Identification of the key genes and long non‑coding RNAs in ankylosing spondylitis using RNA sequencing
Corrigendum in: /10.3892/ijmm.2022.5154
- Authors:
- Published online on: December 20, 2018 https://doi.org/10.3892/ijmm.2018.4038
- Pages: 1179-1192
-
Copyright: © Xu et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Ankylosing spondylitis (AS) is the prototypic and debilitating type of spondyloarthritis, which is an autoimmune disorder (1). AS often affects the axial joints, including the sacroiliac joint and spine, and induces new bone formation and ultimately ankylosis (2,3).
AS is associated with the interplay of genetic risks and environmental triggers, and its etiology and pathogenesis remain largely unknown. A number of studies have reported a strong association between the major histocompatibility complex class I allele human leukocyte antigen B27 (HLA-B27) and AS (4-6). However, the mechanism by which HLA-B27 causes a predisposition to AS remains unclear. T cells and a number of immune pathways, including the interleukin (IL)-17/IL-23 pathway and control of nuclear factor-κB (NF-κB) activation, have been reported to be closely associated with AS (5,6). Currently, it is difficult to diagnose AS during the early stages due to the lack of accurate diagnostic biomarkers.
Long non-coding RNA (lncRNA) is a type of non-protein-coding transcript with a length of >200 nucleotides; they are emerging as key regulators in various biological processes (7). Recent studies have indicated that lncRNAs, including lncRNA-AK001085, lnc-zinc finger protein 354A (ZNF354A)-1, lnc-Lin-54 DREAM MuvB core complex component (LIN54)-1, lnc-Facioscapulohumeral muscular dystrophy region gene 2 family member C (FRG2C)-3 and lnc-ubiquitin specific peptidase 50 (USP50)-2, serve roles in AS (8,9). Some researchers have also revealed that mRNAs, such as programmed cell death 1 (PDCD1) (1), caspase recruitment domain-containing protein 11 (CARD11) (10), phospholipase Cγ1 (PLCG1) (11,12) and DNA methyltransferase 1 (DNMT1) (13), may be involved in the pathogenesis of AS.
In the present study, the key differentially expressed (DE) mRNAs (DEmRNAs) and DElncRNAs in AS were identified using RNA sequencing and bioinformatics analysis. DElncRNA-DEmRNA co-expression network construction, identification of nearby target DEmRNAs of DElncRNAs and functional annotation of DEmRNAs were performed in order to understand the biological functions of the key DEmRNAs and DElncRNAs in AS.
Materials and methods
Patients and samples
Three patients with AS and three normal controls were enrolled in the present study from 2nd Affiliated Hospital, School of Medicine, Zhejiang University (Zhejiang, China). The patients with AS were aged 37, 36 and 40 years old, and all were HLA-B27+ and male. These patients were diagnosed with AS based on kyphotic deformity, bilateral damage of the sacroiliac joint observed in the computed tomography results, and spinal fusion and sacroiliac arthrodesis observed in the X-ray results. All of the patients had not received treatment with non-steroidal anti-inflammatory drugs or biologics and they had not exhibited complications. The normal controls were aged 35, 36 and 37 years old and were male. None of the six participants had any other type of autoimmune disease. Blood samples were obtained from all six participants. All of the participants submitted written informed consent and the present study was approved by the Ethics Committee of 2nd Affiliated Hospital, School of Medicine, Zhejiang University.
Library preparation and high-throughput sequencing
Total RNA was isolated from the blood samples with TRIzol reagent (Invitrogen; Thermo Fisher Scientific, Inc., Waltham, MA, USA) according to the manufacturer's instructions. A NanoDrop ND-2000 spectrophotometer (Thermo Fisher Scientific, Inc.) was used to check the concentration and purity of the RNA. The integrity of the RNA was confirmed using 2% agarose gel and the RNA integrity number (RIN) was obtained using an Agilent 2100 Bioanalyzer instrument (Agilent Technologies, Inc., Santa Clara, CA, USA). The thresholds of total RNA for cDNA library construction were as follows: i) Amount of RNA, >5 μg; ii) concentration of RNA; ≥200 ng/ml; iii) 1.8<optical density (OD)260/280<2.2; iv) 2.0<OD260/280; and v) RIN value, >8.0.
Firstly, the ribosomal RNA was removed from the total RNA using a Ribo-Zero Magnetic kit (Epicentre; Illumina, Inc., San Diego, CA, USA). In addition, 'Soap' software (version 1.03; soap.genomics.org.cn/SOAPdenovo-Trans.html#intro2) was used to compare the reads of the rRNA, and then to write a perl script to remove it from the FastQ file. Subsequently, the cDNA library for RNA sequencing was contrasted using the TruSeq RNA Sample Prep kit (Illumina, Inc.). Briefly, the retrieved RNA was fragmented by adding First Strand Master Mix and then first-strand cDNA was generated using the First Strand Master mix and Super Script II reverse transcription (Invitrogen; Thermo Fisher Scientific, Inc.) with the following reaction conditions: 25°C for 10 min, 42°C for 50 min and 70°C for 15 min. Following purification of the product with Agencourt RNA Clean XP beads (Beckman Coulter, Inc., Brea, CA, USA), Second Strand Master mix and dATP, dGTP, dCTP and dUTP mix (Beckman Coulter, Inc.) were added to synthesize the second-strand cDNA (16°C for 1 h). Subsequently, the purified cDNA was combined with End Repair Mix (Thermo Fisher Scientific, Inc.) and incubated at 30°C for 30 min. Following purification with beads (Qiagen, Inc., Valencia, CA, USA), A-Tailing mix (Qiagen, Inc.) was added into the reaction system, which was incubated at 37°C for 30 min. Adenylate 3' Ends DNA, Index Adapter and Ligation mix (Qiagen, Inc.) were combined and incubated at 30°C for 10 min. Subsequently, the Uracil-N-Glycosylase enzyme was added into the purified ligation product and incubated at 37°C for 10 min. A total of 15 rounds of polymerase chain reaction (PCR) amplification were conducted with PCR Primer Cocktail (Illumina, Inc.) and PCR Master Mix (Illumina, Inc.) to enrich the cDNA fragments. The PCR products were then purified with AMPure XP beads (Qiagen, Inc.). The qualified libraries were amplified on cBot (Illumina, Inc.) to generate a cluster on the flow cell (TruSeq PE Cluster kit v3-cBot-HS; Illumina, Inc.). The amplified flow cell was sequenced on an Illumina HiSeq X Ten platform (Illumina, Inc.).
Quality control of raw sequence
Quality control of the raw reads derived from the RNA sequencing was performed to obtain clean reads of high quality. Briefly, quality control involved trimming low-quality reads, including adaptor sequences, sequences with a quality score <20 and sequences with an N-base rate of raw reads >10%, using SeqPrep (version 1.2; github.com/jstjohn/SeqPrep) and Sickle (version V3.4.0; github.com/najoshi/sickle).
Clean reads mapping
Sequences were aligned to the human reference genome GRCh38.p7 (Ensembl v84; www.ensembl.org/index.html) using hierarchical indexing for the spliced alignment of the transcripts programme HISAT2 (version 2.1.0; ccb.jhu.edu/software/hisat2/index.shtml). Then, StringTie (version v1.3.4; ccb.jhu.edu/software/stringtie/) was used to assemble and quantify the transcripts in each sample. Ultimately, differential gene expression analysis was performed with Ballgown (version 3.7; www.bioconductor.org/packages/release/bioc/html/ballgown.html) in R environment.
Identification of DEmRNAs and DElncRNAs
Using Ballgown, the DEmRNAs and DElncRNAs between the patients with AS and normal controls were identified with P<0.05. The false discovering rate (FDR)-adjusted P-value of the test statistic was used. Hierarchical clustering of the DElncRNAs and DEmRNAs expression profile was performed using hcluster in R language (version 3.3.3; stat.ethz.ch/R-manual/R-devel/library/stats/html/hclust.html).
AS-specific protein-protein interaction (PPI) network construction
PPI networks of the top 30 up- and down-regulated DEmRNAs, respectively, were constructed using BioGRID (version 3.5; thebiogrid.org).
AS-specific weighted DEmRNA-DElncRNA co-expression network analyses
Weighted Gene Co-expression Network Analysis (WGCNA_1.64-1; horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/) (14) is an R package for weighted correlation network analysis, which is also known as weighted gene co-expression network analysis. Using WGCNA, AS-specific weighted DEmRNA-DElncRNA co-expression network analysis was performed. The pairwise Pearson's correlation coefficients (PCCs) between the DElncRNAs and DEmRNAs in patients with AS were calculated. DElncRNA-DEmRNA pairs with |PCC value ≥0.90| and P<0.001 were used to construct an AS-specific weighted DEmRNA-DElncRNA co-expression network, which was deciphered using the online-based software GeneCodis3 (genecodis.cnb.csic.es/analysis).
Functional analyses of DEmRNAs co-expressed with DElncRNAs in AS
The DEmRNAs of these DElncRNA-DEmRNA pairs with |PCC value ≥0.90| and P<0.001 were used to conduct Gene Ontology (GO; www.geneontology.org/) and Kyoto Encyclopedia of Genes and Genomes (KEGG; www.genome.jp/kegg/) molecular pathway enrichment analysis using GeneCoDis3.
DEmRNAs close to DElncRNAs with cis-regulatory effects
Previous studies have reported that lncRNAs can regulate genes that are transcribed near to them, consistent with activity in cis-regulatory elements (15,16). Therefore, in the present study DEmRNAs that were close cis targets of DElncRNAs were identified by searching for DEmRNAs that were transcribed within a 100-kb window up- or downstream of DElncRNAs between the patients with AS and the normal controls (17).
Validation of the expression of DEmRNAs and DElncRNAs using the GSE73754 and GSE25101 datasets
The mRNA expression profile of 52 patients with AS and 20 normal controls (Canadian cohort) in the GSE73754 dataset (GPL10558 Illumina HumanHT-12 V4.0 expression beadchip), which was downloaded from the Gene Expression Omnibus (GEO; www.ncbi.nlm.nih.gov/gds). The mRNA and lncRNA expression profile of 16 patients with AS and 16 normal controls (Australian cohort) in the GSE25101 dataset (GPL6947 Illumina HumanHT-12 V3.0 expression beadchip) was also downloaded from the GEO database. The expression levels of the selected DEmRNAs and DElncRNAs between the patients with AS and normal controls in the present study were validated using the GSE73754 and GSE25101 datasets, and the difference in the expression levels was visualized using box plots.
Receiver operating characteristic (ROC) analyses
To assess the diagnostic value of the DEmRNAs in AS, ROC analyses were conducted using pROC package (version 1.13.0) in R language (cran.r-project.org/web/packages/pROC/index.html). The area under the curve (AUC) under the binomial exact confidence interval was calculated. ROC curves were then generated.
Statistical analysis
Values are displayed as the mean ± standard deviation. Student's t-test was used to assess differences among the groups, and P≤0.05 was considered to indicate a statistically significant difference. Co-expression associations between the lncRNAs and the protein-coding genes were estimated using pairwise PCC analysis using R language (version 3.3.3; stat. ethz.ch/R-manual/R-devel/library/stats/html/hclust.html).
Results
Identification of DEmRNAs and DElncRNAs between patients with AS and normal controls
Compared with the normal controls, 1,072 DEmRNAs (320 upregulated and 752 down-regulated) and 372 DElncRNAs (230 upregulated and 142 downregulated) in patients with AS were identified. The top 10 up- and downregulated DEmRNAs and DElncRNAs between the patients with AS and normal controls are shown in Tables I and II, respectively.
 | Table IITop 10 up- and downregulated DElncRNAs between the patients with ankylosing spondylitis and the normal controls. |
MANSC domain containing 1 (MANSC1) and DNMT1 were the most significantly up- and downregulated DEmRNAs between the patients with AS and normal controls, respectively (data not shown). NOTCH1 associated lncRNA in T-cell acute lymphoblastic leukemia 1 (NALT1) and RP11-837J7.4 were the most significantly up- and downregulated DElncRNAs between the patients with AS and normal controls, respectively (data not shown).
AS-specific PPI network construction
The AS-specific PPI network consisted of 159 nodes and 164 edges. The top 10 mRNAs that had the highest degrees were Myc proto-oncogene basic helix-loop-helix transcription factor (MYC; degree=65), heterogeneous nuclear ribonucleoprotein A1 (HNRNPA1; degree= 40), spectrin-a non-erythrocytic 1 (SPTAN1; degree=18), TATA-box binding protein associated factor 10 (TAF10; degree=9), ETS proto-oncogene 1 transcription factor (degree=7), NCK adaptor protein 1 (degree=6), eukaryotic translation initiation factor 4B (degree=5), KIAA1033 (degree=5), extended synaptotagmin 1 (degree=5) and tumor protein 53 (degree=5); of these, MYC, HNRNPA1, SPTAN1 and TAF10 were hub proteins of the AS-specific PPI network (Fig. 1).
AS-specific weighted DEmRNA-DElncRNA co-expression network analysis
A total of 3,505 lncRNA-mRNA pairs were identified, which included 302 DElncRNAs and 602 DEmRNAs with |PCC value ≥0.90| and P<0.001. Based on these lncRNA-mRNA pairs, the negatively and positively co-expressed DEmRNA-DElncRNA interaction networks were constructed, respectively. The top 100 co-expressed DEmRNA-DElncRNA interaction network is presented in Table III. Based on the positively co-expressed DEmRNA-DElncRNA interaction network, MSTRG.8559 (degree=226) and long intergenic non-protein coding RNA 987 (LINC00987; degree=209) were the top 2 DElncRNAs that were co-expressed with the greatest number of DEmRNAs (Fig. 2). The selected co-expression DEmRNA-DElncRNA interaction network is presented in Fig. 2.
| Table IIIThe top 100 co-expressed differentially expressed mRNA-differentially expressed long non-coding RNA interaction network. |
Functional analysis of DEmRNAs co-expressing DElncRNAs between patients with AS and normal controls
Based on the GO enrichment analysis (Fig. 3) of the 602 DEmRNAs that were co-expressed with DElncRNAs in patients with AS and normal controls, the significantly enriched GO terms were as follows: 'DNA repair' (FDR=3.94×10−4; Fig. 3A), 'carbohydrate metabolic process' (FDR=5.66×10−4; Fig. 3A), 'nucleus' (FDR=3.34×10−30; Fig. 3B) and 'protein binding' (FDR=7.54×10−31; Fig. 3C). 'T-cell receptor signaling pathway' (FDR=2.29×10−3; Fig. 4) and 'cell cycle' (FDR=1.52×10−3; Fig. 4) were the significantly enriched KEGG pathways.
DEmRNAs close to DElncRNAs with cis-regulatory effects
A total of 84 DElncRNAs and nearby cis target DEmRNA pairs, which included 73 DElncRNAs and 82 DEmRNAs, were obtained (Table IV).
Validation of the expression of DEmRNAs and DElncRNAs using the GSE73754 and GSE25101 datasets
A total of 4 DEmRNAs (DNMT1, PDCD1, CARD11 and PLCG1) were selected to perform expression validation using the GSE73754 dataset (Fig. 5). The expression of all four DElncRNAs was downregulated in patients with AS when compared with the normal controls, which was generally consistent with the RNA sequencing data (DNMT1, P<0.05; CARD11, P<0.05; PDCD1, P>0.05; PLCG1, P>0.05). Three lncRNAs (cat eye syndrome chromosome region candidate 7, hydatidiform mole associated and imprinted and KIAA0125) and seven DEmRNAs [MANSC1, adenosine triphosphatase sarcoplasmic/endoplasmic reticulum Ca2+ transporting 2 (ATP2A2), myeloid zinc finger 1 (MZF1), PDCD1, DNMT1, CARD11 and PLCG1] were selected to perform expression validation using the GSE25101 dataset (Fig. 6). The expression of KIAA0125 (P>0.05), MANSC1 (P>0.05), ATP2A2 (P>0.05), MZF1 (P>0.05), PDCD1 (P>0.05), DNMT1 (P<0.0001), CARD11 (P<0.05) and PLCG1 (P>0.05) was generally consistent with the RNA sequencing data.
ROC curve analysis
ROC curve analyses and the AUC were used to assess the discriminatory ability of the four DEmRNAs (DNMT1, PDCD1, CARD11 and PLCG1) among the 52 patients with AS and 20 normal controls of the GSE73754 dataset. The AUCs of PDCD1 and PLCG1 were <0.7 (data not shown). The AUCs of CARD11 and DNMT1 were 0.782 and 0.737, respectively (Fig. 7). For AS diagnosis, the sensitivity (proportion of true positive) and 1-specificity (proportion of false positive) of CARD11 were 70.0 and 80.8%, respectively, and for DNMT1 were 75.0 and 69.2%, respectively (Fig. 7).
Discussion
AS is a type of autoimmune disorder that is associated with HLB-27 and T-cells; however, its etiology and pathogenesis remain unclear (18). The delays in the diagnosis of AS and the insufficient responses to the currently available therapeutics supports the requirement for a greater understanding of its pathogenesis.
A previous microarray study identified four lncRNAs, lnc-ZNF354A-1, lnc-LIN54-1, lnc-FRG2C-3 and lnc-USP50-2, that are involved in the abnormal osteogenic differentiation of mesenchymal stem cells (MSCs) in patients with AS. The expression of these four lncRNAs was positively correlated with that of bone morphogenetic protein 2 and Noggin in MSCs from healthy donors (8). A recent study reported that lncRNA-AK001085 was downregulated in patients with AS and served as a potential diagnostic indicator, thus, lncRNA-AK001085 was considered to be a potential suppressor of AS (9). Due to the lack of research, the regulatory mechanism of the majority of lncRNAs in AS remains unknown. In the present study, the key DEmRNAs and DElncRNAs associated with AS were identified and their functions in AS were investigated using RNA sequencing and bioinformatics analysis.
LINC00342 was demonstrated to be upregulated in patients with non-small cell lung cancer and its expression was positively correlated with lymph node metastasis and the Tumor-Node-Metastasis stage (19). Coiled-coil domain containing 26 (CCDC26) is also a tumor-associated lncRNA that regulates the growth of glioma, pancreatic cancer and myeloid leukemia cells (20-22). In the present study, LINC00342 and CCDC26 were downregulated in patients with AS, which suggests that they may serve roles in AS. Further studies are required in order to identify their precise function in AS.
Furthermore, the results of the present study indicated that many novel DElncRNAs may be involved in AS. In order to investigate their functions in AS, a weighted DElncRNA-DEmRNA co-expression network was constructed and functional annotation of the DElncRNAs co-expressed with DElncRNAs was performed. A total of 3,505 DElncRNA-DEmRNA co-expression pairs, which included 302 DElncRNAs and 602 DEmRNAs, were obtained. Based on the DEmRNAs co-expressed with DElncRNAs, 'T-cell receptor signaling pathway' was a significantly enriched pathway. T-cells have been demonstrated by previous studies to serve important roles in the pathology of AS (23-25). HLA-B27-reactive cluster of differentiation-4+ T-cells have been reported to be involved in the pathogenesis of spondyloarthropathies (26). The number of peripheral T-helper (Th)-2 and Th17 lymphocytes has been demonstrated to be increased in AS, which is suggestive of their potential roles in AS (27,28). Therefore, the results of the present study support those of previous studies as well as the importance of the T-cell receptor signaling pathway in AS. Furthermore, the DEmRNAs that were enriched in the T-cell receptor signaling pathway, including NF-κB inhibitor β, CARD11, P21 (Ras-related C3 botulinum toxin substrate 1) activated kinase 6, protein kinase Cθ, PLCG1, PDCD1, FYN proto-oncogene Src family tyrosine kinase, protein kinase B2 and Vav guanine nucleotide exchange factor 3, may be involved with AS by regulating the T-cell receptor signaling pathway.
Among these DEmRNAs, PDCD1 is a known AS-associated gene. PDCD1 is a member of the immunoglobulin superfamily that is expressed on the surface of peripheral T-cells, and regulates T-cell responses and the maintenance of peripheral tolerance (29,30). A previous study demonstrated that the expression levels of PDCD1 on activated T-cells were decreased in patients with AS (1). Downregulation of PDCD1 may be involved in AS by stimulating the activity of T-cells and elevating the production of cytokines, which promotes spinal inflammation and destruction in patients with AS (1). Downregulated PDCD1 was also identified in patients with AS in the present study, which provides evidence to support the results of the previous study.
CARD11 is a shared member of the CARD and CARD-containing membrane-associated guanylate kinase protein 1 families that has been reported to serve a vital role in regulation of inflammation and the immune response (31). Although to the best of our knowledge, there have been no studies that have reported on the association between CARD11 and AS, downregulated CARD11 has been implicated in another type of autoimmune disease, rheumatoid arthritis, via NF-κB activation, reduced Th17 responses and the decreased production of proinflammatory cytokines, including IL-1β, IL-6 and IL-17 (10,32,33). Joint inflammation and destruction were demonstrated to be attenuated by CARD11 small interfering RNA treatment in mice (10). Considering the crucial roles of NF-κB activation, Th17 cells and proinflammatory cytokines in AS, it was hypothesized that CARD11 may also be a key regulator of AS.
Similar to CARD11, PLCG1 has been reported to be associated with other types of autoimmune disease, including multiple sclerosis and lymphoproliferative syndrome (11,12). Furthermore, the interaction between PLCG1 and linker for activation of T-cells (LAT) was revealed to be involved with the activation and proliferation of T-cells (11,12). The production of IL-6 by T-cells is also regulated by PLCG1-LAT (11,12). Therefore, downregulated PLCG1 in the patients with AS in the present study may serve important roles in the progression of AS by regulating T-cells and the production of IL-6.
DNMT1 encodes an enzyme that establishes and regulates patterns of methylated cytosine residues (13). A previous study observed downregulated and hypermethylated DNMT1 in the peripheral blood mononuclear cells of patients with AS when compared with normal controls, which suggests that DNMT1 may be a potential biomarker of AS (13). Thus, the downregulation of DNMT1 observed in patients with AS in the present study is consistent with this previous study.
Based on the ROC analysis in the present study, CARD11 and DNMT1 may have great diagnostic value for AS, and therefore may be potential biomarkers.
LINC00987 was a downregulated lncRNA in the patients with AS in the present study, and was co-expressed with DNMT1, CARD11 and PLCG1. In addition, DNMT1 and PLCG1 were co-expressed with another downregulated lncRNA, MSTRG.8559, in the patients with AS. Furthermore, LINC00987 and MSTRG.8559 were two hub lncRNAs of the positively co-expressed DElncRNA-DEmRNA network, which regulates the majority of the DEmRNAs in AS. It was hypoth-esized that these two DElncRNAs may serve crucial roles by regulating the expression of DNMT1, the T-cell receptor signaling pathway and its associated genes. Further studies are required to further investigate the biological functions of LINC00987 and MSTRG.8559, particularly those in AS.
RP11-837J7.4 and NALT1 were the most significantly down- and upregulated, respectively, DElncRNAs in patients with AS in the present study; however, their biological functions remain known. Further studies are required in order to identify whether these two DElncRNAs could serve as diagnostic biomarkers for AS.
Previous studies have revealed the prevalence of lncRNA-mediated cis regulation on nearby transcription (34-36). The 84 AS-specific DElncRNA and nearby cis target DEmRNA pairs obtained in the present study provide novel information for understanding the biological functions of lncRNAs in AS.
In conclusion, the present study obtained lncRNA and mRNA expression profiles from patients with AS and normal controls using RNA sequencing. A number of key genes, including PDCD1, DNMT1, CARD11 and PLCG1, that are involved in AS were identified. In addition, the results indicated that numerous novel DElncRNAs may be involved in AS. Furthermore, the functions of DElncRNAs in AS were investigated using functional annotation of DEmRNAs co-expressed with DElncRNAs and through the identification of nearby target DEmRNAs of DElncRNAs. These results may support further studies on the potential roles of lncRNAs in AS. However, the sample size for RNA-seq was small, which is a limitation of the present study, therefore, studies with larger sample sizes are required in order to confirm this conclusion.
Funding
No funding was received.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors' contributions
ZX and GC conceived and designed the experiments. ZX, XZ and HL performed the experiments. ZX, XZ and QC analyzed the data. HL and QC were significant contributors in the manuscript. All authors revised the manuscript and have agreed to the publication of this manuscript.
Ethics approval and consent to participate
All of the participants submitted written informed consent and the present study was approved by the Ethics Committee of the 2nd Affiliated Hospital, School of Medicine, Zhejiang University (Zhejiang, China).
Patient consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Abbreviations:
|
AS |
ankylosing spondylitis |
|
DEmRNAs |
differentially expressed mRNAs |
|
DElncRNAs |
differentially expressed long non-coding RNAs |
|
GO |
Gene Ontology |
|
KEGG |
Kyoto Encyclopedia of Genes and Genomes |
|
PPI |
protein-protein interaction |
Acknowledgments
The authors would like to thank Beijing Yangshen Bioinformatic Technology (Beijing, China) for their assistance during high-throughput sequencing and data analysis.
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