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Integrative Genome-Wide Analysis of LongNoncoding RNAs in Diverse Immune Cell Types ofMelanoma PatientsLei Wang1,2, Sara J. Felts3, Virginia P. Van Keulen3, Adam D. Scheid3, Matthew S. Block4,Svetomir N. Markovic4, Larry R. Pease3, and Yuji Zhang1,2

Abstract

Genome-wide identification and characterization of longnoncoding RNAs (lncRNA) in individual immune cell lineageshelps us better understand the driving mechanisms behindmelanoma and advance personalized patient treatment. Toelucidate the transcriptional landscape in diverse immune celltypes of peripheral blood cells (PBC) in stage IV melanoma, weusedwhole transcriptomeRNA sequencing toprofile lncRNAs inCD4þ, CD8þ, and CD14þ PBC from 132 patient samples. Ourintegrative computational approach identified 27,625 expressedlncRNAs, 2,744ofwhichwere novel. Both T cells (i.e., CD4þ andCD8þ PBC) and monocytes (i.e., CD14þ PBC) exhibited differ-ential transcriptional expression profiles between patients withmelanomaandhealthy subjects.Cis-and trans-level coexpressionanalysis suggested that lncRNAsarepotentially involved inmanyimportant immune-related pathways and the programmed celldeath receptor 1 checkpoint pathways. We also identifiednine gene coexpression modules significantly associated with

melanoma status, all of which were significantly enriched forthree mRNA translation processes. Age and melanoma traitsclosely correlated with each other, implying that melanomacontains age-associated immune changes. Our computationalprediction analysis suggests that many cis- and trans-regulatorylncRNAs could interact with multiple transcriptional and post-transcriptional regulatory elements inCD4þ, CD8þ, andCD14þ

PBC, respectively. These results provide novel insights into theregulatory mechanisms involving lncRNAs in individualimmune cell types in melanoma and can help expedite celltype-specific immunotherapy treatments for such diseases.

Significance: These findings elucidate melanoma-associatedchanges to the noncoding transcriptional landscape of distinctimmune cell classes, thus providing cell type-specific guidanceto targeted immunotherapy regimens.CancerRes;78(15);4411–23.�2018 AACR.

IntroductionMelanoma is responsible for the majority of skin cancer–

related death with limited effective treatment available for stageIV patients (1). Despite recent treatment successes of immuno-therapy and targeted agents, the majority of patients developresistance to treatment for reasons that are still largely unknown.To better understand the underlying the disease mechanism,instead of traditional approaches focusing on antibody andcytokine production or the expression of selected cell-associatedmolecules,more comprehensive approaches to investigate the fullspectrum of the transcriptome of individual cell types in periph-eral blood cells (PBC) are desired (2).

Long noncoding RNAs (lncRNA) are highly heterogeneousmolecules with functional versatility due to their diverse struc-tures and interactions with other molecules (3). Recently,deregulation of lncRNAs has been reported to be greatlyinvolved in various human diseases such as cancer (4–6) andheart disease (7, 8). LncRNAs are also widely expressed inimmune cells serving as important regulators of gene expres-sion throughout the immune system (9). However, the contri-bution of lncRNA-related biological activities in variousimmune compartments in response to disease treatment stillremains little known. Integrative analysis of lncRNA expressionprofiling in diverse immune cell types (e.g., CD4þ, CD8þ, andCD14þ cells) will greatly expedite the discovery of lncRNAregulatory mechanisms and shed light on new treatmentoptions for patients with melanoma.

Previous melanoma studies mostly focused on melanomalocated in the skin tissue and the peripheral blood (10), whereasfew have investigated genome-wide transcriptome profiling ofindividual immune cell types in PBCs of patients with melanoma(11). Furthermore, many studies only focused on protein-codinggenes (12, 13), overlooking the vast landscapeof noncoding genessuch as lncRNAs.Hence, an in-depth investigationof lncRNA rolesin individual immune cell types of PBCs will provide a muchricher understanding of molecular regulatory mechanisms inpatients with melanoma.

In this study, we developed an integrative computationalfunctional genomic approach to interrogate the lncRNA expres-sion profiles and their potential regulatory roles in CD4þ, CD8þ,

1Division of Biostatistics and Bioinformatics, University of Maryland Marlene andStewart Greenebaum Comprehensive Cancer Center, Baltimore, Maryland.2Department of Epidemiology and Public Health, University of Maryland Schoolof Medicine, Baltimore, Maryland. 3Department of Immunology, Mayo ClinicCollege of Medicine and Science, Rochester, Minnesota. 4Department of Oncol-ogy, Mayo Clinic College of Medicine and Science, Rochester, Minnesota.

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

Corresponding Authors: Yuji Zhang, University of Maryland School of Medicine,660WRedwoodSt, HH 109C, Baltimore, MD21201. Phone 410-706-8523; E-mail:[email protected]; and Larry R. Pease, [email protected]

doi: 10.1158/0008-5472.CAN-18-0529

�2018 American Association for Cancer Research.

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and CD14þ PBCs of patients with stage IV melanoma. Our studypresented amost comprehensive landscape of lncRNAprofiling inCD4þ, CD8þ, and CD14þ PBCs of patients with melanoma,which holds great potential to provide clues for future celltype–specific immunotherapy treatment.

Materials and MethodsSample collection and isolation of various immune cell types

Participant recruitment and blood sample collection wereconducted using protocols approved by the Mayo Clinic Institu-tional Review Board (IRB#12-002580 and IRB#13-002293).Twenty-three normal healthy subjects were selected from a South-easternMinnesota community cohort of adults self-identifying ashaving no autoimmunity, allergy, immunodeficiency, immuno-suppressive treatments, or cancer. Eleven patientswith a diagnosisof stage IVmelanoma to receive immune therapywere selected.Ofthese donors, two normal healthy subjects and 10 patients withmelanoma commonly contain CD4þ, CD8þ, and CD14þ PBCsamples (Supplementary Table S1).

Isolation of CD4þ, CD8þ, and CD14þ PBCsBlood was drawn into sodium heparin tubes (APP Pharmaceu-

ticals, NDC 63323-540-11). CD4þ, CD8þ, and CD14þ cells wereisolated from whole blood using anti-CD4/CD8/CD14 microbe-ads (130-090-877/130-090-878/130-090-879; Miltenyi Biotec)and automatics purification as per themanufacturer's instructions.A small portion of the cell isolate was set aside for flow cytometryusinganti-CD3APC-H7, anti-CD4PE-Cy7, anti-CD11b-APC, anti-CD8 PE, anti CD14-FITC (Miltenyi Biotec) to assess the purity. Allantibodies were purchased from BD Pharmingen unless otherwiseindicated. Isolated cellswere resuspended at 1million/mL inRPMIcontaining 10% FBS and either immediately lysed in 0.7 mLQIAzol lysis reagent (Qiagen) or incubated for 4 hours at 37�Cwith 25 mL/mL anti-CD3/anti-CD28 Human T-Activator Dyna-beads (111.32D; Invitrogen) for CD4 and CD8 cells or 1 mg/mLpI:C (528906; Calbiochem), LPS (L439; Sigma) CPG ODN2006;Invivogen), and PGN (Sigma; 77140) for CD14 cells. Lysates werestored at �80�C until processing for RNAseq.

RNA and cDNA library preparationRNA was prepared using the Qiagen miRNeasy Kit according to

themanufacturer's directions. RNAsampleswith integrity values of�6.0 by Agilent Bioanalyzer were processed into TruSeq librariesaccording to the manufacturer's instructions (RNA Prep Kit v2;Illumina) by the Mayo Clinic Medical Genome Facility GeneExpression Core. Paired-end DNA adaptors (Illumina) with asingle "T" base overhang at the 30end were immediately ligatedto the "A-tailed" cDNA population. Unique indexes, included inthe standard TruSeq Kits (12-Set A and 12-Set B), were incorpo-rated at the adaptor ligation step for multiplex sample loading onthe flow cells. Libraries (8–10 pmol/L) were loaded onto paired-end flow cells to generate cluster densities of 700,000/mm2 fol-lowing Illumina's standard protocol for cBot and cBot Paired-EndCluster Kit version 3. The flow cells were sequenced as 51 � 2paired-end reads on an Illumina HiSeq 2000 using TruSeq SBSSequencingKit version3andHCSv2.0.12data collection software.Base-calling was performed using RTA version 1.17.21.3.

RNA-seq data analysisRaw RNA-seq reads generated from each RNA-seq library were

assessed for duplication rate and gene coverage using FastQC. The

reads were aligned to the human reference genome (GRCh38)with the GENCODE annotation (gencode.v23.annotation.gtf) byTopHat (Version 2.0.12). The transcriptomes were then recon-structed by the Cufflinks tool (Version 2.2.1).

Identification of known and novel lncRNAsWe compared our reconstructed transcriptome with the GEN-

CODEannotationV23using theCuffcompare script. Basedon thecomparison with the GENCODE annotation, we kept the tran-scripts in the "u" category (i.e., unknown intergenic transcripts),which were considered as candidates of novel long intergenicnoncoding RNAs (lincRNA). Based on the comparison with theGENCODE lncRNA annotation, the transcripts in the "¼" cate-gorywere kept as known lncRNAs, andother transcripts in thenon"¼" category (i.e., the "i," "j," "o," "p," and "x" categories) wereconsidered as candidate novel lncRNAs. These candidate novellncRNAs were then filtered based on (i) the lncRNA features (i.e.,sequence length larger than 200 nt and exon number greater than2), and (ii) the coding potential using the Coding PotentialCalculator algorithm (14), the Pfam database (15), and theCording Potential Assessing Tool (16). The putative novellncRNAs were then obtained (Fig. 1A).

Identification of expressed lncRNAs, uniquely expressedlncRNAs, and differentially expressed lncRNAs

For each PBC type, one lncRNA was considered expressed if itsmean fragments per kilobase of transcript per million mappedreads (FPKM) value was greater than 0.1 in either healthy subjectgroup or stage IV melanoma patient group; one lncRNA wasconsidered uniquely expressed if it expressed only in patientswith stage IV melanoma or normal healthy subjects; one lncRNAwas considered differentially expressed if its expression level hadgreater than a 1.5-fold change and an false discovery rate (FDR)less than 0.1 between two groups using the Cuffdiff script.

MiTranscriptome databaseDifferentially expressed lncRNAs of melanoma in the skin

tissues were downloaded from the MiTranscriptome database(17). We used the liftover tool (http://genome.ucsc.edu/cgi-bin/hgLiftOver) to convert the gene coordinates of these lncRNAsfrom the GRCh37 to the GRCh38 annotation.

Statistical analysisThe principle component analysis (PCA) was conducted using

the prcomp function in the R software. Hierarchical clusteringwasperformed using the pvclust R package (18) with the multiscalebootstrap resampling (i.e., 1,000 time), and P values were com-puted for each of the clusters (e.g., AU, approximately unbiased;BP, bootstrap probability).

The permutation test was used to evaluate the statistical sig-nificance of the epigenetic marks associated with lncRNAs. The Pvalues of the permutation test were calculated by P value ¼ (E þ1)/(Rþ1),whereR is thenumber of permutations (equal to 1,000in our study), and E is the number of permutation test statistics(i.e., number of lncRNAsassociatedwith selected epigeneticmarks)that are greater than or equal to the observed test statistic (i.e.,number of identified lncRNAs associated with epigenetic marks).

Pathway and network enrichment analysisThe IngenuityPathwayAnalysis (IPA) tool (QIAGEN)wasused to

annotate the genes of interest, map and generate putative biologicalprocesses/functions,networks, andpathwaysbasedon themanually

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curated knowledge database of molecular interactions extractedfrom the public literature. The enriched pathways and interactionnetworks were generated using both direct and indirect knownrelationships/connectivity. These pathways and networks wereranked by the enrichment score, which measures the probabilitythat the genes of interest were included in a network by chance.

Weighted gene coexpression network construction and genemodule detection

The R package "WGCNA" was used to construct the weightedgene coexpression network (19). The transcripts with the meanFPKM value great than 0.1 in CD4þ, CD8þ, and CD14þ PBCs ofpatients with stage IV melanoma or normal healthy subjects were

Figure 1.

Identification and characterization of lncRNAs in CD4þ, CD8þ, and CD14þ PBCs. A, The computational bioinformatics workflow to assemble the transcriptomeand identify lncRNAs. B, The overlap of the expressed lncRNAs between patients with stage IV melanoma and healthy donors in CD4þ, CD8þ, and CD14þ PBCs,respectively.C, The expression and distribution of lncRNAs in CD4þ, CD8þ, and CD14þPBCs [putative novel lncRNAs, red; annotated lncRNAs, green; the FPKMvalue(FPKM < 50) for histogram].

Characterization of Long Noncoding RNAs in Melanoma

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used for the analysis. After transforming the FPKM value bylog2(1þFPKM), weighted gene coexpression analysis was per-formed for CD4þ, CD8þ, and CD14þ PBCs subgroups, respec-tively. To minimize the noise and spurious associations, tran-scripts with similar expression pattern(s) were clustered using themodule detection function "blockwiseModules" with the para-meters networkType ¼ "signed" and TOMType ¼ "signed." Theexpression profile of a given module was represented by its firstprincipal component (i.e., module eigengene), which can explainthemost variation of themodule expression levels. Modules withhighly correlated module eigengenes (correlation > 0.75) weremerged together. The module membership (kME) of each genewas calculated by correlating the gene expression profile with themodule eigengene, representing the extent of a gene close to agiven module. The function "overlapTableUsingKME" was usedto assess whether two modules were preserved based on a hyper-geometric test of module kME.

Chromatin immunoprecipitation sequencing data analysisWe used the publicly available chromatin immunoprecipita-

tion sequencing (ChIP-seq) datasets (CD4, CD8, and CD14primary cells) from the Human Epigenome Atlas Release 9(http://www.epigenomeatlas.org) generated by the EpigenomicsRoadmap project (20). The histone mark peaks (i.e., H3K4me3and H3K36me3) were detected using the MACS2 (version 2.1.1;ref. 21) with default parameters (FDR < 0.05). The liftover toolwas used to convert the genome coordinates of the significantpeaks from the GRCh37 to the GRCh38 annotation. We thenidentified the lncRNAs withH3K36me3 or H3K4me3mark peaksin the region of �3 kb transcription start sites of lncRNAs. Wedownloaded the super-enhancer datasets of the CD8 and CD14PBCs from the dbSUPER database and identified the super-enhancer-associated lncRNAs as the ones whose transcriptionstart sites were assigned to super-enhancers within a 50 kbwindow.

Transcription factor binding analysisThe human transcription factor (TF) binding motifs were

retrieved from the ENCODE database (http://compbio.mit.edu/encode-motifs/). Using the FIMO tool (22), we searched TFbinding motifs (P < 0.0001) within the regulatory regions oflncRNAs (i.e., 1 kb upstream and 1 kb downstream from thetranscription start sites of lncRNAs).

Prediction of lncRNA interactionsWe used the RNAplex software (23) to identify potential

lncRNA–lncRNA interactions based on the RNA duplex energyprediction. The RNAplex parameter was set as -e-20.

To investigate potential interactions between miRNAs andlncRNAs, we downloaded all human miRNAs in the miRBasev21 and predicted potential target lncRNAs for eachmiRNA usingthe miRanda software (24) with default parameters.

To identify potential protein–RNAs interactions, we usedthe catRAPID algorithm (25) to predict potential protein–lncRNA interactions based on the information of protein andRNA domains involved in the macromolecular recognition.The protein–lncRNA interactions with the highly ranking dis-tribution (i.e., the stringent parameters: the star rating > 2,RNA-binding motifs, and domains) were considered as signi-ficant hits.

Data availabilityThe sequencing data set has been deposited in the NCBI Gene

Expression Omnibus (GEO) database (GEO accession numberGSE104744).

ResultsSystematic identification and characterization of lncRNAs indiverse immune cell types

To obtain a comprehensive lncRNA catalog of individualimmune cell types, we developed an integrative transcriptomeassembly pipeline designed to identify expressed lncRNAs inour dataset (Fig. 1A). In total, we reconstructed 345,417 tran-scripts from 65,247 loci across all 132 samples (SupplementaryTable S1) using our transcriptome assembly pipeline. Of thesetranscripts, 24,881 were known lncRNAs (SupplementaryTable S2), and 2,744 were putative novel lncRNAs (Supple-mentary Table S3; https://sites.google.com/view/yujizhanglab/data-scripts?authuser¼0). Meanwhile, 19,476 protein-codinggenes (98.7%) in the GENCODE annotation (V23) were alsorecovered in our assembled transcriptome. The high degree ofoverlap provides an independent measure of the comprehen-sive coverage in our dataset.

We observed that patients with stage IVmelanoma and healthysubjects shared thousands of expressed lncRNAs in CD4þ, CD8þ,and CD14þ PBCs respectively (Fig. 1B), that is, the percentage oflncRNAs expressed in both groups is more than 86%. TheselncRNAs were widely distributed across all the chromosomes inthe human genome (Fig. 1C). We further investigated the differ-ential expression profiling between patients with stage IV mela-noma and healthy subjects. In CD4þ PBC, we identified 781differentially expressed genes (including 98 lncRNA genes) and445 differentially expressed transcripts (including 10 lncRNA). InCD8þ PBC, we identified 789 differentially expressed genes(including 88 lncRNA genes) and 292 differentially expressedtranscripts (including 10 lncRNA). In CD14þ PBC, we identified186 differentially expressed genes (including 22 lncRNA genes)and 32 differentially expressed transcripts (including twolncRNAs).

The cell type–specific expression patterns of lncRNAsTo investigate the cell-specific expression profilings of lncRNAs

in CD4þ, CD8þ, and CD14þ PBCs, we compared their expressionprofiles between patients with melanoma and healthy subjects.Overall, lncRNAs were expressed at relatively lower levels incomparison with mRNAs in all three immune cell types. Approx-imately 75% lncRNAs showed low expression level (mean FPKMvalue < 1) in both patients with stage IV melanoma (Fig. 2A) andhealthy subjects (Fig. 2B). We also observed that hundreds oflncRNAs were uniquely expressed in CD4þ, CD8þ, or CD14þ

PBCs of patients with melanoma (Fig. 2C) and healthy subjects(Fig. 2D), respectively. In addition, there weremany differentiallyexpressed lncRNAs between CD4þ, CD8þ, and CD14þ PBCs (Fig.2E and F), particularly between the T-cell subsets (CD4þ or CD8þ

PBCs) and monocytes (CD14þ PBC).To evaluate the cell type- and tissue-specific expression patterns

of lncRNAs, we explored the expression of some known lncRNAsassociated with melanoma in our dataset. First, we examined theexpression profiles of lncRNAs differentially expressed in the skintissue ofmelanoma. Among 426 differentially expressed lncRNAsin the skin tissue of melanoma cataloged in the MiTranscriptome

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database, 63 were also expressed in our dataset. However, theywere not differentially expressed between patients with stage IVmelanoma and healthy subjects in CD4þ, CD8þ, or CD14þ PBCs(Supplementary Fig. S1A). Second,we examined the expression of

seven key immune-associated lncRNAs from one previous report(26) in our datasets (Supplementary Fig. S1B), all of whichshowed no significant expression differences between patientswith stage IV melanoma and healthy subjects in CD4þ, CD8þ, or

Figure 2.

Comparison of the lncRNAs between patients with stage IVmelanoma and healthy subjects in CD4þ, CD8þ, and CD14þ PBCs, including (i) the density distribution ofexpressed lncRNAs in patients with stage IV melanoma (A) and healthy donors (B) of CD4þ, CD8þ, and CD14þ PBCs. (ii) The number of expressed lncRNAsand their overlaps across CD4þ, CD8þ, and CD14þ PBCs in patients with stage IVmelanoma (C) and healthy donors (D). (iii) The differentially expressed lncRNAs bypairwise comparison between CD4þ, CD8þ, and CD14þ PBCs in patients with stage IV melanoma (E) and healthy donors (F).






CD14þ PBCs. Third, we examined the expression of eight keymelanoma-associated lncRNAs from the experimentally sup-ported Lnc2Cancer database (27) and one previous study (28)in our dataset (Supplementary Fig. S1C). Out of eight lncRNAs,MALAT1 and SPRY4-IT1 were expressed in patients with stage IVmelanoma (mean FPKM value > 0.1) and showed differentialexpression patterns between patientswith stage IVmelanoma andhealthy subjects in each of three cell types. Taken together,lncRNAs exhibited tight cell-/tissue-specific expression patternsbetween patients withmelanoma and healthy subjects in all threeimmune cell types.

lncRNAs cis-regulate their neighboring protein-codingtranscripts enriched in important immune-associatedpathways

To investigate potential cis-regulatory roles of lncRNAs onprotein-coding transcripts, we computed the pairwise expressioncorrelations between lncRNAs and their neighboring protein-coding transcripts (i.e., a neighboring protein-coding transcriptneeds to be within a 10 kb distance to a lncRNA). Overall, we

identified 205, 236, and 228 highly correlated lncRNA–mRNApairs (i.e., Spearman correlation coefficient |r| > 0.7, and P <1E�07) in CD4þ, CD8þ, and CD14þ PBCs, respectively (Fig. 3A;Supplementary Table S4). Most of these lncRNAs are knownlncRNAs, of which 7, 12, and 12 are novel lncRNAs in CD4þ,CD8þ, and CD14þ PBCs, respectively. Specifically, 192 lncRNA–mRNA pairs (94%) in CD4þ PBC, 223 lncRNA–mRNA pairs(95%) in CD8þ PBC, and 222 lncRNA–mRNA pairs (97%) inCD14þ PBC contained immune-associated genes based on theimmunologic signatures in the MSigDB database (29).

The functional enrichment analysis by IPA revealed that thelncRNA–mRNA pairs in CD4þ, CD8þ, and CD14þ PBCs weresignificantly enriched in immune-related pathways (Fig. 3B;Supplementary Table S4). In CD4þ PBC, three top upstream reg-ulators including TCF7 (P ¼1.40E�04), IL27 (P ¼1.93E�04), andEBI3 (P ¼ 2.60E�04) were associated with the immune responsepathways. In CD8þ PBC, the top upstream regulator TGFB1(P ¼1.57E�04) belongs to the wnt/b-catenin signaling pathway[�log(P) ¼ 2.07], which plays a critical role in T-cell immunity. In

Figure 3.

LncRNAs potentially cis-regulate the protein-coding transcripts associated with the immune biological processes. A, The heatmap of lncRNAs (i.e., meanFPKM value) from the significant lncRNA–mRNA pairs (i.e., Spearman correlation coefficient |r| >0.7, P <1E�07 in at least one PBC) in CD4þ, CD8þ, and CD14þ PBCs.B, IPA functional enrichment analysis of protein-coding transcripts from the lncRNA-mRNA pairs in CD4þ, CD8þ, and CD14þ PBCs. C, The number of positively andnegatively correlated lncRNAs from the lncRNA–mRNA pairs in CD4þ, CD8þ, and CD14þ PBCs (N_cor, negatively correlated lncRNAs; P_cor, positively correlatedlncRNAs), respectively. D, The number of lncRNAs including the same and opposite transcriptional direction with the protein-coding transcripts in CD4þ, CD8þ, andCD14þ PBCs (O_dir, the opposite direction; S_dir, the same direction), respectively.

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CD14þ PBC, the top upstream regulator MAPK8 (P ¼ 1.43E�05)was associated with the immune response pathway.

We also investigated the relationship of the correlation and thetranscription directions between lncRNAs and their neighboringmRNAs. Over 95% of lncRNA–mRNA pairs were positively cor-related (Fig. 3C), consistent with the previous report on cis-actingnoncoding RNAs (30). Over 70% of lncRNA-mRNA pairs hadopposite transcriptional directions in CD4þ, CD8þ, or CD14þ

PBCs (Fig. 3D). These findings are consistent with previousstudies suggesting that lncRNAs have potential cis-regulatory roleson their neighboring protein-coding genes (31, 32).

lncRNAs trans-regulate protein-coding transcripts enriched inimmune and mRNA translation pathways

To investigate potential trans-regulatory roles of lncRNAs onprotein-coding transcripts, we performed a weighted gene coex-pression network analysis (WGCNA; ref. 19) to identify groups ofcoexpressed transcripts (i.e., modules). We adopted the coding-to-noncoding strategy to identify the lncRNA–mRNA coexpres-sion associations in a module (33): for a module of interest, weidentified statistically significant correlations between eachlncRNA and mRNAs that are important in specific known func-tional processes. The functions of each lncRNAwere then inferredby the "guilty-by-association" approach (34).

Overall, we identified 13, 17, and 17 coexpression modules inCD4þ, CD8þ, and CD14þ PBCs, respectively (Supplementary Fig.S2A). Notably, four modules (|correlation coefficient| > 0.7,P < 1E�6) in CD4þ PBC, four modules (|correlation coefficient|> 0.68, P <1E�6) in CD8þ PBC, and one module (correlation ¼0.74, P ¼10�8) in CD14þ PBC were statistically correlated withthe melanoma status (Fig. 4A–C). Interestingly, these moduleswere also significantly correlated with age (|correlationcoefficient| > 0.57, P < 2E�5; Supplementary Fig. S2A), whereasnone of them were associated with the patient status (M1b,metastasis to the lung; M1c, other metastasis), disease response[stable disease (SD), partial response (PR); and progressive dis-ease (PD); assessed at 3-months post-initiation of ipilimumabtherapy), or sex.

We also explored lncRNAs associated with important immuneprocesses in these nine coexpression modules (trans-regulatorylncRNAs). Specifically, we identified 361, 151, and 3 trans-regu-latory lncRNAs in CD4þ, CD8þ, and CD14þ PBCs significantlyassociated with immune responses pathways respectively [�log(P) > 2; Supplementary Tables S5–S7]. In addition, these ninecoexpression modules were also all enriched in three mRNAtranslation pathways, including EIF2 signaling, mTOR signaling,and regulation of eIF4 and p70S6K signaling pathway [�log(P) >2; Fig. 4A–C]. In total, we identified 228, 158, and 8 trans-regulatory lncRNAs associated with these three pathways inCD4þ, CD8þ, and CD14þ PBCs, respectively (SupplementaryTables S5–S7). Of these trans-regulatory lncRNAs, 184 (81%)and 134 (85%) trans-regulatory lncRNAs were in turquoise mod-ules of CD4þ and CD8þ PBCs (Supplementary Tables S5 and S6),suggesting that thesemodules containmajormolecular regulatorsin these pathways.

Among these nine coexpression modules correlated withthe melanoma status, the transcripts of six modules were signif-icantly overlapped between CD4þ and CD8þ PBCs (P < 1E�10),the transcripts of two modules were significantly overlappedbetween CD4þ and CD14þ PBCs (P < 1E�10), and no transcriptwas significantly overlapped between CD8þ and CD14þ PBCs

(Fig. 4D; Supplementary Fig. S2B). These results further confirmedthat the CD4þ and CD8þ T cells shared more similar expressionprofiles than the CD14þ monocyte.

The lncRNAs associated with programmed cell death receptor 1checkpoint pathways

Using the immune checkpoint blockers to block the interac-tions between programmed cell death receptor 1 (PD-1) and itsligands is a widely acknowledged approach for cancer immuno-therapy. To investigate the impact of PD-1 checkpoint in diverseimmune cell types, we explored PD-1 expression and relatedpathways, including PI3K/PTEN/Akt/mTOR signaling pathwayand the RAS/RAF/MEK/ERK signaling pathway (35).We observedthat the PD-1/PDCD1 gene was upregulated in T-cell subsets(CD4þ or CD8þ PBCs) but not monocytes (CD14þ PBC) ofpatients with melanoma compared with healthy subjects, dom-inantly expressed by the PDCD1-201 transcript (SupplementaryFig. S3A and S3B). We also identified 11 lncRNAs coexpressedwith the PDCD1-201 transcript inCD8PBC. In eight coexpressionmodules of T cells correlated with the melanoma status, 435 and167 trans-regulatory lncRNAs associated with PD-1 checkpointpathways in CD4 and CD8 PBCs, respectively (|correlationcoefficient| > 0.7, P < 1E�6; Supplementary Table S8). Intrigu-ingly, all these modules contain the mTOR signaling pathway(Supplementary Fig. S3C and S3D). The fact that 335 (77%) and133 (80%) trans-regulatory lncRNAs were in turquoise modulesof CD4þ and CD8þ PBCs suggested that the lncRNAs in thesemodules were potentially associated with PD-1 checkpointpathways.

The potential regulation of lncRNA expression at thetranscriptional level

To investigate potential regulatory mechanisms mediatinglncRNA expression at the transcriptional level, we explored theassociation between the lncRNA regions and various biologicalannotations including histone modification sites, super enhan-cers, TF binding sites, and single-nucleotide polymorphisms(SNP).

The histone H3 lysine 4 trimethylation (H3K4me3) is a hall-mark of actively transcribed promoters, and the histone H3 lysine36 trimethylation (H3K36me3) is enriched not only withinactively transcribed gene bodies but also active enhancers (36).To explore the potential epigenetic modification on lncRNAexpression, we classified lncRNAs into the H3K4me3 andH3K36me3 marked groups based on the chromatin status ofputative regulatory regions of lncRNAs in the ChIP-seq datasetsgenerated by the Epigenomics Roadmap project (20). By focusingon the biologically significant lncRNAs in our dataset (i.e.,immune-associated, translation-associated, and differentiallyexpressed lncRNAs) in CD4þ, CD8þ, and CD14þ PBCs, we foundmore lncRNAs marked with H3K4me3 than those marked withH3K36me3 (Fig. 5A and B). CD4þ PBC had the largest number oflncRNAs commonly marked with H3K4me3 and H3K36me3(Fig. 5C). Our permutation test suggested that our lncRNAs wereindeed enriched in epigenetic marks (P < 0.001 based on 10,000permutations; Supplementary Fig. S4 and Table S9). The fact thatmany lncRNAs were marked with the epigenetic modifications(e.g., H3K4me3 and H3K36me3), indicated that these lncRNAswere transcribed in CD4þ, CD8þ, and CD14þ PBCs.

Super-enhancers are large clusters of transcriptional enhancersthat regulate expression of genes associated with specific diseases






Figure 4.

The WGCNA in CD4þ, CD8þ, and CD14þ PBCs. A, In CD4þ PBC, left of the panel represents the coexpression modules highly correlated with melanoma (HD,normal healthy subjects; Mel, patients with stage IV melanoma). Numbers of each square represent the correlation of module and the melanoma trait (correlation >0.7, P < 10�6); color of each square corresponds to correlation: positive correlation (red) and negative correlation (green). The top right of the panelshows the biological functions statistically enriched in these modules, in which the length of bars indicate the significance by IPA analyses [i.e., �log10(P)]. Themiddle right of the panel represents the heatmaps of the expression patterns of all transcripts in this module across all samples (red, increased expression;black, neutral expression; green, decreased expression), in which the barplots show the corresponding module eigengene expression value, the pie chartshows the ratio of mRNAs and lncRNAs in the module, and the numbers of mRNAs and lncRNAs in each module are shown next to the pie chart. Similar results areshown for CD8þ PBC (B) and CD14þ PBC (C), respectively. D, The remarkably conservative modules in these nine modules across CD4þ, CD8þ, and CD14þ

PBCs (P < 1E�10).

Wang et al.





and cell identity (37). Within the biologically significantlncRNAs, we identified 105 lncRNAs annotated by the super-enhancers dbSUPER database (38) in CD8þ and CD14þ PBCs(Fig. 5D), suggesting that these lncRNAs may play importanttranscriptional regulatory roles in immune cells of stage IVpatients with melanoma.

To investigate whether lncRNAs can be potentially regulated byTFs, we searched the regulatory regions of the biologically signif-icant lncRNAs (i.e., upstream 1 kb and downstream 1 kb to thestart sites of lncRNAs) in CD4þ, CD8þ, and CD14þ PBCs for

potential TF binding motifs based on the annotation of theENCODE database (Fig. 5E). We found that all except three ofthese lncRNAs can be potentially bound by more than 200 TFs(Fig. 5F). The fact that these lncRNAs have many TFs in CD4þ,CD8þ, and CD14þ PBCs suggests that they can be similarlyregulated by TFs as mRNAs do.

The genome-wide association studies have identified thou-sands of SNPs associated with cancer susceptibility, over 90% ofwhich are located in noncoding regions of the human referencegenome (39). Using the noncoding somaticmutation annotation

Figure 5.

The impact of transcriptional regulatory elements for the immune-associated, translation-associated, and differently expressed lncRNAs in CD4þ, CD8þ, andCD14þPBCs.A–D, The impact of the epigeneticmodifications.A, The number of lncRNAsmarkedwithH3K4me3.B, The number of lncRNAsmarkedwith H3K36me3.C, The number of lncRNAs marked with H3K4me3 and H3K36me3. D, The number of lncRNAs marked with the super-enhancer in CD8þ and CD14þ PBCs.E and F, The impact of the TFs. E, The number of lncRNAs including TFmotifs. F, Top 20 lncRNAswith the largest number of potential TF. G andH, The impact of theSNPs. G, The number of lncRNAs containing at least one cancer risk-associated SNP. H, Top 20 lncRNAs potentially contained the largest number ofcancer risk-associated SNPs.






(CosmicNCV, release v80) from the COSMIC database (40), weexplored whether the biologically significant lncRNAs containany cancer risk-associated SNPs in CD4þ, CD8þ, or CD14þ PBCs(Fig. 5G). In total, 743 lncRNAs (93%) contained at least onecancer risk-associated SNP except the translation-associatedlncRNAs in CD14þ PBC, 96% of which contain more than twocancer risk-associated SNPs and 14 contain more than 1,000cancer risk-associated SNPs (Fig. 5H). These results suggestedthat most of these lncRNAs are potentially involved in cancerrisk-associated diseases.

The potential regulation of lncRNA expression at theposttranscriptional level

To investigate potential posttranscriptional regulation throughmolecular interaction mechanisms, we explored the possibilitiesof RNA–RNA interactions, miRNA–lncRNA interactions, andprotein–lncRNA interactions involving our lncRNAs.

First, we investigated the lncRNA–lncRNA interactions for thebiologically significant lncRNAs in CD4þ, CD8þ, and CD14þ

PBCs using a RNA duplex energy prediction approach (23). Wefound that these lncRNAs can potentially interact with each other,except two differentially expressed lncRNAs in CD4þ PBCs, threein CD8þ PBCs, and one in CD14þ PBCs (Fig. 6A), each of whichhas more than 20 potentially interacting lncRNAs and over 97%

have more than 100 potentially interacting lncRNAs (Fig. 6B).Their potential interactions with each other suggest that somelncRNAs canpotentially regulate the expressions of other lncRNAsand mRNAs.

The miRNA–lncRNA interactions can regulate lncRNAs eitheras inhibitory decoys or as regulatory targets of miRNAs (41).We investigated potential miRNA–lncRNA interactionsbetween 2,588 annotated human microRNAs (miRBase v21)and our biologically significant lncRNAs in CD4þ, CD8þ, andCD14þ PBCs using the miRanda approach (24). We found thatall the immune-associated and translation-associated lncRNAshave potential miRNA binding sites, except two differentiallyexpressed lncRNAs in CD4þ PBCs, three in CD8þ PBCs, andone in CD14þ PBCs (Fig. 6C). Of these lncRNAs, each lncRNAhad potential interaction with more than 80 miRNAs, over 98%of which have potential interactions with more than 100miRNAs (Fig. 6D). These findings suggested that lncRNAexpression can be potentially regulated by many miRNAs atthe posttranscriptional level.

The protein–lncRNA interactions also play important roles inposttranscriptional regulation of the immune system (42). Weused the catRAPID algorithm (25) to investigate whether thereare any potential such interactions involving 16 most highlyexpressed lncRNAs (i.e., mean FPKM value >100 in patients

Figure 6.

The impact of posttranscriptional regulatory elements for the immune-associated, translation-associated, and differently expressed lncRNAs in CD4þ, CD8þ, andCD14þ PBCs. A and B, The lncRNA–lncRNA interactions. A, The interacted lncRNAs. B, Top 20 lncRNAs with multiple interacted lncRNAs. C and D, ThemiRNA–lncRNA interactions. C, miRNA-targeted lncRNAs. D, Top 20 lncRNAs binding multiple miRNAs.

Wang et al.





Figure 7.

The comprehensive analysis of lncRNAs across CD4þ, CD8þ, and CD14þ PBCs. A, Left of the panel, radar plots showing the number of lncRNAs that are mediatedby multiple regulatory elements. The center of the plot is 0, and a colored dot on the respective axis indicates the number of lncRNAs that are mediated by differentregulatory elements. Lines connecting the number of lncRNAs to the origin of the plot are added to improve visualization. B, Chow–Ruskey diagrams of immune-associatedcis-regulatory and trans-regulatory lncRNAs inCD4þ andCD8þPBCs (CD4C, cis-regulatory lncRNAs inCD4PBC;CD4T, trans-regulatory lncRNAs inCD4PBC;CD8C, cis-regulatory lncRNAs in CD8 PBC; CD8T, trans-regulatory lncRNAs in CD8 PBC). Color of the borders around each intersection corresponds to theoverlapping lncRNAs. The red circle in the middle represents the overlap of all immune-associated cis-regulatory and trans-regulatory lncRNAs. Lighter shades of red,orange, and yellow represent the overlap of fewer immune-associated cis-regulatory and trans-regulatory lncRNAs. Area of each intersection is proportional to numberof lncRNAs within the intersection. Top right of the panel, the overlapping of differentially expressed lncRNAs in CD4þ and CD8þ PBCs (CD4D, differentiallyexpressed lncRNAs in CD4 PBC; CD8D, differentially expressed lncRNAs in CD8 PBC). Bottom right of the panel, the overlapping of translation-associated lncRNAs inCD4þ and CD8þ PBCs (CD4TL, translation-associated lncRNAs in CD4 PBC; CD8TL, translation-associated lncRNAs in CD8 PBC). C, Sixteen most highly expressedlncRNAs interacted with multiple regulatory elements. Red box, lncRNA interacted with regulatory element.






with melanoma). The results showed that all 16 lncRNAs werepotentially bound by multiple RNA-binding proteins (Supple-mentary Table S10). Of these lncRNAs, TCONS_00050166,TCONS_00212088, and TCONS_00050136 could potentiallyinteract with 2204, 1802, and 414 RNA-binding proteins,respectively. These findings suggested that the protein–lncRNAinteractions could serve as one important regulatory role forthese lncRNAs.

DiscussionsManypatientswith stage IVmelanomadevelop resistance upon

their routine treatments such as immunotherapy and targetedtherapy. The underlyingmechanisms leading to resistance remainlargely unknown. During the last decade, lncRNAs have beenrecognized as one of the largest regulatory RNA classes encoded ineukaryotic genomes. Our premise is that genome-wide analysis oflncRNA expression profiling in individual immune cell types inPBCs will expedite the understanding of the immune cell type–specific transcriptional regulatory mechanism in patients withmelanoma, thus providing resources to investigate potentialbiomarkers or therapeutic targets for patients with melanoma ina noninvasive clinical setting in the future. As melanoma is oftencharacterized by immune infiltration disease, we exploredlncRNA expression patterns in CD4þ, CD8þ, and CD14þ PBCsof patients with stage IV melanoma. Overall, lncRNAs exhibitedwidespread expression patterns in the human genome and tightcell-type-specificity in CD4þ, CD8þ, and CD14þ PBCs. Ouranalysis suggested that lncRNAs can not only mediate the expres-sion of protein-coding transcripts in a cis- or trans-regulatorymanner in the immune system (26), but also be regulated bysimilar regulatorymechanisms asmRNAsdo. For instance, almostbiologically significant lncRNAs can be regulated by both tran-scriptional regulatory elements and posttranscriptional regulato-ry elements (e.g., miRNAs and lncRNAs; Fig. 7A). By comparingCD4þ and CD8þ PBCs, we identified 29 overlapping lncRNAsin T cells, including eight immune-associated lncRNAs, 20translation-associated lncRNAs, and one differentially expressedlncRNA (Fig. 7B). TCONS_00060410, TCONS_00227034, andTCONS_00325476 were identified as both the immune-asso-ciated and translation-associated lncRNAs. Sixteen most highlyexpressed lncRNAs in CD4þ, CD8þ, and CD14þ PBCs werepotentially regulated by multiple transcriptional elements andposttranscriptional elements (Fig. 7C). Through the functionalenrichment analysis, we found that our lncRNAs were associ-ated with several immune-related pathways in CD4þ, CD8þ,and CD14þ PBCs. Interestingly, most of the trans-regulatorylncRNAs involved in immune response pathways, PD-1 check-point pathways, and three mRNA translation processes weregrouped in turquoise modules of CD4þ and CD8þ PBCs.Further functional investigation of these lncRNAs will be great-ly appreciated to understand their biological roles in thesepathways.

T cells (i.e., CD4þ and CD8þ PBCs) and monocyte (i.e.,CD14þ PBC) exhibited differentially transcriptional expressionprofiles between patients with melanoma and healthy subjects(Supplementary Fig. S5 and S6). Specifically, the expressionprofiles of protein-coding transcripts in CD4þ and CD8þ T cellswere able to distinguish patients with stage IV melanoma andhealthy subjects. Furthermore, in nine coexpression modulessignificantly associated with the melanoma trait, CD4þ and

CD8þ T cells share more overlapping modules than CD14þ

monocytes.Age is closely related to melanoma status in diverse immune

cells. Using the coexpression network analysis, we found thatage and melanoma traits were both significantly correlated inthe nine coexpression modules in CD4þ, CD8þ, and CD14þ

PBCs. Patient age has been reported as an independent prog-nostic factor for melanoma (43). With the increasing age, theimmune system exhibits age-related changes (44), mainly dueto the dysregulation of T-cell function resulting in T-cell immu-nosenescence. In T cells (CD4þ and CD8þ PBCs), the mTORsignaling pathway was enriched all eight coexpression mod-ules. PD-1 is an immune-checkpoint receptor that mainlyexpresses in T cells negatively regulating human immuneresponse (45). Inhibition of mTOR signaling pathway canextend the life span in many different species (46) and delaythe onset of age-related diseases in mice (47). Because ofpotential age-related changes in immune cells (48), patientswith melanoma in different age groups may need more per-sonalized immunotherapeutic treatments.

There are some limitations in our study. First, CD4þ, CD8þ,and CD14þ PBCs primarily contain CD4 T cell, CD8 T cell, andCD14 monocyte, respectively. However, they are still heteroge-neous cell types that may have distinct transcriptional expressionprofiles. Second, our RNA-seq dataset only contains lncRNAswithploy-A tails, which account for a partial lncRNA transcriptome.Third, the prediction software in our analysis could introducesome computational errors. Although themulti-omics integrativeanalysis provides a wide understanding of the original cause ofdisease (genetic, environmental, or developmental; ref. 49), thelimited sample size and expensive experimental cost prevent suchmulti-omics experiments to be conducted in individual diseases.As the cost of single-cell sequencing technology becomes moreaffordable (50), we expect that an integrative analysis of diverseimmune cell types at single cell level will promote a much deeperunderstanding of the immune response mechanism and morepersonalized treatment to patients with melanoma.

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

Authors' ContributionsConception and design: L. Wang, S.J. Felts, V.P. Van Keulen, A.D. Scheid,L.R. Pease, Y. ZhangDevelopment of methodology: L. Wang, V.P. Van Keulen, Y. ZhangAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): V.P. Van Keulen, M.S. Block, S.N. Markovic,L.R. Pease, Y. ZhangAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis):L. Wang, V.P. Van Keulen, A.D. Scheid, L.R. Pease,Y. ZhangWriting, review, and/or revision of themanuscript: L.Wang, S.J. Felts, V.P. VanKeulen, L.R. Pease, Y. ZhangAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): V.P. Van Keulen, S.N. Markovic, Y. ZhangStudy supervision: Y. Zhang

The costs of publication of this articlewere defrayed in part by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received February 16, 2018; revised April 17, 2018; accepted June 5, 2018;published first June 12, 2018.


Wang et al.




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2018;78:4411-4423. Published OnlineFirst June 12, 2018.Cancer Res Lei Wang, Sara J. Felts, Virginia P. Van Keulen, et al. Diverse Immune Cell Types of Melanoma PatientsIntegrative Genome-Wide Analysis of Long Noncoding RNAs in

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