Integrative analysis of mRNA and lncRNA profiles identified pathogenetic lncRNAs in esophageal squamous cell carcinoma

Accepted Manuscript Integrative analysis of mRNA and lncRNA profiles identified pathogenetic lncRNAs in esophageal squamous cell carcinoma

Weiwei Wang, Chengguo Wei, Pan Li, Li Wang, Wencai Li, Kuisheng Chen, Jianying Zhang, Weijia Zhang, Guozhong Jiang PII: DOI: Reference:

S0378-1119(18)30310-X doi:10.1016/j.gene.2018.03.066 GENE 42688

To appear in:

Gene

Received date: Revised date: Accepted date:

25 January 2018 2 March 2018 20 March 2018

Please cite this article as: Weiwei Wang, Chengguo Wei, Pan Li, Li Wang, Wencai Li, Kuisheng Chen, Jianying Zhang, Weijia Zhang, Guozhong Jiang , Integrative analysis of mRNA and lncRNA profiles identified pathogenetic lncRNAs in esophageal squamous cell carcinoma. The address for the corresponding author was captured as affiliation for all authors. Please check if appropriate. Gene(2017), doi:10.1016/j.gene.2018.03.066

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ACCEPTED MANUSCRIPT Integrative analysis of mRNA and lncRNA profiles identified pathogenetic lncRNAs in esophageal squamous cell carcinoma Weiwei Wanga,b,c,#, Chengguo Weid,#, Pan Lia,b,c, Li Wanga,b,c, Wencai Lia,b,c, Kuisheng Chena,b,c, Jianying Zhange, Weijia Zhangd,*, Guozhong Jianga,b,c,* a. Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou

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450052, China

b. Department of Pathology, School of Basic Medicine, Zhengzhou University, Zhengzhou

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450002, China

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c. Henan Key Laboratory for Tumor Pathology, Zhengzhou 450052, China. d. Divisions of Nephrology, Department of Medicine, Icahn School of Medicine at Mount Sinai,

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New York, New York.

e. Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, Henan,

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China, 450052.

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# These authors contributed equally to this work. * Corresponding Authors: Dr. Guozhong Jiang, Department of Pathology, The First Affiliated

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Hospital of Zhengzhou University, No. 1, Jianshe East Road, Zhengzhou 450052, Henan Province, China, E-mail: [email protected], Tel: +86-0371-67966157; Dr. Weijia

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Zhang, Divisions of Nephrology, Department of Medicine, Icahn School of Medicine at Mount

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Sinai, 1468 Madison Ave, Annenberge 23-16, New York NY 10029, E-mail: [email protected], Tel: +1-212-241-2883 Abstract

Systems biology approaches can help understand pathogenesis of complex human diseases like cancers for identification of potential new therapeutic targets. Here in this study, we performed genome-wide screening for mRNA and lncRNA profiles in esophageal cancer to identify the novel cancer-related mRNA and lncRNA in esophageal squamous cell carcinoma (ESCC). We identified 1260 up-regulated/1445 down-regulated mRNAs and 402 up-regulated/741 downregulated lncRNAs. Further functional analysis revealed differentially expressed genes (DEGs) of

ACCEPTED MANUSCRIPT mRNA and lncRNA are related to different pathways. mRNA pathways are mainly involved in cell cycles while lncRNA pathways are for regulation and metabolic procession. Differentially expressed mRNAs/lncRNAs were validated with qPCR. At last, mRNA and lncRNA coexpression network were built and highly-connected networks were identified, which may provide a mechanism of mRNA expression regulation by lncRNA. In together, we used next generation sequencing data to explore the co-expression networks of lncRNA and mRNA, which

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may elucidate the functions and mechanisms of lncRNA in ESCC.

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Keywords genome-wide screening; esophageal squamous cell carcinoma; mRNA; lncRNA

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1. Introduction

Esophageal cancer (EC) is one of the most commonly diagnosed cancer type in esophagus

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and ranks as the sixth most common cause of death from cancer with an estimated 400,000 deaths (4.9% of the total worldwide)(Ferlay, Soerjomataram et al. 2015). EC comprises of two different

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histopathological forms: esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). The underlying molecular mechanism of these two forms is very distinct.

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As lack of specific symptoms and effective early diagnostic methods, esophageal cancer tends to be diagnosed at a late stage and its 5-year survival rate remains poor, ranging from 10 to 25 %

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(Su, Hu et al. 2011, Ohashi, Miyamoto et al. 2015). In China, ESCC is the predominant form of tumor arising from esophageal epithelial cells. Therefore, it is important to better understand the

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molecular mechanisms of ESCC tumorigenesis and screen biomarkers for the improvement of early diagnosis or treatment of ESCC in Chinese population. In past decades, expression profiling of coding genes and microRNAs have defined important signaling pathways involved in

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tumorigenesis (Su, Hu et al. 2011, Chen, Li et al. 2014, Song, Li et al. 2014). In recent 3 to 5 years, long non coding RNAs, like lncRNAs and circular RNAs have been gradually reported to

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drive many important cancer phenotypes by multiple ways(Beermann, Piccoli et al. 2016, Bhan, Soleimani et al. 2017, Han, Li et al. 2017, Yang, Gao et al. 2018). Thus, the latest knowledge of actively transcribed long noncoding RNAs from high-throughput sequencing reveals even greater complexity about cancer genome regulatory networks. Systems biology approaches can help understand pathogenesis and identify potential new targets for complex diseases such as ESCC. Many transcriptome analysis and files for ESCC(Ito, Shimada et al. 2008, Ma, Bao et al. 2012, Sawada, Niida et al. 2016) have been made available. Long noncoding RNAs (lncRNAs) have been reported to drive many important cancer phenotypes by multiple ways, including epigenetic modification, transcription regulation, RNA

ACCEPTED MANUSCRIPT decay, miRNA sponging and so on (Evans, Feng et al. 2016, Schmitt and Chang 2016). Several groups have identified ESCC-associated lncRNAs (Li, Wu et al. 2013, Li, Zheng et al. 2014, Huang, Chen et al. 2015, Wu, Hu et al. 2017). However the molecular mechanism of ESCC onset and development is still unclear despite of the progress from these reports. In this study, we performed genome-wide screening for mRNA and lncRNA profiles in 7 pairs of esophageal cancer and normal tissues to identify the novel cancer-related mRNA and

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lncRNA in ESCC. Then we build a co-expression network for all of these mRNA and lncRNA to

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build the relationship between mRNA and lncRNA. To our knowledge, this is the first study to

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explore the association networks for mRNA and lncRNA in ESCC and the central networks could be therapeutic targets for ESCC treatments.

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2. Materials and methods

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2.1. Study population and tissue samples

The research protocol was approved by the ethics committee of Zhengzhou University’s Institutional Ethical Review Boards, and all patients had signed informed consents before tissue

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collection. Primary tumors and adjacent non-neoplastic tissues were obtained from 7 patients with ESCC who underwent surgical treatment at the First Affiliated Hospital of Zhengzhou University

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(Henan, China) during August 2016. Tissues were frozen in liquid nitrogen immediately after surgical resection. None of the patients had prior chemotherapy or radiotherapy, nor did they have

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any other serious diseases. All tissues from ESCC patients were histopathologically diagnosed by at least two independent senior pathologists. The clinicopathological characteristics of the

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patients are summarized in Table 1.

2.2. RNA Isolation for RNA Sequencing

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Total RNA from tissues was extracted using the Trizol reagent (Ambion, Catalog No.15596026, USA) according to the manufacturer’s protocol. RNA concentrations were quantified using a Nano-drop Spectrophotometer at a wavelength of 260 nm. RNA samples were analyzed by Bioanalyzer at a concentration of 100–200 ng/μl to verify the concentration and the purity of samples. Only the samples with RNA integrity values of >7.0 were used for mRNA and lncRNA sequencing at the Genomic Core Facility at Novogene Co., Ltd (Beijing, China). 2.3. Bioinformatics Analysis of RNA Sequencing (RNA-seq) Data

ACCEPTED MANUSCRIPT The RNA-seq data were analyzed by following the procedure described below. Briefly, after sequence quality filtering at a cutoff of a minimum quality score Q20 in at least 90% bases, the good-quality reads aligned to Reads were processed and aligned to the University of California Santa Cruz (UCSC) human reference genome and transcriptome (build hg19) using the BurrowsWheeler Aligner (BWA) (Li and Durbin 2009). The reads that are uniquely aligned to the exon and splicing junction sites for each transcript were combined to calculate an expression level for a

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corresponding transcript and further normalized based on reads per kilobase per million reads in

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order to compare transcription levels among samples. The transcripts with a low raw read count <100 in all the samples were excluded for downstream analysis. Gene expression value was

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transformed to the log 2 base scale.

Principle Component Analysis (PCA) was firstly performed to assess the sample correlations

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using the expression data of all the genes. The differentially expressed genes for mRNA and lncRNA in tumor compared with normal were identified by the R package and we selected the

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genes based on limma test adjusted P<0.05 and 1.5-fold change. The gene ontology (GO) and pathway analysis for the differentially expressed genes were then performed with fold change

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cutoff of ≥1.5 using INGENUITY© IPA (www.ingenuity.com/products/ipa) and the online tool Enrichr. The read coverage of gene functional elements was also visualized by the Integrative

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Genome Viewer tool (www.broadinstitute.org/igv/) from the genome alignment file. Heatmap was used to visualize the top 50 differentially expressed genes using the limma test after the

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median center was transformed using Multi-Experiment Viewer software (Saeed, Sharov et al. 2003).

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2.4. Validation by Reverse Transcription-Polymerase Chain Reaction (RT-PCR) Total RNA was reverse-transcribed into complementary DNA (cDNA) using TIANScript II

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cDNA kit (Tiangen biotech, Beijing, China) according to the manufactory protocol. PCR analysis was performed on additional matched ESCC and adjacent non neoplastic tissues for selected mRNAs and lncRNAs. The housekeeping gene glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was used as internal control. Primers for RT-PCR were designed by using Primer-Blast (NCBI) and synthesized at Sangon Biotech Co., Ltd (Shanghai, China). The primer sequences for PCR used in this study were listed as follows: MMP1, forward 5'GATGAAGCAGCCCAGATGTG-3' and reverse 5'-GCTTGACCCTCAGAGACCTT3'; SOX11, forward 5'-CTGCAGATCAAACAGGAGCC-3' and reverse 5'TGAAGCTGTAGTAGAGGCGG-3'; LYPD2, forward 5'-CACCTGCACCACCAACGAAA-3' and

ACCEPTED MANUSCRIPT reverse 5'-CCACATCCGAGGGCTTACACTT-3'; MUC5B, forward 5'AGAGCGGGGACTACATCAAG-3' and reverse 5'-CGTTGTGGGCATAGAACTCG3'; LINC01518, forward 5'-CAGTGACGGAACAGTACCAG-3' and reverse 5'TCACAAACATCCCGCTCT-3'; LINC01614, forward 5'CTCTTTGGAGACTTCAATGTTCCTT-3' and reverse 5'CCTGTCATTGTATAGCCAGGAAATC-3'; PGM5-AS1, forward 5'-

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GACTATGTTGTGAGCCTGCG-3' and reverse 5'-AAAAGGGGAGGGGCAATACA-

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3'; LINC01626, forward 5'-TTCTCCCCATCATCCCAGTG-3' and reverse 5'-

CACATCTCTCTTGGCGCAAA-3'; GAPDH, forward 5'-TTGGTATCGTGGAAGGACTCA-3'

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and reverse 5'-CAGTAGAGGCAGGGATGATGT-3'. GAPDH was used as internal control. The thermocycle conditions are as follows: initial denaturation at 95℃ for 10 minutes, followed by 95℃

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for 20 seconds, 57℃ for 30 seconds, and 72℃ for 30 seconds for 27-29 cycles. Final extension was at 72℃ for 10 minutes. The amplicons were resolved in 1% agarose gel.

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2.5. Statistical analysis

All statistical analyses were performed on SPSS 16.0. Measurement data are presented as

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mean ± standard deviation (SD). Statistical analyses were performed with Student’s t-test (two-

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tailed) and one-way ANOVA as appropriate. P<0.05 was considered as significant.

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3. Results

3.1. mRNA and lncRNA expression profile in ESCC

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We obtained tumor and adjacent normal tissues from ESCC patients for mRNA and lncRNA sequencing (RNA-seq). The principle component analysis (PCA) and the heatmap of

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differentially expressed genes (DEGs) are shown in Fig. 1. From the PCA, normal and cancer samples fell into distinct clusters, indicating that samples within each biologic group have more similarity. We identified 1260 up-regulated and 1445 down-regulated mRNAs (with a cutoff padj<0.05 & FC>2) and 402 up-regulated and 741 down-regulated lncRNAs (with cutoff padj<0.05). All DEGs for mRNA and lncRNA in tumor tissue are listed in Supplemental files (files 1 and 2). Heatmaps to show top 50 DEGs for mRNA (Fig. 1B, upper) and lncRNA (Fig. 1B, lower). Within the differently express genes, we identified some mRNAs (MMP1, SOX11, GST1, LYDP2, MUC5B, EN1, TMRRSS11, CRISP3 and CRNN) and some lncRNAs (LINC01518, LINC01614, PGM5-AS1 and LINC01626) are differentially expressed in ESCC tumors, some of

ACCEPTED MANUSCRIPT which were reported in other cancers but not in ESCC (Liu, Kato et al. 2012, Beltran, Graves et al. 2014, Dai, Li et al. 2017). 3.2. Validation of mRNA and lncRNA by RT-PCR We then found some of our upregulated lncRNAs such as CASC9, HOTAIR, and FOXD2AS1 (Fig. 1B and Supplemental file 2) are well-known tumor-associated lncRNA in ESCC and

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other malignant tumor (Pan, Mao et al. 2016, Guo, Peng et al. 2017), which give us confidence

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for our sequencing results. Then further validate our DEGs, we selected 2 up-regulated lncRNAs (lnc01518, lnc01614), 2 down-regulated lncRNAs (PGM5-AS1, lnc01626), 2 up-regulated

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mRNAs (MMP1, SOX11), and 2 down-regulated mRNAs (LYDP2, MUC5B) to detect their expression in more independent ESCC and normal tissues by RT-PCR (Fig. 1C). The results

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coincided with the RNA-sequencing results well, confirming the reliability of our RNAsequencing results.

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3.3. Enrichment analysis for differentially expressed mRNA and lncRNA Enrichment analysis (GO & Pathway) were performed to reveal the functions of hundreds of

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differentially expressed mRNAs and lncRNAs. Firstly, we analyzed the differentially expressed mRNAs in ESCC tissues and conducted gene ontology analysis (Fig. 2 and 3). The gene ontology

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for the up-regulated mRNA functions are mitotic skeletal system development, cell cycle, nuclear division, down-regulated mRNA are involved in epithelial cell differentiation, porphyrin

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catabolic process, oxidation reduction (Fig. 2A, Supplemental files 3 and 4). For the mRNA DEGs, we found the most significant Kyoto Encyclopedia of Genes and Genomes (KEGG)

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pathways are cell cycle control of chromosomal replication, Hepatic Fibrosis / Hepatic Stellate Cell Activation, Dendritic Cell Maturation (Fig. 3A, Supplemental file 5). Interestingly, the

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functions of differentially expressed lncRNA are different. The gene ontology terms for upregulated lncRNA are folic acid biosynthetic process, positive regulation of G2/M transition of mitotic cell cycle involved in cellular response to nitrogen starvation, positive regulation of cellcell adhesion mediated by integrin. The down-regulated lncRNA are mainly involved in regulation of glycolysis involved in cellular glucose homeostasis, leukotriene metabolic process (Fig. 2B, Supplemental files 6 and 7). The pathways for lncRNA are Adenosine Nucleotides Degradation II, RAN Signaling, and Methylglyoxal Degradation III (Fig. 3B, Supplemental file 8). To our knowledge, this is the first time of transcriptome data revealing the mRNA and lncRNA expression profiling for ESCC, which will provide valuable information for researchers studying ESCC. Our results showed that the different functions between DEG mRNA and

ACCEPTED MANUSCRIPT lncRNA is that the lncRNA are mainly involved in regulation and metabolic procession (DNA, protein synthesis), however, the mRNA DEGs are for cell cycle, which might implicate that lncRNA are up-stream regulators for mRNA DEGs transcription in ESCC, and lncRNA may play key roles in the procession of the cancer development. 3.4. Establishment of correlation for mRNA and lncRNA in ESCC tumor tissues

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The gene co-expression network between lncRNAs and mRNAs in ESCC tissues can reveal

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the relationship between mRNAs and lncRNA. So we performed the correlation analysis for mRNA and lncRNA with their expression values in ESCC tumor tissues and build the mRNA and

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lncRNA association networks (Fig. 4, Supplemental file 9). Many interesting interactions between lncRNA and mRNA were revealed, such as NBAT1 was reported up-regulated in

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neuroblastomas (Pandey, Mitra et al. 2014). Several of the c-MYC–regulator lncRNAs (CCAT1L, CARLo-5, PCAT1, PRNCR1, CCAT2, PVT1) are localized to the same genomic region and

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co-amplified with c-MYC (Huarte 2015). In our study, we identified many lncRNA-mRNA coexpression interactions, by which lncRNA might regulate mRNA expression (Fig. 4). From the top co-expression networks in figure 4, we can see lncRNA FAM197Y5, TCONS_I2_00006579

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and MIR548H2 are the cores of the networks, and they are associated with many mRNAs, like MMP2 (Xu, Wang et al. 2005), ANTXR1 (Chen, Bhat-Nakshatri et al. 2013), SPARC (Tai and

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Tang 2008), which are reported in previous studies playing roles in cancer. This finding indicated that the expression of mRNAs might be regulated by lncRNAs, which implied the prominent

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function of lncRNAs in ESCC carcinogenesis.

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4. Discussion

ESCC is one of the most fatal cancers worldwide, but the etiology remains poorly

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understood. To explore the pathogenesis of ESCC and find novel biomarker for ESCC, we conducted a NGS analysis to screen the whole transcript expression profile in ESCC. Our results reveal that both mRNA and lncRNA play a vital role in pathogenesis of ESCC. Intriguingly, we have identified an aberrantly overexpressed lncRNA in ESCC tissues, and we build the interaction network for mRNA and lncRNA. Life and disease are complex system that involves interactions among many factors of various levels of regulation, including mRNA, lncRNA and so on. Recent advances in various forms of omics technologies have generated a huge amount of data. Using the transcriptome data, we found lncRNA and mRNA DEGs are involved in different terms of GO biological process. The

ACCEPTED MANUSCRIPT mRNA DEGs are for cell cycle, which might implicate that lncRNA are up-stream regulators for mRNA DEGs transcription in ESCC, and lncRNA may play key roles in the procession of the cancer development. There are many studies of mRNA or lncRNA data to explore the pathogenesis of ESCC. Several groups have reported the aberrant lncRNA expression profile in ESCC and identified hundreds of ESCC-associated lncRNAs (Cao, Wu et al. 2013, Pan, Mao et al. 2016, Yao, Huang et al. 2016), some of which could be used as biomarkers for diagnosis or

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prognosis of ESCC. However, all of these studies are from a single prospect, either lncRNA, or

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mRNA. Here in this study, we collected total RNA and performed RNA-seq for the mRNA and

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lncRNA for integrative analysis to build the interactions between both types of data. Many interesting interactions between lncRNA and mRNA were found, one of the novel interesting lncRNA PGM5 has very strong associating with many mRNA in ESCC tumor tissues

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(Supplemental file 7). We found all the associated genes are functionally involved in ubiquitinprotein ligase activity involved in mitotic cell cycle and tumor necrosis factor-mediated signaling

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pathway(the genes are ITAG9, A2M, MYH11, PREX2) (Pastuszak-Lewandoska, Kordiak et al. 2016, Douet-Guilbert, Chauveau et al. 2017, Srijakotre, Man et al. 2017, Tanaka, Kohashi et al.

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2017). Therefore the lncRNAs we identified may play vitriol roles in tumor development and merit further study on how they regulate the cancer-related gene expression.

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5. Conclusions

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In this study, the novel ESCC-related mRNA and lncRNA were identified by genome-wide screening for mRNA and lncRNA profiles in 7 pairs of esophageal cancer and normal tissues. And co-expression network for all of these mRNA and lncRNA were built to explore the

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relationship between mRNA and lncRNA, which may provide a mechanism of mRNA expression

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regulation by lncRNA.

ACCEPTED MANUSCRIPT Conflict of interest statement None of the authors have any relevant conflicts of interests to declare. Acknowledgements This study was supported by grant from National Natural Science Foundation of China (No. 81272371), and the Major Project of Science and Technology in Henan Province

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(No.161100311400), and the Key Research Projects of Henan Higher Education (No.18A310033),

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and the Youth Innovation Fund of The First Affiliated Hospital of Zhengzhou University to

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Weiwei Wang & Pan Li.

ACCEPTED MANUSCRIPT Legends: Table 1. Clinicopathological characteristics of 7 ESCC patient. Fig. 1. PCA analysis between tumor and normal tissues from ESCC patients, heatmaps showing top 50 DEGs for mRNA and lncRNA data and validation by RT-PCR. (A) The principle component analysis (PCA) was performed for transcriptomic data. Genes with

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the highest loadings in the first three principal components were plotted in 3D visualization. PCA

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revealed that the tumor and normal samples form distinct clusters, indicating that samples within

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each biologic group have more similarity. Each symbol represents each biologic sample (black circles, normal tissues; red circles, tumor tissues). (B) Heatmaps to show top 50 differentially expressed genes (DEGs) for mRNA (upper) and lncRNA (lower). (C) Validation of 8 selected

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differentially expressed mRNA and lncRNA, all of the RT-PCR-validated RNAs showed the

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same fold change tendencies as those in the RNA sequencing results (**P < 0.01). Fig. 2. GO terms for mRNA and lncRNA DEGs comparing tumor vs. normal tissues. (A) GO terms for mRNA DEGs comparing tumor vs. normal tissues. (B) GO terms for lncRNA

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regulated DEGs (lower, green color).

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DEGs comparing tumor vs. normal tissues. Up-regulated DEGs (upper, red color) and down-

Fig. 3. KEGG Pathways of enrichment analysis for differentially expressed mRNA and

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lncRNA

(A) KEGG pathways for the differentially expressed mRNA. (B) KEGG pathways for the

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differentially expressed lncRNA.

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Fig. 4. Network of lncRNAs associated mRNAs. lncRNA are labeled as red rectangle, mRNA are labeled as green circle, positive association were indicated as red lines and negative association were indicated as green lines.

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ACCEPTED MANUSCRIPT Abbreviations: DEGs, differentially expressed genes; EC, esophageal cancer; EAC, esophageal adenocarcinoma; ESCC, esophageal squamous cell carcinoma; lncRNA, long noncoding RNA; UCSC, University of California Santa Cruz; BWA, Burrows-Wheeler Aligner; PCA, Principle Component Analysis; GO, gene ontology; RT-PCR, reverse transcription-polymerase chain reaction; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; KEGG, Kyoto Encyclopedia of

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Genes and Genomes.

ACCEPTED MANUSCRIPT Highlights

Genome-wide screening for mRNA and lncRNA profiles in esophageal squamous cell carcinoma (ESCC) was performed.

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3000 up-regulated/2517 down-regulated mRNAs and 402 up-regulated/741 down-regulated lncRNAs were identified

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mRNA and lncRNA co-expression network were built and highly-connected networks were identified for the first time in ESCC.

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