^TM 1000 Spectrophotometer (NanoDrop Technologies, Berlin, Germany) and RNA quality was determined by running aliquots on the 2100 Bioanalyzer (Agilent Technologies, Waldbronn, Germany). For microarray experiments, samples with a 28S/18S ratio < 1.0, RNA integrity number (RIN) < 6.5 or a concentration < 300 ng/μl were excluded.

Microarray experiments and data analysis

1μg of total RNA per sample was used as starting material. rRNA was first removed using the RiboMinus Human/Mouse Transcriptome Isolation Kit (Invitrogen^TM, Life Technologies^TM) and cDNA synthesis was performed using the GeneChip WT cDNA Synthesis Kit (Affymetrix, Santa Clara, CA). The cDNA was fragmented and labeled with biotin using the Affymetrix GeneChip WT Terminal Labeling Kit. Biotinylated targets were hybridized onto a Affymetrix GeneChip Human Exon 1.0 ST Array according to the manufacturer’s instructions. Each array was washed and stained in the Affymetrix Fluidics Station 450 and scanned to generate a CEL file using the Affymetrix GeneChip Scanner 3000 7G and Command Console^® (AGCC) Software (Affymetrix, Santa Clara, CA). The Affymetrix Expression Console Software (version 1.0) was used to perform quality assessment. The exon array data are deposited in GEO with Series accession number GSE47032.

All exon array data were analyzed using Partek Genomic Suite 6.4 software (Partek). The robust multi-array average (RMA) algorithm was used for gene- and exon-level intensity analyses. Data were filtered to consider only those probe sets included in the “Core Meta-Probeset”. The Analysis of Variance (ANOVA) and multi-test correction for p-values in Partek Genomic Suite were used to identify differentially expressed genes and exons. Tissue type (tumor versus NT) was chosen as the candidate variable in the ANOVA model to obtain tumor-related expression changes and splicing events. ANOVA p-values were corrected using Bonferroni method. Lists of genes and exons with significant variation of the expression levels were generated by using a 0.01 FDR criterion as a significant cutoff.

Partek list of significant exon-level probesets (FDR corrected p-value < 0.01) and an exon level ANOVA test list were used to create - by an ad hoc Python script - a database of significant probesets from Exon Array experiments. To use a more stringent analysis criteria we excluded from the final exon lists all those cases in which Exon Array revealed a significant change at a gene-level. The resulting database was employed to explore simple exon skipping events using as reference ASPicDB single exon skipping splicing events after mapping Affymetrix probesets on ASPicDB .gtf files. A single exon from the Partek list was defined significantly skipped only if its adjacent exons were not, by using 0.01 and 0.05 p-value criterions as a significant cutoff for the “skipped” and adjacent exons, respectively. Finally, information about the portion (CDS or UTRs) affected by the splicing event has been added, starting from .gff files generated from ASPicDB.

Pathway analyses

Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems Inc.) was used to perform a global characterization of the over-represented biological functions within the two sets of up-regulated and down-regulated genes in ccRCC. Each data set was screened for the presence of mapped and function eligible genes. The over-represented biological functions were ranked according to the calculated p-values. The enrichment of specific biological processes and pathways among differentially expressed genes was analyzed also using the Database for Annotation, Visualization and Integrated Discovery (DAVID, http://david.abcc.ncifcrf.gov/) bioinformatics resource [21,22].

qRT-PCR experiments and data analysis

Reverse transcription of 500 ng of total RNA was performed using QuantiTect^® Reverse Transcription kit (Qiagen^®) and diluted cDNA (1:3) was used in all following qPCR reactions. Control reverse transcription reactions without RT enzyme were also prepared and controlled by PCR with the same primers and amplification conditions applied for qRT-PCR assays (data not shown).

The geNorm VBA applet for Microsoft Excel [23] was used to determine the most stable housekeeping genes for the samples used in this study, selected from a panel of five reference genes (ACTB, B2M, GAPDH, HPRT1 and RPL13) in 8 of the 18 sample pairs used in qRT-PCR experiments (PS_21, PS_43, PS_44, PS_45, PS_46, PS_47, PS_62 and PS_67). For the geNorm analysis, qRT-PCR experiments were performed in duplicate on the ABI PRISM 7900HT platform (Applied Biosystems^®, Life Technologies^TM) using 1μl of diluted cDNA as template for each reaction with TaqMan^® Universal PCR Master Mix (Applied Biosystems^®, Life Technologies^TM). TaqMan assays from Applied Biosystems^® were used for the amplification of ACTB (Hs99999903_m1), B2M (Hs99999907_m1), GADPH (Hs99999905_m1), HPRT1 (Hs03929098_m1) and RPL13 (Hs00761672_s1) transcripts. No template controls were included as negative controls for each TaqMan assay. Amplification parameters were as follows: hot start at 95°C for 10 min; 50 amplification cycles (94°C for 15 sec, 60°C for 1 min). Results were first analyzed in SDS 2.4 software and then exported in Microsoft Excel in order to be further analyzed by using geNorm which identified ACTB and RPL13 as the best housekeeping genes for these samples.

qRT-PCR primers for reference genes and differentially expressed genes and exons (including their adjacent exons) were designed using Primer3 software [24], and their specificity was checked with both the UCSC “In-Silico PCR” and the NCBI Primer-BLAST tools. Sequences of primers used for qRT-PCR are reported in Table S1. For gene expression analysis qRT-PCR experiments were carried out in duplicate using the ABI PRISM 7900HT platform (Applied Biosystems^®, Life Technologies^TM) using 1μl of diluted cDNA as template for each reaction with QuantiTect^® SYBR Green PCR Master Mix (Qiagen^®). No template controls were included as negative controls for each primer pair. Amplification parameters were as follows: hot start at 95°C for 15 min; 50 amplification cycles (94°C for 15 sec, 62°C for 30 sec, 72°C for 30 sec); dissociation curve step (95° C for 15 sec, 60°C for 15 sec, 95°C for 15 sec). Fluorescence raw data were exported from the SDS 2.4 software (Applied Biosystems^®, Life Technologies^TM) and analyzed with the DART-PCR Excel workbook [25]. Actual amplification efficiency values (E) for each amplicon were used to correct Cq values before analyzing these data by the ΔCq method to compare relative expression results. Expression levels were calculated relative to the mean expression levels of ACTB and RPL13 genes, according to the following formula: relative Expression Ratio (rER) = 2^{(Cq_gene/exonX – [(Cq_ACTB + Cq_RPL13)/2]}. For exon-level analysis a percentage of inclusion was also calculated as the average of the expression ratio of skipped exon relative to both upstream exon and the downstream one, as well as the expression level fold change for the three exons considering each tissue pair.

Confocal laser scanning microscopy

Paraffin-embedded kidney samples were stained for TCF21, LAMA4, KCNJ1 and PTP4A3 (Abcam, Cambridge, UK). Renal biopsies were deparaffinized and underwent epitope unmasking through three microwave cycles at 750W for 5 min in citrate buffer (pH = 6). The slides were incubated with the appropriate blocking solution, primary antibodies (anti-TCF21 1:200, anti-LAMA4 1:500, anti-KCNJ1 1:50, anti-PTP4A3 1:200) and secondary antibodies (Alexa Flour 488 goat anti-rabbit, Molecular Probes, Eugene, OR). All sections were counterstained with TO-PRO-3 (Molecular Probes). Negative controls were prepared with irrelevant antibody. Specific fluorescence was acquired using the confocal microscope Leica TCS SP2 (Leica, Wetzlar, Germany). Fluorescence levels were quantified using Adobe Photoshop software and expressed as area fraction (%).

Statistical analysis

Two-tailed Student's T tests were performed to assess the statistical significance of gene and protein expression levels differences observed between the tumoral and NT ccRCC samples (and also among the different grades of ccRCC). A p-value < 0.05 was considered statistically significant. Statistical analyses for gene expression data were performed by Analysis ToolPak in Microsoft Excel 2010 software, while Statview software package (version 5.0, SAS Inc. Cary, NC) was used for protein statistical analyses.

Differential expression profiling by exon array

Tissue specimens were obtained from 20 patients undergoing radical or partial nephrectomy for unilateral RCC. Table 1 summarizes the main clinical features of the patients included in the study. The morphological features of the tumor tissues corresponded to well-differentiated renal cell carcinomas of the clear cell type, showing prominent cytoplasmic clearing and thin-walled vascular channels. The expression of ~17,800 core transcript clusters were analyzed in the renal tissues of 10 ccRCC tumoral (T) and 10 matched NT samples. A preliminary ANOVA highlighted the “Status” variable (i.e., NT and ccRCC tissues) as the major source of variance, with all other variables considered accounting for the residual variance, with small differences between random variables (i.e., “Patient”) and factors (i.e., “Fuhrman Grade” and “Gender”) (data not shown). “Status” variable was therefore used as a grouping factor in the principal component analysis (PCA) resulting in a clear segregation of NT tissue samples from the neoplastic ones (data not shown).

The gene-level analysis was performed using an ANOVA with all the information about the samples analyzed, with the “Status” variable (discriminating between NT and ccRCC samples), as parameter for comparison. All those transcripts with no significant change in probesets expression (FDR corrected p-value higher or equal to 0.01) or in linear fold change (-1.4 < FC < 1.4 for ccRCC vs NT) were excluded from the final gene list. Only genes passing these filters were retained to improve the statistical relevance of our results.

A list was produced, containing 1,312 up-regulated and 1,150 down-regulated genes in ccRCC, for a total of 2,462 differentially expressed genes (corresponding to 2,468 differentially expressed transcript clusters: 1,316 were up-regulated and 1,152 down-regulated) (Tables S2-S3). Even if the number of genes up-regulated and down-regulated in ccRCC samples was quite similar, down-regulated genes showed a wider range of variation, as their fold changes ranged from -1.4 to -232.9. On the contrary, the fold changes of up-regulated genes ranged from 1.4 to 37.9. Among up-regulated genes, some known transcriptional targets of HIF were found, i.e., CXCR4, TGFB1 and HK2 genes [4].

The exon-level analysis was performed as described above for the gene-level analysis. Probesets with no significant change in expression (FDR corrected p-value higher or equal to 0.01) related to the status parameter were excluded from the final exon list. A more robust analysis was obtained comparing this list with a list of Affymetrix Exon Array probesets corresponding to single exon skipping events collected in ASPicDB [17,18]. In this way, we obtained a list of potentially differentially skipped exons that was further refined using the criteria that upstream and downstream exons should not be significantly differentially expressed (see Materials and Methods section for a more detailed description).

Therefore, a list containing 139 tumor-associated exon inclusion events (where an inclusion event is to be intended as the entire exon triplet involved in the skip event) in 118 genes and 132 normal-associated exon inclusion events in 119 genes (Tables S4-S5) was generated.

Functional Enrichment Analysis

Ingenuity Pathway Analysis (IPA) software was used to perform a global characterization of over-represented biological functions within the two sets of up-regulated and down-regulated genes in ccRCC.

Using the Ingenuity Knowledge Database, differentially expressed genes were functionally characterized: 1266/1312 (96%) and 1121/1150 (97%) within the up- and down-regulated gene set, respectively.

The IPA “Core Analysis” revealed clear differences between biological functions over-represented in the two data sets. Briefly, as shown in Table 2, the set of genes up-regulated in ccRCC is functionally mostly associated with inflammatory response (“Cell-To-Cell Signaling and Interaction”, “Inflammatory Response”, “Infectious Disease”, “Immune Cell Trafficking” and “Lymphoid Tissue Structure and Development”) and cancer (“Cellular Growth and Proliferation”, “Tumor Morphology”, “Cancer”, “Cellular Movement” and “Cell Death”). In particular, as shown in Figure S1, a marked alteration of the immune response seems to be associated with ccRCC. Besides an obvious over-representation of genes associated with cancer (“DNA Replication, Recombination and Repair” and “Cancer”), the down-regulated data set seems to be mostly associated with renal disorders (“Renal and Urological Disease” and “Molecular Transport”) (Table 2 and Figure S2). These data, together with the alteration of several metabolic pathways (“Aminoacid Metabolism”, “Lipid Metabolism”, “Small Molecule Biochemistry” and “Carbohydrate Metabolism”) may suggest a global alteration of the renal function.

The top 5 networks and the top 5 biological functions that were over-represented in the data sets along with the number of genes assigned to each category are shown in Table 2. Moreover, the top functions and the top canonical pathways over-represented in the two data sets are shown in the histograms of Figures S1-S2.

Functional annotation was confirmed by DAVID tool, used to identify the most relevant biological processes and pathways significantly enriched in the up-regulated and down-regulated gene lists (Tables S6-S9).

Gene expression levels of PTP4A3, CAV2, LAMA4, KCNJ1, SFRP1 and TCF21 in ccRCC

To confirm differentially expressed genes detected by the exon array analysis, qRT-PCR validations were performed on an extended set of specimens (18 paired tumor and NT samples) including the already analyzed eight out of ten pairs (see Table 1). Six genes (3 up-regulated and 3 down-regulated in ccRCC vs. NT) were selected from the list of differentially expressed genes depending on their fold change value, their low variability in expression levels among samples and their function somehow correlated with cancer and/or kidney.

The expression levels of these three up-regulated (PTP4A3, CAV2 and LAMA4) and three down-regulated genes (KCNJ1, SFRP1 and TCF21) evaluated by qRT-PCR were in line with those obtained by the expression array (in this case also considering the Fuhrman grade of the ccRCC; Figures 1 and 2). Table 3 summarizes the main functions of the genes analyzed, and Table S10 shows the IPA enriched networks and bio-functions these genes are associated to.

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Figure 1. Gene Expression analysis of PTP4A3, CAV2 and LAMA4 genes.

Expression levels of PTP4A3 (A), CAV2 (B) and LAMA4 (C) genes were calculated relative to the mean expression levels of ACTB and RPL13 genes. From left to right: box plot of log₂ gene expression intensities resulting from microarray analysis (each dot stands for a sample); histogram of the expression levels (mean ± SE) of target genes in NT and ccRCC samples; histogram of the expression levels (mean ± SE) of target genes in NT and G1, G2 and G3 ccRCC samples. * = p-value < 0.05; # = p-value < 0.01; ** = p-value < 0.001.

https://doi.org/10.1371/journal.pone.0078452.g001

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Figure 2. Gene Expression analysis of KCNJ1, SFRP1 and TCF21 genes.

Expression levels of KCNJ1 (A), SFRP1 (B) and TCF21 (C) genes were calculated relative to the mean expression levels of ACTB and RPL13 genes. From left to right: box plot of log₂ gene expression intensities resulting from microarray analysis (each dot stands for a sample); histogram of the expression levels (mean ± SE) of target genes in NT and ccRCC samples; histogram of the expression levels (mean ± SE) of target genes in NT and G1, G2 and G3 ccRCC samples. * = p-value < 0.05; # = p-value < 0.01; ** = p-value < 0.001.

https://doi.org/10.1371/journal.pone.0078452.g002

Among the over expressed genes identified by exon arrays in ccRCC tissue samples, we focused our attention on three genes. PTP4A3 has been found have an enhanced expression in several cancers and its expression level increases significantly with tumor progression and severity (e.g., colorectal, gastric, ovarian and breast carcinomas) [26]; CAV2 was also selected due to the recently demonstrated significant association between its expression level and breast cancer basal-like and triple-negative immunophenotype, and because it was found to have a prognostic impact on breast cancer-specific survival [27]; the gene and protein expression of LAMA4 was found to correlate with tumor invasion and metastasis in hepatocellular carcinoma and in angiotropic melanoma areas [28,29]. As shown in Figure 1A-C, gene expression alterations measured by exon array (fold change = 3.004, p-value = 1.46E-08 for PTP4A3; fold change = 5.998, p-value = 5.97E-10 for CAV2; fold change = 9.751, p-value = 5.72E-11 for LAMA4) were confirmed by RT-qPCR validation. Indeed, even if slightly smaller fold changes were observed in the wider set of samples by qRT-PCR, the up-regulation of these genes still maintained a statistical significance. Of note, we detected 2.12 fold increase of their expression levels in tumoral samples compared to NT samples (p-value = 0.00094) for PTP4A3 (Figure 1A), 3.64 for CAV2 (p-value = 4.05E-06) (Figure 1B), and 7.72 (p-value = 1.63E-06) for LAMA4 (Figure 1C). Intriguingly, these gene expression alterations were confirmed in all but one sample pair for LAMA4 gene, while in 14 and 15 out of 18 samples for PTP4A3 and CAV2, respectively (data not shown). Although we detected some inter-individual variability, we noticed that the expression level of CAV2 gene was quite constant among samples in the NT condition and only in few cases were CAV2 mRNA levels higher than in non-paired tumor samples (data not shown). As far as the tumor grade was concerned, a significant alteration among the different histological grades was observed only for LAMA4, with a remarkable decrease of its expression levels passing from G2 to G3 ccRCC (Figure 1C).

Among the down-regulated genes identified by exon arrays in ccRCC tissue samples we selected the following genes: KCNJ1, SFRP1 and TCF21. This selection was mainly in view of the pivotal role of these genes in renal function (KCNJ1), cancer (SFRP1) and cell differentiation (TCF21). KCNJ1 is a kidney specific channel affecting not only the transport of potassium ions but indirectly also the normal balance of sodium and other ions in the body [30]. SFRP1 is a tumor suppressor, which has already been associated not only to carcinogenesis but also, to some extent, with the metastatic potential of cancer cells [31]. TCF21 is a transcription factor demonstrated in lung and head and neck cancers to be inactivated by epigenetic mechanisms [32]. Moreover, in exon array analysis these three genes showed a huge alteration of their expression levels in terms of down-regulation: we detected a fold change for KCNJ1 of -53.69 (p-value = 1.35E-15), for SFRP1 of -24.5 (p-value = 4.56E-15), and for TCF21 of -8.741 (p-value = 6.42E-17) as shown in Figure 2A-C. Increasing the number of the samples analyzed, these deregulations were fully validated in qRT-PCR assays. Indeed, comparing ccRCC samples to NT tissue samples, KCNJ1 showed a decrease of its expression levels to -133.8 (p-value = 2.94E-05) (Figure 2A), SFRP1 resulted down-regulated with a fold change of -55.1 (p-value = 1.74E-08) (Figure 2B), while TCF21 expression dropped -16.8 times (p-value = 6.70E-11) (Figure 2C). These genes proved to be very promising candidate biomarkers as their overall expression level was significantly different in NT and ccRCC conditions in all subjects investigated. No significant differences were found between different Fuhrman tumor grades.

Protein expression levels of LAMA4, KCNJ1, TCF21 and PTP4A3 in ccRCC

To further support the gene expression data we measured the corresponding intracellular protein levels of LAMA4, KCNJ1, TCF21 and PTP4A3 in ccRCC renal biopsies (n=6) compared with the corresponding NT kidney tissue, by means of immunofluorescence technique. We found that LAMA4+ cells were barely detectable in NT kidney and their expression was limited to the small and medium renal artery walls (Figure 3A), while we observed a significant increase (p < 0.005) of LAMA4 in ccRCC tumor tissue, with a distribution similar to the peritubular capillary network (Figure 3B). On the contrary, we detected a significant decrease of TCF21 (Figure 3D and E) and mostly KCNJ1 expression (Figure 3G and H) in RCC tissue when compared with normal kidney tissue (p < 0.005 and p < 0.0001, respectively). Moreover, the number of infiltrating PTP4A3⁺ cells was higher in the tumor compared with normal counterpart, although the difference did not reach statistical significance (data not shown).

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Figure 3. In situ analysis for LAMA4⁺, TCF21⁺ and KCNJ1⁺ using confocal laser scanning microscopy.

RCC tumor tissues (T) were characterized by a significant increase of LAMA4 expression (B) and decrease of TCF21 (E) and KCNJ1 (H) expression as compared to non-tumoral (NT) kidney portion (A, D, G). Nuclei are highlighted with TO-PRO in blue, Quantification of specific protein expression was obtained as described in the Methods section, (C, F, I). Results are expressed as mean ± S.D. For each group, all images (magnification 63X) are from a single patient and are representative of the whole group of patients.

https://doi.org/10.1371/journal.pone.0078452.g003

Differential alternative splicing in LIMK2, DAB2, PTPRF and FDXR transcripts

To validate our exon-level approach, we selected four alternative splicing events among the more statistically significant events resulting from the microarray data analysis. Technical issues related to the primer design for qRT-PCR were also considered in the selection process. In particular, we focused our attention on four cassette exons (CEs) that showed higher inclusion ratio in tumor tissues in order to evaluate their usage as tumor tissue biomarkers.

Only one of the four CEs analyzed resulted differentially included in ccRCC samples. This alternative splicing event occurs in DAB2 transcripts and gives rise to two known alternative spliced forms, p96 and p67, the latter lacking the CE was evaluated in our analysis. DAB2 gene encodes an adaptor molecule involved in multiple receptor-mediated signaling pathways that plays a pivotal role in cellular homeostasis. Moreover, DAB2 plays an important regulatory role in cellular differentiation and acts as a tumor suppressor, at least in part, by stabilizing the beta-catenin degradation complex, thus negatively regulating the Wnt signaling pathway [33]. DAB2 splice forms are expressed in a tissue-specific pattern suggesting that they possess non-overlapping cellular activities. In particular, in renal tissue, p96 is equally or more abundant than p67 [34]. Our analysis confirm this data as we measured a percentage of about 50% of inclusion of the CE in normal kidney tissues (Figure 4A). Interestingly, we observed a differential inclusion of the CE in NT and tumoral tissues. Indeed, as shown in Figure 4A, in ccRCC samples there was a significant increase (p-value = 4.48E-06) of the ratio of the isoforms (i.e., p96 splice variant) including the cassette exon analyzed, and only 15% of mature DAB2 transcripts resulted from the skipping of this exon. This scenario is clearly shown also in Figure 4B, and considering the single condition, it is evident that while no differences in the expression level of the three exons involved in the splicing event were detected for the tumor tissues, in their normal counterparts the skipped exon was two-fold less expressed than its adjacent exons (p-value = 3.25E-05 for the upstream exon and p-value = 4.2E-05 for the downstream exon).

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Figure 4. qRT-PCR analysis of differentially alternative splicing exons identified in DAB2 gene.

Left panel (A) shows the percentage of inclusion of the skipped exon in tumoral (T) and non-tumoral (NT) tissues, calculated as the average of the expression ratio of skipped exon relative to both upstream exon and the downstream one. Right panel (B) shows the relative expression ratio (mean ± SE) of each exon of the triplet involved in the splicing event both in NT and T samples. Expression levels were calculated relative to the mean expression levels of ACTB and RPL13 genes. * = p-value < 0.001.

https://doi.org/10.1371/journal.pone.0078452.g004

A decreased expression of DAB2 has been demonstrated in several cancers including ovarian, breast, prostate, oesophagus, urinary bladder, colon and choriocarcinoma [33]. On the contrary, neither exon array nor qRT-PCR data supported a down-regulation at gene level for DAB2 in ccRCC.

We were unable to validate a differential expression for the other three cassette exons (in LIMK2, PTPRF and FDXR genes). Even if a clear exon skipping event was detected in PTPRF and FDXR transcripts, in none of the three cases did our qRT-PCR experiments show any significant variation in the inclusion ratio of the CEs (Figure S3).