Transcriptome-wide N6-methyladenosine modification profiling of long non-coding RNAs in patients with recurrent implantation failure

Transcriptome-wide N6-methyladenosine modification profiling of long non-coding RNAs in patients with recurrent implantation failure | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Transcriptome-wide N6-methyladenosine modification profiling of long non-coding RNAs in patients with recurrent implantation failure Ting Wang, Lili Zhang, Wenxin Gao, Yidan Liu, Feng Yue, Xiaoling Ma, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4563715/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Oct, 2024 Read the published version in BMC Medical Genomics → Version 1 posted 10 You are reading this latest preprint version Abstract N6-methyladenosine (m 6 A) is involved in most biological processes and actively participates in the regulation of reproduction. According to recently research, long non-coding RNAs (lncRNAs) and their m 6 A modifications are involved in reproductive diseases. In the present study, using m 6 A modified RNA immunoprecipitation sequencing (m 6 A-seq), the m 6 A methylation transcription profiles in recurrent implantation failure (RIF) were established for the first time. 1443 significantly up-regulated m 6 A peaks and 425 significantly down-regulated m 6 A peaks were identified in RIF. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that genes associated differentially methylated lncRNAs were involved in classical p53 signaling pathway and amino acid metabolism. Then, competing endogenous RNA (ceRNA) network revealed a regulatory relationship between lncRNAs, microRNAs (miRNAs) and mRNAs. The m 6 A methylation abundances of lncRNAs were verified by m 6 A-RNA immunoprecipitation (MeRIP)-qPCR in this study. This study will lay a foundation for further exploration of the potential role of m 6 A modification in the pathogenesis of RIF. Recurrent implantation failure N6-methyladenosine m6A modified RNA immunoprecipitation sequencing long non-coding RNAs Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Background With the development of assisted reproductive technology, the success rate has exceeded 50%, but there are still some patients who fail after recurrent implantation, which brings a huge financial and psychological burden to the patients. RIF is usually defined as a failure to achieve clinical pregnancy after at least four high-quality embryos are transferred in women under the age of 40 within at least three fresh or frozen cycles( 1 ). According to the definition, high-quality embryos are transplanted in RIF patients, and low endometrial receptivity is the main cause( 2 ). In normal menstrual cycle, 6–8 days after ovulation is considered as window of implantation (WOI), when endometrial receptivity is highest( 3 ). Pinopodes is a membranous protuberance observed under scanning electron microscope and is considered as a morphological marker of endometrial receptivity( 4 ). Furthermore, there are several molecules change, which are deemed to be associated with endometrial receptivity, have been detected in women with RIF, such as mucin 1, integrin β3, homeobox A10 and leukemia inhibitor factor ( LIF )( 5 – 7 ). However, the precise etiology and pathogenesis of RIF have not been fully revealed. N6-methyladenosine (m 6 A) was first discovered in 1974 and is the most common RNA modification in eukaryotes( 8 ). Current studies have confirmed that the formation, removal, and other functions of m 6 A are accomplished by three types of regulatory proteins( 9 ). M 6 A modification is catalyzed by methyltransferase “writer” complex, which is an enzyme complex containing methyltransferase-like 3 ( METTL3 ), methyltransferase-like 14( METTL14 ), Wilms’ tumor 1-associating protein ( WTAP ) and other proteins possessing methyltransferase capability, like RNA-binding motif protein 15( RBM15 ) and zinc finger CCCH-type containing 13 ( ZC3H13 )( 10 ). In contrast, demethylase complexes include ALKB Homolog 5 ( ALKBH5 ) and Fat mass and obesity associated gene ( FTO ), which remove m 6 A, acting as “erasers”. In addition, m 6 A modified RNA can be recognized and regulated by m 6 A binding protein complexes, including YTH domain family proteins 1–3 (YTHDF1-3), and other proteins which acting as “readers”( 11 ). A study in 2021 confirmed that the mRNA expression levels of METTL16 and WTAP were down-regulated and ALKBH5 and IGF2BP2 were up-regulated in the endometrium of infertile patients( 12 ). Notably, it has been proved that global m 6 A methylation and METTL3 expression were significantly increased in the endometrial tissues from women with RIF ( 13 ). Therefore, these evidences indicate that the critical role of m 6 A modification in endometrium of RIF patients. Long noncoding RNAs (lncRNAs) are a class of noncoding RNAs longer than 200 nucleotides. They were regarded as transcription noise for a long time because of their low expression and non-protein coding features. Recently, lncRNAs have been indicated playing important role in embryo implantation. LncRNA TUNAR might participate in embryo implantation by regulating the adhesion of blastocysts to endometrial epithelium and regulating the proliferation and decidualization of endometrial stromal cells (ESCs)( 14 ). LncRNA CECR3 and other five lncRNAs together with their ceRNA sub-networks might be involved in immunological activity, growth factor binding, vascular proliferation, apoptosis steroid biosynthesis in uterus and preparing endometrium for embryo implantation( 15 ). Therefore, the regulation mediated by lncRNA may affect the process of embryo implantation. In addition, researches have demonstrated that lncRNAs also exhibit abundant m 6 A methylation modification( 16 , 17 ).N6-methyladenosine modification profile of lncRNA were established in many diseases, such as coronary artery disease( 18 ), gastric cancer( 19 ), ameloblastoma( 20 ) and systemic lupus erythematosus( 21 ), etc. However, the transcriptome-wide m 6 A methylome of lncRNA with RIF patients has not been determined to date. In this study, using m 6 A modified RNA immunoprecipitation sequencing (m 6 A-seq), the m 6 A methylation transcription profiles in RIF are established for the first time and differentially methylated peaks are identified. Then, competing endogenous RNA (ceRNA) was constructed by using bioinformatics to reveal a regulatory relationship between lncRNAs, miRNAs and mRNAs. This study comprehensively profiled m 6 A modification of lncRNAs in RIF to provide a new theoretical basis for pathogenic mechanism of RIF in the future. Materials and Methods Patients and tissue samples Subjects were recruited from the first Hospital of Lanzhou University. The inclusion criteria of all subjects recruited include: 1. under the age of 40; 2. with regular menstrual cycles (25–35 days); 3. normal basal serum sex hormone of FSH (< 10 mIU/mL), LH (< 10 mIU/mL) and estradiol (E2 < 50 pg/mL), measured on days 2–3 of the menstrual cycle. Women with one or more of the following situations are excluded:1. peripheral blood showing chromosomal anomaly;2. tested positive for anticardiolipin antibody or lupus anticoagulant; 3. polycystic ovary syndrome (PCOS); 4. uterine anomalies (congenital uterine anomaly, fibroid, polyps, intrauterine adhesions); 5. abnormal blood glucose level or thyroid dysfunction function test; 6. use steroid hormone in the preceding 2 months. Women recruited in RIF group must suffer from failure to achieve clinical pregnancy after being transferred at least four high-quality embryos in a minimum of three cycles( 1 ). Women recruited in control subjects, who were pregnant in the next cycle after sampling, underwent IVF/ICSI-ET due to fallopian tube or male factors. Endometrial sample were obtained on day LH + 7 via pipe suction curettage. After curettage, the endometrial sample was collected into a cryopreservation tube and stored in − 80ºC. A total of 6 women were recruited for our study; 3 were RIF patients and the other 3 women were used for the control group. All endometrial samples were middle secretory phase endometrium by histological diagnoses. This study was approved by the Ethics Committee of First Hospital of Lanzhou University (No: LDYYLL2019-45). RNA Extraction and quality Control Total RNA was isolated from endometrial tissue of control subjects and RIF subjects by TRIzol Reagent (Invitrogen, Gibco-BRL, Bethesda, MD, USA) according to the manufacturer’s instructions. NanoDropND-1000 was utilized to determine the concentration of RNA in each sample, and the ratio between OD260 and OD280 varied from 1.8 to 2.1. The ratio of 18S/28S ribosomal band intensities in 1% agarose gels containing ethidium bromide was used to test the quality of total RNA. Preparation and sequencing of RNA and MeRIP library The m 6 A-Seq service was provided by CloudSeq Inc. (Shanghai, China). In accordance with manufacturer's instructions, total RNA was immunoprecipitated using GenSeq® m 6 A-IP Kit (GenSeq Inc.). In brief, RNA was randomly fragmented to about 200nt using RNA Fragmentation Reagents, and Protein A/G beads were rotated for 1 hour to couple protein to antibody. Bead-linked antibodies were incubated with RNA fragments at 4°C for four hours. After incubation, the RNA/antibody complexes were washed for several times, and then, captured RNA was eluted from the complexes and purified. The manufacturer's instructions were followed to construct RNA libraries for IP and input samples using GenSeq® Low Input Whole RNA Library Prep Kit. Sequencing was carried out on a NovaSeq (Illumina) platform after library quality was evaluated with an Agilent 2100 bioanalyzer. MeRIP-qPCR Total purified RNA was eluted from the complexes after MeRIP. The abundances of LncRNAs were determined using qPCR and normalized to the input. MeRIP-qPCR primers used in this study are presented in Table ​1. Table 1 Primer sequences used for RT-qPCR transcript_id Primer sequences (5’-3’) Forward Reverse ENST00000416361 GGGGAAATGTGGGGAAAAGA GATCAACAGCATCCCAAGGC NR_027047 AGAATCGGGCAGCACTCAG AAGGGAGAAAGAAAGCGTGC ENST00000471664 AGTGACTCTTCTTGGACCAGG ATTGTGCCTCTCCAATCTGC Data analysis Briefly, paired-end reads were harvested from Illumina Novaseq 6000 sequencer and were quality controlled by Q30( 22 ). 3’ adaptor-trimming and low quality reads removing were proceed by cutadapt software (v1.9.3). Firstly, clean reads of all libraries were aligned to the reference genome (UCSC hg19) using Hisat2 software( 23 ) (v2.0.4). Then, HTSeq software (v0.9.1)( 24 ) was used to get the transcript level (lncRNA) raw count as the expression profiling, and edgeR (v3.16.5)( 25 ) was used to perform normalization and differentially expressed lncRNAs were identified. Methylated sites on RNAs which called peaks were identified by MACS software( 26 ). Differentially methylated sites were identified using diffReps( 27 ). The peaks located on lncRNA were screened by home-made scripts and annotated accordingly. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed by the associated genes of differentially methylated lncRNAs( 28 ). The miRNA-target gene prediction software, lncRNASNP2( 29 ) and TargetScan( 30 ) were used to predict miRNAs and mRNAs which were combined for the screened lncRNAs. The ceRNA network was plotted using Cytoscape(v3.9.1)( 31 ). Results Overview of m 6 A methylation map within lncRNAs in the controls and RIFs Genome-wide profiling of m 6 A modified lncRNAs in three biological replicates from the control group (N = 3) and RIF group (N = 3) were performed. The data had been submitted to gene expression omnibus (GEO accession number: GSE205398). 10504(1743 + 8761) and 10512(1751 + 8761) m 6 A peaks in the RIF and control groups were identified respectively, and there were 8761 peaks exist in both RIF group and control group (Fig. 1 A). As shown in Fig. 1B, 10504 m 6 A peaks were mapped to 7766(6782 + 984) lncRNAs in RIF group and 10512 m 6 A peaks were mapped to 7720(6782 + 938) lncRNAs in control group. In 8761 common peaks, 1868(1443 + 425) significantly differentially methylated peaks were identified (fold change ≥ 2 and P ≤ 0.0001). The 1868 differentially methylated m 6 A peaks were located across 623(465 + 158) lncRNAs. Among these lncRNAs, 158 lncRNAs were hypomethylated and 465 were hypermethylated (Fig. 1 C). All methylated lncRNAs were clustered (fold-change ≥ 2.0 and P ≤ 0.00001), indicating that these lncRNAs have different expression patterns in the two groups (Fig. 1 D). A search for motifs that enriched in the 50 bp area around m 6 A peak by DREME software( 32 ). As shown in Fig. 1 E, the identified m 6 A peaks contain RRACH conserved sequence motif (R represents purine, A is m 6 A and H is a non-guanine base), which confirmed the existence of m 6 A methylation mechanism. Distribution of differentially methylated N6-methyladenosine peaks As shown in Fig. 2 A, compared to control group, 1443 significantly up-regulated m 6 A peaks and 425 significantly down-regulated m 6 A peaks were identified in RIF group (fold change ≥ 2 and P ≤ 0.0001). All differentially m 6 A methylated peaks could be found in Supplementary Table 1. The top 10 hypermethylated lncRNAs and hypomethylated lncRNAs were listed respectively in Table 2 and Table 3 . To further understand the differentially m 6 A methylated peaks in RIF, m 6 A modified lncRNAs were divided into the following six categories based on the positional relationship between the m 6 A methylated lncRNA and mRNA: bidirectional, exon sense overlap, intron sense overlap, intron antisense, natural antisense and intergenic( 33 ). Results revealed that 51.02% of the differentially m 6 A methylated lncRNAs were in the exon sense-overlapping group (Fig. 2 B). By analyzing the distribution of m 6 A peaks per lncRNA, we found that the majority of lncRNAs had one m 6 A peak (Fig. 2 C). All differentially m 6 A methylated peaks were mapped to human chromosomes. These m 6 A peaks were found in all chromosomes, except chrY, and were particularly found in chr1, chr16 and chr19 (Fig. 2 D). Table 2 Top ten hypomethylated lncRNAs. Chromosome txStart txEnd transcript_id GeneName Foldchange chr22 17155996 17156936 ENST00000338526 ANKRD62P1-PARP4P3 169.3 chr14 90314776 90314840 ENST00000550103 EFCAB11 140.1 chr9 80864161 80864460 ENST00000536374 CEP78 112.9 chr7 101032148 101032361 ENST00000413033 RP5-1106H14.1 108 chr9 21395981 21396345 ENST00000569618 RP11-354P17.15 102.7 chr3 77679221 77679620 ENST00000470802 ROBO2 100.1 chr8 104029261 104029817 ENST00000518264 NPM1P52 97 chr1 71927243 71927500 ENST00000587066 ZRANB2-AS2 89.8 chr13 78233547 78233560 ENST00000450718 SPTLC1P5 87.6 chr6 27235891 27236080 ENST00000604325 XXbac-BPGBPG24O18.1 85 Table 3 LncRNAs screened for lncRNA-miRNA-mRNA analysis. transcript_id GeneName Foldchange Regulation NR_045659 COLEC11 96.7 hypermethylate ENST00000490872 SLC11A1 93.8 hypermethylate ENST00000539408 GABARAPL1 73.3 hypermethylate ENST00000487643 KLHL6 72.3 hypermethylate ENST00000599041 AC010127.3 67.0 hypermethylate ENST00000550103 EFCAB11 140.1 hypomethylate ENST00000536374 CEP78 112.9 hypomethylate ENST00000413033 RP5-1106H14.1 108 hypomethylate ENST00000569811 GOLGA8B 78.1 hypomethylate ENST00000538693 LINC01152 73.5 hypomethylate Functional analysis of genes associated differentially methylated lncRNA To explore the physiological and pathological significance of m 6 A modification in RIF, GO analysis and KEGG pathway analysis were performed for the genes associated differentially methylated lncRNA. In GO analysis, genes were classified into three functional groups: biological process (BP), cellular component (CC) and molecular function (MF). The top 10 significantly enriched BPs, CCs and MFs of the genes associated hypermethylated lncRNAs were listed in Fig. 3 A, while GO analysis of genes associated hypomethylated lncRNAs were shown in Fig. 3 B. GO analysis reveal that genes associated hypermethylated lncRNAs were significantly enriched in neuron projection, regulation of GTPase activity and nucleoside-triphosphatase regulator activity (Fig. 3 A). Genes associated hypomethylated lncRNAs were significantly enriched in regulation of chromosome segregation, golgi stack, and peptidase activity (Fig. 3 B). For KEGG pathway analysis, we found that genes associated hypermethylated lncRNAs in RIF were significantly associated with choline metabolism, phospholipase D signaling pathway, and valine, leucine and isoleucine degradation (Fig. 3 C). The genes associated hypomethylated lncRNAs were involved in p53 signaling pathway, transcriptional mis regulation in cancer and Natural killer cell mediated cytotoxicity (Fig. 3 D). The phospholipase D signaling pathway (Fig. 4 A) and p53 signaling pathway (Fig. 4 B) were the most significant enriched pathways related to RIF and embryo implantation. Construction of lncRNA-miRNA-mRNA network in RIF To explore the mRNAs regulated by lncRNAs, we screened five lncRNAs with fold changes > 70 out of 454 hypermethylated lncRNAs and five lncRNAs with fold changes > 60 out of 151 hypomethylated lncRNAs (Table 4). A ceRNA network was constructed by lncRNA-miRNA-mRNA association analysis (Fig. 5 ). The network consisted of the top five miRNAs (defined by exact probability from lncRNASNP2) combined with a screened lncRNA and the top five mRNAs (defined by cumulative weighted context + + score) bound to the miRNAs, including 10 lncRNAs, 49 miRNAs and 232 mRNAs (Supplementary Table 2). From this ceRNA network, it is clear that lncRNAs regulate to miRNAs and mRNAs. For instance, LINC01152 could act as a sponge for hsa-miR-6801-3p to affect LIF expression. Conjoint analysis of MeRIP-seq and RNA-seq data In order to explore the association between the expression of lncRNA and m 6 A methylation, a conjoint analysis was conducted for m6A-seq and RNA-Seq data. There were 72 lncRNAs with both different m 6 A methylation levels and expression levels (Supplementary Table 3). Among them, 19 lncRNAs were up-regulated with hypermethylation, 19 lncRNAs were down-regulated with hypermethylation, 33 lncRNAs down-regulated and hypomethylated, and only one lncRNA were up-regulated with hypomethylation. Among the 72 lncRNA in Supplementary Table 3, three of them (ENST00000416361, NR_027047, ENST00000471664) were randomly selected. The m6A methylation abundances are visualized using Integrative genomics viewer (IGV) (Fig. 6 A, 6 B, 6 C) and verified by MeRIP-qPCR (Fig. 6 D, 6 E, 6 F). Discussion RIF is a challenging clinical dilemma. Many studies found that m 6 A modifications play vital roles in reproductive diseases, but the transcriptome-wide m 6 A profiling of lncRNA of RIF has not been characterized yet. In this study, RIF transcriptome-wide lncRNA m 6 A modification profile was established using m 6 A-Seq. By using m 6 A-seq, 1868 differentially methylated lncRNA m 6 A peaks was identified in endometrial tissues of RIF, of which 1443 m 6 A peaks were significantly up-regulated and 425 m 6 A peaks were significantly down-regulated. Furthermore, differential expression patterns of differential methylated lncRNA were explored in RIF and control. Moreover, GO and KEGG pathway analyses were performed to reveal the potential functions of differentially methylated transcripts. A ceRNA network was constructed to reveal a regulatory relationship between lncRNAs, miRNAs and mRNAs. This study comprehensively profiled m 6 A modification patterns of lncRNAs in RIF to indicate directions for the diagnosis and treatment of RIF in the future. M 6 A plays a crucial role in mammalian including human fertility. As early as in 2013, ALKBH5 acting as a m 6 A “erasers”, were identified to impair fertility via affecting the apoptosis of meiotic metaphase-stage spermatocytes( 34 ). Subsequently, several m 6 A regulators were discovered to play an important role in reproductive diseases, such as endometriosis( 35 ), polycystic ovary syndrome( 36 ), primary ovarian insufficiency( 37 ) and so on. In one study reported in 2021( 13 ), colorimetric m 6 A quantification strategy was used to examined m 6 A level from control and RIF patients. Global m 6 A level was significantly increased in the endometrial tissues from women with RIF compared with the controls which is consistent with our study. In the present study, it is also proved that m 6 A level were increased in RIF (Fig. 1 D), indicating that m 6 A is important for the pathogenesis of RIF. It’s also reported non-coding RNAs are important in RIF. HOX family have been clarified as critical regulators in endometrial decidualization( 38 ). HOXA11-AS , one lncRNA in the HOX gene family, was elevated in RIF patients. And the pattern of high HOXA11-AS expression and impaired PKM2 splicing was consistently exist in RIF patients( 39 ). LINC02190 was verified upregulating in RIF endometrium and could decrease the adhesion rate of Ishikawa and JAR cells( 40 ). Numbers of researches indicated the role of LncRNAs in RIF couldn’t be ignored. However, lncRNA modified via m 6 A has not been reported in RIF. What's novel about our study is combining m 6 A with lncRNAs. Classification of m 6 A modified lncRNAs has been analyzed in this study. As shown in Fig. 2 B, most differentially m 6 A modified lncRNAs are exon sense-overlaping. In previous study, the majority of differentially m 6 A modified lncRNAs in colorectal cancer are also exon sense-overlaping( 41 ). However, there are several studies with another conclusion. Most differentially methylated m6A sites within lncRNAs were located in intergenic region, in coronary artery disease( 18 ) and gastric cancer( 19 ). Thus, the dominant m 6 A modified lncRNAs present different categories in different diseases. It's worth noting that exon sense-overlaping lncRNAs were proved being relevant to cell proliferation, invasion and metastasis recently, which indicated an important role in m 6 A modified lncRNAs playing of RIF. In the present study, differentially methylated lncRNAs related to many important biological pathways were detected. In KEGG pathway analysis of genes associated hypermethylated lncRNAs, top 5 terms were showed in Fig. 3 C, we focus on choline metabolism, phospholipase D signaling pathway and amino acid metabolism. Choline metabolism is related to Phospholipase D signaling pathway. Phospholipase D (PLD) is a phospholipase enzyme responsible for hydrolyzing phosphatidylcholine into the lipid signaling molecule, phosphatidic acid and choline( 42 ). Choline is a micronutrient and a methyl donor( 43 ). Arzu Yurci et al.( 44 ) demonstrated that Choline signals were found to lower in RIF group compared to the fertile group through MR spectroscopy. These results suggested that Choline might participate in the pathogenesis of RIF through m 6 A formation. Moreover, genes associated hypermethylated lncRNAs were related to amino acid metabolism, such as valine degradation and histidine metabolism. Increasing evidence demonstrated that amino acid metabolism were key regulators in RIF. In previous studies, researchers found that it exists discrimination at metabolomic level between RIF and repeated implantation success (RIS). Valine was found to be significantly down-regulated in women with RIS when compared with those with RIF( 45 ). Recently, one systematic review revealed alanine, aspartate and glutamate metabolism have the highest impact factor in RIF( 46 ). According to our results, it’s suggested that m 6 A could influence the outcome of embryo implantation by valine degradation and histidine metabolism. In contrast to genes associated hypermethylated lncRNAs, genes associated hypomethylated lncRNAs were mainly enriched in immunological process. For instance, natural killer cell mediated cytotoxicity and chemokine signaling pathway and were list in the top 5 KEGG pathways (Fig. 3 D). Cellular cytotoxicity, the ability to kill other cells, is an important effector mechanism of the immune system. Natural killer (NK) cells are the major mediators of this activity( 47 ). NK cells are recruited and activated by ovarian hormones and have pivotal roles throughout pregnancy. Decidual natural killer (dNK) cells release chemokines that induce trophoblast invasion, tissue remodeling, embryonic development, and placentation. NK cells can also mediate cytotoxicity and carry out immune defense if infected in utero by pathogens( 48 – 50 ). Past research suggested an association between RIF with abnormally elevated uterine NK cells' numbers, as well as with cytotoxicity( 49 ). It has been proposed that intrauterine administration of peripheral blood mononuclear cells (PBMCs) modulates maternal immune response through a cascade of chemokines to favor implantation. The clinical pregnancy rate was significantly improved by this therapy, which was proved reliability through meta-analysis( 51 , 52 ). Based on the results of our study, we hypothesize that m 6 A associated with immunological process could influence pregnancy outcomes in patients with RIF, which may be a novel mechanism of the regulation of immunological process in RIF. Genes associated hypomethylated lncRNAs were also involved in p53 signaling pathway, which is one classical signaling pathways. P53 is well-known as a tumor suppressor, however, little is known about its other functions except tumor suppression. With the aid of the p53MH algorithm, leukemia inhibitory factor ( LIF ) was identified as p53-regulated genes by Dr Arnold Levine’s group( 53 ). P53 is crucial for embryonic implantation through up-regulation of uterine LIF transcription( 54 ). Recently, it has been proposed that the absence of PARP-1 and PARP-2 resulted in increased p53 signaling and an increased population of senescent decidual cells( 55 ). In a case-control survey, 100 cases with RIF and 100 women with the normal pregnancy were involved. This research suggested that p53 gene polymorphisms could be a genetic predisposing factor for RIF( 56 ). Based on the results of our study, m 6 A might participate in p53 signaling pathway, which could explain pathogenic mechanism of RIF. In this study, we constructed a ceRNA network to analyze the regulatory relationship between lncRNAs, miRNAs and mRNA (Fig. 4 ). The potential target genes of several lncRNAs in this ceRNA were related to reproductive disease. For instance, LINC01152 could act as a sponge for hsa-miR-6801-3p to affect LIF expression (Fig. 4 ). LIF activates signal transducer and activator of transcription 3 ( STAT3 ) precursors via uterine LIF receptors (LIFRs), allowing successful blastocyst implantation( 57 ). Except LIF , PRX , CD69 and HLA-F are potential target genes in ceRNA network. The expression of the PRX was significantly decreased in ectopic endometrium compared to that in eutopic or control endometrium( 58 ). In one endometrial immune assessment, sub-fertile populations exhibited increased CD69 activation( 59 ). Lately, the articles published in top journals reported HLA-F fluctuates during the menstrual cycle with high levels during the implantation window. The level of HLA-F protein expression correlates with the number of CD56-positive NK cells in the mid-secretory endometrium( 60 ). The functions of abovementioned genes in decidualization and RIF require further investigation. However, from these genes, it is clear that the network can be great significance to further understand the role of lncRNAs in the occurrence and development of RIF. The m 6 A methylation abundances of 3 lncRNAs (ENST00000416361, NR_027047, ENST00000471664) were verified by MeRIP-qPCR in this study. It is reported that lncRNA ENST00000416361 is upregulated in coronary artery disease and is related to inflammation and lipid metabolism( 61 ). LINC00282 (NR_027047) was potentially relevant to the stimulation of cell proliferation mediated by GnRHa( 62 ). ENST00000471664 was identified for its significant loss of expression in GLIS3 knockout mouse pancreatic islets( 63 ). However, these studies are superficial and limited to bioinformatics. Interestingly, our results indicated that several lncRNA were differential expression with hypermethylated/hypomethylated in RIF. There have been many studies on the involvement of m 6 A in the expression of lncRNAs( 64 , 65 ). We suspected that m 6 A might participate in the expression of lncRNA in RIF, which still need more assay to support. Several limitations exist in the current study, which are due to the character of primary studies. The scale of samples is minimum for high-throughput methods. To make up for these deficiencies, MeRIP-qPCR was used to ensure the accuracy of m 6 A-seq results. In this study, bioinformatics analysis was used which were mainly descriptive. It is necessary to explore the relationship of m 6 A and lncRNAs in RIF in subsequent experiments. Conclusions In summary, this study revealed the m 6 A lncRNA methylation landscape by high-throughput sequencing, which provided new perspective on the pivotal roles of epigenetic changes in RIF. What’s more, a ceRNA network revealed a regulatory relationship between lncRNAs, miRNAs and mRNAs. Further experiments should be carried out to verify the differential methylated lncRNAs in RIF patients. Declarations Ethics Approval The study was approved by the Ethics Committee of Lanzhou University First Affiliated Hospital on 21/2/2019. Consent to Participate All procedures were in accordance with the ethical standards of the institutional research committee and with the Helsinki declaration and its later amendments or comparable ethical standards. Written informed consents were obtained from all patients prior to study commencement. Consent for Publication Not applicable. Availability of data and materials The datasets generated for this study could be found in the online repositories. The names of the dataset and accession number could be found below: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE205398. Conflict of Interest The authors declare no competing interests. Declaration of Funding This work was financially supported by the National Natural Science Foundation of China (82360307), the Talent Innovation and Entrepreneurship Project of Lanzhou City (2022-RC-47), Gansu Provincial Science and Technology Program (20JR10TA715,21JR7RA391), the Medical Innovation and Development Project of Lanzhou University (lzuyxcx-2022-191), Science and Technology Project of Lanzhou (2023-ZD-93), Fund of the First Hospital of Lanzhou University (ldyyyn2022-60), Research Project for Young and Middle-aged Doctors in Reproductive Medicine (BJHPA-2021-SHZHYXZHQNYJ-009), Fund of the First Hospital of Lanzhou University (ldyyyn2019-86). Authors' contributions The first two authors contributed equally to this work. Lin Liu designed and supervised the study. Ting Wang conducted the experiments and drafted the manuscript. Lili Zhang and Xiaoling Ma collected the samples. Wenxin Gao and Yidan Liu contributed the methodology. Feng Yue modified the format. 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Zhao F, Chen T, Zhao X, Wang Q, Lan Y, Liang Y, et al. LINC02190 inhibits the embryo-endometrial attachment by decreasing ITGAD expression. Reproduction (Cambridge, England). 2022;163(2):107-18. Zuo L, Su H, Zhang Q, Wu WY, Zeng Y, Li XM, et al. Comprehensive analysis of lncRNAs N(6)-methyladenosine modification in colorectal cancer. Aging. 2021;13(3):4182-98. May-Dracka TL, Gao F, Hopkins BT, Hronowski X, Chen T, Chodaparambil JV, et al. Discovery of Phospholipase D Inhibitors with Improved Drug-like Properties and Central Nervous System Penetrance. ACS Med Chem Lett. 2022;13(4):665-73. Bekdash RA. Neuroprotective Effects of Choline and Other Methyl Donors. Nutrients. 2019;11(12). Yurci A, Dokuzeylul Gungor N, Gurbuz T. Spectroscopy analysis of endometrial metabolites is a powerful predictor of success of embryo transfer in women with implantation failure: a preliminary study. Gynecol Endocrinol. 2021;37(5):415-21. RoyChoudhury S, Singh A, Gupta NJ, Srivastava S, Joshi MV, Chakravarty B, et al. Repeated implantation failure versus repeated implantation success: discrimination at a metabolomic level. Hum Reprod. 2016;31(6):1265-74. Zhang Y, Zhang T, Wu L, Li TC, Wang CC, Chung JPW. Metabolomic markers of biological fluid in women with reproductive failure: a systematic review of current literatures. Biology of reproduction. 2022. Prager I, Watzl C. Mechanisms of natural killer cell-mediated cellular cytotoxicity. J Leukoc Biol. 2019;105(6):1319-29. Díaz-Hernández I, Alecsandru D, García-Velasco JA, Domínguez F. Uterine natural killer cells: from foe to friend in reproduction. Human reproduction update. 2021;27(4):720-46. Sfakianoudis K, Rapani A, Grigoriadis S, Pantou A, Maziotis E, Kokkini G, et al. The Role of Uterine Natural Killer Cells on Recurrent Miscarriage and Recurrent Implantation Failure: From Pathophysiology to Treatment. Biomedicines. 2021;9(10). Zhang X, Wei H. Role of Decidual Natural Killer Cells in Human Pregnancy and Related Pregnancy Complications. Front Immunol. 2021;12:728291. Yakin K, Oktem O, Urman B. Intrauterine administration of peripheral mononuclear cells in recurrent implantation failure: a systematic review and meta-analysis. Sci Rep. 2019;9(1):3897. Yang DN, Wu JH, Geng L, Cao LJ, Zhang QJ, Luo JQ, et al. Efficacy of intrauterine perfusion of peripheral blood mononuclear cells (PBMC) for infertile women before embryo transfer: meta-analysis. J Obstet Gynaecol. 2020;40(7):961-8. Hu W, Feng Z. The role of p53 in reproduction, an unexpected function for a tumor suppressor. Journal of molecular cell biology. 2019;11(7):624-7. Levine AJ, Tomasini R, McKeon FD, Mak TW, Melino G. The p53 family: guardians of maternal reproduction. Nature reviews Molecular cell biology. 2011;12(4):259-65. Kelleher AM, Setlem R, Dantzer F, DeMayo FJ, Lydon JP, Kraus WL. Deficiency of PARP-1 and PARP-2 in the mouse uterus results in decidualization failure and pregnancy loss. Proceedings of the National Academy of Sciences of the United States of America. 2021;118(40). Mohammadzadeh M, Ghorbian S, Nouri M. Evaluation of clinical utility of P53 gene variations in repeated implantation failure. Molecular biology reports. 2019;46(3):2885-91. Fukui Y, Hirota Y, Saito-Fujita T, Aikawa S, Hiraoka T, Kaku T, et al. Uterine Epithelial LIF Receptors Contribute to Implantation Chamber Formation in Blastocyst Attachment. Endocrinology. 2021;162(11). Yu H, Hao JM, Li X, Li F, Li J, Li L. Decreased Expression of Peroxiredoxin in Patients with Ovarian Endometriosis Cysts. Archives of medical research. 2020;51(7):670-4. Marron K, Walsh D, Harrity C. Detailed endometrial immune assessment of both normal and adverse reproductive outcome populations. Journal of assisted reproduction and genetics. 2019;36(2):199-210. Nilsson LL, Hviid TVF. HLA Class Ib-receptor interactions during embryo implantation and early pregnancy. Human reproduction update. 2022;28(3):435-54. Li P, Yan X, Xu G, Pang Z, Weng J, Yin J, et al. A novel plasma lncRNA ENST00000416361 is upregulated in coronary artery disease and is related to inflammation and lipid metabolism. Molecular medicine reports. 2020;21(6):2375-84. Hadziselimovic F, Verkauskas G, Vincel B, Stadler MB. Testicular expression of long non-coding RNAs is affected by curative GnRHa treatment of cryptorchidism. Basic and clinical andrology. 2019;29:18. Scoville DW, Gruzdev A, Jetten AM. Identification of a novel lncRNA (G3R1) regulated by GLIS3 in pancreatic β-cells. Journal of molecular endocrinology. 2020;65(3):59-67. Zhang L, Wan Y, Zhang Z, Jiang Y, Gu Z, Ma X, et al. IGF2BP1 overexpression stabilizes PEG10 mRNA in an m6A-dependent manner and promotes endometrial cancer progression. Theranostics. 2021;11(3):1100-14. Liu HT, Zou YX, Zhu WJ, Sen L, Zhang GH, Ma RR, et al. lncRNA THAP7-AS1, transcriptionally activated by SP1 and post-transcriptionally stabilized by METTL3-mediated m6A modification, exerts oncogenic properties by improving CUL4B entry into the nucleus. Cell Death Differ. 2022;29(3):627-41 Additional Declarations No competing interests reported. Supplementary Files Supplementarytable1.xlsx Supplementarytable2.xlsx supplementarytable3.xlsx Cite Share Download PDF Status: Published Journal Publication published 11 Oct, 2024 Read the published version in BMC Medical Genomics → Version 1 posted Editorial decision: Revision requested 17 Jul, 2024 Reviews received at journal 12 Jul, 2024 Reviewers agreed at journal 28 Jun, 2024 Reviewers agreed at journal 27 Jun, 2024 Reviews received at journal 26 Jun, 2024 Reviewers agreed at journal 20 Jun, 2024 Reviewers invited by journal 19 Jun, 2024 Editor assigned by journal 12 Jun, 2024 Submission checks completed at journal 11 Jun, 2024 First submitted to journal 11 Jun, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4563715","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":319495336,"identity":"9fd4e528-82ea-4110-a755-e0ae06583997","order_by":0,"name":"Ting Wang","email":"","orcid":"","institution":"The first Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Wang","suffix":""},{"id":319495338,"identity":"7cd5f49d-3fab-47d3-ab86-921249018782","order_by":1,"name":"Lili Zhang","email":"","orcid":"","institution":"The reproductive center, the First Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Zhang","suffix":""},{"id":319495341,"identity":"dcc09a22-14cd-4f73-865a-9719fa8d756a","order_by":2,"name":"Wenxin Gao","email":"","orcid":"","institution":"The first Clinical Medical College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Wenxin","middleName":"","lastName":"Gao","suffix":""},{"id":319495345,"identity":"d60b4d29-00ee-4ccd-ae20-d33c3414a19f","order_by":3,"name":"Yidan Liu","email":"","orcid":"","institution":"The Basic Medical Sciences College of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yidan","middleName":"","lastName":"Liu","suffix":""},{"id":319495346,"identity":"0ff3379c-d8df-4a91-977c-0ad0dcf035d6","order_by":4,"name":"Feng Yue","email":"","orcid":"","institution":"The reproductive center, the First Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Yue","suffix":""},{"id":319495349,"identity":"76c3615c-6314-46ba-9f9d-293d88b20271","order_by":5,"name":"Xiaoling Ma","email":"","orcid":"","institution":"The reproductive center, the First Hospital of Lanzhou University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoling","middleName":"","lastName":"Ma","suffix":""},{"id":319495350,"identity":"bad56a69-4783-4eac-8ad8-a2560d4f7fd3","order_by":6,"name":"Lin Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACPoYEIFkgwcDH3twAEmBsIKSFDazFQIKBjecgaVqADIlEYrWw55hJMBhYJLZJPmz+zMNgI7vhAPOzB3i18LwBaZFIbJNObDDmYUgz3nCAzdwArxaJHISWZB6Gw4kbDvCwSRCnRfJgw2Eehv+kaJFgbGzmYThAhBaeZ8UWQC3GbTyJzYxzDJKNZx5mM8OrhZ89eeMNhoo62X72w4c/vKmwk+073vwMrxYGBg4T6T9wDiiomPGrBwL2xx8IqhkFo2AUjIKRDQDzkD16rEfU/QAAAABJRU5ErkJggg==","orcid":"","institution":"The reproductive center, the First Hospital of Lanzhou University","correspondingAuthor":true,"prefix":"","firstName":"Lin","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-06-11 11:29:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4563715/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4563715/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12920-024-02013-3","type":"published","date":"2024-10-11T15:56:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":59964599,"identity":"f9645b48-eb67-427f-b0f9-e06396fd6cdc","added_by":"auto","created_at":"2024-07-10 01:52:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2011007,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of m6A methylation in RIF. (A) Venn diagram of m6A modification sites identified in lncRNAs from RIF group and control group; (B) Venn diagram of m6A modified lncRNAs from RIF group and control group; (C) General numbers of differentially methylated peaks and associated lncRNAs;(D) Cluster analysis of the m6A modified lncRNA genes in RIF group and control group. Fold-change≥2.0, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.00001;(E) The top five m6A motifs enriched from the RIF group and control group.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4563715/v1/23f46f09c17a72a7710c0d1b.jpg"},{"id":59964598,"identity":"1125086e-750d-4804-a1f7-f59ce26d30c3","added_by":"auto","created_at":"2024-07-10 01:52:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1087730,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of differentially methylated N6-methyladenosine sites. (A) Volcano plots indicating the distribution of differential m6A peaks in RIF. Fold-change≥2.0, P \u0026lt; 0.00001, via Fisher’ s exact test;(B) Positional relationship between differentially m6A methylated lncRNAs and mRNAs;(C) The distribution of altered m6A peaks per lncRNA;(D) Chromosomal distribution of all differentially methylated sites within lncRNAs.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4563715/v1/a05c3bc534381ee0cf33db30.jpg"},{"id":59964594,"identity":"cd53bdce-38b8-4657-96bf-ca5610c4b17b","added_by":"auto","created_at":"2024-07-10 01:52:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2478047,"visible":true,"origin":"","legend":"\u003cp\u003eGene ontology enrichment and pathway analysis of altered m6A lncRNA. (A) The top 10 GO terms of genes with upregulated m6A peaks. (B) The top 10 GO terms of genes with downregulated m6A peaks. (C) The top 5 KEGG pathways of genes with upregulated m6A peaks. (D) The top 5 KEGG pathways of genes with downregulated m6A peaks.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4563715/v1/d1aa6ae1c3229b4e36b8cc86.jpg"},{"id":59965570,"identity":"f0fbae3c-91e5-4335-9da9-79b8c63f9828","added_by":"auto","created_at":"2024-07-10 02:00:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1625704,"visible":true,"origin":"","legend":"\u003cp\u003eSignal pathway diagram of (A) phospholipase D signaling pathway; (B) p53 signaling pathway.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4563715/v1/0be0187d6e75e5917730f294.jpg"},{"id":59965571,"identity":"1e5bdafe-1393-4bf7-b77a-04378a408634","added_by":"auto","created_at":"2024-07-10 02:00:09","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1317783,"visible":true,"origin":"","legend":"\u003cp\u003eThe network of lncRNA-miRNA-mRNA regulation in RIF.\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4563715/v1/1e2019dab9db0338ec31d9c4.jpg"},{"id":59964597,"identity":"71e2c645-da0b-42e2-8b38-e6bd63159462","added_by":"auto","created_at":"2024-07-10 01:52:08","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2027909,"visible":true,"origin":"","legend":"\u003cp\u003eValidation of the m6A methylation abundances of lncRNAs. Integrative genomics viewer (IGV) plots of ENST00000416361 (A), NR_027047 (B) and ENST00000471664 (C) m6A methylation abundances and expression abundances in RIF and control. In the IGV, IP refers methylation level, and input refers expression level. The m6A methylation abundances of ENST00000416361 (D), NR_027047 (E) and ENST00000471664 (F) was validated in the endometrial tissue by MeRIP-qPCR.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4563715/v1/fda000d639e284ab0e1b5de3.jpg"},{"id":66597380,"identity":"7efce2d8-2b9d-4e1f-8173-0b4ce59bdde1","added_by":"auto","created_at":"2024-10-14 16:10:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11235122,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4563715/v1/29114415-f93e-4c73-a87b-02312c3f2606.pdf"},{"id":59964601,"identity":"79d4c0ac-e491-4b05-9858-31d6b9417c92","added_by":"auto","created_at":"2024-07-10 01:52:09","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":371656,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4563715/v1/ba0422a3b7cc610422514a3d.xlsx"},{"id":59964596,"identity":"5ae5b99b-56d5-473f-bf16-4956a1d8567f","added_by":"auto","created_at":"2024-07-10 01:52:08","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":14699,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4563715/v1/63f56161d61573c014ce6320.xlsx"},{"id":59964602,"identity":"8cca3913-0370-4110-8474-afa6c6bc3b5f","added_by":"auto","created_at":"2024-07-10 01:52:09","extension":"xlsx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":45128,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarytable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4563715/v1/edc81bf3624f8115561277d0.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptome-wide N6-methyladenosine modification profiling of long non-coding RNAs in patients with recurrent implantation failure","fulltext":[{"header":"Background","content":"\u003cp\u003eWith the development of assisted reproductive technology, the success rate has exceeded 50%, but there are still some patients who fail after recurrent implantation, which brings a huge financial and psychological burden to the patients. RIF is usually defined as a failure to achieve clinical pregnancy after at least four high-quality embryos are transferred in women under the age of 40 within at least three fresh or frozen cycles(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). According to the definition, high-quality embryos are transplanted in RIF patients, and low endometrial receptivity is the main cause(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In normal menstrual cycle, 6\u0026ndash;8 days after ovulation is considered as window of implantation (WOI), when endometrial receptivity is highest(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Pinopodes is a membranous protuberance observed under scanning electron microscope and is considered as a morphological marker of endometrial receptivity(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Furthermore, there are several molecules change, which are deemed to be associated with endometrial receptivity, have been detected in women with RIF, such as mucin 1, integrin β3, homeobox A10 and leukemia inhibitor factor (\u003cem\u003eLIF\u003c/em\u003e)(\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, the precise etiology and pathogenesis of RIF have not been fully revealed.\u003c/p\u003e \u003cp\u003eN6-methyladenosine (m\u003csup\u003e6\u003c/sup\u003eA) was first discovered in 1974 and is the most common RNA modification in eukaryotes(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Current studies have confirmed that the formation, removal, and other functions of m\u003csup\u003e6\u003c/sup\u003eA are accomplished by three types of regulatory proteins(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). M\u003csup\u003e6\u003c/sup\u003eA modification is catalyzed by methyltransferase \u0026ldquo;writer\u0026rdquo; complex, which is an enzyme complex containing methyltransferase-like 3 (\u003cem\u003eMETTL3\u003c/em\u003e), methyltransferase-like 14(\u003cem\u003eMETTL14\u003c/em\u003e), Wilms\u0026rsquo; tumor 1-associating protein (\u003cem\u003eWTAP\u003c/em\u003e) and other proteins possessing methyltransferase capability, like RNA-binding motif protein 15(\u003cem\u003eRBM15\u003c/em\u003e) and zinc finger CCCH-type containing 13 (\u003cem\u003eZC3H13\u003c/em\u003e)(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). In contrast, demethylase complexes include ALKB Homolog 5 (\u003cem\u003eALKBH5\u003c/em\u003e) and Fat mass and obesity associated gene (\u003cem\u003eFTO\u003c/em\u003e), which remove m\u003csup\u003e6\u003c/sup\u003eA, acting as \u0026ldquo;erasers\u0026rdquo;. In addition, m\u003csup\u003e6\u003c/sup\u003eA modified RNA can be recognized and regulated by m\u003csup\u003e6\u003c/sup\u003eA binding protein complexes, including YTH domain family proteins 1\u0026ndash;3 (YTHDF1-3), and other proteins which acting as \u0026ldquo;readers\u0026rdquo;(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). A study in 2021 confirmed that the mRNA expression levels of \u003cem\u003eMETTL16\u003c/em\u003e and \u003cem\u003eWTAP\u003c/em\u003e were down-regulated and \u003cem\u003eALKBH5\u003c/em\u003e and \u003cem\u003eIGF2BP2\u003c/em\u003e were up-regulated in the endometrium of infertile patients(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Notably, it has been proved that global m\u003csup\u003e6\u003c/sup\u003eA methylation and \u003cem\u003eMETTL3\u003c/em\u003e expression were significantly increased in the endometrial tissues from women with RIF (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Therefore, these evidences indicate that the critical role of m\u003csup\u003e6\u003c/sup\u003eA modification in endometrium of RIF patients.\u003c/p\u003e \u003cp\u003eLong noncoding RNAs (lncRNAs) are a class of noncoding RNAs longer than 200 nucleotides. They were regarded as transcription noise for a long time because of their low expression and non-protein coding features. Recently, lncRNAs have been indicated playing important role in embryo implantation. LncRNA \u003cem\u003eTUNAR\u003c/em\u003e might participate in embryo implantation by regulating the adhesion of blastocysts to endometrial epithelium and regulating the proliferation and decidualization of endometrial stromal cells (ESCs)(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). LncRNA \u003cem\u003eCECR3\u003c/em\u003e and other five lncRNAs together with their ceRNA sub-networks might be involved in immunological activity, growth factor binding, vascular proliferation, apoptosis steroid biosynthesis in uterus and preparing endometrium for embryo implantation(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Therefore, the regulation mediated by lncRNA may affect the process of embryo implantation. In addition, researches have demonstrated that lncRNAs also exhibit abundant m\u003csup\u003e6\u003c/sup\u003eA methylation modification(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).N6-methyladenosine modification profile of lncRNA were established in many diseases, such as coronary artery disease(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), gastric cancer(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e), ameloblastoma(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) and systemic lupus erythematosus(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), etc. However, the transcriptome-wide m\u003csup\u003e6\u003c/sup\u003eA methylome of lncRNA with RIF patients has not been determined to date.\u003c/p\u003e \u003cp\u003eIn this study, using m\u003csup\u003e6\u003c/sup\u003eA modified RNA immunoprecipitation sequencing (m\u003csup\u003e6\u003c/sup\u003eA-seq), the m\u003csup\u003e6\u003c/sup\u003eA methylation transcription profiles in RIF are established for the first time and differentially methylated peaks are identified. Then, competing endogenous RNA (ceRNA) was constructed by using bioinformatics to reveal a regulatory relationship between lncRNAs, miRNAs and mRNAs. This study comprehensively profiled m\u003csup\u003e6\u003c/sup\u003eA modification of lncRNAs in RIF to provide a new theoretical basis for pathogenic mechanism of RIF in the future.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients and tissue samples\u003c/h2\u003e \u003cp\u003eSubjects were recruited from the first Hospital of Lanzhou University. The inclusion criteria of all subjects recruited include: 1. under the age of 40; 2. with regular menstrual cycles (25\u0026ndash;35 days); 3. normal basal serum sex hormone of FSH (\u0026lt;\u0026thinsp;10 mIU/mL), LH (\u0026lt;\u0026thinsp;10 mIU/mL) and estradiol (E2\u0026thinsp;\u0026lt;\u0026thinsp;50 pg/mL), measured on days 2\u0026ndash;3 of the menstrual cycle. Women with one or more of the following situations are excluded:1. peripheral blood showing chromosomal anomaly;2. tested positive for anticardiolipin antibody or lupus anticoagulant; 3. polycystic ovary syndrome (PCOS); 4. uterine anomalies (congenital uterine anomaly, fibroid, polyps, intrauterine adhesions); 5. abnormal blood glucose level or thyroid dysfunction function test; 6. use steroid hormone in the preceding 2 months. Women recruited in RIF group must suffer from failure to achieve clinical pregnancy after being transferred at least four high-quality embryos in a minimum of three cycles(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Women recruited in control subjects, who were pregnant in the next cycle after sampling, underwent IVF/ICSI-ET due to fallopian tube or male factors.\u003c/p\u003e \u003cp\u003eEndometrial sample were obtained on day LH\u0026thinsp;+\u0026thinsp;7 via pipe suction curettage. After curettage, the endometrial sample was collected into a cryopreservation tube and stored in \u0026minus;\u0026thinsp;80\u0026ordm;C. A total of 6 women were recruited for our study; 3 were RIF patients and the other 3 women were used for the control group. All endometrial samples were middle secretory phase endometrium by histological diagnoses. This study was approved by the Ethics Committee of First Hospital of Lanzhou University (No: LDYYLL2019-45).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eRNA Extraction and quality Control\u003c/h2\u003e \u003cp\u003eTotal RNA was isolated from endometrial tissue of control subjects and RIF subjects by TRIzol Reagent (Invitrogen, Gibco-BRL, Bethesda, MD, USA) according to the manufacturer\u0026rsquo;s instructions. NanoDropND-1000 was utilized to determine the concentration of RNA in each sample, and the ratio between OD260 and OD280 varied from 1.8 to 2.1. The ratio of 18S/28S ribosomal band intensities in 1% agarose gels containing ethidium bromide was used to test the quality of total RNA.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePreparation and sequencing of RNA and MeRIP library\u003c/h2\u003e \u003cp\u003eThe m\u003csup\u003e6\u003c/sup\u003eA-Seq service was provided by CloudSeq Inc. (Shanghai, China). In accordance with manufacturer's instructions, total RNA was immunoprecipitated using GenSeq\u0026reg; m\u003csup\u003e6\u003c/sup\u003eA-IP Kit (GenSeq Inc.). In brief, RNA was randomly fragmented to about 200nt using RNA Fragmentation Reagents, and Protein A/G beads were rotated for 1 hour to couple protein to antibody. Bead-linked antibodies were incubated with RNA fragments at 4\u0026deg;C for four hours. After incubation, the RNA/antibody complexes were washed for several times, and then, captured RNA was eluted from the complexes and purified. The manufacturer's instructions were followed to construct RNA libraries for IP and input samples using GenSeq\u0026reg; Low Input Whole RNA Library Prep Kit. Sequencing was carried out on a NovaSeq (Illumina) platform after library quality was evaluated with an Agilent 2100 bioanalyzer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMeRIP-qPCR\u003c/h2\u003e \u003cp\u003eTotal purified RNA was eluted from the complexes after MeRIP. The abundances of LncRNAs were determined using qPCR and normalized to the input. MeRIP-qPCR primers used in this study are presented in Table ​1.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer sequences used for RT-qPCR\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003etranscript_id\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePrimer sequences (5\u0026rsquo;-3\u0026rsquo;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000416361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGGGAAATGTGGGGAAAAGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGATCAACAGCATCCCAAGGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR_027047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGAATCGGGCAGCACTCAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAAGGGAGAAAGAAAGCGTGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000471664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGTGACTCTTCTTGGACCAGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eATTGTGCCTCTCCAATCTGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eBriefly, paired-end reads were harvested from Illumina Novaseq 6000 sequencer and were quality controlled by Q30(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). 3\u0026rsquo; adaptor-trimming and low quality reads removing were proceed by cutadapt software (v1.9.3). Firstly, clean reads of all libraries were aligned to the reference genome (UCSC hg19) using Hisat2 software(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e) (v2.0.4). Then, HTSeq software (v0.9.1)(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) was used to get the transcript level (lncRNA) raw count as the expression profiling, and edgeR (v3.16.5)(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) was used to perform normalization and differentially expressed lncRNAs were identified. Methylated sites on RNAs which called peaks were identified by MACS software(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Differentially methylated sites were identified using diffReps(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The peaks located on lncRNA were screened by home-made scripts and annotated accordingly. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed by the associated genes of differentially methylated lncRNAs(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The miRNA-target gene prediction software, lncRNASNP2(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) and TargetScan(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e) were used to predict miRNAs and mRNAs which were combined for the screened lncRNAs. The ceRNA network was plotted using Cytoscape(v3.9.1)(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eOverview of m\u003csup\u003e6\u003c/sup\u003eA methylation map within lncRNAs in the controls and RIFs\u003c/h2\u003e \u003cp\u003eGenome-wide profiling of m\u003csup\u003e6\u003c/sup\u003eA modified lncRNAs in three biological replicates from the control group (N\u0026thinsp;=\u0026thinsp;3) and RIF group (N\u0026thinsp;=\u0026thinsp;3) were performed. The data had been submitted to gene expression omnibus (GEO accession number: GSE205398). 10504(1743\u0026thinsp;+\u0026thinsp;8761) and 10512(1751\u0026thinsp;+\u0026thinsp;8761) m\u003csup\u003e6\u003c/sup\u003eA peaks in the RIF and control groups were identified respectively, and there were 8761 peaks exist in both RIF group and control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). As shown in Fig.\u0026nbsp;1B, 10504 m\u003csup\u003e6\u003c/sup\u003eA peaks were mapped to 7766(6782\u0026thinsp;+\u0026thinsp;984) lncRNAs in RIF group and 10512 m\u003csup\u003e6\u003c/sup\u003eA peaks were mapped to 7720(6782\u0026thinsp;+\u0026thinsp;938) lncRNAs in control group. In 8761 common peaks, 1868(1443\u0026thinsp;+\u0026thinsp;425) significantly differentially methylated peaks were identified (fold change\u0026thinsp;\u0026ge;\u0026thinsp;2 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.0001). The 1868 differentially methylated m\u003csup\u003e6\u003c/sup\u003eA peaks were located across 623(465\u0026thinsp;+\u0026thinsp;158) lncRNAs. Among these lncRNAs, 158 lncRNAs were hypomethylated and 465 were hypermethylated (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). All methylated lncRNAs were clustered (fold-change\u0026thinsp;\u0026ge;\u0026thinsp;2.0 and \u003cem\u003eP\u0026thinsp;\u0026le;\u003c/em\u003e\u0026thinsp;0.00001), indicating that these lncRNAs have different expression patterns in the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). A search for motifs that enriched in the 50 bp area around m\u003csup\u003e6\u003c/sup\u003eA peak by DREME software(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, the identified m\u003csup\u003e6\u003c/sup\u003eA peaks contain RRACH conserved sequence motif (R represents purine, A is m\u003csup\u003e6\u003c/sup\u003eA and H is a non-guanine base), which confirmed the existence of m\u003csup\u003e6\u003c/sup\u003eA methylation mechanism.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDistribution of differentially methylated N6-methyladenosine peaks\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, compared to control group, 1443 significantly up-regulated m\u003csup\u003e6\u003c/sup\u003eA peaks and 425 significantly down-regulated m\u003csup\u003e6\u003c/sup\u003eA peaks were identified in RIF group (fold change\u0026thinsp;\u0026ge;\u0026thinsp;2 and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.0001). All differentially m\u003csup\u003e6\u003c/sup\u003eA methylated peaks could be found in Supplementary Table\u0026nbsp;1. The top 10 hypermethylated lncRNAs and hypomethylated lncRNAs were listed respectively in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. To further understand the differentially m\u003csup\u003e6\u003c/sup\u003eA methylated peaks in RIF, m\u003csup\u003e6\u003c/sup\u003eA modified lncRNAs were divided into the following six categories based on the positional relationship between the m\u003csup\u003e6\u003c/sup\u003eA methylated lncRNA and mRNA: bidirectional, exon sense overlap, intron sense overlap, intron antisense, natural antisense and intergenic(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Results revealed that 51.02% of the differentially m\u003csup\u003e6\u003c/sup\u003eA methylated lncRNAs were in the exon sense-overlapping group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). By analyzing the distribution of m\u003csup\u003e6\u003c/sup\u003eA peaks per lncRNA, we found that the majority of lncRNAs had one m\u003csup\u003e6\u003c/sup\u003eA peak (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). All differentially m\u003csup\u003e6\u003c/sup\u003eA methylated peaks were mapped to human chromosomes. These m\u003csup\u003e6\u003c/sup\u003eA peaks were found in all chromosomes, except chrY, and were particularly found in chr1, chr16 and chr19 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTop ten hypomethylated lncRNAs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003etxStart\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003etxEnd\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003etranscript_id\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGeneName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFoldchange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17155996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17156936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENST00000338526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eANKRD62P1-PARP4P3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e169.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90314776\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90314840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENST00000550103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEFCAB11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80864161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80864460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENST00000536374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCEP78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e112.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e101032148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101032361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENST00000413033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP5-1106H14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21395981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21396345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENST00000569618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRP11-354P17.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e102.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77679221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e77679620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENST00000470802\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eROBO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104029261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e104029817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENST00000518264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNPM1P52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e71927243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71927500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENST00000587066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZRANB2-AS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e89.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78233547\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78233560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENST00000450718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSPTLC1P5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003echr6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27235891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27236080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENST00000604325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eXXbac-BPGBPG24O18.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLncRNAs screened for lncRNA-miRNA-mRNA analysis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003etranscript_id\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFoldchange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRegulation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNR_045659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCOLEC11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehypermethylate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000490872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSLC11A1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehypermethylate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000539408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGABARAPL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehypermethylate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000487643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKLHL6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehypermethylate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000599041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAC010127.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehypermethylate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000550103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEFCAB11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehypomethylate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000536374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCEP78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e112.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehypomethylate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000413033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRP5-1106H14.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehypomethylate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000569811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGOLGA8B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehypomethylate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eENST00000538693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLINC01152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehypomethylate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eFunctional analysis of genes associated differentially methylated lncRNA\u003c/h2\u003e \u003cp\u003eTo explore the physiological and pathological significance of m\u003csup\u003e6\u003c/sup\u003eA modification in RIF, GO analysis and KEGG pathway analysis were performed for the genes associated differentially methylated lncRNA. In GO analysis, genes were classified into three functional groups: biological process (BP), cellular component (CC) and molecular function (MF). The top 10 significantly enriched BPs, CCs and MFs of the genes associated hypermethylated lncRNAs were listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, while GO analysis of genes associated hypomethylated lncRNAs were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB. GO analysis reveal that genes associated hypermethylated lncRNAs were significantly enriched in neuron projection, regulation of GTPase activity and nucleoside-triphosphatase regulator activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Genes associated hypomethylated lncRNAs were significantly enriched in regulation of chromosome segregation, golgi stack, and peptidase activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor KEGG pathway analysis, we found that genes associated hypermethylated lncRNAs in RIF were significantly associated with choline metabolism, phospholipase D signaling pathway, and valine, leucine and isoleucine degradation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). The genes associated hypomethylated lncRNAs were involved in p53 signaling pathway, transcriptional mis regulation in cancer and Natural killer cell mediated cytotoxicity (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). The phospholipase D signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) and p53 signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) were the most significant enriched pathways related to RIF and embryo implantation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of lncRNA-miRNA-mRNA network in RIF\u003c/h2\u003e \u003cp\u003eTo explore the mRNAs regulated by lncRNAs, we screened five lncRNAs with fold changes\u0026thinsp;\u0026gt;\u0026thinsp;70 out of 454 hypermethylated lncRNAs and five lncRNAs with fold changes\u0026thinsp;\u0026gt;\u0026thinsp;60 out of 151 hypomethylated lncRNAs (Table\u0026nbsp;4). A ceRNA network was constructed by lncRNA-miRNA-mRNA association analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The network consisted of the top five miRNAs (defined by exact probability from lncRNASNP2) combined with a screened lncRNA and the top five mRNAs (defined by cumulative weighted context\u0026thinsp;+\u0026thinsp;+\u0026thinsp;score) bound to the miRNAs, including 10 lncRNAs, 49 miRNAs and 232 mRNAs (Supplementary Table\u0026nbsp;2). From this ceRNA network, it is clear that lncRNAs regulate to miRNAs and mRNAs. For instance, \u003cem\u003eLINC01152\u003c/em\u003e could act as a sponge for hsa-miR-6801-3p to affect \u003cem\u003eLIF\u003c/em\u003e expression.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eConjoint analysis of MeRIP-seq and RNA-seq data\u003c/h2\u003e \u003cp\u003eIn order to explore the association between the expression of lncRNA and m\u003csup\u003e6\u003c/sup\u003eA methylation, a conjoint analysis was conducted for m6A-seq and RNA-Seq data. There were 72 lncRNAs with both different m\u003csup\u003e6\u003c/sup\u003eA methylation levels and expression levels (Supplementary Table\u0026nbsp;3). Among them, 19 lncRNAs were up-regulated with hypermethylation, 19 lncRNAs were down-regulated with hypermethylation, 33 lncRNAs down-regulated and hypomethylated, and only one lncRNA were up-regulated with hypomethylation. Among the 72 lncRNA in Supplementary Table\u0026nbsp;3, three of them (ENST00000416361, NR_027047, ENST00000471664) were randomly selected. The m6A methylation abundances are visualized using Integrative genomics viewer (IGV) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA,\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB,\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC) and verified by MeRIP-qPCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD,\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE,\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eRIF is a challenging clinical dilemma. Many studies found that m\u003csup\u003e6\u003c/sup\u003eA modifications play vital roles in reproductive diseases, but the transcriptome-wide m\u003csup\u003e6\u003c/sup\u003eA profiling of lncRNA of RIF has not been characterized yet. In this study, RIF transcriptome-wide lncRNA m\u003csup\u003e6\u003c/sup\u003eA modification profile was established using m\u003csup\u003e6\u003c/sup\u003eA-Seq.\u0026nbsp;By using m\u003csup\u003e6\u003c/sup\u003eA-seq, 1868 differentially methylated lncRNA m\u003csup\u003e6\u003c/sup\u003eA peaks was identified in endometrial tissues of RIF, of which 1443 m\u003csup\u003e6\u003c/sup\u003eA peaks were significantly up-regulated and 425 m\u003csup\u003e6\u003c/sup\u003eA peaks were significantly down-regulated. Furthermore, differential expression patterns of differential methylated lncRNA were explored in RIF and control. Moreover, GO and KEGG pathway analyses were performed to reveal the potential functions of differentially methylated transcripts. A ceRNA network was constructed to reveal a regulatory relationship between lncRNAs, miRNAs and mRNAs. This study comprehensively profiled m\u003csup\u003e6\u003c/sup\u003eA modification patterns of lncRNAs in RIF to indicate directions for the diagnosis and treatment of RIF in the future.\u003c/p\u003e \u003cp\u003eM\u003csup\u003e6\u003c/sup\u003eA plays a crucial role in mammalian including human fertility. As early as in 2013, \u003cem\u003eALKBH5\u003c/em\u003e acting as a m\u003csup\u003e6\u003c/sup\u003eA \u0026ldquo;erasers\u0026rdquo;, were identified to impair fertility via affecting the apoptosis of meiotic metaphase-stage spermatocytes(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Subsequently, several m\u003csup\u003e6\u003c/sup\u003eA regulators were discovered to play an important role in reproductive diseases, such as endometriosis(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), polycystic ovary syndrome(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e), primary ovarian insufficiency(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) and so on. In one study reported in 2021(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), colorimetric m\u003csup\u003e6\u003c/sup\u003eA quantification strategy was used to examined m\u003csup\u003e6\u003c/sup\u003eA level from control and RIF patients. Global m\u003csup\u003e6\u003c/sup\u003eA level was significantly increased in the endometrial tissues from women with RIF compared with the controls which is consistent with our study. In the present study, it is also proved that m\u003csup\u003e6\u003c/sup\u003eA level were increased in RIF (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD), indicating that m\u003csup\u003e6\u003c/sup\u003eA is important for the pathogenesis of RIF.\u003c/p\u003e \u003cp\u003eIt\u0026rsquo;s also reported non-coding RNAs are important in RIF. HOX family have been clarified as critical regulators in endometrial decidualization(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). \u003cem\u003eHOXA11-AS\u003c/em\u003e, one lncRNA in the HOX gene family, was elevated in RIF patients. And the pattern of high \u003cem\u003eHOXA11-AS\u003c/em\u003e expression and impaired \u003cem\u003ePKM2\u003c/em\u003e splicing was consistently exist in RIF patients(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). \u003cem\u003eLINC02190\u003c/em\u003e was verified upregulating in RIF endometrium and could decrease the adhesion rate of Ishikawa and JAR cells(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Numbers of researches indicated the role of LncRNAs in RIF couldn\u0026rsquo;t be ignored. However, lncRNA modified via m\u003csup\u003e6\u003c/sup\u003eA has not been reported in RIF. What's novel about our study is combining m\u003csup\u003e6\u003c/sup\u003eA with lncRNAs.\u003c/p\u003e \u003cp\u003eClassification of m\u003csup\u003e6\u003c/sup\u003eA modified lncRNAs has been analyzed in this study. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, most differentially m\u003csup\u003e6\u003c/sup\u003eA modified lncRNAs are exon sense-overlaping. In previous study, the majority of differentially m\u003csup\u003e6\u003c/sup\u003eA modified lncRNAs in colorectal cancer are also exon sense-overlaping(\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). However, there are several studies with another conclusion. Most differentially methylated m6A sites within lncRNAs were located in intergenic region, in coronary artery disease(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and gastric cancer(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Thus, the dominant m\u003csup\u003e6\u003c/sup\u003eA modified lncRNAs present different categories in different diseases. It's worth noting that exon sense-overlaping lncRNAs were proved being relevant to cell proliferation, invasion and metastasis recently, which indicated an important role in m\u003csup\u003e6\u003c/sup\u003eA modified lncRNAs playing of RIF.\u003c/p\u003e \u003cp\u003eIn the present study, differentially methylated lncRNAs related to many important biological pathways were detected. In KEGG pathway analysis of genes associated hypermethylated lncRNAs, top 5 terms were showed in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, we focus on choline metabolism, phospholipase D signaling pathway and amino acid metabolism. Choline metabolism is related to Phospholipase D signaling pathway. Phospholipase D (PLD) is a phospholipase enzyme responsible for hydrolyzing phosphatidylcholine into the lipid signaling molecule, phosphatidic acid and choline(\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Choline is a micronutrient and a methyl donor(\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). Arzu Yurci et al.(\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) demonstrated that Choline signals were found to lower in RIF group compared to the fertile group through MR spectroscopy. These results suggested that Choline might participate in the pathogenesis of RIF through m\u003csup\u003e6\u003c/sup\u003eA formation. Moreover, genes associated hypermethylated lncRNAs were related to amino acid metabolism, such as valine degradation and histidine metabolism. Increasing evidence demonstrated that amino acid metabolism were key regulators in RIF. In previous studies, researchers found that it exists discrimination at metabolomic level between RIF and repeated implantation success (RIS). Valine was found to be significantly down-regulated in women with RIS when compared with those with RIF(\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Recently, one systematic review revealed alanine, aspartate and glutamate metabolism have the highest impact factor in RIF(\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). According to our results, it\u0026rsquo;s suggested that m\u003csup\u003e6\u003c/sup\u003eA could influence the outcome of embryo implantation by valine degradation and histidine metabolism.\u003c/p\u003e \u003cp\u003eIn contrast to genes associated hypermethylated lncRNAs, genes associated hypomethylated lncRNAs were mainly enriched in immunological process. For instance, natural killer cell mediated cytotoxicity and chemokine signaling pathway and were list in the top 5 KEGG pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Cellular cytotoxicity, the ability to kill other cells, is an important effector mechanism of the immune system. Natural killer (NK) cells are the major mediators of this activity(\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). NK cells are recruited and activated by ovarian hormones and have pivotal roles throughout pregnancy. Decidual natural killer (dNK) cells release chemokines that induce trophoblast invasion, tissue remodeling, embryonic development, and placentation. NK cells can also mediate cytotoxicity and carry out immune defense if infected in utero by pathogens(\u003cspan additionalcitationids=\"CR49\" citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). Past research suggested an association between RIF with abnormally elevated uterine NK cells' numbers, as well as with cytotoxicity(\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). It has been proposed that intrauterine administration of peripheral blood mononuclear cells (PBMCs) modulates maternal immune response through a cascade of chemokines to favor implantation. The clinical pregnancy rate was significantly improved by this therapy, which was proved reliability through meta-analysis(\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Based on the results of our study, we hypothesize that m\u003csup\u003e6\u003c/sup\u003eA associated with immunological process could influence pregnancy outcomes in patients with RIF, which may be a novel mechanism of the regulation of immunological process in RIF.\u003c/p\u003e \u003cp\u003eGenes associated hypomethylated lncRNAs were also involved in p53 signaling pathway, which is one classical signaling pathways. P53 is well-known as a tumor suppressor, however, little is known about its other functions except tumor suppression. With the aid of the p53MH algorithm, leukemia inhibitory factor (\u003cem\u003eLIF\u003c/em\u003e) was identified as p53-regulated genes by Dr Arnold Levine\u0026rsquo;s group(\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). P53 is crucial for embryonic implantation through up-regulation of uterine \u003cem\u003eLIF\u003c/em\u003e transcription(\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Recently, it has been proposed that the absence of PARP-1 and PARP-2 resulted in increased p53 signaling and an increased population of senescent decidual cells(\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). In a case-control survey, 100 cases with RIF and 100 women with the normal pregnancy were involved. This research suggested that p53 gene polymorphisms could be a genetic predisposing factor for RIF(\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e). Based on the results of our study, m\u003csup\u003e6\u003c/sup\u003eA might participate in p53 signaling pathway, which could explain pathogenic mechanism of RIF.\u003c/p\u003e \u003cp\u003eIn this study, we constructed a ceRNA network to analyze the regulatory relationship between lncRNAs, miRNAs and mRNA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The potential target genes of several lncRNAs in this ceRNA were related to reproductive disease. For instance, \u003cem\u003eLINC01152\u003c/em\u003e could act as a sponge for hsa-miR-6801-3p to affect \u003cem\u003eLIF\u003c/em\u003e expression (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). \u003cem\u003eLIF\u003c/em\u003e activates signal transducer and activator of transcription 3 (\u003cem\u003eSTAT3\u003c/em\u003e) precursors via uterine \u003cem\u003eLIF\u003c/em\u003e receptors (LIFRs), allowing successful blastocyst implantation(\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Except \u003cem\u003eLIF\u003c/em\u003e, \u003cem\u003ePRX\u003c/em\u003e, \u003cem\u003eCD69\u003c/em\u003e and \u003cem\u003eHLA-F\u003c/em\u003e are potential target genes in ceRNA network. The expression of the \u003cem\u003ePRX\u003c/em\u003e was significantly decreased in ectopic endometrium compared to that in eutopic or control endometrium(\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). In one endometrial immune assessment, sub-fertile populations exhibited increased \u003cem\u003eCD69\u003c/em\u003e activation(\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). Lately, the articles published in top journals reported \u003cem\u003eHLA-F\u003c/em\u003e fluctuates during the menstrual cycle with high levels during the implantation window. The level of \u003cem\u003eHLA-F\u003c/em\u003e protein expression correlates with the number of CD56-positive NK cells in the mid-secretory endometrium(\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). The functions of abovementioned genes in decidualization and RIF require further investigation. However, from these genes, it is clear that the network can be great significance to further understand the role of lncRNAs in the occurrence and development of RIF.\u003c/p\u003e \u003cp\u003eThe m\u003csup\u003e6\u003c/sup\u003eA methylation abundances of 3 lncRNAs (ENST00000416361, NR_027047, ENST00000471664) were verified by MeRIP-qPCR in this study. It is reported that lncRNA ENST00000416361 is upregulated in coronary artery disease and is related to inflammation and lipid metabolism(\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). \u003cem\u003eLINC00282\u003c/em\u003e(NR_027047) was potentially relevant to the stimulation of cell proliferation mediated by GnRHa(\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e). ENST00000471664 was identified for its significant loss of expression in GLIS3 knockout mouse pancreatic islets(\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). However, these studies are superficial and limited to bioinformatics. Interestingly, our results indicated that several lncRNA were differential expression with hypermethylated/hypomethylated in RIF. There have been many studies on the involvement of m\u003csup\u003e6\u003c/sup\u003eA in the expression of lncRNAs(\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). We suspected that m\u003csup\u003e6\u003c/sup\u003eA might participate in the expression of lncRNA in RIF, which still need more assay to support.\u003c/p\u003e \u003cp\u003eSeveral limitations exist in the current study, which are due to the character of primary studies. The scale of samples is minimum for high-throughput methods. To make up for these deficiencies, MeRIP-qPCR was used to ensure the accuracy of m\u003csup\u003e6\u003c/sup\u003eA-seq results. In this study, bioinformatics analysis was used which were mainly descriptive. It is necessary to explore the relationship of m\u003csup\u003e6\u003c/sup\u003eA and lncRNAs in RIF in subsequent experiments.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn summary, this study revealed the m\u003csup\u003e6\u003c/sup\u003eA lncRNA methylation landscape by high-throughput sequencing, which provided new perspective on the pivotal roles of epigenetic changes in RIF. What\u0026rsquo;s more, a ceRNA network revealed a regulatory relationship between lncRNAs, miRNAs and mRNAs. Further experiments should be carried out to verify the differential methylated lncRNAs in RIF patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval\u0026nbsp;\u003c/strong\u003eThe study was approved by the Ethics Committee of Lanzhou University First Affiliated Hospital on 21/2/2019.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e All procedures were in accordance with the ethical standards of the institutional research committee and with the Helsinki declaration and its later amendments or comparable ethical standards. Written informed consents were obtained from all patients prior to study commencement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003eThe datasets generated for this study could be found in the online repositories. The names of the dataset \u0026nbsp;and accession number could be found below: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE205398.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Funding\u0026nbsp;\u003c/strong\u003eThis work was financially supported by the National Natural Science Foundation of China (82360307), the Talent Innovation and Entrepreneurship Project of Lanzhou City (2022-RC-47), Gansu Provincial Science and Technology Program (20JR10TA715,21JR7RA391), the Medical Innovation and Development Project of Lanzhou University (lzuyxcx-2022-191), Science and Technology Project of Lanzhou (2023-ZD-93), Fund of the First Hospital of Lanzhou University (ldyyyn2022-60), Research Project for Young and Middle-aged Doctors in Reproductive Medicine (BJHPA-2021-SHZHYXZHQNYJ-009), Fund of the First Hospital of Lanzhou University (ldyyyn2019-86).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e The first two authors contributed equally to this work. Lin Liu designed and supervised the study. Ting Wang conducted the experiments and drafted the manuscript. Lili Zhang and Xiaoling Ma collected the samples. Wenxin Gao and Yidan Liu contributed the methodology. Feng Yue modified the format. All authors have read and approved the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e None.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCoughlan C, Ledger W, Wang Q, Liu F, Demirol A, Gurgan T, et al. Recurrent implantation failure: definition and management. Reprod Biomed Online. 2014;28(1):14-38.\u003c/li\u003e\n\u003cli\u003eMrozikiewicz AE, Ożarowski M, Jędrzejczak P. Biomolecular Markers of Recurrent Implantation Failure-A Review. Int J Mol Sci. 2021;22(18).\u003c/li\u003e\n\u003cli\u003eOchoa-Bernal MA, Fazleabas AT. Physiologic Events of Embryo Implantation and Decidualization in Human and Non-Human Primates. 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Cell Death Differ. 2022;29(3):627-41\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgnm","sideBox":"Learn more about [BMC Medical Genomics](http://bmcmedgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgnm/default.aspx","title":"BMC Medical Genomics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Recurrent implantation failure, N6-methyladenosine, m6A modified RNA immunoprecipitation sequencing, long non-coding RNAs","lastPublishedDoi":"10.21203/rs.3.rs-4563715/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4563715/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eN6-methyladenosine (m\u003csup\u003e6\u003c/sup\u003eA) is involved in most biological processes and actively participates in the regulation of reproduction. According to recently research, long non-coding RNAs (lncRNAs) and their m\u003csup\u003e6\u003c/sup\u003eA modifications are involved in reproductive diseases. In the present study, using m\u003csup\u003e6\u003c/sup\u003eA modified RNA immunoprecipitation sequencing (m\u003csup\u003e6\u003c/sup\u003eA-seq), the m\u003csup\u003e6\u003c/sup\u003eA methylation transcription profiles in recurrent implantation failure (RIF) were established for the first time. 1443 significantly up-regulated m\u003csup\u003e6\u003c/sup\u003eA peaks and 425 significantly down-regulated m\u003csup\u003e6\u003c/sup\u003eA peaks were identified in RIF. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis revealed that genes associated differentially methylated lncRNAs were involved in classical p53 signaling pathway and amino acid metabolism. Then, competing endogenous RNA (ceRNA) network revealed a regulatory relationship between lncRNAs, microRNAs (miRNAs) and mRNAs. The m\u003csup\u003e6\u003c/sup\u003eA methylation abundances of lncRNAs were verified by m\u003csup\u003e6\u003c/sup\u003eA-RNA immunoprecipitation (MeRIP)-qPCR in this study. This study will lay a foundation for further exploration of the potential role of m\u003csup\u003e6\u003c/sup\u003eA modification in the pathogenesis of RIF.\u003c/p\u003e","manuscriptTitle":"Transcriptome-wide N6-methyladenosine modification profiling of long non-coding RNAs in patients with recurrent implantation failure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-10 01:52:01","doi":"10.21203/rs.3.rs-4563715/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-17T11:09:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-07-12T18:59:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321177136333517278552139652037448472196","date":"2024-06-28T13:51:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198856265734037289269000626725168230940","date":"2024-06-27T16:55:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-26T14:43:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"208802685581180187868495781737414164430","date":"2024-06-20T17:23:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-19T18:59:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-12T04:27:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-12T03:01:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medical Genomics","date":"2024-06-11T11:27:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medical-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mgnm","sideBox":"Learn more about [BMC Medical Genomics](http://bmcmedgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mgnm/default.aspx","title":"BMC Medical Genomics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ae036925-124c-4bad-adaf-d314f5dd9a68","owner":[],"postedDate":"July 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-14T16:05:28+00:00","versionOfRecord":{"articleIdentity":"rs-4563715","link":"https://doi.org/10.1186/s12920-024-02013-3","journal":{"identity":"bmc-medical-genomics","isVorOnly":false,"title":"BMC Medical Genomics"},"publishedOn":"2024-10-11 15:56:59","publishedOnDateReadable":"October 11th, 2024"},"versionCreatedAt":"2024-07-10 01:52:01","video":"","vorDoi":"10.1186/s12920-024-02013-3","vorDoiUrl":"https://doi.org/10.1186/s12920-024-02013-3","workflowStages":[]},"version":"v1","identity":"rs-4563715","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4563715","identity":"rs-4563715","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}