{"paper_id":"3fd900c7-cc4e-49ff-ac5f-38881059c13c","body_text":"Endometriosis (EMs), characterized by the subsistence of endometrial-like tissue\n(including stroma and glands) growing outside the uterine cavity, is a common benign\ngynaecological disorder. 1  EMs approximately affects 6% to 10% of women worldwide, mainly during the\nreproductive age. 1  It would induce infertility and various pain, such as pelvic pain,\ndysmenorrhea, and dyspareunia, 2  also a risk of cancerization. 3  Of note, there is still no individual theory that can thoroughly explain all\nthe aspects of EMs, even the classic “retrograde menstruation” hypothesis,\nsuggesting viable endometrial debris refluxed through the fallopian tubes into the\npelvic cavity to implant. 4  Not only such complexity of the disease itself but also the absence of\nsensitive and specific biomarkers challenged the diagnosis and treatment of EMs.\nHence, it is essential to explore the potential molecular mechanisms underlying EMs\nto deepen our understanding of EMs.\nThe non-coding RNAs (ncRNAs), transcribed from the DNA-genome but unable to code\nproteins, function as universal regulators in cellular processes, which could be\ngenerally sorted into two types according to their scale: the small long non-coding\nRNAs (<200 nucleotides in length) and the long non-coding RNAs (⩾200 nucleotides\nin length). 5  The miRNA, one of the most concerned small ncRNAs, has been proved to be\ndysregulated in EMs, but the specific mechanism remained to clarify, 6  particularly in multi-cohorts integrated analysis. The lncRNA, a new star\nwith the advancement of the RNA-sequencing technology, has invoked a research\nupsurge in recent years, with no exception in Ems. 7  Notably, the emerging competing endogenous RNAs (ceRNAs) hypothesis\nmanifested that lncRNAs could serve as a miRNA sponge to regulate the target mRNAs. 8  And this hypothesis had been attested in EMs: the first reported lncRNA H19\nin EMs sponged miRNA let-7 to regulate its downstream gene IGF1R to impact the\nproliferation of endometrial stromal cells. 9  However, few comprehensive analyses of EMs-associated miRNAs and lncRNAs in\nthe ceRNA network’s milieu have been conducted.\nTherefore, we intended to establish an EMs-related ceRNA network to investigate the\nregulatory role of the lncRNA-miRNA-mRNA axis in EMs ( Figure 1 ). As far as we know, this report\nrepresents the first endeavour to construct a lncRNA-associated ceRNA network based\non multiple RNA-sequencing datasets in EMs.\nThe flowchart of endometriosis-associated ceRNA network analysis.\n\nTwo miRNA expression datasets:  GSE105765  (eight paired EC and EU endometrium\ntissue samples), 10  and  GSE121406  (four paired EC and EU endometrial stromal cells), 11  as well as the lncRNA and mRNA expression profile  GSE105764  (same eight\npaired EC and EU endometrium tissue samples in  GSE105765 ), 10  were obtained from the GEO database ( http://www.ncbi.nlm.nih.gov/geo ). All these datasets were\nmeasured by high-throughput RNA-sequencing:  GSE105765  was based on platform\n GPL11154  (Illumina HiSeq 2000),  GSE121406  on platform  GPL18573  (Illumina NextSeq\n500), and  GSE105764  on platform  GPL20301  (Illumina HiSeq 4000). There was no\nneed for ethical approval or informed consent in this study because the data was\npublicly available.\nThe “DESeq2” R package 12  was applied to analyze the differentially expressed microRNAs (DEmiRs)\nwith a threshold of |log 2  fold change (FC)| ⩾ 2 and adjust\n P -value < 0.01; the differentially expressed genes\n(DEGs) and differentially expressed lncRNAs (DELs) with a threshold of\n|log 2  FC| ⩾ 3 and adjust  P -value < 0.01.\nMoreover, intersection analysis was conducted to detect the shared DEmiRs\nbetween  GSE105765  and  GSE121406 .\nIn the light of the ceRNA hypothesis, screened DEGs, DELs, and overlapped DEmiRs\nwere applied to build the lncRNA–miRNA–mRNA regulatory network. The predicted\nlncRNAs interacted with overlapped DEmiRs were mined in downloaded databases\nStarBase v2.0 13  and DIANA-LncBase v2.0, 14  both of which provided the experimentally validated miRNA-lncRNA\ninteractive information. Next, these predicted lncRNAs were further intersected\nwith the identified DELs in  GSE105764 . Additionally, the overlapped\nDEmiRs-targeted mRNAs were predicted from the miRTarBase 15  and StarBase v2.0 13  databases and later were intersected with the identified DEGs in\n GSE105764 . Finally, the filtered DEmiR-DEL and DEmiR-DEG interactive pairs were\nemployed to build a ceRNA regulatory network, which was visualized in software\nCytoscape 3.6.1. 16\nThe DEGs included in the established ceRNA network were performed with GO and\nKEGG pathway enrichment analysis by Enrichr ( http://amp.pharm.mssm.edu/Enrichr/ ), a useful online tool for\nquerying functional annotation and biological information of genes. Retrieved GO\nterms and KEGG pathways with a  P -value < 0.05 were supposed\nto be significantly enriched.\nTo further explore the potential interplay of DEGs in the ceRNA network, the\nSearch Tool for the Retrieval of Interacting Genes database (STRING-Version\n10.0,  http://stringdb.org ) was adopted to create a PPI network with\nthe interaction score > 0.4. Then, the PPI network was carried into Cytoscape 3.6.1 16  for visualization, and the degree score of nodes was analyzed by the\nplugin NetworkAnalyzer. Moreover, 10 hub genes were determined by the “Degree”\nmethod in the plugin CytoHubba.\nThe validation analysis of all DEmiRs in the ceRNA network was performed in\n GSE124010  (based on platform  GPL25134 ). 17  This dataset contained 3 normal endometria (NM) from healthy candidates\nand 3 EU samples from EMs patients. It would be interesting to know whether the\ncandidate DEmiRs in EU  vs.  EC in training datasets were also\nchanged in the EU versus NM in the validation dataset. After the positively\nverified DEmiRs were acquired, their target DEGs and DELs were further chosen to\nvalidate in  GSE86534 , which profiled the mRNA and lncRNA expression in four\npaired EU and EC tissue samples from EMs patients grounded on platform  GPL20115 . 18  The p-value < 0.05 was considered significant.\nTo explore the correlation between verified DEmiRs, and their target DEGs and\nDELs, the miRNA profile in  GSE105765  and mRNA-lncRNA profile in  GSE105764 \nexamined on the same samples were combined to perform the Spearman correlation\nanalysis. Due to the lack of validation datasets detecting the miRNA and\nmRNA-lncRNA profile in the same samples, we only validated the correlation\nbetween target DEGs and DELs in  GSE86534 . The  P -value < 0.05\nwas considered significant.\nGSEA is a computational method to evaluate whether a defined gene set exerts a\nsignificant difference between two biological phenotypes. 19  Since the EU samples might play a fundamental role in the pathogenesis of Ems, 20  we investigated the function of the verified DEmiRs, DEGs, and DELs in EU\nsamples by GSEA analysis. According to the median expression of the verified\nDEmiRs, DEGs and DELs, the EU samples were respectively divided into two groups:\nthe high- and low-expression groups, and the file “h.all.v7.0.symbols.gmt” in\nGSEA websites ( https://www.gsea-msigdb.org/gsea/index.jsp ) was used as the\nreference gene set. The analysis was performed in GSEA software, and the\nstatistical threshold was FDR  q -value < 0.25. Then,\ntop-ranking results were visualized in R software.\n\nWith the criteria of |log2 FC| ⩾ 2 and adjust  P -value < 0.01,\n116 DEmiRs (47 upregulated and 69 downregulated) were obtained from  GSE105765 ,\nalong with 70 DEmiRs (40 upregulated and 30 downregulated) from  GSE121406  ( Figure 2a  and  b ). The intersection\nanalysis showed 27 common DEmiRs (11 upregulated and 16 downregulated) between\n GSE105765  and  GSE121406  ( Figure\n2c ). Additionally, with the criteria of |log2 FC| ⩾ 3 and adjust\n P -value < 0.01, 1352 DEGs (693 upregulated and 659\ndownregulated) and 595 DELs (278 upregulated and 317 downregulated) were\nidentified from  GSE105764  ( Figure 2d  and  e ).\nIdentification of DEmiRs, DELs, and DEGs in endometriosis. (a) Volcano\nplots for DEmiRs between ectopic (EC) and eutopic (EU) endometrium in\n GSE105764 . (b) Volcano plots for DEmiRs between EC and EU endometrium in\n GSE121406 . (c) Venn diagram for the overlapping DEmiRs between  GSE105764 \nand  GSE121406 . (d) Volcano plots for DELs between EC and EU endometrium\nin  GSE105765 . (e) Volcano plots for DEGs between EC and EU endometrium\nin  GSE105765 . DEmiRs, differentially expressed microRNAs; DEGs,\ndifferentially expressed genes; DELs: differentially expressed long\nnon-coding RNAs; EC, ectopic endometrium; EU, eutopic endometrium.\nBased on the filtered DEmiR-DEL and DEmiR-DEG interactive pairs, the EMs-related\nceRNA network was established, including 11 upregulated and 16 downregulated\nDEmiRs, 7 upregulated and 13 downregulated DELs, 48 upregulated and 46\ndownregulated DEGs ( Figure\n3 ).\nCompeting endogenous RNA (ceRNA) network in endometriosis. The red\nindicates the upregulated RNAs in EC compared to EU samples, and the\nblue indicates the downregulated RNAs in EC compared to EU samples. The\nv-shape represents DEmiRs, the diamond represents DELs, and ellipse\nrepresents DEGs. DEmiRs, differentially expressed microRNAs; DELs,\ndifferentially expressed long noncoding RNAs; DEGs, differentially\nexpressed genes. EC, ectopic endometrium; EU, eutopic endometrium.\nA total of 94 DEGs in the ceRNA network were processed with functional enrichment\nanalysis by website Enrichr. The GO analysis revealed that the top five\nsignificantly enriched biological processes (BPs) were Circulatory system\ndevelopment, Positive regulation of stem cell differentiation, Male gonad\ndevelopment, Development of primary male sexual characteristics and Positive\nregulation of transcription ( Figure 4a ); the top five molecular functions (MFs) were\nTranscriptional activator activity, RNA polymerase II transcription regulatory\nregion sequence-specific binding, Cytokine activity, Transforming growth\nfactor-beta receptor binding, Oxidoreductase activity, Acting on the CH-NH2\ngroup of donors and RNA polymerase II transcription factor binding ( Figure 4b ); the top five\ncellular components (CCs) were Bicellular tight junction, Cytoplasmic vesicle,\nActomyosin, Zonula adherens and Paranode region of axon ( Figure 4c ). Moreover, KEGG pathway\nanalysis indicated that these DEGs were primarily concentrated in\nTranscriptional misregulation in cancer, Cytokine-cytokine receptor interaction,\nRetinol metabolism, Tight junction and TNF signaling pathway ( Figure 4d ).\nGO, KEGG pathway, and PPI network analyses of DEGs in the EMs-related\nceRNA network. The top 5 enriched (a) biological processes, (b) cellular\ncomponents, (c) molecular functions, and (d) KEGG pathways of DEGs in\nthe ceRNA network. The horizontal axis represents the number of genes,\nand the vertical axis represents GO terms or KEGG pathway names. All\nentries were ranked by p-value in the ascending order. (e) PPI network\nconstructed by the DEGs in the ceRNA network. According to the degree\nscore calculated by plugin NetworkAnalyzer, the node colour changes\ngradually from blue to red and the node sizes from small to large in the\nascending order. (f) 10 hub DEGs in the PPI network analyzed by the\n“Degree” method in plugin CytoHubba. The node colour changes gradually\nfrom yellow to red in the ascending order according to the degree score.\nGO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; PPI,\nprotein-protein interaction; DEGs, differentially expressed genes.\nBy searching those 94 DEGs in the ceRNA network in the STRING database, with the\ninteraction score > 0.4, a PPI network consisting of 41 nodes and 74 edges\nwas constructed ( Figure\n4e ). Furthermore, according to the degree scores, 10 hub DEGs in the\nPPI network were selected out: GATA4, BDNF, RUNX2, SOX9, GATA6, CXCL8, CEBPA,\nEPCAM, NTF3, CHL1 ( Figure\n4f ).\nAll DEmiRs in the ceRNA network were validated in  GSE124010 . Probably due to the\nlimited sample size, we only found hsa-miR-182-5p were significantly\nlow-expressed in EU samples when compared to NM samples in  GSE124010  ( Figure 5a ). Since\nhsa-miR-182-5p was down-regulated in EC  vs.  EU in training\ndatasets and EU  vs . NM in the validation dataset, it might be a\nconstant dysregulated miRNA in EMs development. Hence, the target DEGs and DELs\nof hsa-miR-182-5p were chosen to validated in  GSE86534  ( Figure 5b ). The results showed that two\nlncRNAs LINC01018 and SMIM25 along with four genes BNC2, CHL1, HMCN1, and PRDM16\nwere significantly upregulated in EC compared to EU samples in  GSE86534  ( Figure 5c ).\nValidation analysis of DEmiRs, DEGs, and DELs in the ceRNA network. (a)\nAll DEmiRs in the ceRNA network were validated in  GSE124010 . (b) The\ntarget DEGs and DELs of hsa-miR-182-5p in the ceRNA network. (c) The\ntarget DEGs and DELs of hsa-miR-182-5p were validated in  GSE86534 .\nDEmiRs, differentially expressed microRNAs; DELs, differentially\nexpressed long noncoding RNAs; DEGs, differentially expressed genes.\n* P -value < 0.05.\nThe correlation analysis in combined data of training datasets  GSE105764  and\n GSE105765  indicated that hsa-miR-182-5p was significantly negatively associated\nwith its target DELs (LINC01018 and SMIM25) and DEGs (BNC2, CHL1, HMCN1,\nPRDM16). Moreover, those target DELs (LINC01018 and SMIM25) were significantly\npositively associated with the target DEGs (BNC2, CHL1, HMCN1, PRDM16) ( Figure 6a ). In the\nvalidation dataset  GSE86534 , LINC01018 and SMIM25 were also proved to be\npositively correlated with BNC2, CHL1, HMCN1, and PRDM16, respectively, although\nthe  P -value was not always lower than 0.05 probably due to the\nsmall sample size ( Figure\n6b ). Noticeably, LINC01018 and CHL1 were respectively the most\nup-regulated DEL and DEG both in the training and validation datasets ( supplement Tables S1  and  S2 ). Moreover, CHL1 was also identified as the hub nodes in the\nPPI network. Hence, we selected CHL1 as the representative DEG in subsequent\nGSEA analysis.\nThe relationship between hsa-miR-182-5p and its target DEGs and DELs. (a)\nThe Spearman correlation analysis of hsa-miR-182-5p and its target DEGs\nand DELs in combined data of training datasets  GSE105764  and  GSE105765 .\n(b) The Spearman correlation analysis of the target DEGs and DELs of\nhsa-miR-182-5p in the validation dataset  GSE86534 . DELs, differentially\nexpressed long noncoding RNAs; DEGs, differentially expressed genes.\n* P -value < 0.05.\nTo investigated the function of hsa-miR-182-5p and its targets in EU samples, the\nGSEA analysis was performed. The EU samples in training datasets were divided\ninto high- and low-expression groups according to the median expression of\nhsa-miR-182-5p, LINC01018, SMIM25, and CHL1, respectively. The results showed\nthat the pathway “INFLAMMATORY_RESPONSE” was activated in high-expressed\nLINC01018 and low-expressed hsa-miR-182-5p EU samples compared to respective\ncontrol samples. Besides, the pathway “INTERFERON_GAMMA_RESPONSE” and\n“TNFA_SIGNALING_VIA_NFKB” were also respectively triggered in high-expressed\nSMIM25 and CHL1 EU samples compared to low-expression controls. Interestingly,\nhigh expression of LINC01018 and low expression of hsa-miR-182-5p were also\nassociated with activation of the pathway “EPITHELIAL_MESENCHYMAL_TRANSITION,” a\nwell-described pathological process in EMs ( Figure 7 ).\nThe GSEA analysis of hsa-miR-182-5p and its targets in EU samples. The\ntop 6 activated pathways in high-expressed LINC01018 (a), SMIM25 (b),\nCHL1 (c), and low-expressed hsa-miR-182-5p (d) EU samples compared to\nrespective controls. And representative pathways were displayed in\nclassic GSEA plots (e), (f), (g), (h). GSEA, gene set enrichment\nanalysis; EU, eutopic endometrium. NES, normalized enrichment score.\n\nEndometriosis (EMs) is a heterogeneous disorder because of the diverse implanting\nlocations with different depth of infiltration and non-specific clinical symptoms. 1  The pathological mechanism of EMs remains enigmatic. Progressively\naccumulating evidence declared that the dysregulation of lncRNA affected miRNA\nactivity, such as the ceRNA hypothesis, which was probably involved in the pathology\nof Ems. 5 , 6  However, the\nlncRNA-associated ceRNA network based on multiple RNA-sequencing datasets remains\nunexplored in EMs.\nTo address this challenge, we established an EMs-associated ceRNA network comprised\nof 11 upregulated and 16 downregulated DEmiRs, 7 upregulated and 13 downregulated\nDELs, 48 upregulated and 46 downregulated DEGs. The GO and KEGG pathway analysis\nindicated that this ceRNA network was related to inflammation-related pathways, such\nas “Cytokine-cytokine receptor interaction” and “TNF signalling pathway.” And the\ninflammatory response is the central link of the genesis of Ems. 21  The validation analysis revealed that hsa-miR-182-5p was not only\ndownregulated in EC  vs . EU samples but also downregulated in the EU\nversus NM samples. Besides, the target DELs (LINC01018 and SMIM25) and DEGs (BNC2,\nCHL1, HMCN1, PRDM16) of hsa-miR-182-5p were proved to be upregulated in EC versus EU\nsamples. The negative correlation of hsa-miR-182-5p and these target DELs and DEGs\nwas proved in training datasets. LINC01018 and SMIM25 were found positively\ncorrelated with BNC2, CHL1, HMCN1, PRDM16 in training and validation datasets. The\nGSEA analysis showed that high expression of LINC01018, SMIM25, and CHL1 (the DEG\nwith the maximum log 2 FC) and low expression of hsa-miR-182-5p would\nactivate inflammation-related pathways in EU samples in EMs. Hence, we supposed that\nLINC01018 and SMIM25 might sponge hsa-miR-182-5p to upregulate downstream genes such\nas CHL1 to promote the development of EMs.\nTo the best of our knowledge, the lncRNA LINC01018 and SMIM25 in our established\nceRNA network are firstly reported in EMs. Wang et al. reported that LINC01018 was\ndownregulated in hepatocellular carcinoma (HCC) tissues, and the over-expression of\nLINC01018 inhibited proliferation and promoted apoptosis of HCC cells via the\nup-regulation of FOXO1 by sponging hsa-miR-182-5p. 22  Notably, a certain degree of proliferation and reduced apoptosis were the key\nfeatures of Ems. 21  However, the expression trend of LINC01018 in HCC in Wang et al.’s study 22  was contrary to our findings in EMs. We supposed that LINC01018 might have\ntissue-specific expression and affect cell proliferation and apoptosis in EMs via\nspecific mechanisms different from those in HCC. Moreover, the upregulation of\nLINC01018 would be induced by fasting in humanized livers. 23  And the genome-wide association study (GWAS) indicated that the expression of\nLINC01018 in the liver was associated with the body mass index (BMI). 23  Interestingly, women with EMs were reported with lower BMI 24  and dysregulated lipid metabolism. 25  Although our GSEA analysis indicated LINC01018 related to inflammatory\nresponse, it would be interesting to know whether LINC01018 affects lipid metabolism\nin EMs in future studies.\nThe SMIM25, also known as LINC01272, was upregulated gastric cancer (GC), and the\nover-expression of SMIM25 promoted the migration and invasion ability of GC cells by\nactivating the epithelial-mesenchymal transition (EMT) process. 26  The EMT defines a process by which epithelial cells lose their cell polarity\nand cell-to-cell adhesion and acquire the migratory and invasive properties to\nbecome mesenchymal cells. 27  These changes are supposed to contribute to the establishment of\nendometriotic lesions in Ems. 27  Moreover, the upregulation of SMIM25 might be an indicator of inflammatory\nbowel disease (IBD) and Crohn disease. 28 , 29  More recently, Hung  et\nal.  reported that SMIM25 was upregulated in unstable plaque and highly\nmonocyte- and macrophage-specific. 30  And the knockdown of SMIM25 significantly reduced the phagocytosis. 30  Hence, this study renamed the SMIM25 as PELATON (plaque enriched lncRNA in\natherosclerotic and inflammatory bowel macrophage regulation). Notably, peritoneal\nmacrophages’ impaired phagocytic ability was found in women with EMs, which might\ncontribute to the failure to eradicate aberrant ectopic cells. 31  Additionally, aberrant SMIM25 expression might influence the endometrial\nreceptivity via the inflammation reaction. 32  Considering the crucial role of inflammation and abnormal immunity in EMs, we\nspeculated possible involvement of SMIM25 in the pathogenesis of endometriosis.\nThe verified DEmiR hsa-miR-182-5p belonged to the miR-183/96/182 family, which might\nadopt a critical role in the process of apoptosis, DNA repair, lipid metabolism, and\nimmune signalling. 33  By RNA-sequencing, microarray profiling, and qRT-PCR validation, the\ndown-regulation of hsa-miR-182-5p was observed in EC compared to EU\nsamples. 10 , 34  Meanwhile, the dysregulation of hsa-miR-182-5p was also found\nin the plasma of EMs patients. 35  Similarly, has-miR-183 was also reported downregulated in EC versus EU\nsamples and EU versus NM samples, thus promoting invasion and suppressing apoptosis\nof endometrial stromal cells by targeting ITGB1P. 36 , 37  It has been reported that\nhsa-miR-182-5p was significantly decreased in atherosclerosis models, and the\nover-expression of hsa-miR-182-5p inhibited the oxidative stress and macrophage\napoptosis by targeting Toll-like receptor 4 (TRL4). 38  Quite a few oxidative stress biomarkers had been found significantly higher\nin women with EMs than healthy controls. 39  Continued oxidative stress would contribute to chronic inflammation, 40  which provides a favourable condition for the implantation and growth of\nendometriotic cells. Besides, the macrophages are abundant in ectopic lesions, in\nthe peritoneal cavity and peritoneal fluid of women with EMs compared to controls. 31  And two phenotypes of macrophages: “classically activated” macrophages and\n“alternatively activated” macrophages, collectively contributed to the mixed pro-\nand anti-inflammatory microenvironment for the establishment of ectopic lesions. 31  Furthermore, hsa-miR-182-5p was decreased in metastatic non-small cell lung\ncancer (NSCLC) tissues compared to primary tumour tissues. 41  And it inhibited the metastasis of lung cancer cells via suppressing the EMT process, 41  a well-known precondition for the initial implantation of endometriotic lesions. 27\nOur GSEA analysis revealed that high expression of CHL1 (cell adhesion molecule L1\nLike), the target genes of has-miR-182-5p with the maximum log2FC, would activate\nthe EMT process. CHL1 is a member of the L1 gene family of neural cell adhesion\nmolecules (L1-CAMs), which involved developing the nervous system and a series of\nmorphogenic events, such as cell migration and adhesion. 42  As the homology of CHL1, L1CAM was upregulated in atypical EMs compared to\ntypical EMs, aggravating pain in EMs by promoting nerve growth. 43  Similarly, CHL1 was also found over-expressed in EMs, 44  although it was reported under-expressed in cervical cancer, 42  breast cancer, 45  nasopharyngeal cancer, 46  and papillary thyroid cancer. 47  The overexpression of CHL1 inhibited the motility of nasopharyngeal cancer\ncells by the suppression of EMT. 46  And the silencing of has-miR-182 promoted the expression of CHL1, thus\nsuppressing the growth and invasion of papillary thyroid carcinoma cells. 47  Notably, enhanced invasion and proliferation and the activated EMT were the\nkey features of EMs. 21 , 27  Thus, CHL1 might act in specific ways to influence these\nprocesses in EMs.\nNevertheless, three other target genes of has-miR-182 were seldom reported in EMs.\nBNC2 (Basonuclin 2) is fundamental for the proliferation of craniofacial mesenchymal\ncells during embryogenesis. 48  The polymorphisms in the BNC2 gene were associated with ovarian cancer but\nnot with EMs, indicating EMs is mediated by BNC2 in other ways. 49  HMCN1 (Hemicentin 1) participates in the architecture of adhesive and\nflexible epithelial cell junctions. 50  The upregulation of HMCN1 was found in ovarian cancer (OC) fibroblasts, thus\npromoting the invasion of OC fibroblasts. 50  PRDM16 (PR Domain Containing 16) was involved in adipose biology and also\nmaintenance of hematopoietic and neuronal stem cells. 51  The deletion of PRDM16 in mice contributed to increased apoptosis of\nhematopoietic stem cells (HSCs). 52  And a steady flow of HSCs would facilitate the angiogenesis and inflammation\nin EMs ectopic lesions. 53\nHowever, our analysis has some limitations. Firstly, due to the scarcity of available\nlncRNA and miRNA datasets of EMs, the sample size in the available training and\nvalidation datasets is small. Expanding the sample size would enhance the\nreliability of the results. Secondly, the datasets are expected to include normal\nendometrium (NM) from healthy women as the normal control to explore the molecular\nchanges in EU samples. Besides, the expression of target genes of hsa-miR-182-5p was\nonly analyzed in the mRNA level, which would be improved by validation in the\nprotein level. Moreover, functional experiments need to be performed to explain the\ndetailed regulatory mechanism of hsa-miR-182-5p in a ceRNA manner in EMs.\n\nIn conclusion, we firstly constructed the lncRNA-associated ceRNA network based on\nmultiple RNA-sequencing datasets in endometriosis. Our study revealed that the\nLINC01018 and SMIM25 sponged miR-182-5p to upregulate downstream genes such as CHL1\nto promote the development of endometriosis, which would provide new insights into\nthe roles of non-coding RNAs in the pathogenesis of endometriosis.\n\nClick here for additional data file.\nSupplemental material, sj-xlsx-1-iji-10.1177_2058738420976309 for LINC01018 and\nSMIM25 sponged miR-182-5p in endometriosis revealed by the ceRNA network\nconstruction by Li Jiang, Mengmeng Zhang, Sixue Wang, Yuzhen Xiao, Jingni Wu,\nYuxin Zhou and Xiaoling Fang in International Journal of Immunopathology and\nPharmacology\nClick here for additional data file.\nSupplemental material, sj-xlsx-2-iji-10.1177_2058738420976309 for LINC01018 and\nSMIM25 sponged miR-182-5p in endometriosis revealed by the ceRNA network\nconstruction by Li Jiang, Mengmeng Zhang, Sixue Wang, Yuzhen Xiao, Jingni Wu,\nYuxin Zhou and Xiaoling Fang in International Journal of Immunopathology and\nPharmacology","source_license":"CC0","license_restricted":false}