Genotype-driven variations in lncRNA expression underlie predisposition to high-grade serous ovarian cancer

Genotype-driven variations in lncRNA expression underlie predisposition to high-grade serous ovarian cancer | 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 Article Genotype-driven variations in lncRNA expression underlie predisposition to high-grade serous ovarian cancer Xin Sheng, Mingming Lu, Yanan Chu, Ziwei Zhu, Danfei Zhu, Siyi Chen, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6929868/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract New effective early diagnosis strategies and therapeutic targets are desperately needed for high-grade serous ovarian cancer (HGSOC). Previous functional interpretation of HGSOC genome-wide association studies (GWAS) largely neglected long non-coding RNAs (lncRNAs). Here, we map genotype effects on the expression of both protein-coding genes (PCGs) and lncRNAs in 348 HGSOC tumor samples. We show that underlying genetic variants are significant drivers of expression changes for both PCGs and lncRNAs. Importantly, lncRNAs account for a higher portion of HGSOC heritability than PCGs. Bayesian colocalization analysis prioritized causal genes in HGSOC tumors and whole blood, such as ARL17A and CRHR1-IT1 . Using single-nucleus transcriptome maps of HGSOC tumors generated by snRandom-seq, we characterized expression patterns of candidate susceptibility genes. Furthermore, we validated the role of CRHR1-IT1 in promoting apoptosis and inhibiting the growth of HGSOC cells. Our study provides new insights into early detection and immunotherapy development for HGSOC. Health sciences/Biomarkers Biological sciences/Cancer/Gynaecological cancer eQTL colocalization lncRNA post-GWAS CRHR1-IT1 ovarian cancer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Invasive epithelial ovarian cancer (EOC) is among the leading causes of gynecologic cancer in women worldwide, with greater than 314,000 new diagnoses and approximately 207,000 deaths each year 1 . Histological subtypes of invasive EOC vary in epidemiological 2 , 3 , 4 and genetic risk factors 5 , clinical responses to platinum-based therapy 6 , 7 , and cells of origin 8 , 9 . High-grade serous ovarian cancer (HGSOC) is the most prevalent histotype, accounting for 60–70% of EOC cases and the majority of EOC mortality 10 , 11 . Current treatments mostly rely on traditional cytoreductive surgery combined with platinum-based chemotherapy, and the overall 5-year survival rate is still below 20% for HGSOC due to the scarcity of effective early diagnosis strategies and the rapid development of chemoresistance 12 , 13 , 14 , 15 , 16 , 17 . Therefore, new oncogenes and tumor suppressors that could serve as effective diagnostic and therapeutic targets for HGSOC, are desperately needed. To prioritize the best targets, it is of critical importance to understand the germline genetic architecture of HGSOC, as previous studies consistently confirmed that genetically supported drug targets could dramatically contribute to the successful development of new therapeutics 18 , 19 . HGSOC has a significant germline genetic component, with an estimated heritability around 20 to 40% 15, 17 , 20 , 21 . Known germline loss-of-function mutations in susceptibility genes, such as BRCA1 , BRCA2 , TP53, BRIP1, MSH6 , and RAD51C/D , only account for about 10% of cases 22 , 23 , 24 , 25 , 26 , 27 . The majority of genetic risk factors for HGSOC is due to common low-penetrance-susceptibility alleles 28 . Large-scale genome-wide association studies (GWASs) have so far identified 19 loci, where common single-nucleotide polymorphisms (SNPs) show statistically significant reproducible association with HGSOC predisposition 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , suggesting that germline genetic variants influence tumorigenesis. However, interpreting the likely biological mechanism of GWAS is challenging, as more than 90% of risk variants lie in the non-protein-coding regions of the genome. Expression quantitative trait locus (eQTL) analysis has been a powerful approach to map risk variants to disease-causing genes 39 , 40 , 41 . By integrating of eQTL and GWAS, several previous studies have successfully prioritized putative susceptibility genes for HGSOC risk loci, such as HOXD9 42 , TERT 42 , CHMP4C 28 , ANKLE1/ABHD8 29 , and GNL2 43 . Although long non-coding RNAs (lncRNAs) dominate the transcriptome in human cells, most efforts to interpret the functional consequences of non-coding risk variants have primarily focused on their regulatory effects on protein-coding genes (PCGs), greatly neglecting lncRNAs, which have been increasingly shown to play critical roles in chemoresistance, tumorigenesis, tumor progression, and metastasis 29 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 . LncRNAs are likely to act as scaffolds, guides, or decoys to alter DNA-protein binding or protein-protein interactions 49 , 52 , resulting in dysregulation in cell proliferation, differentiation and apoptosis 53 , 54 . Previous investigations of the correlation between disease-associated SNPs and lncRNA expression have successfully nominated several potential disease-causing lncRNAs, such as PCAT1 in promoting prostate cancer cell growth 55 and LINC01475 in influencing ulcerative colitis susceptibility 56 . Therefore, systematic interrogation of the regulation from germline variants on lncRNAs (lncRNA-eQTLs) could provide new insights into how risk variants contribute to HGSOC pathogenesis via non-coding genome and improve new therapeutic targets identification. In this study, we generated a comprehensive atlas of eQTLs for both PCGs and lncRNAs, utilizing 348 HGSOC tumor samples with matched high-quality genotype and gene expression data from the Cancer Genome Atlas (TCGA) 57 . Our findings indicate that germline variants play crucial roles in modulating the expression of not only PCGs but also lncRNAs, which could further contribute to HGSOC predisposition. Using Bayesian colocalization analysis, we prioritized 10 susceptibility genes for HGSOC, comprising 6 PCGs and 4 lncRNAs, where genotype-driven gene expression changes are highly associated with HGSOC risk in high-grade ovarian tumors. Five of these genes, such as ARL17A and CRHR1-IT1 , also showed an association with HGSOC predisposition in whole blood. We characterized the expression patterns of susceptibility PCGs and lncRNAs using single-nucleus resolution transcriptome maps of the human HGSOC tumor. In addition, we conducted in vitro assays and validated the functional role of CRHR1-IT1 in promoting apoptosis of ovarian cancer cells. Our study could pave the way for early detection and new therapeutic developments for HGSOC. Materials and Methods Genotype and RNA-seq data processing for eQTL analysis We downloaded matched genotype and quantified gene expression data for 350 primary HGSOC tumor samples from the TCGA data portal ( http://tcga-data.nci.nih.gov/tcga/ ) 57 . The TCGA germline variants data were genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0. Quality control of raw genotype data was performed using PLINK (v2.0) 58 ( http://www.cog-genomics.org/plink2/ ). We excluded samples with misgendering, contamination, or low call rates. SNPs on sex chromosomes, monomorphic SNPs, and those with a minor allele frequency (MAF) < 1%, Hardy–Weinberg equilibrium P-value < 1.0E-06, or call rate 0.2). This left 348 individuals and 771,697 SNPs for further analysis (Supplementary Table 1) . To better understand the genetic background of the TCGA HGSOC samples, we performed Principal Components Analysis (PCA) on the genotype data of these 348 HGSOC subjects, along with 2,504 genotyped data from the 1,000 Genomes Project Phase 3 59 , using Genome-wide Complex Trait Analysis (GCTA) 60 . Most samples in our study clustered amongst the European populations, as shown in Supplementary Fig. 1 . The first 2 principal components (PCs) were used in the linear regression model for eQTL analysis. To increase eQTL discovery power, autosomal variants were pre-phased by SHAPEIT2 61 and subsequently imputed with IMPUTE2 62 , using 1,000 Genomes Project Phase 3 as the reference panel. Before imputation, indels and SNPs absent or incompatible with the 1,000 Genomes Project dataset were removed. Imputed SNPs with MAF < 1%, Hardy-Weinberg Equilibrium P-value < 1E-06, and imputation confidence score INFO (a measure of r 2 ) < 0.4 (estimated by SNPTEST 63 ) were further excluded. Consequently, 8,579,445 high-quality autosomal SNPs were used for eQTL analysis. The TCGA level 3 RNA-seq data were aligned to the human genome based on GENCODE v22 annotations 64 . Next, we normalized the downloaded raw read counts across samples using the TMM method and then estimated gene expression levels as transcripts per million (TPM). Only genes with at least 0.1 TPM in more than two samples were identified as expressed and used for downstream eQTL analysis. eQTL mapping We performed cis -eQTL (further referred to as eQTL) mapping using 348 primary HGSOC samples for both protein-coding genes (PCGs) and lncRNAs (Supplementary Table 2) . Expression measurements for each PCG or lncRNA were quantile-normalized and then transformed into a rank-based standard normal expression using inverse normal-transformed (INT). To remove the effects of latent confounders, we estimated 40 and 60 PEER 65 factors on INT expression by adjusting for age, batch effect, and top 2 genotype PCs for PCG-eQTL and lncRNA-eQTL identification, respectively. The number of PEER factors that could maximize the identified ePCGs/elncRNA (PCGs/lncRNAs that were significantly regulated by at least one SNP) was selected (Supplementary Fig. 2) . eQTL mapping was then performed using the INT gene expression as the outcome and SNP dosage as the independent variable, adjusting for age, batch effect, top 2 genotype PCs, and the estimated PEER factors. Nominal P-values were calculated for all SNPs located within ± 1 Mb of PCGs/lncRNAs using MatrixEQTL 66 with an additive linear regression mode. A permutation procedure was then implemented using FastQTL 67 with the setting “-permute 10000”. We adopted Beta distribution-adjusted empirical P-values to calculate q using Storey’s q method 68 , and identified eGenes with a q cutoff ≤ 0.05. Next, we defined an empirical P threshold, P t , as the empirical P of the PCG/lncRNA closest to the 0.05 FDR threshold. P t was used to estimate the nominal P cutoff for each gene based on the Beta distribution model of the minimum P distribution f(P min ), generated from the permutations for the gene. For each ePCGs/elncRNA, SNPs having a lower nominal P than the gene-level cutoff were identified as eSNPs (SNPs that significantly regulated at least one PCG or lncRNA) 69 . Differentially expressed genes identification To minimize computational batch effects causing by different data analysis pipelines in TCGA and GTEx (v7; http://gtexportal.org/home/ ) 70 cohorts, we downloaded the recomputed gene expression data in the ovaries of the TCGA HGSOC patients and GTEx healthy samples using a consistent analysis protocol from UCSC Xena ( http://xena.ucsc.edu/ ). The estimated gene expression was quantified as transcripts per kilobase million (TPM). Genes with TPM < 0.1 in < 5% of samples were excluded from downstream analysis. Differentially expressed genes (DEGs) between HGSOC tumor and healthy samples were identified using a linear model combined with an empirical Bayes method in the R package limma 71 . This linear regression model adopted gene expression as the outcome and disease status as independent variable, adjusting for age. Genes with a Benjamini-Hochberg FDR < 0.05 were identified as DEGs. Colocalization analysis To evaluate the posterior probabilities of inherited HGSOC risk and expression of both PCGs and lncRNAs sharing common genetic causal variants in a given region, we performed colocalization analysis on summary data of HGSOC GWAS 33 and eQTL data from HGSOC tumors and whole blood (from GTEx v7) 70 using coloc v5.2.3 72 . We adopted the Phelan et al. HGSOC GWAS study based on 13,037 cases and 40,941 controls 33 . HGSOC associated variants with a P-value < 5E-08 were selected. Accounting for the inflation caused by GWAS variants representing the same signal, we performed LD pruning using swiss ( https://github.com/statgen/swiss ) by discarding variants in LD r 2 ≥ 0.8 with the lead SNP at each locus. Genomic regions within ± 100 kb of each pruned GWAS variant, that overlap with ePCGs or elncRNAs, were used in the coloc analysis. Posterior probability was then estimated using summary data of HGSOC GWAS and PCG-eQTLs/lncRNA-eQTLs to identify the colocalization regions. Coloc examines whether HGSOC and gene expression are associated and share the same causal variants (H4). We used PP4 (the posterior probability of H4) > 0.8 as the threshold of colocalization. Sample procurement for snRandom-seq Two tumor specimens for single-nucleus Random-seq were obtained from the Biobank of Zhejiang Cancer Hospital. One specimen was collected from the intestine surface during the primary debulking surgery, and the other was from adipose tissue obtained during surgery for the platinum-resistant recurrence. The high-grade tumor specimens were pathologically reviewed. FFPE single-nucleus transcriptome library construction and sequencing FFPE samples were cut from paraffin block and washed twice with 1 mL Xylene (Aladdin, Cat # X112054) for 5 minutes at room temperature to remove the paraffin. The samples were then gently redehydrated by immersing them in a graded series of ethanol solutions (Aladdin, Cat # E130059), starting with 100% ethanol and ending with 30% ethanol. Next, we utilized the VITApilote high-throughput FFPE single-cell transcriptome kit (Cat #R20123124, M20 Genomics) for pre-library processing, library construction, and purification. The VITAcruizer single-cell preparation instrument DP400 (Cat #E20000131, M20 Genomics) was employed for droplet generation, single-cell partitioning, and encapsulation, and nucleic acid capture. The experiments were carried out according to the instructions of corresponding kits and instruments. The main workflow was as follows: The prepared tissue section was permaeablized and blocked for single-stranded DNA-blocking. Random primers were then added to reverse transcribe the total RNA inside the nuclei from the tissue section, and the resulting cDNA fragments were ligated to adapters within the nuclei. The tissue section was lysed into individual cell nuclei by grinding and enzymatic treatment. The reverse-transcribed single-nucleus suspension was mixed with reagents and barcoding beads containing cell barcode and unique molecular identifiers (UMI). This mixture was encapsuled, captured and barcoded using the VITAcruizer DP400 instrument, resulting in barcoded cDNA strands. PCR amplification was then conducted using the cDNA as the template. The purified products were utilized to construct a single-nucleus library containing P5 and P7 adapters and were sequenced on a Novaseq 6000 sequencer (Illumina) with 150 bp paired-end reads (Supplementary Table 3) . Consumables, reagents, and instruments used in the experiment included: a 20 µm cell sieve (pluriSelect, Cat # 43-10020-20), DAPI (4′,6-diamidino-2-phenylindole) staining solution (Abcam, Cat # ab228549), and an inverted fluorescence microscope (Nikon Eclipse, Ts2-FL). SnRandom-seq data processing Primer sequences and adaptors were trimmed from the raw data. The UMI (8 nts) and cell-specific barcode (30 nts) for each Read1 were then extracted. Sequenced barcodes were merged if they could be uniquely assigned to the same accepted barcode with a Hamming distance of 2 nts or less. Sequencing reads were aligned to the human genome based on GENCODE v22 annotations 64 using the STARsolo module of the STAR aligner (version 2.7.10a) with appropriate parameters 73 . Ambient RNA was removed using SoupX (version 1.6.2) 74 , and the corrected expression data were further analyzed with Seurat (v5.0.2) 75 . For quality control, we excluded features expressed in fewer than 5 cells. Low-quality cells with fewer than 200 features or more than 4,000 features expressed were also excluded. In addition, cells with more than 40% mitochondrial transcripts were filtered out. Samples were integrated and batch effects were removed using the FindIntegrationAnchors function 75 , 76 . PCA was performed and the first 30 PCs were selected to generate a k -nearst neighbor graph with k = 15. Uniform manifold approximation and projection (UMAP) embeddings with a resolution of 0.5 were generated to visualize the data 77 . Cell culture and transfection experiment The human ovarian adenocarcinoma cell line OVCAR­3 (ATCC® HTB-161) was purchased from ATCC (USA) and cultured in the Roswell Park Memorial Institute 1640 Medium (RPMI-1640, Catalog No. 30­2001) containing 20% fetal bovine serum (Gibco, USA) and 0.01 mg/ml bovine insulin (Sigma, Catalog No. I6634). Cells were incubated at 37°C with 5% CO 2 , and the medium was changed every two days. The CRHR1-IT1 sequence was synthesized according to the full-length sequence with an added poly-A tail based on the LINC02210 sequence, NR_026680.3 in NCBI (Supplementary Table 4) , and then subcloned into a pcDNA3.1 (+) vector (Genscript, Shanghai, China). OVCAR­3 cells were seeded in a 24-well plate and incubated at 37°C for 8 hours. Next, the overexpression vector pcDNA3.1(+)- CRHR1-IT1 was transfected into OVCAR­3 cells using Lipofectamine 3000 (Invitrogen, USA) and Opti-MEM (Thermo Fisher Scientific, USA), following the manufacturer’s protocol. Transfection conditions included: control (0 µg/well vector, null condition), mock (0 µg/well verctor, baseline transfection reagents), vehicle (1 µg/well pcDNA3.1(+) vector), 0.25 (0.25 µg/well pcDNA3.1(+)- CRHR1-IT1 vector), 0.5 (0.5 µg/well pcDNA3.1(+)- CRHR1-IT1 vector) and 1 (1 µg/well pcDNA3.1(+)- CRHR1-IT1 vector). After 6 hours of transfection, the cells were washed once with 1X PBS, fresh RPMI-1640 medium was then added, and the cells were grown for 48 hours post-transfection. We collected RNA samples from control, mock, vehicle conditions, as well as from pcDNA3.1(+)- CRHR1-IT1 overexpression-transfected OVCAR­3 cells at vector concentrations of 0.25 µg/well, 0.50 µg/well, and 1.00 µg/well at 24 and 48 hours for sequencing. In total, 24 RNA-seq datasets were obtained, with two biological replicates per transfection condition. RNA isolation and quantitative real-time PCR (qRT-PCR) Cells were lysed, and total RNA was isolated using the PureLink® RNA Mini Kit (Thermo Fisher Scientific, USA) according to the manufacturer's protocol. 1 µg of RNA was used to synthesize cDNA using the PrimeScript™ RT reagent Kit with gDNA Eraser (Takara, Japan). Subsequent qRT-PCR was performed using SuperReal PreMix Plus (SYBR Green) (Tiangen, China). The expression of mRNAs and lncRNAs was normalized to GAPDH . Relative gene expression was calculated by the 2 −ΔCT method. The primer sequences for CRHR1-IT1 were as follows: Forward 5'-CCCCACTTCTAGGCTTGGTT-3'; Reverse 5'-TCTGTAGCGGAGGGAATGGA-3'. The primer sequences for GAPDH were as follows: Forward 5’-GGAGCGAGATCCCTCCAAAAT-3'; Reverse 5'-GGCTGTTGTCATACTTCTCATGG-3'. The primer sequences for CMYC, CRHR1, FAS , JUN and PARP1 are presented in Supplementary Table 5 . All qRT-PCR reactions were performed using a CFX96 TOUCH/GelDoc XR + qRT-PCR machine (Bio-Rad, USA), with three technical replicates for each reaction. Reads processing of collected RNA-seq data from OVCAR-3 cells RNA was isolated and purified using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). RNA quality was assessed with the Agilent Bioanalyzer 2100 (Agilent, CA, USA), with RIN scores above 7, and confirmed by electrophoresis with denaturing agarose gel. Poly (A) RNA was purified from 1 µg total RNA using Dynabeads Oligo (dT) 25-61005 (Thermo Fisher, CA, USA) through two rounds of purification. The poly (A) RNA was then fragmented into small pieces using the Magnesium RNA Fragmentation Module (NEB, cat.e6150, USA) at 94℃ for 5–7 minutes. The cleaved RNA fragments were reverse-transcribed to cDNA using SuperScript™ II Reverse Transcriptase (Invitrogen, cat. 1896649, USA). Strand-specific RNA-seq libraries were prepared using the dUTP method. 2 × 150 bp paried-end sequencing was then performed on the Illumina NovaSeq X Plus. Next, sequence quality was assessed using FastQC (v0.11.9). Adaptors and low-quality bases were trimmed using Trim-galore (v0.6.6). The remaining RNA-seq reads were then aligned to the human genome (hg19/GRCh37) using STAR (v2.7.10a) 78 based on GENCODE v24 annotations 79 . Gene expression levels were quantified using RSEM (v1.3.1) 80 . Weighted Gene Co-Expression Network Analysis (WGCNA) Weighted Gene Co-Expression Network Analysis (WGCNA) was performed on 24 RNA-seq data using the R package WGCNA 81 . The top 50% variablely expressed genes were used. Modules with at least 30 genes were selected. The expression correlation between CRHR1-IT1 and genes in each module was then estimated. Genes with the absolute value of gene significance (GS) > 0.75 and module membership (MM) > 0.75 were defined as key genes for that module. Functional enrichment analysis and network construction Functional enrichment analysis was performed using Metascape ( http://metascape.org/ ) with default settings 82 . Network analysis was implemented in ConsensusPathDB ( http://cpdb.molgen.mpg.de ) 83 , which integrates various interaction networks, including binary and complex protein-protein interactions, genetic interactions, metabolic pathways, signaling pathways, gene regulatory networks, drug-target interactions, and biochemical pathways. Incucyte apoptosis assay for CRHR1-IT1 After 6 hours of cell transfection, cells were washed once with 1X PBS and added fresh RPMI-1640 medium. CellEvent™ Caspase-3/7 Green Detection Reagent (Thermo Fisher Scientific, USA) was then added to the cells, which were subsequently incubated in an Incucyte SX5 incubator for 42 hours. Statistical analysis of qRT-PCR and cell apoptosis assays Statistical analysis of qRT-PCR and cell apoptosis assays was performed using GraphPad Prism 8.0. Data are presented as the mean ± SE, and experiments were repeated at least three times. Two-sided P-values were calculated using unpaired t-test analysis for the qRT-PCR assay. For the cell apoptosis assay, two-sided P-values were estimated using one-way ANOVA. Results were considered significant at *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001. Results Genotype-driven expression changes of PCGs and lncRNAs To understand the contribution of germline variants to expression changes, we investigated the association between genotype and local expression variations of both PCGs and lncRNAs ( Fig. 1 A ) . After stringent quality control (Materials and Methods) , we selected 348 HGSOC tumor samples (Supplementary Table 1) with matched high-quality genotype and gene expression data to identify PCG-eQTLs. And for lncRNA-eQTL analysis, we adopted quantified expression data of lncRNAs from the same 348 HGSOC tumor samples. We performed eQTL analysis using a linear regression model for PCGs and lncRNAs, respectively, and interrogated SNPs located within a ± 1 Mb ( cis ) window of each PCG or lncRNA. The PCG-eQTL analysis identified 7,145 eGenes (that is, genes associated with at least one SNP) at a Storey’s q -value ≤ 0.05. Our results successfully replicated the previously identified significant SNP-gene pairs in the ovary from the GTEx with π 1 = 0.99 (where π 1 represents the proportion of true positives) 84 , aligning with our own and others’ previous identification of genotype-driven expression changes (eQTLs) from the same tissue 39 , 41 , 85 . The number of identified eGenes is also in line with previous eQTL study in 44 human tissues from the GTEx Consortium (Supplementary Fig. 3) 84 . For example, we found that the genetic variant rs35478347 had a robust effect (two-sided P-value = 6.0E-06) on DHX58 expression ( Fig. 1 B ) . And the lncRNA-eQTL analysis identified 572 elncRNAs (that is, lncRNAs associated with at least one SNP) (Supplementary Table 6) . For instance, we observed that the expression of the lncRNA LOC284581 (hg19 chr1:205,781,868 − 205,875,038) was significantly correlated with rs9438393 (two-sided P-value = 3.02E-74) in HGSOC tumor samples ( Fig. 1 C ) . Intriguingly, we found that the variance explained by the top associated SNPs decreased dramatically from that for lncRNA expression to protein-coding gene expression and to HGSOC risk ( Fig. 1 D ) , suggesting that expression changes of lncRNAs explain a higher portion of HGSOC heritability than PCGs. Moreover, the regulatory significance of the lead SNP on each ePCG or elncRNA increased for SNPs closer to the transcription start site (TSS), suggesting that germline variations in promoter regions have stronger regulatory effects on expression of both PCGs and lncRNAs ( Figs. 1 E and 1 F ) . In addition, the regulatory effects of germline variants on lncRNAs were generally stronger than on PCGs ( Figs. 1 E and 1 F ) , consistent with a previous study indicating that lncRNA eQTLs tend to have larger effect sizes than protein-coding eQTLs 56 . Given the substantial genotype-driven expression changes, we next investigated the potential role of germline variants in HGSOC predisposition ( Fig. 1 G ) . First, we identified differentially expressed genes (DEGs) in the ovaries of the HGSOC patients (N = 419 from TCGA) and healthy samples (N = 88 from GTEx). We identified 2,689 up-regulated and 4,616 down-regulated genes as DEGs ( Fig. 1 H ) , with 76.9% being protein-coding genes and 19.9% being lncRNAs (Supplementary Fig. 4) . To ascertain the contribution of germline variants in HGSOC predisposition, we overlapped our DEGs and eQTL results. We found that about 6.82% of the identified DEGs, including 404 PCGs and 94 lncRNAs, could be driven by underlying germline variants ( Fig. 2 I and Supplementary Table 7) . For instance, the eGene DHX58 , whose expression changes were regulated by germline variant rs35478347 ( Fig. 1 B ) , also showed significant differences (two-sided P-value = 1.31E-43) in expression between HGSOC tumors and healthy samples (Supplementary Fig. 5) . In summary, our results suggested that underlying germline variants play important roles in driving expression changes of both PCGs and lncRNAs, which could further contribute to the HGSOC-associated DEGs. Genotype-driven expression changes are enriched for HGSOC-associated GWAS loci As the vast majority of GWAS-identified HGSOC risk loci reside in non-coding regions of the genome, these variants might function through regulatory effects on the expression of putative causal PCGs or lncRNAs ( Fig. 2 A ) . To understand the underlying biological mechanisms of the observed statistical association between SNPs and HGSOC predisposition, we first overlapped genome-wide significant loci in HGSOC GWAS 33 and in our eQTL signals for both PCGs and lncRNAs, respectively. Genotype-driven expression changes of 51 PCGs and 20 lncRNAs showed associations with HGSOC risk ( Fig. 2 B ) . Several putative HGSOC risk genes, such as HOXB4 , LERK1 , MYO19 , MEIOB , MRRF , C3orf33 , and CCDC23 , have been identified as associated with epithelial ovarian cancer in previous independent studies 28 , 86 , 87 , 88 , 89 , 90 , 91 , 92 . Although the top significant GWAS signals were enriched on chromosomes 3, 4, 8, and 9, we observed a strong enrichment of candidate susceptibility genes for HGSOC on chromosome 17 after overlapping with eQTLs. These loci are adjacent to the known EOC and breast cancer marker gene BRCA1 on chromosome 17. Intriguingly, out of these 51 PCGs, 16 genes (about 31%) have also been previously implicated as candidate breast cancer susceptibility genes (Supplementary Table 8) , including MAPT 28 , 93 , LGALS7 94 , TBC1D3 95 , IQCG 96 and CCL4 97 etc. In addition, the elncRNA ERVK13-1 (Endogenous Retrovirus Group K13 Member 1) has previously been shown an association with breast cancer 98 . It appears that HGSOC and breast cancer share an extensive expression-based pleiotropy at these loci, consistent with previous observations 28 , 34 . Next, we performed a Bayesian statistical framework established in colocalization (coloc) analysis 72 to prioritize disease-causing genes, whose genotype-driven expression changes are associated with GWAS signals ( Fig. 2 C ) , by integrating HGSOC GWAS with the ovarian tumor eQTL data. Our analysis nominated 10 susceptibility genes for HGSOC, including 6 PCGs and 4 lncRNAs ( Fig. 2 D and Supplementary Table 9) . Consistently, all 6 PCGs— CHMP4C , ARL17A , ARL17B , LRRC37A , LRRC37A2 , and MAPT —have been previously identified as HGSOC susceptibility genes using a transcriptome-wide association study (TWAS) 28 , demonstrating that our observations could be successfully replicated using different statistical methods. Notably, around ± 100 kb window of SNP rs142045106, we identified one protein-coding gene MAPT and 3 lncRNAs, including CRHR1-IT1 , KANSL1-AS1 , and LOC102724391 , as putative disease-causing genes for HGSOC. This suggests a likely functional convergence of MAPT and its adjacent lncRNAs at this locus. Emerging studies have reported that MAPT expression showed an association with estrogen receptor expression in breast cancer, and an imperfect estrogen-response element has also been identified in the promoter region of MAPT 93 , 99 , 100 . In addition, the tau protein encoded by MAPT , was found almost exclusively bound to microtubules, which plays a pivotal role in mitosis in cancer development as well 101 . Insights into early detection and immunotherapy for HGSOC HGSOC is often diagnosed at a very late stage and is characterized by an immune-suppressive tumor microenvironment (TME) with few tumor infiltrating lymphocytes, rendering it largely refractory to checkpoint inhibitor immunotherapies 102 , 103 . To gain new insights into early detection and treatment of HGSOC, we investigated putative disease-causing genes in whole blood by integrating HGSOC GWAS with whole blood eQTL data (from GTEx v7) 70 using Bayesian-test-based colocalization analysis. As illustrated in Fig. 2 D, our analysis nominated 12 PCGs and 5 lncRNAs in whole blood. Of these genes, HOXB2 28 , HOXB3 104, 105 , PLEKHM1 106 , RCCD1 28, 34 , and KANSL1 28, 107, 108 have been previously implicated as susceptibility genes for epithelial ovarian cancer. Intriguingly, KANSL1 has been previously identified as an oncogene, whereas the colocalized lncRNA KANSL1-AS1 showed an opposite trend by assessing the 5-year survival time of 170 HGSOC patients 107 . In addition, the variant rs76640332 within KANSL1 loci was previously reported to be associated with lymphocyte percentage of leukocytes (two-sided P GWAS = 5E-13) 109 . Next, we interrogated whether these susceptibility genes identified in whole blood could potentially contribute to the early detection for HGSOC. We overlapped the colocalized genes in the ovarian tumor and whole blood ( Fig. 2 D ) . ARL17A , LRRC37A2 , and CRHR1-IT1 were found to show significant differences in expression between HGSOC tumors and healthy samples ( Figs. 2 E and 2 F, Supplementary Fig. 6) . We also found that the expression of the overlapped elncRNA KANSL-AS1 correlated with the EOC marker gene BRCA2 expression in the TCGA HGSOC tumor samples ( Fig. 2 G ) . These observations highlighted that the shared colocalized genes in both ovarian tumors and whole blood likely play important roles in the early diagnosis of HGSOC. Using the genome-scale integrated analysis of gene networks in tissues (GIANT 2.0) 110 , we found that the colocalized genes KANSL1 , LRRC37A2 , and ARL17A were strongly co-expressed in CD8 + T cells and enriched for chromatin organization and regulation of nitrogen compound metabolic process ( Fig. 2 H and Supplementary Table 10) . Nitrogen is one of the essential requirements for de novo synthesis of many important nitrogenous compounds, including amino acids and nucleotides, which support the growth of both cancer and immune cells 111 . The competition for available nitrogen between tumor cells and infiltrating CD8 + T cells might further alter the TME 111 . CD8 + T cells are pivotal to the efficacy of cancer immunotherapies 112 . Emerging studies have shown that variations in the fraction of infiltrating CD8 + T cells in the TME can lead to different outcomes in prognosis and treatment response in various malignancies, including breast cancer and HGSOC 113 , 114 , 115 . Single-nucleus resolution transcriptome maps for the human HGSOC tumor Next, we characterized the expression atlas of PCGs and lncRNAs at a cellular resolution for human HGSOC tumors using snRandom-seq 116 , a droplet-based, highly sensitive, and full-length single-nuclei RNA sequencing method for FFPE samples. This technique captures full-length total RNAs with random primers ( Fig. 3 A ) , allowing for the detection of more long non-coding RNAs compared to other scRNA-seq technologies, such as 10x Genomics scRNA-seq. We manually assigned the clusters to known cell types according to previously reported cell type marker genes ( Figs. 3 B and 3 C ) 117 , 118 , 119 , 120 . Our snRandom-seq analysis characterized the expression patterns of 13,602 PCGs and 2,713 lncRNAs at single-nucleus resolution in 20,564 human HGSOC tumor cells ( Figs. 3 B and 3 C ) . Given the strong enrichment of candidate susceptibility genes on chromosome 17 ( Fig. 2 B ) by overlapping HGSOC GWAS with tumor eQTLs, we investigated the single-cell expression patterns of these genes. More than 70% of them showed high expression levels in the immune cells of HGSOC tumors (Supplementary Fig. 7) . Intriguingly, this dataset further confirmed the enriched co-expressed network ( Fig. 2 H ) for the colocalized genes KANSL1 , LRRC37A2 , and ARL17A in T cells of HGSOC tumors ( Fig. 3 D ) . Interestingly, KANSL1 showed a T-cell-specific expression, which aligns with previous observations indicating its association with the lymphocyte percentage of leukocytes 109 . In addition, we also observed that colocalized genes ARL17A , LRRC37A2 , LRRC37A , and KANSL1-AS1 , identified in both HGSOC tumors and whole blood ( Fig. 2 D ) , showed high expression levels in immune cells, cancer cells, and proliferative cancer cells. And MAPT, CHMP4C and ARL17B , identified in HGSOC tumors, were more likely to express specifically in cancer cells and proliferative cancer cells (Supplementary Fig. 8) . The putative roles of CRHR1-IT1 and KANSL1-AS1 in ovarian cancer Our analysis nominated two putative causal lncRNAs, CRHR1-IT1 and KANSL1-AS1 , in chr17q21 for HGSOC. We identified 91 and 65 colocalized signals (with PP4 ranging from 0.80 to 1.00) (Supplementary Table 11) for CRHR1-IT1 and KANSL1-AS1 , respectively. 56 of these colocalized signals were observed from the same genomic regions. For instance, centered around the HGSOC GWAS SNP of rs146761208 (two-sided P GWAS = 6.90E-10), colocalized signals were observed for both CRHR1-IT1 ( Fig. 3 E ) and KANSL1-AS1 (Supplementary Fig. 9) . In this region, the HGSOC-associated GWAS variants were also the causal variants that influence the expression of CRHR1-IT1 and KANSL1-AS1 . However, our scRNA-seq data of HGSOC tumors showed very low expression of the colocalized lncRNA CRHR1-IT1 ( Fig. 3 E ) . Combining this with our previous observation shown in Fig. 2 F, we hypothesized a potential protective role of CRHR1-IT1 in HGSOC development. Functional insights into the lncRNA CRHR1-IT1 To validate the putative roles of CRHR1-IT1 in HGSOC, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis using gradient transfection in the HGSOC cell line OVCAR-3. We explored the biological pathways associated with CRHR1-IT1 expression changes using RNA-seq data collected at 24 and 48 hours after transfection. Transfection conditions included: control (0 µg/well vector, null condition), mock (0 µg/well verctor, baseline transfection reagents), vehicle (1 µg/well pcDNA3.1(+) vector), 0.25 (0.25 µg/well pcDNA3.1(+)- CRHR1-IT1 vector), 0.5 (0.5 µg/well pcDNA3.1(+)- CRHR1-IT1 vector) and 1 (1 µg/well pcDNA3.1(+)- CRHR1-IT1 vector) (Materials and Methods) . We found that transcript expression levels of CRHR1 , the protein-coding gene corresponding to the lncRNA CRHR1-IT1 ( CRHR1 intronic transcript 1), were markedly altered after transfection with pcDNA3.1(+)- CRHR1-IT1 in OVCAR-3 cells (Supplementary Fig. 10) . By performing a weighted gene co-expression network analysis (WGCNA) 81 , we identified 9 modules in OVCAR-3 cells and assessed the correlation between the expression of genes within these modules and the lncRNA CRHR1-IT1 ( Figs. 4 A and 4 B ) . The ‘green’ and ‘brown’ modules exhibited the most significant negative and positive correlations, respectively ( Fig. 4 B ) . Of the 357 genes in the ‘green’ module, 309 were identified as down-regulated in response to CRHR1-IT1 , whereas 956 of the 1,251 genes in the ‘brown’ module were up-regulated by CRHR1-IT1 (Supplementary Table 12) . Functional analysis of these genes indicated enrichment in biological processes regulating cell growth and ovarian function, such as cell cycle, apoptosis, and oocyte meiosis ( Fig. 4 C ) . As shown in Fig. 4 D, we observed that 309 down-regulated genes exibhited a dramatic decrease in expression at 24 hours, while 956 up-regulated genes showed observable increase in expression at 48 hours, following transfection with pcDNA3.1(+)- CRHR1-IT1 in OVCAR-3 cells, compared to the control, mock, and vehicle conditions. Interestingly, the up-regulated genes were likely involved in regulating cell death processes, such as the positive regulation of apoptotic process, whereas the down-regulated genes were enriched in cell cycle processes ( Fig. 4 E ) . These findings suggest that CRHR1-IT1 likely regulates HGSOC cell growth by initially inhibiting cell cycle progression and subsequently promoting apoptosis. Next, we identified potential transcription factors (TFs) that might regulate CRHR1-IT1 by combining results from multiple analytical approaches. First, using TRRUST v2 database 121 , we found 37 enriched TFs, such as TP53 , ATF6 , and JUN (Supplementary Fig. 11A) , likely regulators of those 1,265 genes showing CRHR1-IT1 -associated expression. Second, we prioritized CRHR1-IT1 -associated genes by assessing gene significance (GS) and module membership (MM) using WGCNA. Our analysis identified 48 key genes from the ‘green’ and ‘brown’ modules, which showed the strongest expression association with CRHR1-IT1 (Supplementary Fig. 11B and Supplementary Table 13) . Third, we performed a sequence-based interaction prediction for CRHR1-IT1 using AnnoLnc2 122 , revealing 337 genes that may interact with CRHR1-IT1 (Supplementary Table 14) . We then evaluated the overlap of predicted TFs for CRHR1-IT1 , and JUN was the only TF present in all three analyses (Supplementary Fig. 11C) . In addition, ChIP-seq data from various cell types confirmed that JUN binds to CRHR1-IT1 , highlighting its potential regulatory role (Supplementary Table 15) 122 . Our RNA-seq data and qRT-PCR results further suggested a strong positive correlation between CRHR1-IT1 and JUN expression (Pearson’s correlation = 0.92, two-sided P-value = 1.32E-10; Fig. 4 F and Supplementary Fig. 12 ). JUN , a crucial transcription factor, which has been previously reported to participate in diverse biological processes including apoptosis 123 , 124 , 125 , 126 , 127 , 128 , 129 , was also found to be significantly down-regulated in HGSOC samples ( Fig. 4 G ) . Subsequent network analysis using the ConsensusPathDB 83 molecular interaction data from 31 public repositories ( http://cpdb.molgen.mpg.de ) revealed that JUN potentially regulate several apoptosis-related genes, such as ATF3 , DDIT3 , TP53, BRCA1 , and RARG ( Fig. 4 H ) . Notably, ATF3 has been previously reported to promote apoptosis in cancer cells 130 , 131 , 132 . These findings suggest that CRHR1-IT1 may co-express with JUN to regulate apoptosis in HGSOC cells. An increase in CRHR1-IT1 expression promotes apoptosis of HGSOC cells To determine whether CRHR1-IT1 plays a role in the development of HGSOC cells, we performed a gradient transfection experiment in OVCAR-3 cells. CRHR1-IT1 expression was significantly increased after transfection with pcDNA3.1(+)- CRHR1-IT1 in HGSOC cells (Supplementary Figs. 10A-10B) . Next, we used IncuCyte SX5 and Caspase-3/7 Green Detection Reagent to measure the fluorescence-activated cell intensity in transfected OVCAR­3 cells. We observed a dramatic increase in the proportion of apoptotic cells with higher transfection concentrations ( Figs. 5 A and 5 B ) . And the rate of apoptosis accelerated as the concentration increased at 24 and 48 hours after transfection ( Figs. 5 B- 5 D ) . We also found that lncRNA CRHR1-IT1 slowed down the growth rate of non-apoptotic cells (Supplementary Fig. 13) . Moreover, our qRT-PCR results further confirmed that the expression of genes known to play important roles in apoptosis, including FAS , CMYC , and PARP1 , were significantly correlated with lncRNA CRHR1-IT1 expression ( Figs. 5 E- 5 G ) . These results suggested that elevated CRHR1-IT1 expression could promote apoptosis in OVCAR-3 cells. In summary, our findings highlight the significant role of the lncRNA CRHR1-IT1 in promoting apoptosis and inhibiting the growth of OVCAR-3 cells, suggesting that CRHR1-IT1 could be a promising new therapeutic target for HGSOC. Discussion In this study, we defined genotype-driven expression changes for both PCGs (PCG-eQTL) and lncRNAs (lncRNA-eQTL). We demonstrated that lncRNAs account for a relatively higher portion of HGSOC heritability compared to PCGs. Our Bayesian-test-based colocalization analysis prioritized 10 HGSOC susceptibility genes, such as MAPT , CHMP4C , KANSL1-AS1 , and CRHR1-IT1 , in HGSOC tumors. We also identified 17 putative causal PCGs and lncRNAs in whole blood, which could contribute to the early detection and immunotherapy for HGSOC. Using the single-nucleus transcriptome map generated by snRandom-seq for the human HGSOC tumor, we further characterized the cell-type-dependent expression patterns of the candidate susceptibility PCGs and lncRNAs for HGSOC. Finally, we focused on the lncRNA CRHR1-IT1 and demonstrated its crucial role in promoting apoptosis in HGSOC cells. We believe this study, which estimates genotype-driven expression variations of HGSOC susceptibility lncRNAs, is unique. We defined lncRNA-eQTLs to understand the genotype effect from germline variants on the expression of lncRNAs. Our findings suggest that lncRNA expression changes could explain more variations in HGSOC heritability than PCGs. It is noteworthy that a considerable proportion of expression variations associated with HGSOC predisposition are driven by underlying germline variants for both lncRNAs and PCGs. We illustrated that the germline variant rs35478347 influenced the expression of the protein-coding gene DHX58 , one of the differentially expressed genes between HGSOC tumors and healthy samples. DHX58 encodes for the RIG-I-like receptor LGP2, which has been previously reported to suppress RIG-I signaling 133 and protect tumor cells from ionizing radiation 134 , 135 . These findings indicate that it will be critical to integrate genotype-driven expression changes of lncRNAs and PCGs into future DEG identification to distinguish between genetically and environmentally driven expression differences. We integrated HGSOC-associated germline variants (from GWAS) and genotype-driven expression changes of PCGs (PCG-eQTLs) and lncRNAs (lncRNA-eQTLs) to prioritize putative causal genes for HGSOC. Our initial overlapping results of HGSOC GWAS and eQTL data indicate that approximately 31% of putative susceptibility genes for HGSOC are shared with breast cancer, including MAPT , LGALS7 and ERVK13-1 , with a strong enrichment on chromosome 17. Using Bayesian-test-based colocalization analysis, we identified 10 susceptibility genes for HGSOC with high fidelity. Notably, all 6 identified PCGs— CHMP4C , ARL17A , ARL17B , LRRC37A , LRRC37A2 , and MAPT —showed genotype-driven expression changes associated with HGSOC predisposition in our study, which were successfully replicated in a previous study using a different statistical method, TWAS 136 . We demonstrate that further investigations on the identified colocalized genes in whole blood provide new insights into early detection and immunotherapy for HGSOC. This is best illustrated by the shared colocalized genes, such as ARL17A , LRRC37A2, KANSL1-AS1 , and CRHR1-IT1 , whose expression and HGSOC predisposition are not only associated but also share common genetic causal variants in both tumor and whole blood. Interestingly, these observations were confirmed using the snRandom-seq data of the human HGSOC tumor, which could reveal more comprehensive expression patterns of lncRNAs at cellular resolution compared to other scRNA-seq technologies. Furthermore, our study highlighted multiple lines of converging evidence suggesting a potential protective role for CRHR1-IT1 in HGSOC development. We illustrate that elevated CRHR1-IT1 expression could promote apoptosis and inhibit the growth of OVCAR-3 cells. In conclusion, we identified genotype-driven expression changes of both PCGs and lncRNAs in HGSOC tumors. Our study highlights the importance of integrating lncRNA-eQTLs to understand the genetic underpinning of HGSOC pathogenesis. We illustrate the putative role of the lncRNA CRHR1-IT1 in HGSOC development and suggest that lncRNAs could serve as novel therapeutic targets for HGSOC. Declarations Disclosure and competing interest statement Ethics approval and consent to participate Genotype, quantified gene expression data, and clinical records of the 350 HGSOC tumor samples used in this study comply with ethical regulations. Approval and informed consent for collection and sharing were already obtained by the relevant consortia from TCGA. HGSOC tumor specimens for snRandom-seq were obtained from the Biobank of the Zhejiang Cancer Hospital, and the sample collection was approved by the Ethics Committee of the Zhejiang Cancer Hospital. Consent for publication Not applicable. Conflict of Interest The authors declare no competing interests. Availability of Data and Materials Matched genotype and quantified gene expression data for 350 primary HGSOC tumor samples were downloaded from the GDC Data Portal as well (http://www.cancer.gov/ccg/research/genome-sequencing/tcga). The recomputed gene expression data in the ovaries of the TCGA HGSOC patients and GTEx healthy samples were downloaded from UCSC Xena (http://xena.ucsc.edu). The eQTL data for lncRNAs and PCGs are available via a searchable public website https://zenodo.org/records/13760103. RNA sequencing data of OVCAR-3 cells and human HGSOC tumor snRandom-seq data generated in this study are publicly available in the GSA database (https://ngdc.cncb.ac.cn/gsa-human/) under accession numbers HRA009466 137, 138 and HRA008122, respectively. Acknowledgements This work was supported by grants from the National Natural Science Foundation of China [32300534, 32270659 to X.S., 32371537 to Y.K., and 82304907 to G.J.], the Open Project of Jiangsu Provincial Science and Technology Resources (Clinical Resources) Coordination Service Platform [TC2023006 to X.S.], the Key R&D Program of Zhejiang Province [2024SSYS0021 to X.S.], and the hundred talents program of Zhejiang University to X.S.; the Youth Innovation Promotion Association of Chinese Academy of Science [2022098 to J.Z.]; We thank the data resources of HGSOC tumor samples provided by the Cancer Genome Atlas (TCGA, http://cancergenome.nih.gov) managed by the NCI and NHGRI. We thank the technical support from the Core Facilities, Liangzhu Laboratory, Zhejiang University. We thank Dr. Chunjie Jiang and Dr. Fan Wu for assistance in lncRNA characterization. We thank Biorender (© BioRender – biorender.com) for support in schematic representations. Author contributions X.S. designed the study and supervised the computational analyses. X.S. and Y.K. designed and supervised the experimental validation in cell lines. X.S. and J.G. designed and supervised the snRandom-seq on HGSOC samples with the help of Y. W.. X.S., M.L., D.Z. and C.G. performed computational analyses and manuscript writing. Y.C., Z.Z. and S.C. performed the experimental validation of CRHR1-IT1 in cell lines and manuscript writing with the assisstance of F.K.-M. C., X. F., N. K., S. L.. and M.L. analyzed data with the help of C.B., Q.Q., C.G., M.J., H.Z., J.W., J. Z., and Z. Z. References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A , et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians 2021, 71 (3) : 209-249. Risch HA, Marrett LD, Jain M, Howe GR. 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09:50:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6929868/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6929868/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":85283257,"identity":"43ccfdf8-b9fd-459a-904c-b50bbeb2f437","added_by":"auto","created_at":"2025-06-24 08:38:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":698697,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGenotype-driven expression changes of PCGs and lncRNAs contribute to HGSOC predisposition.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Schematic representation of PCG-eQTL and lncRNA-eQTL; genotype-expression association study. Expression changes regulated by a nearby SNP.\u003c/p\u003e\n\u003cp\u003e(B) An example of PCG-eQTL. The genotype of SNP rs35478347 and the gene expression of \u003cem\u003eDHX58\u003c/em\u003e association in HGSOC tumors (N= 348 samples; two-sided P-value=6.0E-06). Center lines show the medians; box limits indicate the 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles; whiskers extend to the 5\u003csup\u003eth\u003c/sup\u003e and 95\u003csup\u003eth\u003c/sup\u003e percentiles. Two-sided P-value was calculated by linear regression model.\u003c/p\u003e\n\u003cp\u003e(C) An example of lncRNA-eQTL. The genotype of SNP rs9438393 and the expression of the lncRNA \u003cem\u003eLOC284581\u003c/em\u003e association in HGSOC tumors (N= 348 samples; two-sided P-value=3.02E-74). Center lines show the medians; box limits indicate the 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles; whiskers extend to the 5\u003csup\u003eth\u003c/sup\u003e and 95\u003csup\u003eth\u003c/sup\u003e percentiles. Two-sided P-value was calculated by linear regression model.\u003c/p\u003e\n\u003cp\u003e(D)\u0026nbsp;Distribution of the variance explained in expression of PCGs, lncRNAs, and HGSOC predisposition using the best eSNPs (the lead eQTL). Two-sided P-values were calculated using Student's t-test. Statistical significance is indicated by *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, and ****p \u0026lt; 0.0001.\u003c/p\u003e\n\u003cp\u003e(E)\u0026nbsp;The strength of association (y-axis) of the best eSNPs (the lead eQTL) decreases as the distance (x-axis) from the transcription start site of PCGs increases (N=348 samples).\u003c/p\u003e\n\u003cp\u003e(F)\u0026nbsp;The strength of association (y-axis) of the best eSNPs (the lead eQTL) decreases as the distance (x-axis) from the transcription start site of lncRNAs increases (N=348 samples).\u003c/p\u003e\n\u003cp\u003e(G)\u0026nbsp;Both genetic variants and environmental factors contribute to differentially expressed patterns between HGSOC tumor and healthy samples. Genotype-driven expression changes can be uncovered by eQTL analysis, while differentially expressed gene (DEG) identification detects HGSOC-associated genes.\u003c/p\u003e\n\u003cp\u003e(H)\u0026nbsp;Volcano plot. The x-axis is the negative base 2 log of the fold change in gene expression between HGSOC tumor samples (N=419) and healthy samples (N=88). The y-axis represents the negative base 10 log of the significance P-value estimated from DEG identification.\u003c/p\u003e\n\u003cp\u003e(I)\u0026nbsp;The number of eGenes influenced by at least one genetic variant, identified by eQTL analysis. DEGs were identified between HGSOC tumor samples (N=419) and healthy samples (N=88). Overlapping DEGs, including 404 PCGs and 94 lncRNAs, could be driven by underlying germline variants.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6929868/v1/a97d7017bebee3d4d3e1da2e.png"},{"id":85283256,"identity":"d39cf158-10c8-4794-953b-938b43d477e1","added_by":"auto","created_at":"2025-06-24 08:38:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1177276,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComprehensive gene prioritization provides new insights into early detection and immunotherapy for HGSOC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)\u0026nbsp;Illustration of the integration analysis of HGSOC GWAS with eQTL data for PCGs and lncRNAs.\u003c/p\u003e\n\u003cp\u003e(B)\u0026nbsp;Miami plots showing overlapped loci that reached genome-wide significance in HGSOC GWAS and in our eQTL data for both PCGs (top) and lncRNAs (bottom), respectively. The orange dots define prioritized PCGs and lncRNAs. The x-axis represents chromosomal location. The y-axis represents the negative base 10 log of the significance P-value. Two-sided P-value was obtained from the HGSOC GWAS study \u003csup\u003e33\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e(C)\u0026nbsp;Illustration of the Bayesian colocalization analysis. This method integrates GWAS and eQTL data to identify loci where the causal variants associated with HGSOC and gene expression are shared (colocalization).\u003c/p\u003e\n\u003cp\u003e(D)\u0026nbsp;We identified 15 protein-coding genes and 7 lncRNAs, where HGSOC predisposition (GWAS) and gene expression (PCG-eQTL/lncRNA-eQTL) share causal genetic variants. Green circles denote colocalized genes identified in HGSOC tumors. Yellow circles denote colocalized genes identified in whole blood.\u003c/p\u003e\n\u003cp\u003e(E)\u0026nbsp;An example of DEG, \u003cem\u003eARL17A\u003c/em\u003e, between HGSOC tumor samples (N = 419) and healthy samples (N = 88). Two-sided P-value = 3.98E-48. Center lines show the medians; box limits indicate the 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles; whiskers extend to the 5\u003csup\u003eth\u003c/sup\u003e and 95\u003csup\u003eth\u003c/sup\u003e percentiles; outliers are represented by dots.\u003c/p\u003e\n\u003cp\u003e(F)\u0026nbsp;An example of differientially expressed lncRNA, \u003cem\u003eCRHR1-IT1\u003c/em\u003e, between HGSOC tumor samples (N = 419) and healthy samples (N = 88). Two-sided P-value = 5.30E-45. Center lines show the medians; box limits indicate the 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles; whiskers extend to the 5\u003csup\u003eth\u003c/sup\u003e and 95\u003csup\u003eth\u003c/sup\u003e percentiles; outliers are represented by dots.\u003c/p\u003e\n\u003cp\u003e(G) Expression correlation between the lncRNA \u003cem\u003eKANSL1-AS1\u003c/em\u003e (x-axis) and \u003cem\u003eBRCA2\u003c/em\u003e (y-axis) in HGSOC tumor samples (N = 348). Two-sided P-value = 0.04.\u003c/p\u003e\n\u003cp\u003e(H) Pathway analysis performed by GIANT 2.0 \u003csup\u003e110\u003c/sup\u003e, suggesting that colocalized genes \u003cem\u003eKANSL1\u003c/em\u003e, \u003cem\u003eLRRC37A2,\u003c/em\u003e and \u003cem\u003eARL17A\u003c/em\u003e, were strongly co-expressed in CD8\u003csup\u003e+\u003c/sup\u003e T cells and enriched for chromatin organization and regulation of nitrogen compound metabolic process.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6929868/v1/07e8ee1dcb0fab7aca146f70.png"},{"id":85283265,"identity":"b6388829-6207-4d80-a6fa-47a443cde663","added_by":"auto","created_at":"2025-06-24 08:38:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1485677,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSingle-nucleus resolution transcriptome maps for the human HGSOC tumor.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Experimental scheme for snRandom-seq.\u003c/p\u003e\n\u003cp\u003e(B) UMAP plot of single-nucleus transcriptome data from human HGSOC tumors (N = 20,564 cells). Ten distinct cell clusters were identified using previously reported cell type marker genes \u003csup\u003e117, 118, 119, 120\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e(C) Violin plots showing the expression levels of representative marker genes across the 10 cell clusters.\u003c/p\u003e\n\u003cp\u003e(D) Heatmap illustrating the expression patterns of genes involved in the enriched co-expression network for colocalized genes\u003cem\u003e KANSL1, LRRC37A2,\u003c/em\u003e and \u003cem\u003eARL17A \u003c/em\u003ein T cells of HGSOC tumors. Mean expression values of the genes were calculated in each cluster. The color scheme represents the Z-score, derived from normalized gene expression levels, indicating relative expression compared to other cell types.\u003c/p\u003e\n\u003cp\u003e(E) LocusZoom plots of HGSOC GWAS variants (top) \u003csup\u003e33\u003c/sup\u003e and HGSOC tumor lnc-eQTLs (bottom) on \u003cem\u003eCRHR1-IT1\u003c/em\u003e. The x-axis shows the ± 200 kb genomic region around rs146761208. The y-axis represents GWAS significance as -log\u003csub\u003e10\u003c/sub\u003e(two-sided P-value). Each data point represents a variant, with color indicating r\u003csup\u003e2\u003c/sup\u003e (linkage disequilibrium). N = 53,978 individuals for HGSOC GWAS; N= 348 individuals for HGSOC tumor lncRNA-eQTLs.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6929868/v1/20dd72ea4d54692aabb0957d.png"},{"id":85283261,"identity":"fed3dce6-cd87-453a-bd65-bcb3b10f2946","added_by":"auto","created_at":"2025-06-24 08:38:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1267545,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCo-expression network analysis indicates the potential role of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCRHR1-IT1\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e in HGSOC development.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) WGCNA cluster dendrogram and module assignment. Colors in the horizontal bar represent different modules, with genes clustered into 9 distinct modules.\u003c/p\u003e\n\u003cp\u003e(B) Moduel-trait relationships. Pearson’s correlation between \u003cem\u003eCRHR1-IT1\u003c/em\u003e expression and each module is shown. Two-sided P-values represent the significance of these correlations.\u003c/p\u003e\n\u003cp\u003e(C) Functional annotation (KEGG pathway) of genes in the ‘green’ and ‘brown’ modules, which exihibited the strongest correlations with \u003cem\u003eCRHR1-IT1\u003c/em\u003e expression.\u003c/p\u003e\n\u003cp\u003e(D) Expression levels of 956 up-regulated genes and 295 down-regulated genes from the ‘green’ and ‘brown’ modules in OVCAR-3 cells. Expression values were calculated from 24 RNA-seq data collected at 24 and 48 hours after transfection with vector concentrations of: control (0 μg/well vector, null condition), mock (0 μg/well vector, baseline transfection reagents), vehicle (1 μg/well pcDNA3.1 (+) vector), and increasing concentrations of pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e vector: 0.25 μg/well, 0.5 μg/well, and 1 μg/well.\u003c/p\u003e\n\u003cp\u003e(E) Functional annotation of up-regulated genes (top) and down-regulated genes (bottom) in the ‘green’ and ‘brown’ modules.\u003c/p\u003e\n\u003cp\u003e(F)\u0026nbsp; Expression correlation between \u003cem\u003eJUN \u003c/em\u003eand \u003cem\u003eCRHR1-IT1\u003c/em\u003e. The x-axis represents the log\u003csub\u003e2\u003c/sub\u003e transformed expression of \u003cem\u003eCRHR1-IT1\u003c/em\u003e, and the y-axis is the log\u003csub\u003e2\u003c/sub\u003e transformed expression of \u003cem\u003eJUN\u003c/em\u003e. N = 24 samples.\u003c/p\u003e\n\u003cp\u003e(G) An example of DEG, \u003cem\u003eJUN\u003c/em\u003e, between HGSOC tumor samples (N = 419) and healthy samples (N = 88). Two-sided P-value = 2.93E-23. Center lines show the medians; box limits indicate the 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles; whiskers extend to the 5\u003csup\u003eth\u003c/sup\u003e and 95\u003csup\u003eth\u003c/sup\u003e percentiles; outliers are represented by dots.\u003c/p\u003e\n\u003cp\u003e(H) Network analysis performed by ConsensusPathDB\u003csup\u003e83\u003c/sup\u003e, suggesting that \u003cem\u003eJUN\u003c/em\u003e potentially regulates several apoptosis-related genes, including \u003cem\u003eATF3\u003c/em\u003e, \u003cem\u003eDDIT3\u003c/em\u003e, \u003cem\u003eTP53, BRCA1, \u003c/em\u003eand\u003cem\u003e RARG\u003c/em\u003e. Seed nodes are shown in black, and intermediate nodes are shown in purple. ‘Andr_human_gene’ represents human androgen receptor.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6929868/v1/75a7544d20903232cfc5d579.png"},{"id":85283258,"identity":"8465955e-def4-47da-b9a0-8e647b6ee69a","added_by":"auto","created_at":"2025-06-24 08:38:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1533595,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIncreased expression of lncRNA \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eCRHR1-IT1 \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003epromotes apoptosis in OVCAR-3 cells.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Representative photographs of cell apoptosis (caspase-3/7 detection reagent displayed as green fluorescence) after transfection with the pcDNA3.1 (+) or pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e vectors for 24 or 48 hours. Transfection conditions include: control (0 μg/well vector, null condition), mock (0 μg/well vector, baseline transfection reagents), vehicle (1 μg/well pcDNA3.1 (+) vector), and increasing concentrations of pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e vector: 0.25 μg/well, 0.5 μg/well, and 1 μg/well. One image per condition is shown out of 48 analyzed images (16 images per technical triplicate) taken at 10x magnification.\u003c/p\u003e\n\u003cp\u003e(B) Real-time growth monitoring of OVCAR-3 cells was performed using the Incucyte live imaging and analysis system, starting 6 hours after transfection. Caspase-3/7 green fluorescence-positive signals represent early apoptotic cells, and negative phase signals represent non-apoptotic cells. Data point is the mean of 3 independent wells per time point ± SE. Transfection conditions include: control (0 μg/well vector, null condition), mock (0 μg/well vector, baseline transfection reagents), vehicle (1 μg/well pcDNA3.1 (+) vector), and increasing concentrations of pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e vector: 0.25 μg/well, 0.5 μg/well, and 1 μg/well.\u003c/p\u003e\n\u003cp\u003e(C) The ratio of early apoptotic cells to non-apoptotic cells after transfection with the \u003cem\u003eCRHR1-IT1\u003c/em\u003e_pcDNA3.1(+) vector for 24 hours. Transfection conditions include: control (0 μg/well vector, null condition), mock (0 μg/well vector, baseline transfection reagents), vehicle (1 μg/well pcDNA3.1 (+) vector), and increasing concentrations of pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e vector: 0.25 μg/well, 0.5 μg/well, and 1 μg/well. The results are shown as mean ± SE of three technical replicates per group. Statistical significance is indicated by *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, and ****p \u0026lt; 0.0001, determined by an unpaired t-test.\u003c/p\u003e\n\u003cp\u003e(D) Ratio of early apoptotic cells to non-apoptotic cells after transfection with the \u003cem\u003eCRHR1-IT1\u003c/em\u003e_pcDNA3.1(+) vector for 48 hours. Transfection conditions include: control (0 μg/well vector, null condition), mock (0 μg/well vector, baseline transfection reagents), vehicle (1 μg/well pcDNA3.1 (+) vector), and increasing concentrations of pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e vector: 0.25 μg/well, 0.5 μg/well, and 1 μg/well. Results are shown as mean ± SE of three technical replicates per group. Statistical significance is indicated by *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, and ****p \u0026lt; 0.0001, determined by an unpaired t-test.\u003c/p\u003e\n\u003cp\u003e(E) Expression of \u003cem\u003eFAS \u003c/em\u003e(24 hours, \u003cem\u003etop\u003c/em\u003e; 48 hours, \u003cem\u003ebottom\u003c/em\u003e)\u003cem\u003e \u003c/em\u003eand \u003cem\u003eCRHR1\u003c/em\u003e in OVCAR-3 cells at 24 and 48 hours after transfection.\u003c/p\u003e\n\u003cp\u003e(F) Expression of \u003cem\u003eCMYC \u003c/em\u003e(24 hours, \u003cem\u003etop\u003c/em\u003e; 48 hours, \u003cem\u003ebottom\u003c/em\u003e)\u003cem\u003e \u003c/em\u003eand \u003cem\u003eCRHR1 \u003c/em\u003ein OVCAR-3 cells at 24 and 48 hours after transfection.\u003c/p\u003e\n\u003cp\u003e(G) Expression of \u003cem\u003eCRHR1 \u003c/em\u003e(24 hours, \u003cem\u003etop\u003c/em\u003e; 48 hours, \u003cem\u003ebottom\u003c/em\u003e)\u003cem\u003e \u003c/em\u003eand \u003cem\u003eCRHR1 \u003c/em\u003ein OVCAR-3 cells at 24 and 48 hours after transfection. Transfection conditions include: control (0 μg/well vector, null condition), mock (0 μg/well vector, with baseline transfection reagents), vehicle (1 μg/well pcDNA3.1 (+) vector), and increasing concentrations of pcDNA3.1(+)-CRHR1-IT1 vector: 0.25 μg/well, 0.5 μg/well, and 1 μg/well. Results are shown as mean ± SE of three technical replicates per group. Significance levels are indicated as *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001 and ****p \u0026lt; 0.0001, determined by unpaired t-test.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6929868/v1/8d94e39811a50e0e1417db48.png"},{"id":85312830,"identity":"46c2f331-9822-444c-ae79-8785cfe16bd9","added_by":"auto","created_at":"2025-06-24 13:58:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7405542,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6929868/v1/04f25081-ca02-45d1-ba9c-260d645a09a7.pdf"},{"id":85285029,"identity":"03a7a0c4-2eaa-4f3f-861e-d8ed2f181ed6","added_by":"auto","created_at":"2025-06-24 08:54:01","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":149201,"visible":true,"origin":"","legend":"Supplementary Tables","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6929868/v1/991178e27158e290878cd8a9.xlsx"},{"id":85283263,"identity":"5554c944-7a9b-48e5-bf23-2c1a8734ea9b","added_by":"auto","created_at":"2025-06-24 08:38:01","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":907400,"visible":true,"origin":"","legend":"Supplementary Figures","description":"","filename":"SupplementaryFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6929868/v1/86ed1c874d9081ed1ca7d36a.pdf"}],"financialInterests":"(Not answered)","formattedTitle":"Genotype-driven variations in lncRNA expression underlie predisposition to high-grade serous ovarian cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInvasive epithelial ovarian cancer (EOC) is among the leading causes of gynecologic cancer in women worldwide, with greater than 314,000 new diagnoses and approximately 207,000 deaths each year\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Histological subtypes of invasive EOC vary in epidemiological\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and genetic risk factors\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, clinical responses to platinum-based therapy\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, and cells of origin\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. High-grade serous ovarian cancer (HGSOC) is the most prevalent histotype, accounting for 60\u0026ndash;70% of EOC cases and the majority of EOC mortality\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Current treatments mostly rely on traditional cytoreductive surgery combined with platinum-based chemotherapy, and the overall 5-year survival rate is still below 20% for HGSOC due to the scarcity of effective early diagnosis strategies and the rapid development of chemoresistance\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Therefore, new oncogenes and tumor suppressors that could serve as effective diagnostic and therapeutic targets for HGSOC, are desperately needed. To prioritize the best targets, it is of critical importance to understand the germline genetic architecture of HGSOC, as previous studies consistently confirmed that genetically supported drug targets could dramatically contribute to the successful development of new therapeutics\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHGSOC has a significant germline genetic component, with an estimated heritability around 20 to 40%\u003csup\u003e15, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Known germline loss-of-function mutations in susceptibility genes, such as \u003cem\u003eBRCA1\u003c/em\u003e, \u003cem\u003eBRCA2\u003c/em\u003e, \u003cem\u003eTP53, BRIP1, MSH6\u003c/em\u003e, and \u003cem\u003eRAD51C/D\u003c/em\u003e, only account for about 10% of cases\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. The majority of genetic risk factors for HGSOC is due to common low-penetrance-susceptibility alleles\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. Large-scale genome-wide association studies (GWASs) have so far identified 19 loci, where common single-nucleotide polymorphisms (SNPs) show statistically significant reproducible association with HGSOC predisposition\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, suggesting that germline genetic variants influence tumorigenesis. However, interpreting the likely biological mechanism of GWAS is challenging, as more than 90% of risk variants lie in the non-protein-coding regions of the genome. Expression quantitative trait locus (eQTL) analysis has been a powerful approach to map risk variants to disease-causing genes\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. By integrating of eQTL and GWAS, several previous studies have successfully prioritized putative susceptibility genes for HGSOC risk loci, such as \u003cem\u003eHOXD9\u003c/em\u003e\u003csup\u003e42\u003c/sup\u003e, \u003cem\u003eTERT\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eCHMP4C\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eANKLE1/ABHD8\u003c/em\u003e\u003csup\u003e29\u003c/sup\u003e, and \u003cem\u003eGNL2\u003c/em\u003e\u003csup\u003e43\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough long non-coding RNAs (lncRNAs) dominate the transcriptome in human cells, most efforts to interpret the functional consequences of non-coding risk variants have primarily focused on their regulatory effects on protein-coding genes (PCGs), greatly neglecting lncRNAs, which have been increasingly shown to play critical roles in chemoresistance, tumorigenesis, tumor progression, and metastasis\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. LncRNAs are likely to act as scaffolds, guides, or decoys to alter DNA-protein binding or protein-protein interactions\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e, resulting in dysregulation in cell proliferation, differentiation and apoptosis\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Previous investigations of the correlation between disease-associated SNPs and lncRNA expression have successfully nominated several potential disease-causing lncRNAs, such as \u003cem\u003ePCAT1\u003c/em\u003e in promoting prostate cancer cell growth\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eLINC01475\u003c/em\u003e in influencing ulcerative colitis susceptibility\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. Therefore, systematic interrogation of the regulation from germline variants on lncRNAs (lncRNA-eQTLs) could provide new insights into how risk variants contribute to HGSOC pathogenesis via non-coding genome and improve new therapeutic targets identification.\u003c/p\u003e \u003cp\u003eIn this study, we generated a comprehensive atlas of eQTLs for both PCGs and lncRNAs, utilizing 348 HGSOC tumor samples with matched high-quality genotype and gene expression data from the Cancer Genome Atlas (TCGA)\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Our findings indicate that germline variants play crucial roles in modulating the expression of not only PCGs but also lncRNAs, which could further contribute to HGSOC predisposition. Using Bayesian colocalization analysis, we prioritized 10 susceptibility genes for HGSOC, comprising 6 PCGs and 4 lncRNAs, where genotype-driven gene expression changes are highly associated with HGSOC risk in high-grade ovarian tumors. Five of these genes, such as \u003cem\u003eARL17A\u003c/em\u003e and \u003cem\u003eCRHR1-IT1\u003c/em\u003e, also showed an association with HGSOC predisposition in whole blood. We characterized the expression patterns of susceptibility PCGs and lncRNAs using single-nucleus resolution transcriptome maps of the human HGSOC tumor. In addition, we conducted \u003cem\u003ein vitro\u003c/em\u003e assays and validated the functional role of \u003cem\u003eCRHR1-IT1\u003c/em\u003e in promoting apoptosis of ovarian cancer cells. Our study could pave the way for early detection and new therapeutic developments for HGSOC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGenotype and RNA-seq data processing for eQTL analysis\u003c/h2\u003e \u003cp\u003eWe downloaded matched genotype and quantified gene expression data for 350 primary HGSOC tumor samples from the TCGA data portal (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tcga-data.nci.nih.gov/tcga/\u003c/span\u003e\u003cspan address=\"http://tcga-data.nci.nih.gov/tcga/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e57\u003c/sup\u003e. The TCGA germline variants data were genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0. Quality control of raw genotype data was performed using PLINK (v2.0)\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.cog-genomics.org/plink2/\u003c/span\u003e\u003cspan address=\"http://www.cog-genomics.org/plink2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We excluded samples with misgendering, contamination, or low call rates. SNPs on sex chromosomes, monomorphic SNPs, and those with a minor allele frequency (MAF)\u0026thinsp;\u0026lt;\u0026thinsp;1%, Hardy\u0026ndash;Weinberg equilibrium P-value\u0026thinsp;\u0026lt;\u0026thinsp;1.0E-06, or call rate\u0026thinsp;\u0026lt;\u0026thinsp;95% were removed as well. In total, we excluded 2 contaminated samples with high PIHAT values (\u0026gt;\u0026thinsp;0.2). This left 348 individuals and 771,697 SNPs for further analysis \u003cb\u003e(Supplementary Table\u0026nbsp;1)\u003c/b\u003e. To better understand the genetic background of the TCGA HGSOC samples, we performed Principal Components Analysis (PCA) on the genotype data of these 348 HGSOC subjects, along with 2,504 genotyped data from the 1,000 Genomes Project Phase 3\u003csup\u003e59\u003c/sup\u003e, using Genome-wide Complex Trait Analysis (GCTA)\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Most samples in our study clustered amongst the European populations, as shown in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e. The first 2 principal components (PCs) were used in the linear regression model for eQTL analysis.\u003c/p\u003e \u003cp\u003eTo increase eQTL discovery power, autosomal variants were pre-phased by SHAPEIT2\u003csup\u003e61\u003c/sup\u003e and subsequently imputed with IMPUTE2\u003csup\u003e62\u003c/sup\u003e, using 1,000 Genomes Project Phase 3 as the reference panel. Before imputation, indels and SNPs absent or incompatible with the 1,000 Genomes Project dataset were removed. Imputed SNPs with MAF\u0026thinsp;\u0026lt;\u0026thinsp;1%, Hardy-Weinberg Equilibrium P-value\u0026thinsp;\u0026lt;\u0026thinsp;1E-06, and imputation confidence score INFO (a measure of r\u003csup\u003e2\u003c/sup\u003e)\u0026thinsp;\u0026lt;\u0026thinsp;0.4 (estimated by SNPTEST\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e) were further excluded. Consequently, 8,579,445 high-quality autosomal SNPs were used for eQTL analysis.\u003c/p\u003e \u003cp\u003eThe TCGA level 3 RNA-seq data were aligned to the human genome based on GENCODE v22 annotations\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. Next, we normalized the downloaded raw read counts across samples using the TMM method and then estimated gene expression levels as transcripts per million (TPM). Only genes with at least 0.1 TPM in more than two samples were identified as expressed and used for downstream eQTL analysis.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eeQTL mapping\u003c/h3\u003e\n\u003cp\u003eWe performed \u003cem\u003ecis\u003c/em\u003e-eQTL (further referred to as eQTL) mapping using 348 primary HGSOC samples for both protein-coding genes (PCGs) and lncRNAs \u003cb\u003e(Supplementary Table\u0026nbsp;2)\u003c/b\u003e. Expression measurements for each PCG or lncRNA were quantile-normalized and then transformed into a rank-based standard normal expression using inverse normal-transformed (INT). To remove the effects of latent confounders, we estimated 40 and 60 PEER\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e factors on INT expression by adjusting for age, batch effect, and top 2 genotype PCs for PCG-eQTL and lncRNA-eQTL identification, respectively. The number of PEER factors that could maximize the identified ePCGs/elncRNA (PCGs/lncRNAs that were significantly regulated by at least one SNP) was selected \u003cb\u003e(Supplementary Fig.\u0026nbsp;2)\u003c/b\u003e. eQTL mapping was then performed using the INT gene expression as the outcome and SNP dosage as the independent variable, adjusting for age, batch effect, top 2 genotype PCs, and the estimated PEER factors. Nominal P-values were calculated for all SNPs located within \u0026plusmn;\u0026thinsp;1 Mb of PCGs/lncRNAs using MatrixEQTL\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e with an additive linear regression mode. A permutation procedure was then implemented using FastQTL\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e with the setting \u0026ldquo;-permute 10000\u0026rdquo;. We adopted Beta distribution-adjusted empirical P-values to calculate \u003cem\u003eq\u003c/em\u003e using Storey\u0026rsquo;s \u003cem\u003eq\u003c/em\u003e method\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e, and identified eGenes with a \u003cem\u003eq\u003c/em\u003e cutoff\u0026thinsp;\u0026le;\u0026thinsp;0.05. Next, we defined an empirical P threshold, \u003cem\u003eP\u003c/em\u003e\u003csub\u003et\u003c/sub\u003e, as the empirical \u003cem\u003eP\u003c/em\u003e of the PCG/lncRNA closest to the 0.05 FDR threshold. \u003cem\u003eP\u003c/em\u003e\u003csub\u003et\u003c/sub\u003e was used to estimate the nominal P cutoff for each gene based on the Beta distribution model of the minimum P distribution f(P\u003csub\u003emin\u003c/sub\u003e), generated from the permutations for the gene. For each ePCGs/elncRNA, SNPs having a lower nominal P than the gene-level cutoff were identified as eSNPs (SNPs that significantly regulated at least one PCG or lncRNA)\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eDifferentially expressed genes identification\u003c/h3\u003e\n\u003cp\u003eTo minimize computational batch effects causing by different data analysis pipelines in TCGA and GTEx (v7; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gtexportal.org/home/\u003c/span\u003e\u003cspan address=\"http://gtexportal.org/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e70\u003c/sup\u003e cohorts, we downloaded the recomputed gene expression data in the ovaries of the TCGA HGSOC patients and GTEx healthy samples using a consistent analysis protocol from UCSC Xena (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://xena.ucsc.edu/\u003c/span\u003e\u003cspan address=\"http://xena.ucsc.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The estimated gene expression was quantified as transcripts per kilobase million (TPM). Genes with TPM\u0026thinsp;\u0026lt;\u0026thinsp;0.1 in \u0026lt;\u0026thinsp;5% of samples were excluded from downstream analysis. Differentially expressed genes (DEGs) between HGSOC tumor and healthy samples were identified using a linear model combined with an empirical Bayes method in the R package limma\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. This linear regression model adopted gene expression as the outcome and disease status as independent variable, adjusting for age. Genes with a Benjamini-Hochberg FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were identified as DEGs.\u003c/p\u003e\n\u003ch3\u003eColocalization analysis\u003c/h3\u003e\n\u003cp\u003eTo evaluate the posterior probabilities of inherited HGSOC risk and expression of both PCGs and lncRNAs sharing common genetic causal variants in a given region, we performed colocalization analysis on summary data of HGSOC GWAS\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and eQTL data from HGSOC tumors and whole blood (from GTEx v7)\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e using coloc v5.2.3\u003csup\u003e72\u003c/sup\u003e. We adopted the Phelan et al. HGSOC GWAS study based on 13,037 cases and 40,941 controls\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. HGSOC associated variants with a P-value\u0026thinsp;\u0026lt;\u0026thinsp;5E-08 were selected. Accounting for the inflation caused by GWAS variants representing the same signal, we performed LD pruning using \u003cem\u003eswiss\u003c/em\u003e (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/statgen/swiss\u003c/span\u003e\u003cspan address=\"https://github.com/statgen/swiss\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) by discarding variants in LD r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.8 with the lead SNP at each locus. Genomic regions within \u0026plusmn;\u0026thinsp;100 kb of each pruned GWAS variant, that overlap with ePCGs or elncRNAs, were used in the coloc analysis. Posterior probability was then estimated using summary data of HGSOC GWAS and PCG-eQTLs/lncRNA-eQTLs to identify the colocalization regions. Coloc examines whether HGSOC and gene expression are associated and share the same causal variants (H4). We used PP4 (the posterior probability of H4)\u0026thinsp;\u0026gt;\u0026thinsp;0.8 as the threshold of colocalization.\u003c/p\u003e\n\u003ch3\u003eSample procurement for snRandom-seq\u003c/h3\u003e\n\u003cp\u003eTwo tumor specimens for single-nucleus Random-seq were obtained from the Biobank of Zhejiang Cancer Hospital. One specimen was collected from the intestine surface during the primary debulking surgery, and the other was from adipose tissue obtained during surgery for the platinum-resistant recurrence. The high-grade tumor specimens were pathologically reviewed.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFFPE single-nucleus transcriptome library construction and sequencing\u003c/h2\u003e \u003cp\u003eFFPE samples were cut from paraffin block and washed twice with 1 mL Xylene (Aladdin, Cat # X112054) for 5 minutes at room temperature to remove the paraffin. The samples were then gently redehydrated by immersing them in a graded series of ethanol solutions (Aladdin, Cat # E130059), starting with 100% ethanol and ending with 30% ethanol. Next, we utilized the VITApilote high-throughput FFPE single-cell transcriptome kit (Cat #R20123124, M20 Genomics) for pre-library processing, library construction, and purification. The VITAcruizer single-cell preparation instrument DP400 (Cat #E20000131, M20 Genomics) was employed for droplet generation, single-cell partitioning, and encapsulation, and nucleic acid capture. The experiments were carried out according to the instructions of corresponding kits and instruments. The main workflow was as follows: The prepared tissue section was permaeablized and blocked for single-stranded DNA-blocking. Random primers were then added to reverse transcribe the total RNA inside the nuclei from the tissue section, and the resulting cDNA fragments were ligated to adapters within the nuclei. The tissue section was lysed into individual cell nuclei by grinding and enzymatic treatment. The reverse-transcribed single-nucleus suspension was mixed with reagents and barcoding beads containing cell barcode and unique molecular identifiers (UMI). This mixture was encapsuled, captured and barcoded using the VITAcruizer DP400 instrument, resulting in barcoded cDNA strands. PCR amplification was then conducted using the cDNA as the template. The purified products were utilized to construct a single-nucleus library containing P5 and P7 adapters and were sequenced on a Novaseq 6000 sequencer (Illumina) with 150 bp paired-end reads \u003cb\u003e(Supplementary Table\u0026nbsp;3)\u003c/b\u003e. Consumables, reagents, and instruments used in the experiment included: a 20 \u0026micro;m cell sieve (pluriSelect, Cat # 43-10020-20), DAPI (4\u0026prime;,6-diamidino-2-phenylindole) staining solution (Abcam, Cat # ab228549), and an inverted fluorescence microscope (Nikon Eclipse, Ts2-FL).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSnRandom-seq data processing\u003c/h3\u003e\n\u003cp\u003ePrimer sequences and adaptors were trimmed from the raw data. The UMI (8 nts) and cell-specific barcode (30 nts) for each Read1 were then extracted. Sequenced barcodes were merged if they could be uniquely assigned to the same accepted barcode with a Hamming distance of 2 nts or less. Sequencing reads were aligned to the human genome based on GENCODE v22 annotations\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e using the STARsolo module of the STAR aligner (version 2.7.10a) with appropriate parameters\u003csup\u003e\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u003c/sup\u003e. Ambient RNA was removed using SoupX (version 1.6.2)\u003csup\u003e\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e\u003c/sup\u003e, and the corrected expression data were further analyzed with Seurat (v5.0.2)\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e. For quality control, we excluded features expressed in fewer than 5 cells. Low-quality cells with fewer than 200 features or more than 4,000 features expressed were also excluded. In addition, cells with more than 40% mitochondrial transcripts were filtered out. Samples were integrated and batch effects were removed using the FindIntegrationAnchors function\u003csup\u003e\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e. PCA was performed and the first 30 PCs were selected to generate a \u003cem\u003ek\u003c/em\u003e-nearst neighbor graph with \u003cem\u003ek\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15. Uniform manifold approximation and projection (UMAP) embeddings with a resolution of 0.5 were generated to visualize the data\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eCell culture and transfection experiment\u003c/h3\u003e\n\u003cp\u003eThe human ovarian adenocarcinoma cell line OVCAR\u0026shy;3 (ATCC\u0026reg; HTB-161) was purchased from ATCC (USA) and cultured in the Roswell Park Memorial Institute 1640 Medium (RPMI-1640, Catalog No. 30\u0026shy;2001) containing 20% fetal bovine serum (Gibco, USA) and 0.01 mg/ml bovine insulin (Sigma, Catalog No. I6634). Cells were incubated at 37\u0026deg;C with 5% CO\u003csub\u003e2\u003c/sub\u003e, and the medium was changed every two days.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eCRHR1-IT1\u003c/em\u003e sequence was synthesized according to the full-length sequence with an added poly-A tail based on the LINC02210 sequence, NR_026680.3 in NCBI \u003cb\u003e(Supplementary Table\u0026nbsp;4)\u003c/b\u003e, and then subcloned into a pcDNA3.1 (+) vector (Genscript, Shanghai, China). OVCAR\u0026shy;3 cells were seeded in a 24-well plate and incubated at 37\u0026deg;C for 8 hours. Next, the overexpression vector pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e was transfected into OVCAR\u0026shy;3 cells using Lipofectamine 3000 (Invitrogen, USA) and Opti-MEM (Thermo Fisher Scientific, USA), following the manufacturer\u0026rsquo;s protocol. Transfection conditions included: control (0 \u0026micro;g/well vector, null condition), mock (0 \u0026micro;g/well verctor, baseline transfection reagents), vehicle (1 \u0026micro;g/well pcDNA3.1(+) vector), 0.25 (0.25 \u0026micro;g/well pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e vector), 0.5 (0.5 \u0026micro;g/well pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e vector) and 1 (1 \u0026micro;g/well pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e vector). After 6 hours of transfection, the cells were washed once with 1X PBS, fresh RPMI-1640 medium was then added, and the cells were grown for 48 hours post-transfection. We collected RNA samples from control, mock, vehicle conditions, as well as from pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e overexpression-transfected OVCAR\u0026shy;3 cells at vector concentrations of 0.25 \u0026micro;g/well, 0.50 \u0026micro;g/well, and 1.00 \u0026micro;g/well at 24 and 48 hours for sequencing. In total, 24 RNA-seq datasets were obtained, with two biological replicates per transfection condition.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRNA isolation and quantitative real-time PCR (qRT-PCR)\u003c/h2\u003e \u003cp\u003eCells were lysed, and total RNA was isolated using the PureLink\u0026reg; RNA Mini Kit (Thermo Fisher Scientific, USA) according to the manufacturer's protocol. 1 \u0026micro;g of RNA was used to synthesize cDNA using the PrimeScript\u0026trade; RT reagent Kit with gDNA Eraser (Takara, Japan). Subsequent qRT-PCR was performed using SuperReal PreMix Plus (SYBR Green) (Tiangen, China). The expression of mRNAs and lncRNAs was normalized to \u003cem\u003eGAPDH\u003c/em\u003e. Relative gene expression was calculated by the 2\u003csup\u003e\u0026minus;ΔCT\u003c/sup\u003e method. The primer sequences for \u003cem\u003eCRHR1-IT1\u003c/em\u003e were as follows: Forward 5'-CCCCACTTCTAGGCTTGGTT-3'; Reverse 5'-TCTGTAGCGGAGGGAATGGA-3'. The primer sequences for \u003cem\u003eGAPDH\u003c/em\u003e were as follows: Forward 5\u0026rsquo;-GGAGCGAGATCCCTCCAAAAT-3'; Reverse 5'-GGCTGTTGTCATACTTCTCATGG-3'. The primer sequences for \u003cem\u003eCMYC, CRHR1, FAS\u003c/em\u003e, \u003cem\u003eJUN\u003c/em\u003e and \u003cem\u003ePARP1\u003c/em\u003e are presented in \u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e. All qRT-PCR reactions were performed using a CFX96 TOUCH/GelDoc XR\u0026thinsp;+\u0026thinsp;qRT-PCR machine (Bio-Rad, USA), with three technical replicates for each reaction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eReads processing of collected RNA-seq data from OVCAR-3 cells\u003c/h2\u003e \u003cp\u003eRNA was isolated and purified using TRIzol reagent (Invitrogen, Carlsbad, CA, USA). RNA quality was assessed with the Agilent Bioanalyzer 2100 (Agilent, CA, USA), with RIN scores above 7, and confirmed by electrophoresis with denaturing agarose gel. Poly (A) RNA was purified from 1 \u0026micro;g total RNA using Dynabeads Oligo (dT) 25-61005 (Thermo Fisher, CA, USA) through two rounds of purification. The poly (A) RNA was then fragmented into small pieces using the Magnesium RNA Fragmentation Module (NEB, cat.e6150, USA) at 94℃ for 5\u0026ndash;7 minutes. The cleaved RNA fragments were reverse-transcribed to cDNA using SuperScript\u0026trade; II Reverse Transcriptase (Invitrogen, cat. 1896649, USA). Strand-specific RNA-seq libraries were prepared using the dUTP method. 2 \u0026times; 150 bp paried-end sequencing was then performed on the Illumina NovaSeq X Plus. Next, sequence quality was assessed using FastQC (v0.11.9). Adaptors and low-quality bases were trimmed using Trim-galore (v0.6.6). The remaining RNA-seq reads were then aligned to the human genome (hg19/GRCh37) using STAR (v2.7.10a)\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e based on GENCODE v24 annotations\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e. Gene expression levels were quantified using RSEM (v1.3.1)\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eWeighted Gene Co-Expression Network Analysis (WGCNA)\u003c/h2\u003e \u003cp\u003eWeighted Gene Co-Expression Network Analysis (WGCNA) was performed on 24 RNA-seq data using the R package WGCNA\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e. The top 50% variablely expressed genes were used. Modules with at least 30 genes were selected. The expression correlation between \u003cem\u003eCRHR1-IT1\u003c/em\u003e and genes in each module was then estimated. Genes with the absolute value of gene significance (GS)\u0026thinsp;\u0026gt;\u0026thinsp;0.75 and module membership (MM)\u0026thinsp;\u0026gt;\u0026thinsp;0.75 were defined as key genes for that module.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis and network construction\u003c/h2\u003e \u003cp\u003eFunctional enrichment analysis was performed using Metascape (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://metascape.org/\u003c/span\u003e\u003cspan address=\"http://metascape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with default settings\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u003c/sup\u003e. Network analysis was implemented in ConsensusPathDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cpdb.molgen.mpg.de\u003c/span\u003e\u003cspan address=\"http://cpdb.molgen.mpg.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e83\u003c/sup\u003e, which integrates various interaction networks, including binary and complex protein-protein interactions, genetic interactions, metabolic pathways, signaling pathways, gene regulatory networks, drug-target interactions, and biochemical pathways.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIncucyte apoptosis assay for\u003c/b\u003e \u003cb\u003eCRHR1-IT1\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAfter 6 hours of cell transfection, cells were washed once with 1X PBS and added fresh RPMI-1640 medium. CellEvent\u0026trade; Caspase-3/7 Green Detection Reagent (Thermo Fisher Scientific, USA) was then added to the cells, which were subsequently incubated in an Incucyte SX5 incubator for 42 hours.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis of qRT-PCR and cell apoptosis assays\u003c/h2\u003e \u003cp\u003eStatistical analysis of qRT-PCR and cell apoptosis assays was performed using GraphPad Prism 8.0. Data are presented as the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SE, and experiments were repeated at least three times. Two-sided P-values were calculated using unpaired t-test analysis for the qRT-PCR assay. For the cell apoptosis assay, two-sided P-values were estimated using one-way ANOVA. Results were considered significant at *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and ****p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eGenotype-driven expression changes of PCGs and lncRNAs\u003c/h2\u003e \u003cp\u003eTo understand the contribution of germline variants to expression changes, we investigated the association between genotype and local expression variations of both PCGs and lncRNAs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. After stringent quality control \u003cb\u003e(Materials and Methods)\u003c/b\u003e, we selected 348 HGSOC tumor samples \u003cb\u003e(Supplementary Table\u0026nbsp;1)\u003c/b\u003e with matched high-quality genotype and gene expression data to identify PCG-eQTLs. And for lncRNA-eQTL analysis, we adopted quantified expression data of lncRNAs from the same 348 HGSOC tumor samples. We performed eQTL analysis using a linear regression model for PCGs and lncRNAs, respectively, and interrogated SNPs located within a\u0026thinsp;\u0026plusmn;\u0026thinsp;1 Mb (\u003cem\u003ecis\u003c/em\u003e) window of each PCG or lncRNA. The PCG-eQTL analysis identified 7,145 eGenes (that is, genes associated with at least one SNP) at a Storey\u0026rsquo;s \u003cem\u003eq\u003c/em\u003e-value\u0026thinsp;\u0026le;\u0026thinsp;0.05. Our results successfully replicated the previously identified significant SNP-gene pairs in the ovary from the GTEx with π\u003csub\u003e1\u003c/sub\u003e\u0026thinsp;=\u0026thinsp;0.99 (where π\u003csub\u003e1\u003c/sub\u003e represents the proportion of true positives)\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e, aligning with our own and others\u0026rsquo; previous identification of genotype-driven expression changes (eQTLs) from the same tissue\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u003c/sup\u003e. The number of identified eGenes is also in line with previous eQTL study in 44 human tissues from the GTEx Consortium \u003cb\u003e(Supplementary Fig.\u0026nbsp;3)\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e. For example, we found that the genetic variant rs35478347 had a robust effect (two-sided P-value\u0026thinsp;=\u0026thinsp;6.0E-06) on \u003cem\u003eDHX58\u003c/em\u003e expression \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. And the lncRNA-eQTL analysis identified 572 elncRNAs (that is, lncRNAs associated with at least one SNP) \u003cb\u003e(Supplementary Table\u0026nbsp;6)\u003c/b\u003e. For instance, we observed that the expression of the lncRNA \u003cem\u003eLOC284581\u003c/em\u003e (hg19 chr1:205,781,868\u0026thinsp;\u0026minus;\u0026thinsp;205,875,038) was significantly correlated with rs9438393 (two-sided P-value\u0026thinsp;=\u0026thinsp;3.02E-74) in HGSOC tumor samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Intriguingly, we found that the variance explained by the top associated SNPs decreased dramatically from that for lncRNA expression to protein-coding gene expression and to HGSOC risk \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e, suggesting that expression changes of lncRNAs explain a higher portion of HGSOC heritability than PCGs. Moreover, the regulatory significance of the lead SNP on each ePCG or elncRNA increased for SNPs closer to the transcription start site (TSS), suggesting that germline variations in promoter regions have stronger regulatory effects on expression of both PCGs and lncRNAs \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. In addition, the regulatory effects of germline variants on lncRNAs were generally stronger than on PCGs \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE and \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e, consistent with a previous study indicating that lncRNA eQTLs tend to have larger effect sizes than protein-coding eQTLs\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eGiven the substantial genotype-driven expression changes, we next investigated the potential role of germline variants in HGSOC predisposition \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e. First, we identified differentially expressed genes (DEGs) in the ovaries of the HGSOC patients (N\u0026thinsp;=\u0026thinsp;419 from TCGA) and healthy samples (N\u0026thinsp;=\u0026thinsp;88 from GTEx). We identified 2,689 up-regulated and 4,616 down-regulated genes as DEGs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eH\u003cb\u003e)\u003c/b\u003e, with 76.9% being protein-coding genes and 19.9% being lncRNAs \u003cb\u003e(Supplementary Fig.\u0026nbsp;4)\u003c/b\u003e. To ascertain the contribution of germline variants in HGSOC predisposition, we overlapped our DEGs and eQTL results. We found that about 6.82% of the identified DEGs, including 404 PCGs and 94 lncRNAs, could be driven by underlying germline variants \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eI \u003cb\u003eand Supplementary Table\u0026nbsp;7)\u003c/b\u003e. For instance, the eGene \u003cem\u003eDHX58\u003c/em\u003e, whose expression changes were regulated by germline variant rs35478347 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e, also showed significant differences (two-sided P-value\u0026thinsp;=\u0026thinsp;1.31E-43) in expression between HGSOC tumors and healthy samples \u003cb\u003e(Supplementary Fig.\u0026nbsp;5)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn summary, our results suggested that underlying germline variants play important roles in driving expression changes of both PCGs and lncRNAs, which could further contribute to the HGSOC-associated DEGs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eGenotype-driven expression changes are enriched for HGSOC-associated GWAS loci\u003c/h2\u003e \u003cp\u003eAs the vast majority of GWAS-identified HGSOC risk loci reside in non-coding regions of the genome, these variants might function through regulatory effects on the expression of putative causal PCGs or lncRNAs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. To understand the underlying biological mechanisms of the observed statistical association between SNPs and HGSOC predisposition, we first overlapped genome-wide significant loci in HGSOC GWAS\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e and in our eQTL signals for both PCGs and lncRNAs, respectively. Genotype-driven expression changes of 51 PCGs and 20 lncRNAs showed associations with HGSOC risk \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Several putative HGSOC risk genes, such as \u003cem\u003eHOXB4\u003c/em\u003e, \u003cem\u003eLERK1\u003c/em\u003e, \u003cem\u003eMYO19\u003c/em\u003e, \u003cem\u003eMEIOB\u003c/em\u003e, \u003cem\u003eMRRF\u003c/em\u003e, \u003cem\u003eC3orf33\u003c/em\u003e, and \u003cem\u003eCCDC23\u003c/em\u003e, have been identified as associated with epithelial ovarian cancer in previous independent studies\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u003c/sup\u003e. Although the top significant GWAS signals were enriched on chromosomes 3, 4, 8, and 9, we observed a strong enrichment of candidate susceptibility genes for HGSOC on chromosome 17 after overlapping with eQTLs. These loci are adjacent to the known EOC and breast cancer marker gene \u003cem\u003eBRCA1\u003c/em\u003e on chromosome 17. Intriguingly, out of these 51 PCGs, 16 genes (about 31%) have also been previously implicated as candidate breast cancer susceptibility genes \u003cb\u003e(Supplementary Table\u0026nbsp;8)\u003c/b\u003e, including \u003cem\u003eMAPT\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e\u003c/sup\u003e, \u003cem\u003eLGALS7\u003c/em\u003e\u003csup\u003e94\u003c/sup\u003e, \u003cem\u003eTBC1D3\u003c/em\u003e\u003csup\u003e95\u003c/sup\u003e, \u003cem\u003eIQCG\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e and \u003cem\u003eCCL4\u003c/em\u003e\u003csup\u003e97\u003c/sup\u003e etc. In addition, the elncRNA \u003cem\u003eERVK13-1\u003c/em\u003e (Endogenous Retrovirus Group K13 Member 1) has previously been shown an association with breast cancer\u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e\u003c/sup\u003e. It appears that HGSOC and breast cancer share an extensive expression-based pleiotropy at these loci, consistent with previous observations\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNext, we performed a Bayesian statistical framework established in colocalization (coloc) analysis\u003csup\u003e\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e to prioritize disease-causing genes, whose genotype-driven expression changes are associated with GWAS signals \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e, by integrating HGSOC GWAS with the ovarian tumor eQTL data. Our analysis nominated 10 susceptibility genes for HGSOC, including 6 PCGs and 4 lncRNAs \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD \u003cb\u003eand Supplementary Table\u0026nbsp;9)\u003c/b\u003e. Consistently, all 6 PCGs\u0026mdash;\u003cem\u003eCHMP4C\u003c/em\u003e, \u003cem\u003eARL17A\u003c/em\u003e, \u003cem\u003eARL17B\u003c/em\u003e, \u003cem\u003eLRRC37A\u003c/em\u003e, \u003cem\u003eLRRC37A2\u003c/em\u003e, and \u003cem\u003eMAPT\u003c/em\u003e\u0026mdash;have been previously identified as HGSOC susceptibility genes using a transcriptome-wide association study (TWAS)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, demonstrating that our observations could be successfully replicated using different statistical methods. Notably, around \u0026plusmn;\u0026thinsp;100 kb window of SNP rs142045106, we identified one protein-coding gene \u003cem\u003eMAPT\u003c/em\u003e and 3 lncRNAs, including \u003cem\u003eCRHR1-IT1\u003c/em\u003e, \u003cem\u003eKANSL1-AS1\u003c/em\u003e, and \u003cem\u003eLOC102724391\u003c/em\u003e, as putative disease-causing genes for HGSOC. This suggests a likely functional convergence of \u003cem\u003eMAPT\u003c/em\u003e and its adjacent lncRNAs at this locus. Emerging studies have reported that \u003cem\u003eMAPT\u003c/em\u003e expression showed an association with estrogen receptor expression in breast cancer, and an imperfect estrogen-response element has also been identified in the promoter region of \u003cem\u003eMAPT\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e, \u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e. In addition, the tau protein encoded by \u003cem\u003eMAPT\u003c/em\u003e, was found almost exclusively bound to microtubules, which plays a pivotal role in mitosis in cancer development as well\u003csup\u003e\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eInsights into early detection and immunotherapy for HGSOC\u003c/h2\u003e \u003cp\u003eHGSOC is often diagnosed at a very late stage and is characterized by an immune-suppressive tumor microenvironment (TME) with few tumor infiltrating lymphocytes, rendering it largely refractory to checkpoint inhibitor immunotherapies\u003csup\u003e\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e\u003c/sup\u003e. To gain new insights into early detection and treatment of HGSOC, we investigated putative disease-causing genes in whole blood by integrating HGSOC GWAS with whole blood eQTL data (from GTEx v7)\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e using Bayesian-test-based colocalization analysis. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD, our analysis nominated 12 PCGs and 5 lncRNAs in whole blood. Of these genes, \u003cem\u003eHOXB2\u003c/em\u003e\u003csup\u003e28\u003c/sup\u003e, \u003cem\u003eHOXB3\u003c/em\u003e\u003csup\u003e104, 105\u003c/sup\u003e, \u003cem\u003ePLEKHM1\u003c/em\u003e\u003csup\u003e106\u003c/sup\u003e, \u003cem\u003eRCCD1\u003c/em\u003e\u003csup\u003e28, 34\u003c/sup\u003e, and \u003cem\u003eKANSL1\u003c/em\u003e\u003csup\u003e28, 107, 108\u003c/sup\u003e have been previously implicated as susceptibility genes for epithelial ovarian cancer. Intriguingly, \u003cem\u003eKANSL1\u003c/em\u003e has been previously identified as an oncogene, whereas the colocalized lncRNA \u003cem\u003eKANSL1-AS1\u003c/em\u003e showed an opposite trend by assessing the 5-year survival time of 170 HGSOC patients\u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e\u003c/sup\u003e. In addition, the variant rs76640332 within \u003cem\u003eKANSL1\u003c/em\u003e loci was previously reported to be associated with lymphocyte percentage of leukocytes (two-sided P\u003csub\u003eGWAS\u003c/sub\u003e = 5E-13)\u003csup\u003e109\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNext, we interrogated whether these susceptibility genes identified in whole blood could potentially contribute to the early detection for HGSOC. We overlapped the colocalized genes in the ovarian tumor and whole blood \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. \u003cem\u003eARL17A\u003c/em\u003e, \u003cem\u003eLRRC37A2\u003c/em\u003e, and \u003cem\u003eCRHR1-IT1\u003c/em\u003e were found to show significant differences in expression between HGSOC tumors and healthy samples \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF, \u003cb\u003eSupplementary Fig.\u0026nbsp;6)\u003c/b\u003e. We also found that the expression of the overlapped elncRNA \u003cem\u003eKANSL-AS1\u003c/em\u003e correlated with the EOC marker gene \u003cem\u003eBRCA2\u003c/em\u003e expression in the TCGA HGSOC tumor samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e. These observations highlighted that the shared colocalized genes in both ovarian tumors and whole blood likely play important roles in the early diagnosis of HGSOC.\u003c/p\u003e \u003cp\u003eUsing the genome-scale integrated analysis of gene networks in tissues (GIANT 2.0)\u003csup\u003e\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e\u003c/sup\u003e, we found that the colocalized genes \u003cem\u003eKANSL1\u003c/em\u003e, \u003cem\u003eLRRC37A2\u003c/em\u003e, and \u003cem\u003eARL17A\u003c/em\u003e were strongly co-expressed in CD8\u003csup\u003e+\u003c/sup\u003e T cells and enriched for chromatin organization and regulation of nitrogen compound metabolic process \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH \u003cb\u003eand Supplementary Table\u0026nbsp;10)\u003c/b\u003e. Nitrogen is one of the essential requirements for \u003cem\u003ede novo\u003c/em\u003e synthesis of many important nitrogenous compounds, including amino acids and nucleotides, which support the growth of both cancer and immune cells\u003csup\u003e\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u003c/sup\u003e. The competition for available nitrogen between tumor cells and infiltrating CD8\u003csup\u003e+\u003c/sup\u003e T cells might further alter the TME\u003csup\u003e\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u003c/sup\u003e. CD8\u003csup\u003e+\u003c/sup\u003e T cells are pivotal to the efficacy of cancer immunotherapies\u003csup\u003e\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e. Emerging studies have shown that variations in the fraction of infiltrating CD8\u003csup\u003e+\u003c/sup\u003e T cells in the TME can lead to different outcomes in prognosis and treatment response in various malignancies, including breast cancer and HGSOC\u003csup\u003e\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSingle-nucleus resolution transcriptome maps for the human HGSOC tumor\u003c/h2\u003e \u003cp\u003eNext, we characterized the expression atlas of PCGs and lncRNAs at a cellular resolution for human HGSOC tumors using snRandom-seq\u003csup\u003e\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u003c/sup\u003e, a droplet-based, highly sensitive, and full-length single-nuclei RNA sequencing method for FFPE samples. This technique captures full-length total RNAs with random primers \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e, allowing for the detection of more long non-coding RNAs compared to other scRNA-seq technologies, such as 10x Genomics scRNA-seq.\u0026nbsp;We manually assigned the clusters to known cell types according to previously reported cell type marker genes \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e, \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e\u003c/sup\u003e. Our snRandom-seq analysis characterized the expression patterns of 13,602 PCGs and 2,713 lncRNAs at single-nucleus resolution in 20,564 human HGSOC tumor cells \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. Given the strong enrichment of candidate susceptibility genes on chromosome 17 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e by overlapping HGSOC GWAS with tumor eQTLs, we investigated the single-cell expression patterns of these genes. More than 70% of them showed high expression levels in the immune cells of HGSOC tumors \u003cb\u003e(Supplementary Fig.\u0026nbsp;7)\u003c/b\u003e. Intriguingly, this dataset further confirmed the enriched co-expressed network \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eH\u003cb\u003e)\u003c/b\u003e for the colocalized genes \u003cem\u003eKANSL1\u003c/em\u003e, \u003cem\u003eLRRC37A2\u003c/em\u003e, and \u003cem\u003eARL17A\u003c/em\u003e in T cells of HGSOC tumors \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. Interestingly, \u003cem\u003eKANSL1\u003c/em\u003e showed a T-cell-specific expression, which aligns with previous observations indicating its association with the lymphocyte percentage of leukocytes\u003csup\u003e\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/sup\u003e. In addition, we also observed that colocalized genes \u003cem\u003eARL17A\u003c/em\u003e, \u003cem\u003eLRRC37A2\u003c/em\u003e, \u003cem\u003eLRRC37A\u003c/em\u003e, and \u003cem\u003eKANSL1-AS1\u003c/em\u003e, identified in both HGSOC tumors and whole blood \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e, showed high expression levels in immune cells, cancer cells, and proliferative cancer cells. And \u003cem\u003eMAPT, CHMP4C\u003c/em\u003e and \u003cem\u003eARL17B\u003c/em\u003e, identified in HGSOC tumors, were more likely to express specifically in cancer cells and proliferative cancer cells \u003cb\u003e(Supplementary Fig.\u0026nbsp;8)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThe putative roles of\u003c/b\u003e \u003cb\u003eCRHR1-IT1\u003c/b\u003e \u003cb\u003eand\u003c/b\u003e \u003cb\u003eKANSL1-AS1\u003c/b\u003e \u003cb\u003ein ovarian cancer\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOur analysis nominated two putative causal lncRNAs, \u003cem\u003eCRHR1-IT1\u003c/em\u003e and \u003cem\u003eKANSL1-AS1\u003c/em\u003e, in chr17q21 for HGSOC. We identified 91 and 65 colocalized signals (with PP4 ranging from 0.80 to 1.00) \u003cb\u003e(Supplementary Table\u0026nbsp;11)\u003c/b\u003e for \u003cem\u003eCRHR1-IT1\u003c/em\u003e and \u003cem\u003eKANSL1-AS1\u003c/em\u003e, respectively. 56 of these colocalized signals were observed from the same genomic regions. For instance, centered around the HGSOC GWAS SNP of rs146761208 (two-sided P\u003csub\u003eGWAS\u003c/sub\u003e = 6.90E-10), colocalized signals were observed for both \u003cem\u003eCRHR1-IT1\u003c/em\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e and \u003cem\u003eKANSL1-AS1\u003c/em\u003e \u003cb\u003e(Supplementary Fig.\u0026nbsp;9)\u003c/b\u003e. In this region, the HGSOC-associated GWAS variants were also the causal variants that influence the expression of \u003cem\u003eCRHR1-IT1\u003c/em\u003e and \u003cem\u003eKANSL1-AS1\u003c/em\u003e. However, our scRNA-seq data of HGSOC tumors showed very low expression of the colocalized lncRNA \u003cem\u003eCRHR1-IT1\u003c/em\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. Combining this with our previous observation shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF, we hypothesized a potential protective role of \u003cem\u003eCRHR1-IT1\u003c/em\u003e in HGSOC development.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFunctional insights into the lncRNA\u003c/b\u003e \u003cb\u003eCRHR1-IT1\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo validate the putative roles of \u003cem\u003eCRHR1-IT1\u003c/em\u003e in HGSOC, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis using gradient transfection in the HGSOC cell line OVCAR-3. We explored the biological pathways associated with \u003cem\u003eCRHR1-IT1\u003c/em\u003e expression changes using RNA-seq data collected at 24 and 48 hours after transfection. Transfection conditions included: control (0 \u0026micro;g/well vector, null condition), mock (0 \u0026micro;g/well verctor, baseline transfection reagents), vehicle (1 \u0026micro;g/well pcDNA3.1(+) vector), 0.25 (0.25 \u0026micro;g/well pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e vector), 0.5 (0.5 \u0026micro;g/well pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e vector) and 1 (1 \u0026micro;g/well pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e vector) \u003cb\u003e(Materials and Methods)\u003c/b\u003e. We found that transcript expression levels of \u003cem\u003eCRHR1\u003c/em\u003e, the protein-coding gene corresponding to the lncRNA \u003cem\u003eCRHR1-IT1\u003c/em\u003e (\u003cem\u003eCRHR1\u003c/em\u003e intronic transcript 1), were markedly altered after transfection with pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e in OVCAR-3 cells \u003cb\u003e(Supplementary Fig.\u0026nbsp;10)\u003c/b\u003e. By performing a weighted gene co-expression network analysis (WGCNA)\u003csup\u003e\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e, we identified 9 modules in OVCAR-3 cells and assessed the correlation between the expression of genes within these modules and the lncRNA \u003cem\u003eCRHR1-IT1\u003c/em\u003e \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. The \u0026lsquo;green\u0026rsquo; and \u0026lsquo;brown\u0026rsquo; modules exhibited the most significant negative and positive correlations, respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Of the 357 genes in the \u0026lsquo;green\u0026rsquo; module, 309 were identified as down-regulated in response to \u003cem\u003eCRHR1-IT1\u003c/em\u003e, whereas 956 of the 1,251 genes in the \u0026lsquo;brown\u0026rsquo; module were up-regulated by \u003cem\u003eCRHR1-IT1\u003c/em\u003e \u003cb\u003e(Supplementary Table\u0026nbsp;12)\u003c/b\u003e. Functional analysis of these genes indicated enrichment in biological processes regulating cell growth and ovarian function, such as cell cycle, apoptosis, and oocyte meiosis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD, we observed that 309 down-regulated genes exibhited a dramatic decrease in expression at 24 hours, while 956 up-regulated genes showed observable increase in expression at 48 hours, following transfection with pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e in OVCAR-3 cells, compared to the control, mock, and vehicle conditions. Interestingly, the up-regulated genes were likely involved in regulating cell death processes, such as the positive regulation of apoptotic process, whereas the down-regulated genes were enriched in cell cycle processes \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. These findings suggest that \u003cem\u003eCRHR1-IT1\u003c/em\u003e likely regulates HGSOC cell growth by initially inhibiting cell cycle progression and subsequently promoting apoptosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNext, we identified potential transcription factors (TFs) that might regulate \u003cem\u003eCRHR1-IT1\u003c/em\u003e by combining results from multiple analytical approaches. First, using TRRUST v2 database\u003csup\u003e\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e\u003c/sup\u003e, we found 37 enriched TFs, such as \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003eATF6\u003c/em\u003e, and \u003cem\u003eJUN\u003c/em\u003e \u003cb\u003e(Supplementary Fig.\u0026nbsp;11A)\u003c/b\u003e, likely regulators of those 1,265 genes showing \u003cem\u003eCRHR1-IT1\u003c/em\u003e-associated expression. Second, we prioritized \u003cem\u003eCRHR1-IT1\u003c/em\u003e-associated genes by assessing gene significance (GS) and module membership (MM) using WGCNA. Our analysis identified 48 key genes from the \u0026lsquo;green\u0026rsquo; and \u0026lsquo;brown\u0026rsquo; modules, which showed the strongest expression association with \u003cem\u003eCRHR1-IT1\u003c/em\u003e \u003cb\u003e(Supplementary Fig.\u0026nbsp;11B and Supplementary Table\u0026nbsp;13)\u003c/b\u003e. Third, we performed a sequence-based interaction prediction for \u003cem\u003eCRHR1-IT1\u003c/em\u003e using AnnoLnc2\u003csup\u003e122\u003c/sup\u003e, revealing 337 genes that may interact with \u003cem\u003eCRHR1-IT1\u003c/em\u003e \u003cb\u003e(Supplementary Table\u0026nbsp;14)\u003c/b\u003e. We then evaluated the overlap of predicted TFs for \u003cem\u003eCRHR1-IT1\u003c/em\u003e, and \u003cem\u003eJUN\u003c/em\u003e was the only TF present in all three analyses \u003cb\u003e(Supplementary Fig.\u0026nbsp;11C)\u003c/b\u003e. In addition, ChIP-seq data from various cell types confirmed that \u003cem\u003eJUN\u003c/em\u003e binds to \u003cem\u003eCRHR1-IT1\u003c/em\u003e, highlighting its potential regulatory role \u003cb\u003e(Supplementary Table\u0026nbsp;15)\u003c/b\u003e\u003csup\u003e\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e\u003c/sup\u003e. Our RNA-seq data and qRT-PCR results further suggested a strong positive correlation between \u003cem\u003eCRHR1-IT1\u003c/em\u003e and \u003cem\u003eJUN\u003c/em\u003e expression (Pearson\u0026rsquo;s correlation\u0026thinsp;=\u0026thinsp;0.92, two-sided P-value\u0026thinsp;=\u0026thinsp;1.32E-10; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF \u003cb\u003eand Supplementary Fig.\u0026nbsp;12\u003c/b\u003e). \u003cem\u003eJUN\u003c/em\u003e, a crucial transcription factor, which has been previously reported to participate in diverse biological processes including apoptosis\u003csup\u003e\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e, \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e, \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e, \u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e, \u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e, \u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e, \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e\u003c/sup\u003e, was also found to be significantly down-regulated in HGSOC samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e. Subsequent network analysis using the ConsensusPathDB\u003csup\u003e\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u003c/sup\u003e molecular interaction data from 31 public repositories (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cpdb.molgen.mpg.de\u003c/span\u003e\u003cspan address=\"http://cpdb.molgen.mpg.de\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) revealed that \u003cem\u003eJUN\u003c/em\u003e potentially regulate several apoptosis-related genes, such as \u003cem\u003eATF3\u003c/em\u003e, \u003cem\u003eDDIT3\u003c/em\u003e, \u003cem\u003eTP53, BRCA1\u003c/em\u003e, and \u003cem\u003eRARG\u003c/em\u003e \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH\u003cb\u003e)\u003c/b\u003e. Notably, \u003cem\u003eATF3\u003c/em\u003e has been previously reported to promote apoptosis in cancer cells\u003csup\u003e\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e, \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e\u003c/sup\u003e. These findings suggest that \u003cem\u003eCRHR1-IT1\u003c/em\u003e may co-express with \u003cem\u003eJUN\u003c/em\u003e to regulate apoptosis in HGSOC cells.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAn increase in\u003c/b\u003e \u003cb\u003eCRHR1-IT1\u003c/b\u003e \u003cb\u003eexpression promotes apoptosis of HGSOC cells\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo determine whether \u003cem\u003eCRHR1-IT1\u003c/em\u003e plays a role in the development of HGSOC cells, we performed a gradient transfection experiment in OVCAR-3 cells. \u003cem\u003eCRHR1-IT1\u003c/em\u003e expression was significantly increased after transfection with pcDNA3.1(+)-\u003cem\u003eCRHR1-IT1\u003c/em\u003e in HGSOC cells \u003cb\u003e(Supplementary Figs.\u0026nbsp;10A-10B)\u003c/b\u003e. Next, we used IncuCyte SX5 and Caspase-3/7 Green Detection Reagent to measure the fluorescence-activated cell intensity in transfected OVCAR\u0026shy;3 cells. We observed a dramatic increase in the proportion of apoptotic cells with higher transfection concentrations \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. And the rate of apoptosis accelerated as the concentration increased at 24 and 48 hours after transfection \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. We also found that lncRNA \u003cem\u003eCRHR1-IT1\u003c/em\u003e slowed down the growth rate of non-apoptotic cells \u003cb\u003e(Supplementary Fig.\u0026nbsp;13)\u003c/b\u003e. Moreover, our qRT-PCR results further confirmed that the expression of genes known to play important roles in apoptosis, including \u003cem\u003eFAS\u003c/em\u003e, \u003cem\u003eCMYC\u003c/em\u003e, and \u003cem\u003ePARP1\u003c/em\u003e, were significantly correlated with lncRNA \u003cem\u003eCRHR1-IT1\u003c/em\u003e expression \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE-\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eG\u003cb\u003e)\u003c/b\u003e. These results suggested that elevated \u003cem\u003eCRHR1-IT1\u003c/em\u003e expression could promote apoptosis in OVCAR-3 cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn summary, our findings highlight the significant role of the lncRNA \u003cem\u003eCRHR1-IT1\u003c/em\u003e in promoting apoptosis and inhibiting the growth of OVCAR-3 cells, suggesting that \u003cem\u003eCRHR1-IT1\u003c/em\u003e could be a promising new therapeutic target for HGSOC.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we defined genotype-driven expression changes for both PCGs (PCG-eQTL) and lncRNAs (lncRNA-eQTL). We demonstrated that lncRNAs account for a relatively higher portion of HGSOC heritability compared to PCGs. Our Bayesian-test-based colocalization analysis prioritized 10 HGSOC susceptibility genes, such as \u003cem\u003eMAPT\u003c/em\u003e, \u003cem\u003eCHMP4C\u003c/em\u003e, \u003cem\u003eKANSL1-AS1\u003c/em\u003e, and \u003cem\u003eCRHR1-IT1\u003c/em\u003e, in HGSOC tumors. We also identified 17 putative causal PCGs and lncRNAs in whole blood, which could contribute to the early detection and immunotherapy for HGSOC. Using the single-nucleus transcriptome map generated by snRandom-seq for the human HGSOC tumor, we further characterized the cell-type-dependent expression patterns of the candidate susceptibility PCGs and lncRNAs for HGSOC. Finally, we focused on the lncRNA \u003cem\u003eCRHR1-IT1\u003c/em\u003e and demonstrated its crucial role in promoting apoptosis in HGSOC cells.\u003c/p\u003e \u003cp\u003eWe believe this study, which estimates genotype-driven expression variations of HGSOC susceptibility lncRNAs, is unique. We defined lncRNA-eQTLs to understand the genotype effect from germline variants on the expression of lncRNAs. Our findings suggest that lncRNA expression changes could explain more variations in HGSOC heritability than PCGs. It is noteworthy that a considerable proportion of expression variations associated with HGSOC predisposition are driven by underlying germline variants for both lncRNAs and PCGs. We illustrated that the germline variant rs35478347 influenced the expression of the protein-coding gene \u003cem\u003eDHX58\u003c/em\u003e, one of the differentially expressed genes between HGSOC tumors and healthy samples. \u003cem\u003eDHX58\u003c/em\u003e encodes for the RIG-I-like receptor LGP2, which has been previously reported to suppress RIG-I signaling\u003csup\u003e\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e\u003c/sup\u003e and protect tumor cells from ionizing radiation\u003csup\u003e\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e, \u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e\u003c/sup\u003e. These findings indicate that it will be critical to integrate genotype-driven expression changes of lncRNAs and PCGs into future DEG identification to distinguish between genetically and environmentally driven expression differences.\u003c/p\u003e \u003cp\u003eWe integrated HGSOC-associated germline variants (from GWAS) and genotype-driven expression changes of PCGs (PCG-eQTLs) and lncRNAs (lncRNA-eQTLs) to prioritize putative causal genes for HGSOC. Our initial overlapping results of HGSOC GWAS and eQTL data indicate that approximately 31% of putative susceptibility genes for HGSOC are shared with breast cancer, including \u003cem\u003eMAPT\u003c/em\u003e, \u003cem\u003eLGALS7\u003c/em\u003e and \u003cem\u003eERVK13-1\u003c/em\u003e, with a strong enrichment on chromosome 17. Using Bayesian-test-based colocalization analysis, we identified 10 susceptibility genes for HGSOC with high fidelity. Notably, all 6 identified PCGs\u0026mdash;\u003cem\u003eCHMP4C\u003c/em\u003e, \u003cem\u003eARL17A\u003c/em\u003e, \u003cem\u003eARL17B\u003c/em\u003e, \u003cem\u003eLRRC37A\u003c/em\u003e, \u003cem\u003eLRRC37A2\u003c/em\u003e, and \u003cem\u003eMAPT\u003c/em\u003e\u0026mdash;showed genotype-driven expression changes associated with HGSOC predisposition in our study, which were successfully replicated in a previous study using a different statistical method, TWAS\u003csup\u003e\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e\u003c/sup\u003e. We demonstrate that further investigations on the identified colocalized genes in whole blood provide new insights into early detection and immunotherapy for HGSOC. This is best illustrated by the shared colocalized genes, such as \u003cem\u003eARL17A\u003c/em\u003e, \u003cem\u003eLRRC37A2, KANSL1-AS1\u003c/em\u003e, and \u003cem\u003eCRHR1-IT1\u003c/em\u003e, whose expression and HGSOC predisposition are not only associated but also share common genetic causal variants in both tumor and whole blood. Interestingly, these observations were confirmed using the snRandom-seq data of the human HGSOC tumor, which could reveal more comprehensive expression patterns of lncRNAs at cellular resolution compared to other scRNA-seq technologies.\u003c/p\u003e \u003cp\u003eFurthermore, our study highlighted multiple lines of converging evidence suggesting a potential protective role for \u003cem\u003eCRHR1-IT1\u003c/em\u003e in HGSOC development. We illustrate that elevated \u003cem\u003eCRHR1-IT1\u003c/em\u003e expression could promote apoptosis and inhibit the growth of OVCAR-3 cells.\u003c/p\u003e \u003cp\u003eIn conclusion, we identified genotype-driven expression changes of both PCGs and lncRNAs in HGSOC tumors. Our study highlights the importance of integrating lncRNA-eQTLs to understand the genetic underpinning of HGSOC pathogenesis. We illustrate the putative role of the lncRNA \u003cem\u003eCRHR1-IT1\u003c/em\u003e in HGSOC development and suggest that lncRNAs could serve as novel therapeutic targets for HGSOC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosure and competing interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenotype, quantified gene expression data, and clinical records of the 350 HGSOC tumor samples used in this study comply with ethical regulations. Approval and informed consent for collection and sharing were already obtained by the relevant consortia from TCGA. HGSOC tumor specimens for snRandom-seq were obtained from the Biobank of the Zhejiang Cancer Hospital, and the sample collection was approved by the Ethics Committee of the Zhejiang Cancer Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMatched genotype and quantified gene expression data for 350 primary HGSOC tumor samples were downloaded from the GDC Data Portal as well (http://www.cancer.gov/ccg/research/genome-sequencing/tcga). The recomputed gene expression data in the ovaries of the TCGA HGSOC patients and GTEx healthy samples were downloaded from UCSC Xena (http://xena.ucsc.edu). The eQTL data for lncRNAs and PCGs are available via a searchable public website https://zenodo.org/records/13760103. RNA sequencing data of OVCAR-3 cells and human HGSOC tumor snRandom-seq data generated in this study are publicly available in the GSA database (https://ngdc.cncb.ac.cn/gsa-human/) under accession numbers HRA009466\u003csup\u003e137, 138\u003c/sup\u003e and HRA008122, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China [32300534, 32270659 to X.S., 32371537 to Y.K., and 82304907 to G.J.], the Open Project of Jiangsu Provincial Science and Technology Resources (Clinical Resources) Coordination Service Platform [TC2023006 to X.S.], the Key R\u0026amp;D Program of Zhejiang Province [2024SSYS0021 to X.S.], and the hundred talents program of Zhejiang University to X.S.; the Youth Innovation Promotion Association of Chinese Academy of Science [2022098 to J.Z.]; We thank the data resources of HGSOC tumor samples provided by the Cancer Genome Atlas (TCGA, http://cancergenome.nih.gov) managed by the NCI and NHGRI. We thank the technical support from the Core Facilities, Liangzhu Laboratory, Zhejiang University. We thank Dr. Chunjie Jiang and Dr. Fan Wu for assistance in lncRNA characterization. We thank Biorender (\u0026copy; BioRender \u0026ndash; biorender.com) for support in schematic representations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.S. designed the study and supervised the computational analyses. X.S. and Y.K. designed and supervised the experimental validation in cell lines. X.S. and J.G. designed and supervised the snRandom-seq on HGSOC samples with the help of Y. W.. X.S., M.L., D.Z. and C.G. performed computational analyses and manuscript writing. Y.C., Z.Z. and S.C. performed the experimental validation of \u003cem\u003eCRHR1-IT1\u003c/em\u003e in cell lines and manuscript writing with the assisstance of F.K.-M. C., X. F., N. K., S. L.. and M.L. analyzed data with the help of C.B., Q.Q., C.G., M.J., H.Z., J.W., J. Z., and Z. Z.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A\u003cem\u003e, et al.\u003c/em\u003e Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. \u003cem\u003eCA: a cancer journal for clinicians\u003c/em\u003e 2021, \u003cstrong\u003e71\u003c/strong\u003e(3)\u003cstrong\u003e:\u003c/strong\u003e 209-249.\u003c/li\u003e\n \u003cli\u003eRisch HA, Marrett LD, Jain M, Howe GR. 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Database resources of the national genomics data center, China National Center for Bioinformation in 2022. \u003cem\u003eNucleic Acids Research\u003c/em\u003e 2022, \u003cstrong\u003e50\u003c/strong\u003e(D1)\u003cstrong\u003e:\u003c/strong\u003e D27.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"eQTL, colocalization, lncRNA, post-GWAS, CRHR1-IT1, ovarian cancer","lastPublishedDoi":"10.21203/rs.3.rs-6929868/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6929868/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNew effective early diagnosis strategies and therapeutic targets are desperately needed for high-grade serous ovarian cancer (HGSOC). Previous functional interpretation of HGSOC genome-wide association studies (GWAS) largely neglected long non-coding RNAs (lncRNAs). Here, we map genotype effects on the expression of both protein-coding genes (PCGs) and lncRNAs in 348 HGSOC tumor samples. We show that underlying genetic variants are significant drivers of expression changes for both PCGs and lncRNAs. Importantly, lncRNAs account for a higher portion of HGSOC heritability than PCGs. Bayesian colocalization analysis prioritized causal genes in HGSOC tumors and whole blood, such as \u003cem\u003eARL17A\u003c/em\u003e and \u003cem\u003eCRHR1-IT1\u003c/em\u003e. Using single-nucleus transcriptome maps of HGSOC tumors generated by snRandom-seq, we characterized expression patterns of candidate susceptibility genes. Furthermore, we validated the role of \u003cem\u003eCRHR1-IT1\u003c/em\u003e in promoting apoptosis and inhibiting the growth of HGSOC cells. Our study provides new insights into early detection and immunotherapy development for HGSOC.\u003c/p\u003e","manuscriptTitle":"Genotype-driven variations in lncRNA expression underlie predisposition to high-grade serous ovarian cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-24 08:37:56","doi":"10.21203/rs.3.rs-6929868/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"69e76594-ab9b-4a05-b091-001952cf3e4f","owner":[],"postedDate":"June 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":50348318,"name":"Health sciences/Biomarkers"},{"id":50348319,"name":"Biological sciences/Cancer/Gynaecological cancer"}],"tags":[],"updatedAt":"2025-06-24T13:49:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-24 08:37:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6929868","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6929868","identity":"rs-6929868","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}