Tumor Biol. DOI 10.1007/s13277-016-4927-z

ORIGINAL ARTICLE

Long-noncoding RNAs in basal cell carcinoma Michael Sand 1,2 & Falk G. Bechara 1 & Daniel Sand 3 & Thilo Gambichler 1 & Stephan A. Hahn 4 & Michael Bromba 2 & Eggert Stockfleth 1 & Schapoor Hessam 1

Received: 30 December 2015 / Accepted: 28 January 2016 # International Society of Oncology and BioMarkers (ISOBM) 2016

Abstract Long noncoding RNAs (lncRNAs) are fundamental regulators of pre- and post-transcriptional gene regulation. Over 35,000 different lncRNAs have been described with some of them being involved in cancer formation. The present study was initiated to describe differentially expressed lncRNAs in basal cell carcinoma (BCC). Patients with BCC (n = 6) were included in this study. Punch biopsies were harvested from the tumor center and nonlesional epidermal skin (NLES, control, n = 6). Microarray-based lncRNA and mRNA expression profiles were identified through screening for 30,586 lncRNAs and 26,109 protein-coding transcripts (mRNAs). The microarray data were validated by RT-PCR in a second set of BCC versus control samples. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of mRNAs were performed to

Electronic supplementary material The online version of this article (doi:10.1007/s13277-016-4927-z) contains supplementary material, which is available to authorized users.

assess biologically relevant pathways. A total of 1851 lncRNAs were identified as being significantly up-regulated, whereas 2165 lncRNAs were identified as being significantly down-regulated compared to nonlesional skin (p < 0.05). Oncogenic and/or epidermis-specific lncRNAs, such as CASC15 or ANRIL, were among the differentially expressed sequences. GO analysis showed that the highest enriched GO targeted by up-regulated transcripts was Bextracellular matrix.^ KEGG pathway analysis showed the highest enrichment scores in BFocal adhesion.^ BCC showed a significantly altered lncRNA and mRNA expression profile. Dysregulation of previously described lncRNAs may play a role in the molecular pathogenesis of BCC and should be subject of further analysis. Keywords Long noncoding RNAs . Basal cell carcinoma . Noncoding RNAs . Epithelial skin cancer . Nonmelanoma skin cancer

Introduction * Michael Sand [email protected]

1

Dermatologic Surgery Unit, Department of Dermatology, Venereology and Allergology, Ruhr-University Bochum, 44791 Bochum, Germany

2

Department of Plastic Surgery, St. Josef Hospital, Catholic Clinics of the Ruhr Peninsula, 45257 Essen, Germany

3

University of Michigan Kellogg Eye Center, Ann Arbor, MI 48105, USA

4

Department of Internal Medicine, Knappschaftskrankenhaus University of Bochum, Zentrum für Klinische Forschung, Labor für Molekulare Gastroenterologische Onkologie, 44780 Bochum, Germany

It is widely recognized that almost 70–98 % of all transcriptional output in human cells consists of noncoding RNAs (ncRNAs), whereas only 1.9 % are actually transcribed into protein-coding mRNAs [1]. This expanding group of ncRNAs can be divided into two groups, small ncRNAs (sncRNAs) and long ncRNAs (lncRNAs). The most prominent members belonging to the group of sncRNAs are microRNAs (miRNAs), which represent pivotal regulators of protein expression on a post-transcriptional level and are 17–23 nt long, exerting both tumor suppressor and oncogenic effects that participate in basal cell carcinoma (BCC) formation, which is the most common form of skin cancer and human cancer in general [2–7]. Similar to miRNAs,

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expression can potentially result in cancer formation. This is very likely as there is growing evidence that lncRNAs are involved in malignancies and tumor development, in general [13, 18, 19]. Although most other forms of cancer, including cutaneous melanoma, have been successfully investigated regarding differential lncRNA expression, nonmelanoma skin cancers such as BCC, which affects keratinocytes, have not yet been systematically evaluated [10]. This study was performed to describe differentially expressed lncRNAs in BCC tissue.

lncRNAs are defined as nonprotein-coding RNAs with tissue-specific expression and have been shown to be decisive regulators by controlling gene expression on the preand post-transcriptional level [8]. In contrast to the wellstudied group of sncRNAs, lncRNAs range in length from >200 nt up to 100 kb [9]. A vast number of studies have demonstrated their pivotal role in controlling tissue development and their strong impact on fundamental central cellular processes, such as epigenetic modifications of chromatin, gene regulation, nuclear import, and X chromosome inactivation, in both normal and disease tissue [10]. The initial presumption that ncRNA sequences are unnecessary transcriptional noise or accumulated transcriptional waste originating from evolutionary processes that were genetically carried from one evolutionary state to the next has been shown to be vastly untrue. LncRNAs have currently emerged as the largest group of molecules transcribed by human translational machinery. The number and diversity of lncRNAs massively exceeds the approximately 19,000 protein-coding human genes, and the characterization of their intracellular role is constantly being updated [11]. The tremendous biological diversity of lncRNAs hints at their various levels of regulation, which includes epigenetic, transcriptional, and posttranscriptional gene regulation, as well as regulation of sncRNAs, such as miRNAs [12]. One important characteristic is their ability to form ribonucleoprotein complexes that directly regulate gene expression in the nucleus. As previously mentioned, lncRNAs have a broad range of effects and have been shown to control cell cycle, proliferation, differentiation, and apoptosis; more functions are still being discovered [13]. The maintenance of stem cells (keratinocyte progenitor) and terminal differentiation of keratinocytes in the epidermis have been shown to be affected by a defined set of lncRNA genes [14–17]. Keratinocyte homeostasis depends on lncRNAs as they play key roles in differentiation. At the same time, disturbance of keratinocyte lncRNA Table 1 Characteristics of the BCC and control specimens

Materials and methods The procedures performed in this study were in accordance with the ethical standards of the institutional research committee and with the 1964 Declaration of Helsinki and its later amendments and comparable ethical standards. Informed consent was obtained from all individuals who were included in the study. Samples A total of 12 samples (6 BCC and 6 controls) from 12 individuals (5 women, 7 men, mean age 71.1 years) were enrolled in this study (Table 1). All individuals were of Caucasian origin and had no skin disease other than BCC. There is no statistical difference in age between BCC and control (p > 0.05). While excising BCCs with cold steel under local anesthesia, 4-mm punch biopsies were taken from the center of the tumor and from nonlesional epithelial skin (control). These samples were immediately placed in RNAlater (Qiagen, Hilden, Germany) and stored at −80 °C. Total RNA was extracted with TRIzol (Life Technologies, Carlsbad, USA) according to the manufacturer’s protocol. RNA quantity and quality were measured with a NanoDrop

Sample ID

Sex

Age

Localization

Histology

Invasion depth (mm)

BCC_1 BCC_2 BCC_3 BCC_4

W W M W

72 70 80 78

Face Face Pectoral Face

Nodular BCC Nodular BCC Nodular BCC Nodular BCC

2.8 2.5 2.2 1.2

BCC_5 BCC_6 Control_1 Control_2 Control_3 Control_4 Control_5 Control_6

W M M W M M M M

54 60 78 62 46 77 94 82

Capillitium Face Face Face Face Face Capillitium Capillitium

Nodular BCC Nodular BCC Non-lesional skin Non-lesional skin Non-lesional skin Non-lesional skin Non-lesional skin Non-lesional skin

5.9 2.8 n.a. n.a. n.a. n.a. n.a. n.a.

RP11-758 M4.4

AC073135.3 AK095285 RP11-8 L2.1 AC022311.1

RP11-697 M17.1 AC073135.3

LINC00340 FER1 L6-AS2 RP5-1121H13.3

KC6 RP11-350F16.1 XLOC_004478 RP1-27K12.4 RP1-27K12.4

XLOC_004478 AC008175.10 XLOC_004478 XLOC_004478 RP11-26 L20.3

XLOC_004478 RP11-789C1.1 CTD-2049J23.2

XLOC_010713 LINC00230B

ENST00000422555 uc003 hoz.1 ENST00000510016 ENST00000432314

ENST00000522183 ENST00000419104

NR_015410 ENST00000520031 ENST00000412348

NR_002838 ENST00000524012 TCONS_00010750 ENST00000508884 ENST00000508884

TCONS_00010748 ENST00000400578 TCONS_00010751 TCONS_00010749 ENST00000558730

TCONS_00010747 ENST00000504509 ENST00000497452

TCONS_00022102 uc004fto.3

H19 uc.110

ENST00000442037 uc.110-

AX746826 RP11-519 M16.1

RP6-24A23.7 LOC727982 XLOC_012686

ENST00000564206 NR_034134 TCONS_00026624

uc003wnu.1 ENST00000500092

AC073135.3 AC073135.3

ENST00000411596 ENST00000453982

ENST00000518128

Gene symbol

Comparing BCC versus control

Seq name

Table 2

1726 605 1247 547 547 1636 392 1272 1704 763 657 469 2425 689 483

−68.02 −55.85 −55.23 −54.12 −49.65 −44.45 −39.28 −38.81 −36.91 −34.68

1904 1746 417

833 559

567 2838 468 627

3949 1963

2810

798 243

4996 2674 290

312 852

RNA length

+45.61 −98.75 −78.92 −73.79 −73.79

+48.44 +46.66 +46.66

+53.04 +50.91

+62.99 +60.39 +56 +54.86

+70.77 +65.37

+71.73

+72.73 +72.43

+116.93 +115.95 +87.97

+183.72 +144.88

Fold change

chr13 chrY

chr5 chr4 chr3

chr5 chrY chr5 chr5 chr16

chr20 chr8 chr5 chr6 chr6

chr6 chr8 chr8

chr8 chr3

chr3 chr4 chr4 chr2

chr7 chr4

chr8

chr11 chr2

chrX chr2 chr18

chr3 chr3

Chrom

Intergenic Intergenic

Intergenic Intergenic Natural antisense

Intergenic Intergenic Intergenic Intergenic Intronic antisense

Intergenic Intergenic Intergenic Natural antisense Natural antisense

Intergenic Natural antisense Intergenic

Intergenic Intergenic

Intergenic Intergenic Intergenic Intergenic

Intron sense-overlapping Intergenic

Intronic antisense

Intergenic Intergenic

Exon sense-overlapping Intergenic Intronic antisense

Intergenic

Relationship

IL12A

IRX6

GCLC isoform a GCLC isoform b

FER1 L6

PTPRN2

PI15

AC006305.1

RP6-24A23.6

Associated_gene_name

LincRNAs identified by Cabili et al. UCSC_knowngene

LincRNAs identified by Cabili et al. GENCODE GENCODE

LincRNAs identified by Cabili et al. GENCODE LincRNAs identified by Cabili et al. LincRNAs identified by Cabili et al. GENCODE

RefSeq GENCODE LincRNAs identified by Cabili et al. GENCODE GENCODE

RefSeq GENCODE GENCODE

GENCODE GENCODE

GENCODE UCSC_knowngene GENCODE GENCODE

UCSC_knowngene GENCODE

GENCODE

GENCODE UCR

GENCODE RefSeq LincRNAs identified by Cabili et al.

GENCODE pseudogene

Source

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Characteristics of the top 20 differentially expressed lncRNAs (fold change ≥2 and p < 0.05) in BCC sorted by fold change (see Supplementary Material for the full list of 1851 up- and 2165 down-regulated lncRNAs)

RefSeq RefSeq UTY isoform 7 UTY isoform 8 Exon sense-overlapping Exon sense-overlapping 6025 6025 UTY UTY NR_047614 NR_047614

−32.09 −32.09

chrY chrY

RefSeq RefSeq RefSeq RefSeq GPC5 UTY isoform 4 UTY isoform 5 UTY isoform 6 Intronic antisense Exon sense-overlapping Exon sense-overlapping Exon sense-overlapping chr13 chrY chrY chrY 503 6025 6025 6025 −32.32 −32.09 −32.09 −32.09 GPC5-AS1 UTY UTY UTY NR_049776 NR_047614 NR_047614 NR_047614

RNA length Fold change Gene symbol Seq name

Table 2 (continued)

Chrom

Relationship

Associated_gene_name

Source

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ND-1000 spectrophotometer, and RNA integrity was assessed by standard denaturing agarose gel electrophoresis. Two groups of samples, BCC_1, BCC_2, and BCC_3 and Control_1, Control_2, and Control_3, were used for microarray scanning of lncRNA and mRNA expression. The samples BCC_4, BCC_5, and BCC_6 and Control_4, Control_5, and Control_6 were used for qRT-PCR validation as a second independent group of samples. Microarrays Microarrays were performed with Arraystar Human LncRNA Microarray V3.0 (Rockville, Maryland, USA), which is designed for global profiling of human lncRNAs and protein-coding transcripts (mRNAs). The following target sequences were represented in the array: 30,586 lncRNAs, 26,109 coding transcripts (mRNAs), 709 lncRNAs containing an open reading frame and sharing the same start codon with protein-coding transcripts, and 577 transcribed pseudogenes. The following lncRNA sources were represented in the array: 3991 lncRNAs from Reference Sequence (RefSeq; http://www.ncbi.nlm.nih.gov/ refseq/), 12,694 lncRNAs from the UCSC Known Genes dataset (Known Genes 6; http://genome.ucsc.edu/cgi-bin/ hgTables), 20,596 lncRNAs from the GENCODE database of annotations for all human protein-coding and noncoding genes, 1492 lncRNAs from the RNAdb database (http:// research.imb.uq.edu.au/rnadb/), 1112 lncRNAs from the Noncoding RNA Expression Database (NRED; http://jsmresearch.imb.uq.edu.au/nred/cgi-bin/ncrnadb.pl), 287 lncRNAs from the lncRNAdb (http://lncrnadb.com/), 2592 lincRNAs described by Khalil et al., 14,353 transcripts expressed from 4662 stringently defined human lincRNA genes described by Cabili et al., 475 ultraconserved regions (UCRs) described by Bejerano et al. (http://users. soe.ucsc.edu/~jill/ultra.html), 407 transcribed regions within the four HOX loci in humans identified by Rinn et al., and 3019 human lncRNAs with enhancer-like functions described by Orom et al. [16, 20–28]. RNA labeling and array hybridization Sample labeling and array hybridization were performed according to the Agilent One-Color Microarray-Based Gene Expression Analysis protocol (Agilent Technology, Santa Clara, USA) as previously described [29]. After removal of rRNA (mRNA-ONLY™ Eukaryotic mRNA Isolation Kit, Epicentre), mRNA was purified from total RNA. A mixture of oligo(dT) and random priming method (Arraystar Flash RNA Labeling Kit, Arraystar, Rockville, USA) was used to amplify each of the samples and transcribe them into fluorescent cRNA along the entire length of the transcripts. Labeled cRNAs were then purified by the RNeasy Mini Kit (Qiagen,

+47.95 +46.82 +46.33 +41.81 +41.15

LGR5 PCDH11X FBN3

GLI1 PCDH11Y NTRK3

PCDH11X LRP2 PRAMEF20 PRAME

FRG2C

COL11A1 CABP1 PCDH11X PDGFA

PDLIM3 HSD3B1

ALOX15B

ALOX15B FADS2 GAL OLIG3

AWAT1 PIP SEC14 L6

ELOVL3 PSAPL1 THRSP FAR2

NM_003667 ENST00000395337 NM_032447

NM_001160045 ENST00000333703 NM_001007156

ENST00000361724 NM_004525 ENST00000316412 NM_206956

NM_001124759

ENST00000353414 NM_004276 NM_001168360 NM_033023

NM_014476 ENST00000235547

NM_001039131

NM_001039130 NM_004265 NM_015973 ENST00000367734

NM_001013579 NM_002652 NM_001193336

NM_152310 NM_001085382 NM_003251 ENST00000182377

2853 1680 2508 2643 3149 778 2196 1411 591 1261 1347 4700 1182 2095

−191.23 −126.47 −116.11 −103.92 −75.84 −75.71 −56.45 −52.36 −46.49 −46.05 −45.43 −43.12

7174 1021 8365 2740

2069

4603 15735 1597 2197

3414 4710 4141

2880 4767 8967

1263 2079 8278 845

RNA length

+31.78 −210.81

+39.27 +39.17 +32,36 +31

+53.59 +53.02 +50.81

+59.45 +59.05 +56.39

+130.34 +119.18 +93.72 +63.28

FOXI3 CHGA PCDH11X MZB1

NM_001135649 NM_001275 NM_001168362 NM_016459

Fold change

Gene symbol

Seq Name

chr10 chr4 chr11 chr12

chrX chr7 chr22

chr17 chr11 chr11 chr6

chr17

chr4 chr1

chr1 chr12 chrX chr7

chr3

chrX chr2 chr1 chr22

chr12 chrY chr15

chr12 chrX chr19

chr2 chr14 chrX chr5

Chrom

83401 768239 7069 55711

158833 5304 730005

247 9415 51083 167826

247

27295 3283

1301 9478 27328 5154

100288801

27328 4036 645425 23532

2735 83259 4916

8549 27328 84467

344167 1113 27328 51237

Entrez ID

Elongation of very long chain fatty acids protein 3 Proactivator polypeptide-like 1 preproprotein Thyroid hormone-inducible hepatic protein Fatty acyl CoA reductase 2 [Source: HGNC Symbol;Acc:25531]

Arachidonate 15-lipoxygenase B isoform a Fatty acid desaturase 2 Galanin peptides preproprotein Oligodendrocyte transcription factor 3 [Source:HGNC Symbol;Acc:18003] Acyl-CoA wax alcohol acyltransferase 1 Prolactin-inducible protein precursor Putative SEC14-like protein 6

PDZ and LIM domain protein 3 isoform a Hydroxy-delta-5-steroid dehydrogenase, 3 beta- and steroid delta-isomerase 1 [Source: HGNC Symbol;Acc:5217] Arachidonate 15-lipoxygenase B isoform b

Collagen, type XI, alpha 1 [Source:HGNC Symbol;Acc:2186] Calcium-binding protein 1 isoform 2 Protocadherin-11 X-linked isoform e precursor Platelet-derived growth factor subunit A isoform 2 preproprotein

Protein FRG2-like-2

Protocadherin 11 X-linked [Source:HGNC Symbol;Acc:8656] Low-density lipoprotein receptor-related protein 2 precursor PRAME family member 20 [Source:HGNC Symbol;Acc:25224] Melanoma antigen preferentially expressed in tumors

Xinc finger protein GLI1 isoform 2 Protocadherin 11 Y-linked [Source:HGNC Symbol;Acc:15813] NT-3 growth factor receptor isoform c precursor

Leucine-rich repeat-containing G-protein coupled receptor 5 precursor Protocadherin 11 X-linked [Source:HGNC Symbol;Acc:8656] Fibrillin-3 precursor

Forkhead box protein I3 Chromogranin-A preproprotein Protocadherin-11 X-linked isoform g precursor Plasma cell-induced resident endoplasmic reticulum protein precursor

Product

Table 3 Comparing BCC vs. control. Characteristics of the top 20 differentially expressed mRNAs (fold change ≥2 and p < 0.05) in BCC sorted by fold change (see Supplementary Material for the full list of 1477 up- and 1567 down-regulated mRNAs)

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Long-chain-fatty-acid-CoA ligase ACSBG1 isoform 1 40S ribosomal protein S4, Y isoform 1 23205 6192 3010 910 ACSBG1 RPS4Y1 NM_015162 NM_001008

−30.88 −27.59

chr15 chrY

Phosphodiesterase 6A, cGMP-specific, rod, alpha [Source:HGNC Symbol;Acc:8785] 5145 5642 PDE6A ENST00000255266

−31.56

chr5

Arylacetamide deacetylase-like 3 [Source: HGNC Symbol;Acc:32037] Oleoyl-ACP hydrolase [Source:HGNC Symbol;Acc:25625] Caspase recruitment domain-containing protein 18 Arylacetamide deacetylase-like 3 isoform 1 126767 55301 59082 126767 chr1 chr10 chr11 chr1 3832 1675 670 4049 −37.26 −36.57 −35.59 −34.48 AADACL3 OLAH CARD18 AADACL3 ENST00000332530 ENST00000378228 NM_021571 NM_001103170

RNA length Fold change Gene symbol Seq Name

Table 3 (continued)

Chrom

Entrez ID

Product

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Hilden, Germany) according to the manufacturer’s protocol. A NanoDrop ND-1000 spectrophotometer was used to measure the concentration and specific activity of the labeled cRNAs (pmol Cy3/μg cRNA). Then, 5 μl of 10× blocking agent and 1 μl of 25× fragmentation buffer were added to 1 μg of each labeled cRNA, and the mixture was heated at 60 °C for 30 min. Next, 25 μl of 2× GE hybridization buffer was added to dilute the labeled cRNA. Finally, 50 μl of the hybridization solution was dispensed into the gasket slide and assembled onto the lncRNA expression microarray slide, which was incubated for 17 h at 65 °C in an Agilent Hybridization Oven (SureHyb Microarray Hybridization Chamber, Agilent Technologies, Santa Clara, USA). The hybridization arrays were washed, fixed, and scanned with an Agilent DNA Microarray Scanner G2505C (Agilent Technologies, Santa Clara, USA). Quantitative real-time reverse transcription polymerase chain reaction To validate the microarray data, the expression levels of the three up-regulated lncRNA genes, ENST00000510016, ENST00000560097, and ENST00000560054; the three down-regulated lncRNA genes, ENST00000504509, ENST00000523831, and ENST00000558730; and the reference gene, β actin, were determined in a second set of BCC (BCC_4, BCC_5, BCC_6) and control (Control_4, Control_5, Control_6) samples. Briefly, cDNA was synthesized; a standard curve was prepared, and gene expression was determined by real-time reverse transcription polymerase chain reaction (RT-PCR). The following components were mixed together for first-strand cDNA synthesis: 1 μl of 10 μM oligo(dT)18 primer, 0.3 μl of 10 μM random (N9) primer, 1.0 μg of total RNA, 1 μl of 10 mM dNTP Mix (Invitrogen, Waltham, USA), and sterile distilled water, for a total volume of 13 μl. The mixture was heated to 65 °C for 5 min, followed by incubation on ice for at least 1 min. The contents of the tube were mixed by pipetting gently up and down after adding the following components: 4 μl of 5× first-strand buffer (Invitrogen, Waltham, USA), 1 μl of 0.1 M DL-Dithiothreitol (DTT; Invitrogen, Waltham, USA), 1 μl of RNase Inhibitor (Enzymatics, Beverly, USA), and 1 μl of SuperScript III RT (Invitrogen, Waltham, USA). After incubation at 50 °C for 60 min, the reaction was inactivated by heating at 70 °C for 15 min (ViiA 7 Real-Time PCR System, Applied Biosystems, Carlsbad, USA). A total of 200 μl of ddH2O was added to every 20 μl of cDNA synthesis reaction. The finished first-strand cDNA synthesis reaction was maintained on ice until standard curve preparation. Then, 5 μl of the Arraystar PCR Master Mix (Arraystar Inc., Rockville, USA), 1 μl of 10 μM PCR

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forward primer, 1 μl of 10 μM PCR reverse primer, 1 μl of diluted first strand cDNA, and 2 μl of ddH2O were mixed together for a total volume of 10 μl. The following primer pairs were used: ENST00000510016 (F 5′GTCAGCACCGCAGCAAATC-3′; R 5′-CATCACTCA CCGCTGTTTTCA-3′), ENST00000560097 (F 5′GAGTGACCCGATTTTCCAGC-3′; R 5′-GGTCTCTCTGACCTTCTCCTTG-3′), E N S T 0 0 0 0 0 5 6 0 0 5 4 ( F 5 ′ - G C AT TA G C G T T T T T TATTGGAG-3′; R: 5′-AATGCCCTTTTAGGCTTGAT-3′), ENST00000504509 (F 5′-GA ACAAGACCAGGA GGAGGTTT-3′; R 5′-GCCAATCATCGCTGTGAGGTA3′), ENST00000523831 (F 5′-GTGTAGAGAGTGGT GTGGGGA-3′; R 5′-GCTTGGCTCATAGGTCAGGAT-3′), and ENST00000558730 (F 5′-ACACCCAACATCC ACACCCTx-3′; R 5′-TGCAGATCCTCATCCCGTAA-3′). The 384-well PCR array plates were loaded, and RTPCR was run in relative quantification mode with triplicate measurements. The following temperature profiles were used: polymerase activation/denaturation at 95 °C for 10 min, followed by 40 amplification cycles at 95 °C for 10 s and 60 °C for 1 min. To quantify the PCR products, a standard curve was constructed using serial tenfold dilutions (from 1 to 10−6). Finally, RTPCR was prepared by mixing 5 μl of Arraystar PCR Master Mix, 0.6 μl of 10 μM PCR forward primer, 0.6 μl of 10 μM PCR reverse primer, 2 μl of diluted first-strand cDNA synthesis reaction, and 1.8 μl of ddH2O to a total volume of 10 μl. The following temperature profiles were used: polymerase activation/

denaturation 95 °C for 10 min, followed by 40 amplification cycles at 95 °C for 10 s and 60 °C for 1 min. The gene concentration of each sample was generated by ABI7900 Analysis Software SDS2.3 (Applied Biosystems, Carlsbad, USA). Bioinformatics data analysis The data from this study were deposited in the Gene Expression Omnibus (GEO) of the National Center for Biotechnology Information and are accessible through GEO Series accession number GSE74858 (http://www.ncbi.nlm. nih.gov/geo/query/acc.cgi?acc=GSE74858). Array images were analyzed with the Agilent Feature Extraction software (version 11.0.1.1). The GeneSpring GX v12.0 software package (Agilent Technologies, Santa Clara, USA) was used for quantile normalization and subsequent data processing. Quantile normalization was applied to the raw data. In detail, the lncRNAs and mRNAs that had at least three out of six samples flagged in Marginal or Present (BAll Targets Value^) were chosen for data analysis. Volcano plot filtering between two groups (BCC vs. control) was used to identify differentially expressed lncRNAs and mRNAs with statistical significance. R software (version 2.15, http://www.r-project.org/) was used for hierarchical clustering to arrange samples into groups based on their expression levels. Pathway analysis and Gene Ontology (GO) analysis were performed with the standard enrichment computation method.

Fig. 1 Scatterplots of lncRNA (a) and mRNA (b) showing normalized signal values (log2 scaled) for BCC versus control. All points above and below the green fold change lines indicate a greater-than-2.0-fold change of lncRNA/mRNA between BCC versus control

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Fig. 2 Volcano plots showing differentially expressed lncRNAs (a) and mRNAs (b); red squares represent differentially expressed lncRNAs. The two vertical lines represent the filtering criteria of FC ≥ 2

GO and KEGG pathway analysis GO analysis, which is a functional analysis associating differentially expressed mRNAs with GO categories derived from GO (http://www.geneontology.org), was performed as previously described [30]. GO composed of three structured networks of defined terms (cellular component (cc), biological process (bp), and molecular function (mf)) that describe gene product attributes has enabled a broader understanding of the microarray data [31]. This was made possible by grouping genes of interest into cc, bp, and mf categories. The significance of GO term enrichment in differentially expressed mRNAs was assessed by calculating enrichment scores with Fisher’s exact test, comparing a portion of the gene list in the BCC group to the control group, and p value and false discovery rates (FDR) using the method described by Benjamini and Hochberg [32]. Furthermore, a pathway analysis for differentially expressed mRNAs was performed based on the latest Kyoto Encyclopedia of Genes and Genomes (KEGG, http://www. genome.jp/kegg) database to determine biological pathways for the significant enrichment of differentially expressed mRNAs with a possible impact on BCC tumor formation. Statistical analysis For statistical analysis of lncRNA and mRNA microarray expression data, an unpaired t test was performed after

adjusting the original p values by applying the algorithm devised by Benjamini and Hochberg using MedCalc software version 15.2 (MedCalc, Mariakerke, Belgium) [32]. To identify differential lncRNA and mRNA expression between the BCC and control samples, fold changes (FC) were calculated and filtered. Statistical significance and robust detection were evaluated to identify differentially expressed lncRNA and mRNA in BCC. Statistical significance was defined as differential expression by a p ≤ 0.05 with an FC ≥ 2.0 and selected as differentially repressed by a p ≤ 0.05 with an FC ≥ −2.0. Using Microsoft Excel’s data sort and filter functionalities (Microsoft, Redmond, USA), analysis outputs were filtered and differentially expressed with lncRNAs and mRNAs ranked by FC and p value (p ≤ 0.05). Microarray data were visualized by the Bheatmap.2^ function in the Bgplots^ package that was used for heat map generation. Cluster analysis with Euclidean distance as a measure was performed as previously described [33, 34]. Correlation analysis was performed by calculating Pearson’s correlation coefficient r for each sample within the two groups and for all pairwise comparisons. Fig. 3 Cluster analysis of lncRNA (a) and mRNA (b) expression shows a„ strong similarity within the two groups (BCC and non-lesional skin) with clustering. The color scale reflects the signal intensity converted to log2 and ranges from blue (low intensity) to white (moderate intensity) to red (strong intensity). The dendrogram (left) reflects the hierarchical similarity

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Results Quality control RNA quantity and quality were measured with a NanoDrop ND-1000 spectrophotometer, and RNA integrity was assessed by standard denaturing agarose gel electrophoresis after RNA extraction and prior to sample labeling. The inclusion criteria for RNA were an O.D. A260/280 ratio between 1.8 and 2.1 and an O.D. A260/230 ratio >1.8. RNA integrity was successfully tested by denaturing agarose gel electrophoresis. The 28S and 18S ribosomal RNA bands were sharp and intense. The intensity of the upper band, which was approximately twice that of the lower band, and the absence of a high-molecular-weight smear or band above the 28S ribosomal RNA indicated that there was no DNA contamination. RT-PCR showed that the three upregulated lncRNAs were up-regulated and that the three down-regulated lncRNAs were down-regulated, validating microarray data in a second independent set of BCC versus control samples. The following ratios for gene expression were calculated (BCC/control) for the up-regulated genes, ENST00000510016 (2.35), ENST00000560097 (276.07), and ENST00000560054 (4.08), and the down-regulated genes, ENST00000504509 (0.03), ENST00000523831 (0.32), and ENST00000558730 (0.01). Identification of differentially expressed lncRNAs and mRNAs in BCC A total of 1851 lncRNAs were significantly up-regulated, and 2165 lncRNAs were significantly down-regulated with the criteria of FC ≥ 2 and p < 0.05 (Table 2). The most up-regulated lncRNA was AC073135.3 (FC 183.72), and the most down-regulated was RP11350F16.1 (FC 98.75). Using the same criteria, FC ≥ 2 and p < 0.05, 1477 mRNAs were up-regulated and 1567 mRNAs down-regulated (Table 3). The most up-regulated mRNA was forkhead box protein I3 (FOXI3, FC 130.34), whereas the most down-regulated mRNA was hydroxydelta-5-steroid dehydrogenase, 3 beta- and steroid deltaisomerase 1 (HSD3B1, FC −210.81). Scatterplot analysis showed variations in the lncRNA and mRNA expression profiles in the BCC samples compared with the control samples (Fig. 1a, b). To visualize the relationship between the fold changes and the statistical significance of differentially expressed lncRNAs and mRNAs, volcano plots were constructed (Fig. 2a, b). Furthermore, the lncRNA and mRNA expression patterns across samples were distinguishable in the heat map generated by hierarchical clustering (Fig. 3a, b). Correlation analysis of lncRNA and mRNA expression showed high correlations within the two groups of BCC and control

samples (Pearson’s correlation coefficient r > 0.9). The results from the correlation analyses are summarized in Fig. 4a, b. GO and KEGG pathway analysis To determine the potential role of differentially expressed mRNAs in BCC, GO categories of cc, bp, and mf were analyzed as previously described [35, 36]. Enriched GO terms Bextracellular matrix^ (cc), Banatomical structure morphogenesis^ (bp), and Bmetal ion binding^ (mf) were the most significantly up-regulated mRNAs (p ≤ 0.05; FDR ≤ 0.05) (Fig. 5). The analysis of the down-regulated mRNAs showed that the Bmembrane region^ (cc), Blipid metabolic process^ (bp), and Boxidoreductase activity^ (mf) were the most significant (p ≤ 0.05; FDR ≤ 0.05). The KEGG pathway analysis showed that BFocal adhesion^ BExtracellular matrix (ECM) receptor interaction,^ and the cell cycle-regulating BPI3K-Akt signaling pathway^ showed the highest enrichment scores in the group of up-regulated mRNAs; specifically, BPeroxisome,^ BFatty acid metabolism,^ and BBiosynthesis of unsaturated fatty metabolism^ showed the highest enrichment scores in the groups of down-regulated mRNAs (p ≤ 0.05; FDR ≤ 0.05).

Discussion The role of lncRNAs in cancer has recently been addressed in numerous studies in a variety of malignant tissues. In addition, differential expression functional studies have shown the enormous potential of lncRNAbased therapeutic mechanisms. Xing et al. showed that lncRNA participates in the direct coordination of protein recruitment and the indirect regulation of transcription factors, which can be targeted by lncRNA silencing [37]. In a mouse model, they showed that knockdowns of BCAR4 lncRNA significantly reduced breast cancer metastasis. Although BCC is the most frequently occurring tumor in humans being semi-malignant, slow growing, and locally infiltrating, there has been no global investigation of differential lncRNA expression in BCC [38, 39]. The present study showed that a variety of lncRNAs were differentially expressed in BCC and, similar to miRNAs, may play an important role in BCC pathology. The gene AC073135.3 encoding the two most upregulated lncRNAs, ENST00000411596 and ENST00000453982, has been found to be up-regulated in lung adenocarcinoma and glioma cells [40]. The gene RP6-24A23.7 splicing lncRNA sequence ENST00000564206 was predicted to be associated with lung neoplasm, glioma, colorectal neoplasm, melanoma, pancreatic neoplasm, neuroblastoma, and breast neoplasm based on a model of LncRNA Functional Similarity

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Fig. 4 Heat map of correlation coefficient r analysis (BCC vs. control) for lncRNA (a) and mRNA expression (b). The color scale on the left reflects the correlation of samples and runs from white (low correlation) to

blue (medium correlation) to red (high correlation) (BCC basal cell carcinoma)

Calculations based on the information of MiRNA (LFSCM), which calculates functional similarities of

lncRNA and disease-specific lncRNA-miRNA interactions [41]. Interestingly, lncRNAs, similar to circular

Fig. 5 Differentially expressed mRNAs functionally classified by GO analysis (5A-5C; filtering criteria p ≤ 0.05 with a FC ≥ 2.0) and KEGG pathway analysis (5D). Up- or down-regulated mRNAs in BCC were

analyzed in three GO categories: a biological process (bp), b cellular component (cc), and c molecular function (mf)

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RNAs, influence gene expression by acting as miRNA sponges [10]. They can contain multiple miRNA-binding sites capable of binding target miRNAs by complimentary base pairing, which regulates mRNA expression. ENST00000442037 is spliced from the gene H19, which has been shown to play an important role in gastric cancer through its mature product hsa-miR-675, which targets the tumor suppressor Runt domain transcription factor1 (RUNX1) [42, 43]. For future studies, it would be interesting to determine if a similar lncRNA-miRNA-tumor suppressor gene axis can be identified for BCC. ENST00000500092 spliced from RP11-519 M16.1 was predicted to target hsamiR-19b-1, a member of oncogenic oncomiR-1 (miRNa-1792), and hsa-miR-106a, which shares seed region identity with hsa-miR-106b [44, 45]. In a previous study, we showed that hsa-miR-19b-1 and hsa-miR-106b are both significantly upregulated in BCC [4]. ENST00000522183 spliced from RP11-697 M17 uses hsamiR-130a and hsa-miR-145-3p as target sequences. hsa-miR130a was up-regulated, and hsa-miR-145-3p down-regulated, in a previous BCC miRNA expression profiling study [4]. AC073135.3 was up-regulated in lung adenocarcinoma [40]. LINC00340 (cancer susceptibility candidate 15, CASC15) is associated with neuroblastoma and is increased during melanoma progression [46]. It has further been shown to be an independent predictor of disease recurrence in melanoma patients with stage III lymph node metastasis and plays a key role in switching between proliferative and invasive states in melanoma cells [47]. KC6 splicing NR_002838 was described in keratoconus, which is a degenerative proliferative disorder of the eye characterized by conical-shaped cornea epithelium [48]. Hombach et al. reviewed lncRNAs involved in epidermal homeostasis and disease [10]. When comparing previously described relevant skin-associated lncRNAs with our data set, several important similarities were found. The SPRY4 intronic transcript 1 (SPRY4-IT1) was up-regulated in our study and is up-regulated in several different tumors, including melanoma, gastric cancer, and breast and prostate cancer [10]. Antisense noncoding RNA in the INK4 locus (ANRIL), which is also known as CDKN2B antisense RNA 1, is associated with a variety of malignancies, such as melanoma, breast tumors, small cell lung cancer, hepatocellular carcinoma, and cervical cancer [49, 50]. In our data set, CDKN2B was significantly up-regulated; however, ANRIL was not differentially expressed (p < 0.05). Tissue differentiationinducing nonprotein-coding RNA (TINCR or PLAC2) was up-regulated in differentiating keratinocytes, together with Staufen1 building a protein-lncRNA complex that regulates genes required for terminal differentiation and maintaining high levels of pivotal epidermal differentiation genes, such as fillagrin and loricrin [51–53]. In our study, it was upregulated in BCC; however, it did not reach statistical

significance (p > 0.05). Differentiation antagonizing nonprotein coding RNA (DANCR or ANCR) suppressed epidermal differentiation in progenitors and was slightly down-regulated in BCC (p > 0.05) [14]. Recently, both TINCR and ANCR were shown to regulate the v-maf avian musculoaponeurotic fibrosarcoma oncogene homolog (MAF, also known as c-maf) and the v-maf avian musculoaponeurotic fibrosarcoma oncogene homolog B (MAFB), which were shown to bind and control the expression of known epidermal differentiation transcription factor genes, such as GRHL3, ZNF750, KLF4, and PRDM1 [15]. The lncRNAs TINCR and ANCR regulate MAF/MAFB-induced epidermal transcription factors representing an lncRNA transcription factor network that is essential for epidermal differentiation and is possibly involved in epithelial skin cancer, such as BCC. The lncRNA psoriasis associated nonprotein coding RNA induced by stress (PRINS) regulates interferon-induced protein 6-16 (G1P3), exhibits anti-apoptopic effects in keratinocytes, and is down-regulated in psoriasis [54]. PRINS was downregulated in BCC, but this change did not reach statistical significance (p > 0.05). The HOXA distal transcript antisense RNA (HOTTIP) important for transcriptional activation can activate HoxA genes by recruiting histone-modifying enzymes in an intracellular phenomenon described as locus control [55]. In vitamin D receptor (VDR) deleted mouse keratinocytes, which are more prone to UV-induced skin cancer development, the oncogenic lncRNA HOTTIP was significantly increased [56]. HOTTIP was also increased in BCC, but this increase did not reach statistical significance (p > 0.05).

Conclusions In conclusion, the results of this study showed the first evidence for lncRNA and mRNA differential expression in BCC compared to NLES (control). Differential expression of lncRNAs in BCC was noticeable and therefore suggests a potentially important role for lncRNAs in BCC formation. The differentially expressed lncRNAs described in this work represent a foundation for future functional studies which could potentially lead to lncRNA-based therapy of advanced or metastatic BCC. Compliance with ethical standards Conflicts of interest None Financial disclosure All authors hereby disclose any commercial associations that may pose or create a conflict of interest with the information presented in this manuscript. The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this paper. Daniel Sand was supported by the Heed Ophthalmic Foundation. Ethics This study conformed to local requirements following ethical and investigational committee review, informed consent, and other

Tumor Biol. statutes or regulations regarding the protection of the rights and welfare of human subjects participating in medical research (Ethical Review Board of Ruhr-University Bochum, Germany).

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