{"paper_id":"14a34cf8-75e9-4c7a-9ac4-a32e16ae20c9","body_text":"J Exp Pathol. 2022\nVolume 3, Issue 2\nJournal of Experimental Pathology Research Article\n40\nJ Exp Pathol. 2022;3(2):40-54.\nDifferential Expression of Long Non-coding Ribonucleic Acid \n(RNA) Genes in Endometriosis-associated Ovarian Cancer \n(EAOC): A Pilot Meta-analysis for Pathological Insights and \nPotential Diagnostic Biomarker Identification\nA Finall 1,*, D James 2, M Quintela-Vazquez 2, RS Conlan 2\n1Department of Cellular and Molecular Pathology, Morriston Hospital, Swansea Bay University Health Board, Swansea, SA6 6NL, \nUK\n2Reproductive Biology and Gynaecology Oncology, Medical School, Swansea University, Singleton Park, Swansea, SA2 8PP , UK\n*Correspondence should be addressed to Dr A Finall, alison.finall3@wales.nhs.uk\n Received date:  October 28 , 2022 , Accepted date: November 24, 2022\n Citation:  Finall A, James D, Quintela-Vazquez M, Conlan RS. Differential Expression of Long Non-coding Ribonucleic Acid \n(RNA) Genes in Endometriosis-associated Ovarian Cancer (EAOC): A Pilot Meta-analysis for Pathological Insights and \nPotential Diagnostic Biomarker Identification. J Exp Pathol. 2022;3(2):40-54.\n Copyright: © 2022 Finall A, et al.  This is an open-access article distributed under the terms of the Creative Commons Attribution \nLicense, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author  and source \nare credited.\nAbstract\nIntroduction: Endometrioid and clear cell carcinomas of the ovary are the most common subtypes of epithelial malignancy arising from \nendometriosis and are often termed endometriosis-associated ovarian carcinomas (EAOCs). There is a paucity of experimental evidence in \nthe medical literature regarding the role of long non-coding ribonucleic acid (RNA) gene expression in the pathogenesis of these carcinomas.\nPurpose: There is a need to develop understanding of the pathogenesis of these carcinomas for neoplastic risk stratification in endometriosis \nand to develop novel diagnostic biomarkers. Clear cell carcinoma of the ovary, in particular, has a poor prognosis as a result of resistance to \nstandard platinum-based chemotherapy.\nMethods:  RNAseq datasets from EAOCs were downloaded from Gene Expression Omnibus (GEO) and compared with normal ovarian control \nsequences using a customized bioinformatic pipeline. \nResults: We found 88 differentially expressed non-coding RNA molecules present in both endometrioid and clear cell carcinoma types \ncompared with controls. A further 117 were specifically differentially expressed in the endometrioid carcinoma group and 128 in clear cell \ncarcinoma samples alone. Genes of interest for further study from the 88 shared set in both EAOC types include CASC9, RP4-561L24.3, SLC2A1-\nAS1, LUCAT1, XIST, CASC15, and MIR99AHG. These genes appear to influence ferroptosis as a common pathway. \nConclusions: Alterations in the ferroptosis pathway may be a key event in development of EAOC in ovarian endometriosis patients. Further \nwork is required to elucidate the function of the candidate RNA genes identified in this study by in-vitro, cell line and cultured organoid \nexperiments. These candidate RNA gene biomarkers have potential clinical utility in early diagnosis, risk stratification of endometriosis, and \npost-surgical monitoring. \nKeywords: Long non-coding RNA, Endometriosis- associated ovarian carcinoma, Pathogenesis, RNA sequencing, Endometrioid \nadenocarcinoma, Clear cell carcinoma, Ovarian endometriosis\n\n                                                                                                                                                      \n  Finall A, James D, Quintela-Vazquez M, Conlan RS. Differential Expression of Long Non-coding Ribonucleic Acid \n(RNA) Genes in Endometriosis-associated Ovarian Cancer (EAOC): A Pilot Meta-analysis for Pathological Insights \nand Potential Diagnostic Biomarker Identification. J Exp Pathol. 2022;3(2):40-54.\nJ Exp Pathol. 2022\nVolume 3, Issue 2\n41\nIntroduction\nClinical pathology\nOvarian cancer affects 15 women per 100,000 in Europe \n[1] but it is not one disease.  Epithelial malignancy the most \ncommon type of ovarian malignancy and defines the groups \ntermed carcinoma [2,3]. Other malignant subtypes include \nsarcomas, germ cell tumors and sex-cord stromal tumors [1]. \nOvarian carcinomas are subdivided based on histological \nfeatures, the most common being high-grade serous \ncarcinoma which makes up around 70% of ovarian carcinomas \n[2,4]. Up to 10% of ovarian carcinomas are endometrioid sub-\ntype, having phenotypic and molecular resemblances to \nendometroid adenocarcinomas that arise in the endometrial \ncavity [3,5] Clear cell carcinoma (OCCC) and endometrioid \ncarcinoma of the ovary (EnOC) occur on a background of \novarian endometriosis in as many as 70% of cases [6,7]. \nOCCC is equally as common as endometroid type, perhaps \nreflecting this shared origin and collectively are often referred \nto as Endometriosis-Associated Ovarian Carcinomas (EAOCs). \nMucinous carcinoma is less common than endometriosis-\nrelated carcinomas at around 3% of ovarian carcinomas [2]. \nMucinous and low-grade serous carcinomas are rare. Low-\ngrade serous carcinoma has a distinct molecular origin from \nhigh-grade serous carcinoma and is regarded as entirely \nseparate entity despite the similarity in their names [2,4,8]. \nOvarian endometriosis is a common estrogen dependent \ndisease affecting up to 10% of reproductive-age women \naround the world [18,19]. It is characterized by the presence \nof endometrial glands and stroma in sites outside the \nendometrial cavity [19]. There is an increased lifetime risk of \novarian endometriosis progressing to malignancy of around \n1% [20-22]. There is genomic and histological data to suggest \nthat malignancy occurs through an intermediate, dysplastic \nstage called atypical endometriosis [20,23-26].There is \ngenomic, gene expression, and immunohistochemical \nevidence to support the theory that endometriosis is a pre-\nmalignant condition [27-32] (Table 1). \nGene expression data\nIt has been shown that increased expression of genes CCNB2, \nCORO2A, CSNK1G1, FRMD8, LIN54, PDK1, PEX6 and LIN00664 \nis associated with shorter progression free survival times as \ncompared with serous carcinomas where the converse was \nobserved [33]. This observation reinforces the importance of \nappropriately segregating ovarian carcinoma subtypes when \nlooking for clinically significant gene expression profiles [33].  \nTassi and colleagues found that FOXM1 was differentially \nexpressed between a combined ovarian endometrioid, and \nclear cell carcinoma group as compared with high-grade \nserous carcinoma and that this was associated with a poorer \nprognosis in non-serous carcinoma subtypes [34]. Another \ntissue microarray study showed over-expression of GLRX, \nSLC16A3, MKL1, GNE, KIFC3, NAP1, ABCC3, NDRG1, TST, EML2, \nNP , RAP1GA1, AKR1C1, IGFBP3, ARHB, IMPA2, COL4A2, ANXA4, \nSLC4A3, FGFR4, TFAP2A, PTPRM, SMTN, ARHGAP8, and C1QTNF6 \nin OCCC [35]. In contrast, ESR1, ITPR2, WFDC2, FGFRL1, NFIA, \nTable 1. Characteristics of Epithelial Ovarian Carcinoma subtypes.\nHistological Diagnosis\nHigh-grade serous\n[1,9-12] \nEndometrioid\n[2,6,12,13]\nClear cell\n[2,6,12,13]\nMucinous\n[2,12,13] \nLow-grade serous\n[1,14-17]\nIncidence (% of OC) 70 10 10 3 5-10\nAverage age at diagnosis 63 56 51 54 47\nGene mutations\nTP53\nBRCA1/2\nRAD51C/D\nBRIP1\nMSI genes\nARID1A, PTEN, \nCTNNB1, PIK3CA\nARID1A\nPIK3CA\nKRAS, HER2 \namplification\nTP53, c-myc\nKRAS\nNRAS BRAF\nPositive IHC CK7, ER, WT1, p16, \np53, PAX8\nVimentin, ER, PR, \nPAX8 CK7, napsinA CK7, CK20, cdx2 CK7, ER, WT1, PAX8\nFIGO Stage at diagnosis\n51% stage III \n29% stage IV\n58-64% stage I 58-64% stage I 58-64% stage I 78% stage I\nPlatinum-based \nchemotherapy response More than 70% 60% 22-56% 20-60% 4-40%\nFive-year survival 10-26.9% 82% 66% 71% 88%\nAbbreviations: FIGO: International Federation of Gynecology and Obstetrics; OC: Ovarian Carcinoma; IHC: Immunohistochemistry\n\n                                                                                                                                                      \n  Finall A, James D, Quintela-Vazquez M, Conlan RS. Differential Expression of Long Non-coding Ribonucleic Acid \n(RNA) Genes in Endometriosis-associated Ovarian Cancer (EAOC): A Pilot Meta-analysis for Pathological Insights \nand Potential Diagnostic Biomarker Identification. J Exp Pathol. 2022;3(2):40-54.\nJ Exp Pathol. 2022\nVolume 3, Issue 2\n42\nSELENBP1, CDH2, PKIB, SCNN1A, IGFBP2, ID4, CMAS, FLOT1, \nCYP4B1, UBE2F3, GAS1, WT1, EFNB2, MAP1B, DDR1, APOA1B1, \nTSC22, TRIP7, and EDN1 were under-expressed in the same \nstudy [35]. It should be noted that these findings are based \non expression data from just six patients. Having said this, \nhigh gene expression levels for ANXA4 (annexinA4) and GLRX \n(glutaredoxin thiotransferase) have been replicated in another \nstudy using different experimental techniques [36]. \nNon-coding RNA \nThere is emerging data to suggest that elements of the \nhuman non-coding genome make a contribution to the \npathogenesis of endometriosis-associated ovarian cancers \n(EAOC) [37]. The non-coding genome plays a part in the \ndevelopment of malignancies across a range of tumor types \nthrough transcriptional regulation and control of protein \ntranslation by non-coding RNA molecules [38-40]. \nThe RNA molecules responsible for regulating the protein  \ncoding genome are divided into long (more than 200 \nnucleotides) and short molecules (<200 nucleotides). Small \nRNA molecules include microRNAs (miRNAs) which can direct \nmessenger RNA for degradation before translation. piRNA \nmolecules are PIWI-protein interacting and responsible for \nsilencing transposons in the human genome [41]. Short \nRNA molecules may be derived from transfer RNA molecules \n(tsRNA) and these can stabilize messenger RNA for translation \nin opposition to microRNAs [42]. LncRNA genes play a role \nin human carcinogenesis by binding to and regulating \ntranscription factors for protein coding genes, inactivating \nmicroRNAs that target messenger RNA transcripts for \ndegradation, modifying protein function and cellular \nlocalization, influencing chromatin and histone modification, \nand regulating alternative splicing of mRNA [43]. These \nfunctions can affect a number of cell-signaling pathways in the \ndevelopment of cancer including control of cell proliferation, \napoptosis and propagating epithelial-mesenchymal transition \nwhich is said to confer the ability of epithelial cells to invade \nconnective tissue and metastasize [44-48] (Figure 1). \nLong non-coding RNA \nIt has been shown that many of the non-coding somatic \nmutations present in EAOC converge on the PAX8 pathway in \na range of ovarian cancer subtypes including endometrioid \nand clear cell subtypes [49]. Endometrial endometrioid \nadenocarcinoma of the uterine corpus has overlapping \nmolecular pathogenetic characteristics compared with \nendometrioid adenocarcinoma of the ovary [50]. LncRNA \nmolecule MALAT1 has been shown to be involved in the \npathogenesis of endometrioid adenocarcinoma arising from \nthe endometrial cavity by promoting epithelial-mesenchymal \ntransition [51]. NEAT 1, OVAAL, H19, and HOTAIR have also been \nshown to have altered expression profiles in endometrioid \nadenocarcinoma [52-56].\nOther lncRNA molecules that have been implicated in \novarian carcinogenesis are derived from studies that do not \nspecify the histological subtype of ovarian malignancy. This is \nlargely due to the use of ovarian carcinoma cell lines, most of \nwhich derive from high-grade serous carcinoma. The lncRNA   \n \n \n  \nFigure 1: Classification of RNA molecules according to size and cellular function [49].\n\n                                                                                                                                                      \n  Finall A, James D, Quintela-Vazquez M, Conlan RS. Differential Expression of Long Non-coding Ribonucleic Acid \n(RNA) Genes in Endometriosis-associated Ovarian Cancer (EAOC): A Pilot Meta-analysis for Pathological Insights \nand Potential Diagnostic Biomarker Identification. J Exp Pathol. 2022;3(2):40-54.\nJ Exp Pathol. 2022\nVolume 3, Issue 2\n43\ngenes differentially expressed in cell lines include ANRIL, \nBC200, HULC, HST2, HOST2, GAS5, PTAF, SOX2OT, DGCR5, PC3A, \nFAL1, ABO73614, LSINCT5, PVT1, LINK-A, HOXA11-AS, PVT1, \nTUG1, UCA1, ZFAS1, the majority of which are said to behave \nas oncogenes [56-61]. \nThis study is a meta-analysis of published RNA sequencing \n(RNA-seq) data sets generated through high-throughput \nsequencing methods for differential expression analysis \nusing a customized bioinformatics pipeline. The aim was to \ndocument the differential gene expression profile of EAOC \nwith focus on lncRNA genes. Secondarily, the function and \npathway involvement of these lncRNA genes was to be sought \nfrom in silico  tools and databases for insights into EAOC \npathogenesis. \nMethods\nThis study is a meta-analysis of data generated by RNA \nsequencing by other researchers posted in a public access \nonline database for the purpose of further analysis.\nData set identification\nAn online search of the NCBI (National Center for \nBiotechnology Information) gene expression omnibus \n(GEO) [62] repository was performed using keywords ovary, \nendometriosis-associated ovarian carcinoma, ovarian cancer, \natypical endometriosis, endometriosis, clear cell carcinoma \nand ovarian endometrioid carcinoma to identify suitable \nRNA-seq datasets. The search results were further filtered \nusing the terms ‘homo sapiens’ , and ‘expression profiling \nby high throughput sequencing’ . Tissue samples from \novarian carcinomas other than EAOCs, metastatic disease, \nfetal and embryonic tissues, fluid cytology samples, stem \ncells, circulating tumor cells and cell lines were excluded. \nApplication of exclusion criteria yielded 1960 human RNA-\nseq data sets; five were for normal ovarian tissue (Geo \nAccession numbers GSM1010948, GSE127873, GSE137608, \nGSE135485, GSE18927), four were for endometriosis-related \ncontrols (eutopic and ectopic endometrial tissues and normal \nendometrium from healthy patients; accession numbers \nGSE118928, GSE99949, GSE87809, GSE87810), and one for \nEAOC samples (GSE121103). There were no RNA-seq data sets \navailable that included atypical endometriosis samples. \nDatasets were downloaded using the NCBI SRA-toolkit.\nClinical details of ovarian carcinoma tissue samples\nPatient information for each of the source samples used to \ngenerate the RNA-seq data for GSE121103 is given in Table \n2. One of the endometrioid carcinoma samples (EnOC) failed \nto generate reads of sufficient quality for publication to GEO \nbut the investigators do not specify which of the samples this \nrefers to in their paper [37].\nBioinformatic pipeline\nFastq files are first assessed for quality using FastQC [63]. \nThe RNA sequencing reads were aligned to the reference \ngenome using STAR aligner using the GeneCounts argument \nfor the quantMode flag, which generates a gene count table \nfile labelled ReadsPerGene.out.tab. The resulting alignment \nfrom STAR generates a file of summary mapping statistics \nannotated as Log.final.out. This gives an indication of the \nquality of the sample analyzed by reflecting the proportion of \ninput reads, the average RNA molecule read length versus the \nnumber of unmapped and chimeric reads and mismatch rate. \nA data matrix of factors informed the normalization step which \nwas carried out by using the DESeq2 package. DESeq2 adjusts \nfor the variation in expression counts according to variation \nin the read depth for each gene by using a generalized linear  \nTable 2. EAOC patient samples clinical details [37].\nAge Ethnicity Tumour grade Tumour stage\nOCCC 1 45 Asian 3 IIIC\nOCCC 2 47 White Hispanic 3 IIIC\nOCCC 3 61 Unknown 3 IIIC\nOCCC 4 38 Unknown 3 IIIB\nOCCC 5 52 Unknown 3 IIIB\nEnOC 1 64 Hispanic 3 IV\nEnOC 2 50 White Hispanic 1 IB\nEnOC 3 35 White Hispanic 2 IC\nEnOC 4 42 White Hispanic 2 IC\nEnOC 5 41 White Hispanic 1 IC\nAbbreviations: OCCC: Ovarian Clear Cell Carcinoma; EnOC: Endometrioid adenocarcinoma of the Ovary.\n\n                                                                                                                                                      \n  Finall A, James D, Quintela-Vazquez M, Conlan RS. Differential Expression of Long Non-coding Ribonucleic Acid \n(RNA) Genes in Endometriosis-associated Ovarian Cancer (EAOC): A Pilot Meta-analysis for Pathological Insights \nand Potential Diagnostic Biomarker Identification. J Exp Pathol. 2022;3(2):40-54.\nJ Exp Pathol. 2022\nVolume 3, Issue 2\n44\nmodel and creates an estimate of moderated variance of genes \nby comparing the variance of the gene in question versus \nthe average variance of all the genes present in the dataset \n[64]. Following normalization, DESeq2 performs differential \nexpression analysis to calculates the fold change in expression \nfor each transcript between a control sample set and a sample \nof interest. DESeq2 calculates a P-value, corrected for multiple \nsampling, to indicate whether the fold change is statistically \nsignificant. Statistical significance was set at the 0.05 level. The \ngene lists were filtered for base mean expression (absolute \nexpression level) >10 and log fold change >2.\nBiological interpretation\nThe location-based display function in Ensembl was used \nto find the region detail map for each lncRNA gene and \ninterrogated to find the nearest protein coding gene according \nto current annotations in the GRCh38 reference [65,66]. There \nis evidence that most long non-coding transcripts exert their \ntranscriptional influence by acting on protein coding genes in \ncis, that is to say, by acting upon genes that are located near \nto them in the genome [67-70]. RNA databases ‘RNAcentral’ , \n‘Rfam’ , ‘NONCODE’ , ‘LNCipedia’ , ‘LNCBook’ , and ‘lncrnadb’ were \nalso interrogated to provide up-to-date information regarding \nall aspects of lncRNA genes identified. Online bioinformatics \ntools and databases, including NCBI, Ensembl, OMIM, Clinvar, \nGenecards, RISE, Diana and PubMed were used to identify \nfunctional annotations and pathway interactions for lncRNA  \ntranscripts and their cis-located protein coding genes [71-78]. \nEthical considerations\nMeta-analysis was chosen as the method of study, in part, \ndue to the temporal constraints on ethical review but also \nbecause of financial limitations. Specific ethical approval was \nnot required for this study as it was a re-analysis of published \npatient sequencing data in the public domain. The tissue \nsamples used in the original study by Lin et al, 2019 [61] were \ncollected with informed consent and given approval by the \nethical review board of the University of Southern California.\nResults\nThe samples for normal endometrium were of insufficient \nquality to use as control material for this study. Normal ovarian \ntissue was therefore used as control material as sequencing \nread outs were of good quality.\nThe primary aim of describing the differential gene \nexpression of EAOCs was achieved. The secondary aim of \nfunctional characterization was also achieved but required \nassumption of in cis function of all lncRNA genes to inform \ninterpretation. A total of 35,697 transcripts were differentially \nexpressed in the ovarian endometrioid adenocarcinoma \n(EnOC) sample set from 4 patients (n=4) and 33,939 transcripts \nfrom the ovarian clear cell carcinoma (OCCC) samples (n=5). \nBoth transcript expression lists were filtered by removing all \nprotein coding genes, microRNAs (less than 200 nucleotides in \nlength), processed transcripts, pseudogenes (processed and \nunprocessed), small nuclear RNA (snRNA) and small nucleolar \n(snoRNA), mitochondrial RNA and molecules classified as \nmiscellaneous, leaving only transcripts annotated as lncRNA. \nThe total transcript expression list included 333 lncRNAs \nsignificantly up-regulated or down-regulated in endometrioid \nand clear cell adenocarcinoma groups with 88 being present in \nboth the endometrioid and clear cell adenocarcinoma groups. \nThere was differential expression of 117 lncRNA transcripts in \nthe endometrioid group alone and 128 differentially expressed \ntranscripts in the clear cell group (Figure 2). \nThe top ten most over expressed transcripts, in decreasing \norder, in the overlapping group of 88 lincRNA transcripts were \nRP11-456B22.8, LINC00958, LINC00621, RP11-529E15.1, RP11-\n3J1.1, RP11-4K16.2, LINC01320, U47924.27, CASC9, and RP1-\n86C11.7 as measured by absolute log fold change (LFC) >2.  \n \n \n  \nOvarian Clear Cell \nCarcinoma \n \nOvarian Endometrioid \nCarcinoma \n \nFigure 2. Venn diagram illustrating number of overlapping lncRNA molecules between groups of ovarian endometriosis-related adenocar-\ncinomas (filtered by LFC >2, BM>10 and p<0.05).\n\n                                                                                                                                                      \n  Finall A, James D, Quintela-Vazquez M, Conlan RS. Differential Expression of Long Non-coding Ribonucleic Acid \n(RNA) Genes in Endometriosis-associated Ovarian Cancer (EAOC): A Pilot Meta-analysis for Pathological Insights \nand Potential Diagnostic Biomarker Identification. J Exp Pathol. 2022;3(2):40-54.\nJ Exp Pathol. 2022\nVolume 3, Issue 2\n45\nThe most under-expressed lincRNA transcripts in the \noverlapping group compared with control samples were \nRP4-561L24.3, RP11-108M9.3, AC084082.3, RP4-535B20.1, \nCTD2332E11.2, RP11-473M20.16, LINC00324, RP11-613D13.8, \nAP001172.3, and RP5-875O13.1, with RP4-561L24.3  being the \nmost under-expressed with a LFC of -11.29. See Table 3.\nThe most differentially overexpressed lincRNA transcripts \nin the endometrioid carcinoma group were  RP11-6.08O21.1, \nAC011288.2, LINC01123, LLINC01508, RP11-400N13.2, RP11-\n319E16.2, LINC010206, RP1-60O19.1, CTC-304I17, and RP11-\n89K21.1 whilst those with most reduced expression out of \nthe 117 lincRNA transcripts identified were fewer in number. \nThey are RP11-1100L3.8, GATA6-AS1, RNU12, RP11-323I15.5,  \nLINC00602, and RP11-95H3.1. See Table 4.\nOf the 128 lncRNA transcripts found in the clear cell \ncarcinoma group the following were most over-expressed: \nLINC00668, LINC00858, RP11-190J1.3, LINC01446, LINC01518, \nRP11-528A4.2, RP11-356C4.5, LINC01559, CTD-2008P7.8,  RP11-\n346C4.3. The greatest reduction in expression compared \nwith control normal ovary included ENOX1-AS1, AP000962.2, \nOVAAL, RP11-400K9.4, RP11- 1081M.51, LINC01539, LINC00924, \nRP11-826N14.4, GAS1RR, and LINC01018. See Table 5.\nThe Ensembl genome region detail map showed protein \ncoding genes located near to the lncRNA transcripts \ndifferentially expressed in our meta-analysis. Of note, the \nTable 3: Overlapping gene expression between ovarian endometrioid and clear cell adenocarcinoma types as ranked by log fold change \n(priority OCCC). All p values less than 0.05.\nGene name Expression level Log Fold Change\nEnOC             OCCC EnOC        OCCC\nRP11-456B22.8 38.41 45.80 9.53 10.23\nLINC00958 92.48 248.13 10.72 9.62\nLINC00621 399.39 142.08 9.83 9.46\nRP11-529E15.1 30.39 30.08 8.91 8.95\nRP11-3J1.1 39.98 30.85 8.25 8.76\nRP11-4K162 26.01 19.70 8.46 8.51\nLINC01320 624.39 596.55 7.66 8.39\nU47924.27 23.19 21.39 7.32 7.95\nCASC9 50.44 82.20 7.84 7.55\nRP1-86C11.7 12.28 14.06 7.83 7.39\nRP4-561L24.3 3555.92 2716.14 -11.87 -11.29\nRP11-108M9.3 556.1 424.11 -11.22 -9.93\nAC084082.3 240.31 181.83 -8.22 -7.26\nRP4-535B20.1 29.40 21.43 -8.23 -6.04\nCTD-2332E11.2 140.74 107.36 -5.85 -5.62\nRP11-473M20.16 229.46 172.49 -6.03 -5.38\nLINC00324 230.15 169.80 -6.37 -5.33\nRP11- 613D13.8 156.39 115.61 -5.82 -5.12\nAP001172.3 18.88 15.71 -5.56 -4.87\nRP5-875O13.1 28.88 21.69 -4.77 -4.51\nAbbreviations: EnOC: Ovarian Endometrioid adenocarcinoma; OCCC: Ovarian Clear Cell Carcinoma. Colour code: Red = Over-expression; \nGreen = Under-expression.\n\n                                                                                                                                                      \n  Finall A, James D, Quintela-Vazquez M, Conlan RS. Differential Expression of Long Non-coding Ribonucleic Acid \n(RNA) Genes in Endometriosis-associated Ovarian Cancer (EAOC): A Pilot Meta-analysis for Pathological Insights \nand Potential Diagnostic Biomarker Identification. J Exp Pathol. 2022;3(2):40-54.\nJ Exp Pathol. 2022\nVolume 3, Issue 2\n46\nTable 4: Differential gene expression of ovarian endometrioid adenocarcinoma (EnOC) group as ranked by log fold change.\nGene Name Expression level Log fold change P value\nRP11-608O21.1 35.93 9.15 1.67E-08\nAC011288.2 27.25 9.11 9.16E-07\nLINC01123 29.45 8.78 2.49E-07\nLINC01508 22.18 8.68 8.71E-07\nRP11-400N13.2 17.80 8.44 6.73E-06\nRP11-319E16.2 20.7 8.32 3.69E-05\nLINC01206 21.03 8.0 4.06E-05\nRP1-60O19.1 16.41 8.02 4.48E-05\nCTC-304I17.6 18.01 7.83 4.66E-05\nRP11-89K21.1 83.85 7.81 3.04E-09\nRP11-1100L3.8 90.92 -2.07 0.0016\nGATA6-AS1 68.26 -2.65 0.0002\nRNU12 13.14 -2.6 0.005\nRP11-323I15.5 15.44 -2.70 0.004\nLINC00602 12.94 -3.73 0.002\nRP11-95H3.1 48.37 -3.74 8.05E-05\nRed = Over-expression; Green = Under-expression\nTable 5: Differential gene expression in ovarian clear cell adenocarcinomas (OCCC) as ranked by log fold change. \nGene Name Expression level LFC P value\nLINC00668 508.97 11.72 1.23E-20\nLINC00858 35.23 9.63 8.86E-09\nRP11-190J1.3 38.88 9.63 3.54E-08\nLINC01446 24.76 8.79 8.23E-05\nLINC01518 30.61 8.26 1.58E-06\nRP11-528A4.2 18.89 7.91 5.62E-05\nRP11-356C4.5 11.15 7.61 8.16E-05\nLINC01559 102.25 7.49 4.19E-05\nCTD-2008P7.8 11.03 7.22 0.00034\nRP11-346C4.3 22.28 6.96 7.43E-05\n \nENOX1-AS1 13.79 -5.26 2.32E-05\nAP000962.2 35.17 -5.82 1.71E-05\nOVAAL 10.51 -6.16 0.0013\nRP11-400K9.4 45.77 -6.29 1.97E-08\nRP11- 1081M.51 10.16 -6.32 0.00056\n\n                                                                                                                                                      \n  Finall A, James D, Quintela-Vazquez M, Conlan RS. Differential Expression of Long Non-coding Ribonucleic Acid \n(RNA) Genes in Endometriosis-associated Ovarian Cancer (EAOC): A Pilot Meta-analysis for Pathological Insights \nand Potential Diagnostic Biomarker Identification. J Exp Pathol. 2022;3(2):40-54.\nJ Exp Pathol. 2022\nVolume 3, Issue 2\n47\nLINC01539 14.00 -6.40 0.0020\nLINC00924 71.39 -6.90 3.01E-10\nRP11-826N14.4 13.56 -6.90 0.00032\nGAS1RR 29.61 -6.92 2.16E-06\nLINC01018 31.93 -6.99 1.18E-06\nRed = Over-expression; Green = Under-expression\n \nTable 6.  Target genes based on in cis function. Summarizes findings based on data from Ensembl genome map information, Lincipedia, \nRNAcentral, and Genecards. \nEndometrioid Adenocarcinoma Both CCC and Endometrioid Clear Cell Carcinoma (CCC)\nLncRNA greatest log \nfold change\nlncRNA highest \nexpression level\nLncRNA greatest log \nfold change\nlncRNA highest \nexpression level\nLncRNA greatest log \nfold change\nlncRNA highest \nexpression level\nlncSLIT2\nSLIT2\nLINC01695\nN6AMT1\nRP11-456B2 \n2.8\nRNF223\nRP4-561L24.3\nBCAR3\nGCLM\nDNTTIP2\nLINC00668\nLAMA1\nARHGAP28\nXIST\nTSIX\nHIF1A\nAC011288.2\nARL4A\nRP11-191L9.4\nTBC1D22A\nLINC00958\nTEAD1 \nCARMN\nPCYOX1L\nLINC00858\nLRIT1\nRGR\nC1orf132\nCD34\nCD46\nLINC01123\nMALL\nBLACAT1\nLEMD1\nLINC00621\nSGCG\nSLC2A1-AS1\nSLC2A1\nHIF-1alpha\nRP11-190J1.3\nFBXW4\nLINC00668\nLAMA1\nARHGAP28\nLINC01508\nDIRAS2\nLUCAT1\nADGRV1\nNRF2\nRP11-529E15.1\nFAM98A\nLINC00478\nUSP25\nLINC01446\nVS2MTA\nPOM121L12\nMIRLET7BHG\nPRR34\nLINC02474\nDUSP10\nLINC02604\nTMEM248\nRP11-3J1.1\nLCORL\nSLIT2\nPWRN1\nNPAP1\nLINC01518\nZNF338\nRP11-54H7.4\nMYO16\nLNCNFT53-2\nNFT3\nNRAD1\nLACC1\nCCDC122\nMAL2-AS1\nMAL2\nCASC15\nPRL\nSOX4\nCDKAL1\nLINC02038\nOPA1\nHES1\nRP11-20D14.6\nRIMKLB\nLINC01206\nSOX2\nKRT80-4\nNR4A1\nLINC01320\nFAM98A\nCH507-513H4.6\nKCNE1B\nRP11-356C4.5\nPRDM7\nHCG11\nBNT1A1\nHMGN4\nRP1-60O19.1\nPDSS2\nRP-11-89K21.1\nMIR200CHG\nPHB2\nLINC01320\nFAM98A\nLINC01559\nGRIN2B\nMIR29A\nKLF4\nMKLN1\nCTC-304I17.6 LINC00937\nCASC9\nHN4A\nRP11-108M9.3 CTD-2008P7.8 RP11-554D15.3\nLncRNA genes are in black, adjacent protein coding genes identified in Ensembl are in blue. Genes involve in ferroptosis are shown in red.\n\n                                                                                                                                                      \n  Finall A, James D, Quintela-Vazquez M, Conlan RS. Differential Expression of Long Non-coding Ribonucleic Acid \n(RNA) Genes in Endometriosis-associated Ovarian Cancer (EAOC): A Pilot Meta-analysis for Pathological Insights \nand Potential Diagnostic Biomarker Identification. J Exp Pathol. 2022;3(2):40-54.\nJ Exp Pathol. 2022\nVolume 3, Issue 2\n48\nprotein coding gene GCLM (Glutamate-cysteine ligase, \nmodifier subunit) is located near to RP4-561L24.3, HIF-1α  \n(Hypoxia Inducible Factor1, subunit alpha)  is located near to \nSLC2A1-AS1 lncRNA, USP25 (Ubiquitin Specific Protease 25)  is \nlocated near to LINC00478, SOX4 (SRY-box 4)  is located near \nto CASC15 and HNF4α (Hepatocyte Nuclear Factor 4-alpha) \nis located near to CASC9 within the group of differentially \nexpressed transcripts found in both endometrioid and clear \ncell carcinomas (see central two columns of Table 6). NRF2 \n(Nuclear-Factor Erythroid2-Related Factor)  is located near to \nLUCAT1 within the data for endometrioid adenocarcinoma \ntranscripts. The protein coding gene HIF-1α  is also near to XIST, \nwhich was differentially expressed in the clear cell carcinoma \ngroup. Examination of KEGG [79] pathways, PathCards [80] \nand other integrated functional databases [81,82] showed \nthat genes in red (see Table 6) were involved in ferroptosis, an \niron-dependent form of programmed cell death [83-89].\nDiscussion\nLong non-coding RNAs have several modes of function in \nhuman cells and these fall into three broad categories; post-\nmRNA processing, chromatin reprogramming and regulation \nof protein coding gene transcription and enhancer sites [90]. \nMost lncRNA transcripts are said regulate transcription factors \nof nearby protein coding gene and are thus cis-acting [68-\n70,91]. There is a paucity of published literature regarding \nlncRNA function with mRNA and miRNA interactions for the \nmajority of lncRNA genes listed in the results above (see Table \n6). Understanding that many long non-coding RNA molecules \nfunction in cis  allowed detailed exploration of the genomic \nsites of origin of these molecules and generation of a list of \nprotein coding genes that represent the most likely targets \nto be controlled by the lncRNAs identified. Analysis of these \ngenes with long-non-coding RNA species in the Genecards \ndatabase has identified potential molecular function and \nhighlighted biological pathways in which they function. For \nexample, lncRNA cancer susceptibility 9 (CASC9) is situated next \nto the hepatocyte nuclear factor 4 gamma (HNF4G) protein \ncoding gene (also known as NR2A2). \nOVAAL (ovarian adenocarcinoma amplified lncRNA) \nexpression has been reported as amplified in ovarian high-\ngrade serous carcinoma [92]. In this study, OVAAL showed \nreduced expression in ovarian clear cell carcinoma (log \nfold change -6.16, p=0.001). Research suggests that OVAAL \nbehaves as an oncogene by enhancing cell survival through \ninitiation of the RAF/MEK/ERK pathway and avoiding cellular \nsenescence mechanisms [93]. However, down-regulated \nexpression of OVAAL, as identified in this study, appears \nto contradict this evidence and would suggest a tumor \nsuppressor function in vivo. \nFindings by Zou et al., 2015 support the hypothesis that \nCASC9 interacts with protein coding HNF4G by acting in-cis \nbased on evidence from datamining bioinformatic online  \ndatabases [94]. HNF4G is one of a subfamily of liver-specific \ntranscription factors important for organ development in \nutero [95]. HNF4 has also been shown to be overexpressed \nin Islet of Langerhans cells of the pancreas in young people \nwith maturity onset diabetes [96]. The morphological feature \nof cytoplasmic clearing in OCCC is due to intracytoplasmic \nglycogen accumulation and this may be due to alterations \nin glycogen metabolism as a result of alterations in HNF4G \nfunction (Ji et al, 2018). HNF4G has been documented as \nbeing involved in OCCC pathogenesis [37]. Also, strong \nimmunohistochemical expression of a different subset of \nHNF, HNF1beta, has been shown in OCCC but this is not used \nin routine diagnostic histopathological practice due to a lack \nof specificity and distinct morphology [97]. Furthermore, the \ngenes for the group of transcription factors comprising HNF4 \nand HNF1 are found on different chromosomes and activate \ntranscription of different cytochrome p450 enzymes [98]. \nCdc42 interacts with breast cancer anti-estrogen resistance \nprotein 3 (BCAR3) which interacts with RP4-561L24.3. Cdc42 \ncodes for a cell membrane protein found in macrophages \nand is responsible, in part, for coordinated and effective \nphagocytosis [99]. Reduced expression of cdc42 protein \non the surface of macrophages has been shown to differ \nbetween tissues with endometriosis and EAOC. Loss of cdc42 \nexpression may play a role in the malignant transformation \nof endometriosis. Further, cdc42 protein also plays a key role \nin the MAPK pathway that controls cellular proliferation, anti-\napoptosis and cellular differentiation.\nIn addition, BCAR3, RP4-561L24.3  is co-located with GCLM \nand DNTTIP2  on chromosome 1 [100]. In this meta-analysis, \nRP4-561L24.3 was expressed at the highest overall level in \nthe combined EAOC group but simultaneously differentially \nunder-expressed in comparison with controls. RP4-561L24.3 \nmay influence ferroptotic pathways in the pathogenesis of \nendometriosis-associated carcinoma via its interaction with \nGCLM [101]. Ferroptosis is a form of programmed cell death \nresulting from a combination of iron and lipid peroxidation \nin mitochondria with a toxic accumulation of reactive oxygen \nspecies [102]. Resistance to ferroptosis is thought to be critical \ndevelopment in the pathogenesis of endometriosis [103]. This \nmakes biological sense in a context of endometriosis where \nthe local tissue environment will contain increased amounts of \niron-rich hemosiderin as a consequence of menstrual bleeding \n[103]. However, the role of ferroptosis in the development of \nendometriosis-associated malignancy has yet to be described \nin full [89]. A recent report showed an increased sensitivity \nof EAOCs to Erastin therapy, which targets the ferroptosis \npathway, leads to increased rates of cell death [89]. If resistance \nto ferroptosis is an important mechanism in the development \nof EAOC, one could hypothesize that gene expression studies \nwill show increased expression of tumor suppressor genes in \nnormal cells with reduced differential expression in malignant \ncells [104,105]. This suggests that RP4-561L24.3 functions as \na tumor suppressor as it is the most down regulated lncRNA  \n\n                                                                                                                                                      \n  Finall A, James D, Quintela-Vazquez M, Conlan RS. Differential Expression of Long Non-coding Ribonucleic Acid \n(RNA) Genes in Endometriosis-associated Ovarian Cancer (EAOC): A Pilot Meta-analysis for Pathological Insights \nand Potential Diagnostic Biomarker Identification. J Exp Pathol. 2022;3(2):40-54.\nJ Exp Pathol. 2022\nVolume 3, Issue 2\n49\nmolecule in this EAOC dataset in the combined endometrioid \nand clear cell group (log fold change -11.87, p value= 3.97x \n10-34).\nFurther evidence of a role for ferroptosis in the development \nof EAOC comes from the finding that LUCAT1 controls NRF2 \nexpression via miRNA-495 [106]. NRF2 (also known as NFE2L2) \nhas been shown to have a critical role in ferroptosis [101]. NRF2 \n(Nuclear-Factor Erythroid2-Related Factor ) is a transcription \nfactor that regulates genes with promotors containing anti-\noxidant response elements [85]. Genes regulated by NRF2 are \nupregulated in response to oxygen free radicals released in a \ncontext of inflammation and injury as seen in ovarian tissues \naffected by endometriosis [107].\nIt is also interesting to note that cells with high levels of nrf2 \nprotein activity are enriched for gamma-glutamyl peptides \nwhich are produced as a result of GCLC/GCLM activity [86]. \nKang et al. suggest that GCLM may be regulated in cis by \nRP4-561L24.3. It would make sense that reduced differential \nexpression of genes known to act in the ferroptosis pathway \nplay a key role in EAOC pathogenesis as it is known that \nmenstrual cycling in endometriotic lesions results in \ngeneration of excessive irons and free radicals [89].\nSLC2A1 is a member of the family of solute carriers.  The anti-\nsense lncRNA molecule SLC2A1-AS1 is expressed at a high level \nin our EAOC data. This regulates transcription of SLC2A1 on the \nsense strand of DNA [108]. Transcription of SLC2A1 is induced \nunder hypoxic conditions and influenced by increasing levels \nof hypoxia inducible factor 1 alpha (HIF-1alpha) [109]. \nSOX4 is said to be involved in the pathway regulating \nferroptosis [110,111]. This may be regulated in cis by CASC15 \ndue to co-location with SOX4 in the human genome [100]. \nSOX4 (SRY-related HMG-box 4) is a transcription factor that has \nbeen implicated in carcinogenesis as high expression of SOX4 \nis thought to contribute to dedifferentiation, cell survival and \nepithelial-mesenchymal transition [112]. On the basis of its \nmolecular interactions, CASC15 may have an important role \nto play in development of EAOC. Researchers suggest that \nCASC15 functions as a tumor suppressor gene [113] in which \ncase one would expect to see reduced levels of expression \ncompared with controls in this study. This is not the case, \nhowever, as CASC15 was shown to be expressed at a high level \nand was not differentially expressed. This casts some doubt on \nthe role of CASC15 in the development of EAOC and requires \nfurther experimental investigation.\nMir-99a-Let7c Cluster Host Gene (MIR99AHG), otherwise known \nas LINC00478, resides near to protein coding gene Ubiquitin \nSpecific Peptidase 25 (USP25) . USP25 is a peptidase enzyme \nresponsible for cleavage of ubiquitin from proteins destined \nfor cellular degradation by proteolysis in the proteosome \n[114]. It thereby prevents target proteins from breakdown and \nhas been shown to interact with components of the MAPK \npathway [115]. USP25 also inhibits induction of ferroptosis in \nmalignancy [116].\nXIST has been documented as having an influence on \nferroptosis by suppressing glutathione-S transferase (GST) \nand increasing glutathione synthase levels [117]. XIST was \n \n \n \n \n \n \n \n  \nFerroptosis\nCASC9 via \nHN4A\nSLC2A1-\nAS1 via \nHIF-1alpha\nRP4-\n561L24.3 \nvia GCLM\nCASC15 via \nSOX4\nMIR999HG \nvia USP25\nLUCAT1 via \nNRF2\nXIST via \nHIF1A-AS1\nFigure 3: Summary of lncRNA molecules converging on ferroptosis.\n\n                                                                                                                                                      \n  Finall A, James D, Quintela-Vazquez M, Conlan RS. Differential Expression of Long Non-coding Ribonucleic Acid \n(RNA) Genes in Endometriosis-associated Ovarian Cancer (EAOC): A Pilot Meta-analysis for Pathological Insights \nand Potential Diagnostic Biomarker Identification. J Exp Pathol. 2022;3(2):40-54.\nJ Exp Pathol. 2022\nVolume 3, Issue 2\n50\nexpressed at the highest overall level in OCCC but was \nsimultaneously down regulated expression level 5357, log \nfold change -2.68, p value= 0.0001. There is evidence in the \nRISE database of RNA-RNA interactions that XIST interacts with \nanti-sense RNA HIF1alpha-AS1 which regulates transcription of \nthe hypoxia inducible factor from the opposite coding strand \nof DNA [74]. HIF1alpha is also induces expression of SCL2A1-\nAS1 under hypoxic conditions [109] (Figure 3).\nStrengths and Limitations of This Study\nLimitations of this study include not having access to the \nsource tissue to confirm the disease entities listed in the \npatient clinical information table were histologically accurate. \nThere is concern regarding the specimen classified as FIGO \ngrade 3 endometrioid adenocarcinoma of the ovary as this \nis a difficult histological diagnosis to make. The morphology \nof grade 3 endometrioid adenocarcinoma is often solid, \npoorly differentiated and similar to high-grade serous \ncarcinoma.  The distinction relies upon the use of a panel of \nimmunohistochemistry markers which were not described in \noriginal publication [37]. \nAnother limitation of the study was the use of normal ovarian \ntissue as a source of control RNA sequence for comparison \nwith the malignant tissues. It would have been more accurate \nto use ovarian endometriosis samples given that this is said \nto be the cell of origin for most EAOCs [6] but these were of \ninsufficient quality for use.\nThe online databases used to study the pathways are at an \nearly stage in evolution with gaps in knowledge content. \nIn many cases there is no known functional information or \ninteractions regarding the lncRNA species identified in this \nmeta-analysis. Furthermore, many of the assertions made \nare based on in silico  prediction rather than experimental \ndata. This indicates a need for further work by, for example, \nperforming knockdown experiments in organoid models \nof endometriosis and EAOCs to help classify function and \ncontribute to the knowledge base. A major assumption of \nthe interpretation of our data rests on a cis- rather than trans-\nregulatory function of the lncRNA molecules identified in this \nwork [69].\nConclusions\nLncRNA genes  RP4-561L24.3, CASC15, CASC9, SLC2A-AS1, \nLUCAT1, XIST and MIR99AHG are candidate biomarkers for \nfurther exploration in the pathogenesis of EAOC. Some of these \nlncRNA molecules may have an influence on the ferroptosis \npathway by cis-acting regulation of transcription factors of \nnearby protein coding genes [69]. In vitro  experiments are \nrequired to provide evidence in respect of this hypothesis. \nNumerous online databases have incomplete information \nrelating to lncRNA function and interactions that makes \nfurther experimentation necessary.\nFerroptosis is a form of programmed cell death that is \ninduced in response to cellular stresses involving iron and \nlipid metabolism in mitochondria and might play a part in \nthe pathogenesis of EAOC. LncRNA molecules identified in \nthis meta-analysis may inform clinical diagnostic pathways \nby development of non-invasive methods of detection for \nearly diagnosis, risk stratification of endometriosis and more \neffective post-surgical patient monitoring for recurrence.\nConflicts of Interest\nThe authors declare no conflicts of interest.\nReferences\n1. Tavassoli FA, Devilee P . Pathology and genetics of tumours of the \nbreast and female genital organs. 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