GWAS meta-analysis identifies five susceptibility loci for endometrial cancer

doi:10.1016/j.ebiom.2025.105830

. 2025 Aug:118:105830.

doi: 10.1016/j.ebiom.2025.105830. Epub 2025 Jul 8.

GWAS meta-analysis identifies five susceptibility loci for endometrial cancer

Dhanya Ramachandran¹, Xuemin Wang², Triin Laisk³, Ying Zheng⁴, Nathan Ingold², Daffodil M Canson⁵, Pik Fang Kho², Bianca J Naumann⁶, Carly J Chapman², Kristine Bousset¹, Anna V Krause¹, Peter Schürmann¹, Britta Wieland¹, Patricia Hanel¹, Fabienne Hülse¹, Norman Häfner⁷, Ingo Runnebaum⁸, Natalia Dubrowinskaja⁹, Nurzhan Turmanov¹⁰, Tatyana Yugay¹¹, Zura Berkutovna Yessimsiitova¹², Frédéric Amant¹³, Daniela Annibali¹⁴, Matthias W Beckmann¹⁵, Clara Bodelon¹⁶, Daniel D Buchanan¹⁷, Chu Chen¹⁸, Megan A Clarke¹⁹, Linda S Cook²⁰, Immaculata De Vivo²¹, Wout De Wispelaere¹⁴, Mengmeng Du²², Douglas F Easton²³, Julius Emons¹⁵, Peter A Fasching¹⁵, Christine M Friedenreich²⁴, Grace Gallagher²², Graham G Giles²⁵, Ellen L Goode²⁶, Holly R Harris²⁷, David J Hunter²⁸, David L Kolin²⁹, Peter Kraft³⁰, James V Lacey³¹, Diether Lambrechts³², Lingeng Lu³³, George L Mutter³⁴, Jeffin Naduparambil²², Kelli O'Connell²², Alpa V Patel¹⁶, Paul D P Pharoah³⁵, Timothy R Rebbeck³⁶, Fulvio Ricceri³⁷, Harvey A Risch³³, Matthias Ruebner¹⁵, Carlotta Sacerdote³⁸, Rodney J Scott³⁹, V Wendy Setiawan⁴⁰, Xiao-Ou Shu⁴¹, Melissa C Southey⁴², Emma Tham⁴³, Ian Tomlinson⁴⁴, Constance Turman⁴⁵, Nicolas Wentzensen¹⁹, Wanghong Xu⁴⁶, Herbert Yu⁴⁷, Wei Zheng⁴¹, Amanda B Spurdle⁵, Yosef Yarden⁴⁸; Estonian Biobank Research Team³; Reedik Mägi³, Peter Hillemanns¹, Dylan M Glubb⁶, Thilo Dörk⁴⁹, Tracy A O'Mara⁵⁰

Affiliations

¹ Gynaecology Research Unit, Hannover Medical School, Hannover, Germany.
² Cancer Research Program, QIMR Berghofer, Brisbane, Queensland, Australia.
³ Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.
⁴ Gynaecology Research Unit, Hannover Medical School, Hannover, Germany; Department of Gynecology, The First Hospital of Hebei Medical University, Shijiazhuang, China.
⁵ Population Health Program, QIMR Berghofer, Brisbane, Queensland, Australia.
⁶ Cancer Research Program, QIMR Berghofer, Brisbane, Queensland, Australia; School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
⁷ Department of Gynaecology, Jena University Hospital - Friedrich Schiller University, Jena, Germany.
⁸ Department of Gynaecology, Jena University Hospital - Friedrich Schiller University, Jena, Germany; RU21 GmbH, Jena, Germany.
⁹ Gynaecology Research Unit, Hannover Medical School, Hannover, Germany; Clinic for Rheumatology and Immunology, Hannover Medical School, Hannover, Germany.
¹⁰ Gynaecology Research Unit, Hannover Medical School, Hannover, Germany; Rahat Clinics, Almaty, Kazakhstan; KRH Clinic Neustadt am Rübenberge, Neustadt am Rübenberge, Germany.
¹¹ Rahat Clinics, Almaty, Kazakhstan.
¹² Department of Biodiversity and Bioresources, al-Farabi Kazakh National University, Almaty, Kazakhstan.
¹³ Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University Hospitals KU Leuven, University of Leuven, Leuven, Belgium; Gynecological Oncology Laboratory, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium.
¹⁴ Gynecological Oncology Laboratory, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium.
¹⁵ Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany.
¹⁶ Department of Population Science, American Cancer Society, Atlanta, GA, USA.
¹⁷ Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia; Collaborative Centre for Genomic Cancer Medicine, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia.
¹⁸ Epidemiology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA.
¹⁹ Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
²⁰ Epidemiology, School of Public Health, University of Colorado, Aurora, CO, USA; Community Health Sciences, University of Calgary, Calgary, AB, Canada.
²¹ Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
²² Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
²³ Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK; Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.
²⁴ Department of Cancer Epidemiology and Prevention Research, Alberta Health Services, Calgary, AB, Canada; Departments of Oncology and Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
²⁵ Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
²⁶ Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
²⁷ Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Epidemiology, University of Washington, Seattle, WA, USA.
²⁸ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Nuffield Department of Population Health, University of Oxford, Oxford, UK.
²⁹ Brigham and Women's Hospital, Boston, MA, USA.
³⁰ Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
³¹ Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, USA; City of Hope Comprehensive Cancer Center, City of Hope, Duarte, CA, USA.
³² Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium; VIB Center for Cancer Biology, VIB, Leuven, Belgium.
³³ Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA.
³⁴ Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
³⁵ Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, USA.
³⁶ Harvard T.H. Chan School of Public Health, Boston, MA, USA; Dana-Farber Cancer Institute, Boston, MA, USA.
³⁷ Department of Clinical and Biological Sciences, Centre for Biostatistics, Epidemiology, and Public Health (C-BEPH), University of Turin, Turin, Italy.
³⁸ Unit of Epidemiology, Local Health Unit of Novara, Novara, Italy; Department of Health Sciences, University of Eastern Piedmont, Novara, Italy.
³⁹ Division of Molecular Medicine, Pathology North, John Hunter Hospital, Newcastle, New South Wales, Australia; Discipline of Medical Genetics, School of Biomedical Sciences and Pharmacy, Faculty of Health, University of Newcastle, Callaghan, New South Wales, Australia; Hunter Medical Research Institute, John Hunter Hospital, Newcastle, New South Wales, Australia.
⁴⁰ Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
⁴¹ Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA.
⁴² Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia; Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia.
⁴³ Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; Clinical Genetics and Genomics, Karolinska University Hospital, Stockholm, Sweden.
⁴⁴ Department of Oncology, University of Oxford, Oxford, UK.
⁴⁵ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
⁴⁶ School of Public Health, Fudan University, Shanghai, China.
⁴⁷ Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA.
⁴⁸ Weizmann Institute of Science, Rehovot, Israel.
⁴⁹ Gynaecology Research Unit, Hannover Medical School, Hannover, Germany. Electronic address: Doerk.Thilo@mh-hannover.de.
⁵⁰ Cancer Research Program, QIMR Berghofer, Brisbane, Queensland, Australia; School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia. Electronic address: Tracy.OMara@qimrb.edu.au.

PMID: 40633141
PMCID: PMC12275056
DOI: 10.1016/j.ebiom.2025.105830

GWAS meta-analysis identifies five susceptibility loci for endometrial cancer

Dhanya Ramachandran et al. EBioMedicine. 2025 Aug.

. 2025 Aug:118:105830.

doi: 10.1016/j.ebiom.2025.105830. Epub 2025 Jul 8.

Authors

Affiliations

¹ Gynaecology Research Unit, Hannover Medical School, Hannover, Germany.
² Cancer Research Program, QIMR Berghofer, Brisbane, Queensland, Australia.
³ Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia.
⁴ Gynaecology Research Unit, Hannover Medical School, Hannover, Germany; Department of Gynecology, The First Hospital of Hebei Medical University, Shijiazhuang, China.
⁵ Population Health Program, QIMR Berghofer, Brisbane, Queensland, Australia.
⁶ Cancer Research Program, QIMR Berghofer, Brisbane, Queensland, Australia; School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia.
⁷ Department of Gynaecology, Jena University Hospital - Friedrich Schiller University, Jena, Germany.
⁸ Department of Gynaecology, Jena University Hospital - Friedrich Schiller University, Jena, Germany; RU21 GmbH, Jena, Germany.
⁹ Gynaecology Research Unit, Hannover Medical School, Hannover, Germany; Clinic for Rheumatology and Immunology, Hannover Medical School, Hannover, Germany.
¹⁰ Gynaecology Research Unit, Hannover Medical School, Hannover, Germany; Rahat Clinics, Almaty, Kazakhstan; KRH Clinic Neustadt am Rübenberge, Neustadt am Rübenberge, Germany.
¹¹ Rahat Clinics, Almaty, Kazakhstan.
¹² Department of Biodiversity and Bioresources, al-Farabi Kazakh National University, Almaty, Kazakhstan.
¹³ Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University Hospitals KU Leuven, University of Leuven, Leuven, Belgium; Gynecological Oncology Laboratory, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium.
¹⁴ Gynecological Oncology Laboratory, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium.
¹⁵ Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany.
¹⁶ Department of Population Science, American Cancer Society, Atlanta, GA, USA.
¹⁷ Colorectal Oncogenomics Group, Department of Clinical Pathology, Melbourne Medical School, The University of Melbourne, Melbourne, Victoria, Australia; Collaborative Centre for Genomic Cancer Medicine, Victorian Comprehensive Cancer Centre, Parkville, Victoria, Australia.
¹⁸ Epidemiology Program, Fred Hutchinson Cancer Center, Seattle, WA, USA.
¹⁹ Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
²⁰ Epidemiology, School of Public Health, University of Colorado, Aurora, CO, USA; Community Health Sciences, University of Calgary, Calgary, AB, Canada.
²¹ Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
²² Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center, New York, NY, USA.
²³ Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK; Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, UK.
²⁴ Department of Cancer Epidemiology and Prevention Research, Alberta Health Services, Calgary, AB, Canada; Departments of Oncology and Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.
²⁵ Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia; Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia.
²⁶ Division of Epidemiology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.
²⁷ Program in Epidemiology, Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA; Department of Epidemiology, University of Washington, Seattle, WA, USA.
²⁸ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Nuffield Department of Population Health, University of Oxford, Oxford, UK.
²⁹ Brigham and Women's Hospital, Boston, MA, USA.
³⁰ Division of Cancer Epidemiology and Genetics, Department of Health and Human Services, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
³¹ Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, USA; City of Hope Comprehensive Cancer Center, City of Hope, Duarte, CA, USA.
³² Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium; VIB Center for Cancer Biology, VIB, Leuven, Belgium.
³³ Chronic Disease Epidemiology, Yale School of Public Health, New Haven, CT, USA.
³⁴ Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
³⁵ Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, USA.
³⁶ Harvard T.H. Chan School of Public Health, Boston, MA, USA; Dana-Farber Cancer Institute, Boston, MA, USA.
³⁷ Department of Clinical and Biological Sciences, Centre for Biostatistics, Epidemiology, and Public Health (C-BEPH), University of Turin, Turin, Italy.
³⁸ Unit of Epidemiology, Local Health Unit of Novara, Novara, Italy; Department of Health Sciences, University of Eastern Piedmont, Novara, Italy.
³⁹ Division of Molecular Medicine, Pathology North, John Hunter Hospital, Newcastle, New South Wales, Australia; Discipline of Medical Genetics, School of Biomedical Sciences and Pharmacy, Faculty of Health, University of Newcastle, Callaghan, New South Wales, Australia; Hunter Medical Research Institute, John Hunter Hospital, Newcastle, New South Wales, Australia.
⁴⁰ Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
⁴¹ Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA.
⁴² Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia; Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia.
⁴³ Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden; Clinical Genetics and Genomics, Karolinska University Hospital, Stockholm, Sweden.
⁴⁴ Department of Oncology, University of Oxford, Oxford, UK.
⁴⁵ Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
⁴⁶ School of Public Health, Fudan University, Shanghai, China.
⁴⁷ Epidemiology Program, University of Hawaii Cancer Center, Honolulu, HI, USA.
⁴⁸ Weizmann Institute of Science, Rehovot, Israel.
⁴⁹ Gynaecology Research Unit, Hannover Medical School, Hannover, Germany. Electronic address: Doerk.Thilo@mh-hannover.de.
⁵⁰ Cancer Research Program, QIMR Berghofer, Brisbane, Queensland, Australia; School of Biomedical Sciences, Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia. Electronic address: Tracy.OMara@qimrb.edu.au.

PMID: 40633141
PMCID: PMC12275056
DOI: 10.1016/j.ebiom.2025.105830

Abstract

Background: Endometrial cancer is the most common gynaecological cancer in high-income countries. In addition to environmental risk factors, genetic predisposition contributes towards endometrial cancer development but is still incompletely defined.

Methods: Building on genome-wide association studies (GWASs) by the Endometrial Cancer Association Consortium, we conducted a GWAS meta-analysis of 17,278 endometrial cancer cases and 289,180 controls, incorporating biobank samples from the UK, Finland, Estonia and Japan.

Findings: GWAS analysis identified five additional risk loci (3p25.2, 3q25.2, 6q22.31, 12q21.2, and 17q24.2). Corresponding gene-based analyses supported findings for three of the five loci, at NAV3 (12q21.2), PPARG (3p25.2), and BPTF (17q24.2), as well as two additional candidate risk regions at ATF7IP2 (16p13.2-p13.13) and RPP21 (6p22.1). Validation genotyping in further independent case-control series replicated the most significant locus at 12q21.2 and corroborated risk variants located intronic to NAV3, the gene for Neuron Navigator 3. Downregulation of NAV3 in endometrial cell lines accelerated cell division and wound healing capacity whereas NAV3 overexpression reduced cell survival and increased cell death, indicating that NAV3 acts as a tumour suppressor in endometrial cells.

Interpretation: Our large study extends the number of genome-wide significant risk loci identified for endometrial carcinoma by about one-third and proposes a role of NAV3 as a tumour suppressor in this common cancer.

Funding: This study was mainly supported by funding from the Wilhelm Sander Foundation, Germany, and the National Health and Medical Research Council (NHMRC) of Australia. A complete list of funding organisations is provided in the acknowledgements.

Keywords: Endometrial carcinoma; GWAS; Luciferase; NAV3; eQTL.

PubMed Disclaimer

Conflict of interest statement

Declaration of interests D. Ramachandran received intramural funding from Hannover Medical School. M. Clark. has stock options in AbbVie and is employed by AbbVie on work not related to current manuscript. 1U01CA250476-01A1 to I. De Vivo and G. L. Mutter. NCI P30CA008748, U01CA250476 to M. Du. J. Emons receives honoraria for lectures from Pfizer, Eisai, AstraZeneca, Novartis, participates in the advisory board for these and MSD, receives travel money from AstraZeneca. Grant awards 3U01-CA199277-07S1 and 3U01-CA199277-08S1 from the NCI to J. V. Lacey for whole genome sequencing for participants in the California Teachers Study, which contributed data to this manuscript. P. A. Fasching receives grants from BioNTech, Cepheid, Pfizer; consulting fees, honoraria for lectures, and participates in an advisory board for Novartis, Pfizer, Roche, Daiichi-Sankyo, AstraZeneca, Lilly, Eisai, Merck Sharp & Dohme, Pierre Fabre, SeaGen, Agendia, Sanofi Aventis, Gilead, Mylan. D. L. Kolin holds stocks of Abbott laboratories, Alcon Inc., Becton Dickinson, Novartis, Pfizer, and UnitedHealth Group. G. L. Mutter received consulting fees from Bayer as personal consultant in pathology to perform diagnostic safety reads for clinical trials. A. V. Patel is on the NCI board of scientific counsellors. NIH funding to H. Risch. E. Tham received grants from Region Stockholm, the Swedish Childhood Cancer Fund (Barncancerfonden) and the Swedish Cancer Fund (Cancerfonden). E. Tham is a board member of Anna Dahlbäck’s memorial fund. Grants from CRUK and Genome Canada to D. Easton. NIH grant to P. Kraft. CRUK grants to P. Pharoah. D. Lambrechts receives annual funding from VIB. D. Glubb received payment from the University of Sharjah for grant reviewing. Grant from Wilhelm Sander Foundation to T. Dörk and P. Hillemanns. T. A. O’Mara received funding from the US Department of Defence and Worldwide Cancer Research. None of the sponsors had any role in the design, data generation or result interpretation in this study. All other authors declare no competing interests.

Figures

**Fig. 1**
**GWAS meta-analysis for endometrial cancer.** (a) Workflow showing the different studies that contributed summary statistics and QC steps involved, (b) Manhattan plot displaying chromosomes on the x-axis in alternating black and light grey tones, with -log₁₀ p values after meta-analysis on the y-axis, genome-wide significance threshold is set at 5 × 10⁻⁸ in a red line, and variants underlying the five genomic loci identified in this study are coloured as red dots, (c) Quantile-quantile plot of expected versus observed p values from the GWAS summary statistics, (d) Manhattan plot after genome-wide gene-based MAGMA analysis displays chromosomes on the x-axis in alternating black and light grey hues, with -log₁₀ p values on the y-axis. Genome-wide significance threshold is set to 2.45 × 10⁻⁶ and all significant genes above this threshold are labelled, with the gene names coloured in red and blue. Loci that only come up in the MAGMA analysis are labelled in red whereas loci that were identified in both the meta-analysis and MAGMA analysis are in blue.

**Fig. 2**
**Five genomic loci for endometrial cancer.** LocusZoom plots at (a) 3p25.2, (b) 3q26.2, (c) 6q22.31, (d) 12q21.2, and (e) 17q24.2 show chromosomal position on the x-axis, genes located within 1 Mbp of the lead SNP, −log₁₀ p values on the left y-axis, recombination rate on the right y-axis, and linkage with the lead SNP is colour coded according to the legend (r² ≥ 0.8 in red, 0.6–08 in yellow, 0.4–0.6 in green, 0.2–0.4 in light blue and 0–0.2 in dark blue).

**Fig. 3**
**Functional genetic analysis of the 12q21.2 risk locus.** (a) Mapping of credible risk variants, protein coding *NAV3* transcripts and promoter/enhancer HiChIP looping data at the 12q21.2 locus reveals colocalisation of risk variants with a HiChIP anchor that loops to the promoter region of a *NAV3* transcript (ENST00000552895.5). (b) Zooming into the highlighted box in Panel A, this panel illustrates that a risk variant (rs1842126) within the HiChIP anchor localises to a putative *NAV3* enhancer identified in endometrial tumours. Panels (c) and (d) show normalised luciferase activity from reporter gene assays involving the empty pGL3-Basic vector, a reporter gene construct containing the promoter of the ENST00000552895.5 *NAV3* isoform, and *NAV3* promoter reporter gene constructs with the putative enhancer carrying risk or reference alleles of rs1842126 in E6E7hTERT and Ishikawa cells, respectively. Error bars denote 95% confidence intervals for experiments conducted in triplicate. p values were determined by two-way ANOVA followed by Dunnett’s multiple comparisons test (∗p < 0.05, ∗∗∗p < 0.001).

**Fig. 4**
**Functional assessment after *NAV3* silencing in Ishikawa and E6E7hTERT cells.** (a) Fold changes after qRT-PCR for *NAV3* levels in Ishikawa cells (left panel), and E6E7hTERT cells (right panel). Each dot represents a biological replicate. p value indicated after paired t-test, (b) Western blot for NAV3 in Ishikawa cells (left panel), and E6E7hTERT cells (right panel) after silencing using siRNA, together with DNA-PK as housekeeper, high exp indicates higher exposure time, whereas low exp indicates lower exposure time, (c) Mitotic time per cell (in minutes) after silencing *NAV3* in Ishikawa cells (left panel), and E6E7hTERT cells (right panel). Each dot represents a cell in three biological experiments. p value indicated after unpaired t-test, (d) Proportion of cells that finished mitosis on the y-axis, time (in minutes) on the x-axis, after silencing *NAV3* in Ishikawa cells (left panel), and E6E7hTERT cells (right panel). Data shown from three biological experiments, (e) Percentage reduction of scratch area (relative to 0 h) on the y-axis and time (in hours) on the x-axis, after silencing *NAV3* in Ishikawa cells (left panel), and E6E7hTERT cells (right panel). Three biological experiments were performed in Ishikawa cells, and a single biological experiment was taken for E6E7hTERT cells. p value indicated with asterisk in multiple comparisons after ANOVA. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001, (f) Normalised cell index showing impedance in xCELLigence experiments after silencing *NAV3* in Ishikawa cells (left panel), and E6E7hTERT cells (right panel). Time in hours on the x-axis. Data shown from a single biological experiment with five technical replicates in both cell lines. p value indicated with asterisk in multiple comparisons after ANOVA. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001, ∗∗∗∗p < 0.0001, (g) Percentage of dead cells after propidium iodide staining after silencing *NAV3* in Ishikawa cells (left panel), and E6E7hTERT cells (right panel). Treatment on the x-axis. Data shown from four biological experiments in Ishikawa and three biological replicates in E6E7hTERT cells. p value indicated after paired t-test.

**Fig. 5**
**Functional assessment after *NAV3* overexpression in Ishikawa cells.** (a) Fold changes after qRT-PCR for *NAV3* levels in Ishikawa cells transfected with pEGFPN1-NAV3WT and pEGFPN1-NAV3D1047N plasmids versus untreated controls. Each dot represents a biological replicate. p value indicated after paired t-test, (b) Immunoblotting of NAV3 and DNAPK in Ishikawa cells transfected with pEGFPN1, pEGFPN1-NAV3WT and pEGFPN1-NAV3D1047N, (c) Immunocytochemistry to visualize NAV3 overexpression in pEGFPN1-NAV3WT (upper panel) or pEGFPN1-NAV3D1047N (lower panel) transfected Ishikawa cells (NAV3 in red), with DAPI (for nuclear staining, in blue), and GFP fluorescence (in green), with the last panel showing the merged overlay, (d) Average cell numbers after transfecting Ishikawa cells with pEGFPN1-NAV3WT and pEGFPN1-NAV3D1047N plasmids at 72 h after transfection. Each dot represents a biological replicate. p value indicated after paired t-test, (e) Proportion of green cells remaining after transfecting Ishikawa cells with pEGFPN1-NAV3WT and pEGFPN1-NAV3D1047N plasmids. Each dot represents a cell from three biological experiments, with log rank p value shown, (f) Percentage of dead cells after DAPI staining after overexpression of pEGFPN1-NAV3WT and pEGFPN1-NAV3D1047N plasmids in Ishikawa cells. Data shown from four biological experiments. p value indicated after paired t-test, (g) Time to death in non-surviving green cells after transfecting Ishikawa cells with pEGFPN1-NAV3WT and pEGFPN1-NAV3D1047N plasmids. Each dot represents a cell from three biological experiments, with log rank p value shown.

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[1] Sung H., Ferlay J., Siegel R.L., et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–249. https://onlinelibrary.wiley.com/doi/10.3322/caac.21660 Available from: - DOI - PubMed

[2] Sung H., Ferlay J., Siegel R.L., et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209–249. https://onlinelibrary.wiley.com/doi/10.3322/caac.21660 Available from: - DOI - PubMed

[3] Siegel R.L., Miller K.D., Wagle N.S., Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17–48. https://acsjournals.onlinelibrary.wiley.com/doi/10.3322/caac.21763 Available from: - DOI - PubMed

[4] Siegel R.L., Miller K.D., Wagle N.S., Jemal A. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17–48. https://acsjournals.onlinelibrary.wiley.com/doi/10.3322/caac.21763 Available from: - DOI - PubMed

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GWAS meta-analysis identifies five susceptibility loci for endometrial cancer

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GWAS meta-analysis identifies five susceptibility loci for endometrial cancer

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