Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J Clin. 2021;71(3):209–49. https://doi.org/10.3322/caac.21660.

Article  Google Scholar 

Kurman RJ, International agency for research on cancer, world health organization, eds. WHO classification of tumours of female reproductive organs. 4th ed. International agency for research on cancer; 2014.

Gershenson DM, Sun CC, Lu KH, et al. Clinical behavior of stage II-IV low-grade serous carcinoma of the ovary. Obstet Gynecol. 2006;108(2):361–8. https://doi.org/10.1097/01.AOG.0000227787.24587.d1.

Article  PubMed  Google Scholar 

du Bois A, Reuss A, Pujade-Lauraine E, Harter P, Ray-Coquard I, Pfisterer J. Role of surgical outcome as prognostic factor in advanced epithelial ovarian cancer: a combined exploratory analysis of 3 prospectively randomized phase 3 multicenter trials: by the Arbeitsgemeinschaft gynaekologische onkologie studiengruppe ovarialkarzinom (AGO-OVAR) and the groupe d’investigateurs nationaux pour les etudes des cancers de l’Ovaire (GINECO). Cancer. 2009;115(6):1234–44. https://doi.org/10.1002/cncr.24149.

Article  CAS  PubMed  Google Scholar 

Gadducci A, Guarneri V, Peccatori FA, et al. Current strategies for the targeted treatment of high-grade serous epithelial ovarian cancer and relevance of BRCA mutational status. J Ovarian Res. 2019;12(1):9. https://doi.org/10.1186/s13048-019-0484-6.

Article  PubMed  PubMed Central  Google Scholar 

Vergote I, Tropé CG, Amant F, et al. Neoadjuvant chemotherapy or primary surgery in stage IIIC or IV ovarian cancer. N Engl J Med. 2010;363(10):943–53. https://doi.org/10.1056/NEJMoa0908806.

Article  CAS  PubMed  Google Scholar 

Lheureux S, Gourley C, Vergote I, Oza AM. Epithelial ovarian cancer. The Lancet. 2019;393(10177):1240–53. https://doi.org/10.1016/S0140-6736(18)32552-2.

Article  Google Scholar 

Kipps E, Tan DSP, Kaye SB. Meeting the challenge of ascites in ovarian cancer: new avenues for therapy and research. Nat Rev Cancer. 2013;13(4):273–82. https://doi.org/10.1038/nrc3432.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zeppernick F, Meinhold-Heerlein I. The new FIGO staging system for ovarian, fallopian tube, and primary peritoneal cancer. Arch Gynecol Obstet. 2014;290(5):839–42. https://doi.org/10.1007/s00404-014-3364-8.

Article  CAS  PubMed  Google Scholar 

Naora H, Montell DJ. Ovarian cancer metastasis: integrating insights from disparate model organisms. Nat Rev Cancer. 2005;5(5):355–66. https://doi.org/10.1038/nrc1611.

Article  CAS  PubMed  Google Scholar 

Ford CE, Werner B, Hacker NF, Warton K. The untapped potential of ascites in ovarian cancer research and treatment. Br J Cancer. 2020;123(1):9–16. https://doi.org/10.1038/s41416-020-0875-x.

Article  PubMed  PubMed Central  Google Scholar 

Lau TS, Chan LKY, Wong ECH, et al. A loop of cancer-stroma-cancer interaction promotes peritoneal metastasis of ovarian cancer via TNFα-TGFα-EGFR. Oncogene. 2017;36(25):3576–87. https://doi.org/10.1038/onc.2016.509.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chen Y, McAndrews KM, Kalluri R. Clinical and therapeutic relevance of cancer-associated fibroblasts. Nat Rev Clin Oncol. 2021;18(12):792–804. https://doi.org/10.1038/s41571-021-00546-5.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Sahai E, Astsaturov I, Cukierman E, et al. A framework for advancing our understanding of cancer-associated fibroblasts. Nat Rev Cancer. 2020;20(3):174–86. https://doi.org/10.1038/s41568-019-0238-1.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Baslan T, Hicks J. Unravelling biology and shifting paradigms in cancer with single-cell sequencing. Nat Rev Cancer. 2017;17(9):557–69. https://doi.org/10.1038/nrc.2017.58.

Article  CAS  PubMed  Google Scholar 

Armingol E, Officer A, Harismendy O, Lewis NE. Deciphering cell–cell interactions and communication from gene expression. Nat Rev Genet. 2021;22(2):71–88. https://doi.org/10.1038/s41576-020-00292-x.

Article  CAS  PubMed  Google Scholar 

Izar B, Tirosh I, Stover EH, et al. A single-cell landscape of high-grade serous ovarian cancer. Nat Med. 2020;26(8):1271–9. https://doi.org/10.1038/s41591-020-0926-0.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Shih AJ, Menzin A, Whyte J, et al. Identification of grade and origin specific cell populations in serous epithelial ovarian cancer by single cell RNA-seq. PLoS ONE. 2018;13(11):e0206785. https://doi.org/10.1371/journal.pone.0206785.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Hu Z, Artibani M, Alsaadi A, et al. The repertoire of serous ovarian cancer non-genetic heterogeneity revealed by single-cell sequencing of normal fallopian tube epithelial cells. Cancer Cell. 2020;37(2):226-242.e7. https://doi.org/10.1016/j.ccell.2020.01.003.

Article  CAS  PubMed  Google Scholar 

Winterhoff BJ, Maile M, Mitra AK, et al. Single cell sequencing reveals heterogeneity within ovarian cancer epithelium and cancer associated stromal cells. Gynecol Oncol. 2017;144(3):598–606. https://doi.org/10.1016/j.ygyno.2017.01.015.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kan T, Wang W, Ip PP, et al. Single-cell EMT-related transcriptional analysis revealed intra-cluster heterogeneity of tumor cell clusters in epithelial ovarian cancer ascites. Oncogene. 2020;39(21):4227–40. https://doi.org/10.1038/s41388-020-1288-2.

Article  CAS  PubMed  Google Scholar 

Pietilä EA, Gonzalez-Molina J, Moyano-Galceran L, et al. Co-evolution of matrisome and adaptive adhesion dynamics drives ovarian cancer chemoresistance. Nat Commun. 2021;12(1):3904. https://doi.org/10.1038/s41467-021-24009-8.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047–8. https://doi.org/10.1093/bioinformatics/btw354.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Wingett SW, Andrews S. FastQ screen: a tool for multi-genome mapping and quality control. F1000Res. 2018;7:1338. https://doi.org/10.12688/f1000research.15931.2.

Article  PubMed  PubMed Central  Google Scholar 

Dobin A, Davis CA, Schlesinger F, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21. https://doi.org/10.1093/bioinformatics/bts635.

Article  CAS  PubMed  Google Scholar 

Anders S, Pyl PT, Huber W. HTSeq–a python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166–9. https://doi.org/10.1093/bioinformatics/btu638.

Article  CAS  PubMed  Google Scholar 

Afgan E, Baker D, Batut B, et al. The galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Res. 2018;46(W1):W537–44. https://doi.org/10.1093/nar/gky379.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Starruß J, de Back W, Brusch L, Deutsch A. Morpheus: a user-friendly modeling environment for multiscale and multicellular systems biology. Bioinformatics. 2014;30(9):1331–2. https://doi.org/10.1093/bioinformatics/btt772.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. https://doi.org/10.1186/s13059-014-0550-8.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Kuleshov MV, Jones MR, Rouillard AD, et al. Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic Acids Res. 2016;44(W1):W90-97. https://doi.org/10.1093/nar/gkw377.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chen EY, Tan CM, Kou Y, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinform. 2013;14(1):128. https://doi.org/10.1186/1471-2105-14-128.

Article  Google Scholar 

Xie Z, Bailey A, Kuleshov MV, et al. Gene set knowledge discovery with enrichr. Curr Protoc. 2021;1(3):90.

Article  Google Scholar 

Edgar R. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207–10. https://doi.org/10.1093/nar/30.1.207.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888-1902.e21. https://doi.org/10.1016/j.cell.2019.05.031.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jin S, Guerrero-Juarez CF, Zhang L, et al. Inference and analysis of cell-cell communication using cell chat. Nat Commun. 2021;12(1):1088. https://doi.org/10.1038/s41467-021-21246-9.

Article  CAS  PubMed  PubMed Central