• 1.

    Heintz AP, Odicino F, Maisonneuve P, Quinn MA, Benedet JL, Creasman WT, et al. Carcinoma of the ovary. FIGO 26th Annual Report on the Results of Treatment in Gynecological Cancer. Int J Gynaecol Obstet. 2006;95(Suppl 1):S161–92.

    PubMed 

    Google Scholar
     

  • 2.

    Jayson GC, Kohn EC, Kitchener HC, Ledermann JA. Ovarian cancer. Lancet. 2014;384(9951):1376–88.

    PubMed 

    Google Scholar
     

  • 3.

    Menon U, Karpinskyj C, Gentry-Maharaj A. Ovarian cancer prevention and screening. Obstet Gynecol. 2018;131(5):909–27.

    PubMed 

    Google Scholar
     

  • 4.

    Burger RA, Brady MF, Bookman MA, Fleming GF, Monk BJ, Huang H, et al. Incorporation of bevacizumab in the primary treatment of ovarian cancer. N Engl J Med. 2011;365(26):2473–83.

    CAS 
    PubMed 

    Google Scholar
     

  • 5.

    Bickell NA, Egorova N, Prasad-Hayes M, Franco R, Howell EA, Wisnivesky J, et al. Secondary surgery versus chemotherapy for recurrent ovarian cancer. Am J Clin Oncol. 2018;41(5):458–64.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 6.

    Bristow RE, Tomacruz RS, Armstrong DK, Trimble EL, Montz FJ. Survival effect of maximal cytoreductive surgery for advanced ovarian carcinoma during the platinum era: a meta-analysis. J Clin Oncol. 2002;20(5):1248–59.

    PubMed 

    Google Scholar
     

  • 7.

    Lheureux S, Braunstein M, Oza AM. Epithelial ovarian cancer: evolution of management in the era of precision medicine. CA Cancer J Clin. 2019;69(4):280–304.

    PubMed 

    Google Scholar
     

  • 8.

    Coleman RL, Spirtos NM, Enserro D, Herzog TJ, Sabbatini P, Armstrong DK, et al. Secondary surgical cytoreduction for recurrent ovarian cancer. N Engl J Med. 2019;381(20):1929–39.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 9.

    Hoppenot C, Eckert MA, Tienda SM, Lengyel E. Who are the long-term survivors of high grade serous ovarian cancer? Gynecol Oncol. 2018;148(1):204–12.

    PubMed 

    Google Scholar
     

  • 10.

    Coleman RL, Fleming GF, Brady MF, Swisher EM, Steffensen KD, Friedlander M, et al. Veliparib with first-line chemotherapy and as maintenance therapy in ovarian cancer. N Engl J Med. 2019;381(25):2403–15.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 11.

    González-Martín A, Pothuri B, Vergote I, DePont Christensen R, Graybill W, Mirza MR, et al. Niraparib in patients with newly diagnosed advanced ovarian cancer. N Engl J Med. 2019;381(25):2391–402.

    PubMed 

    Google Scholar
     

  • 12.

    Ledermann J, Harter P, Gourley C, Friedlander M, Vergote I, Rustin G, et al. Olaparib maintenance therapy in patients with platinum-sensitive relapsed serous ovarian cancer: a preplanned retrospective analysis of outcomes by BRCA status in a randomised phase 2 trial. Lancet Oncol. 2014;15(8):852–61.

    CAS 
    PubMed 

    Google Scholar
     

  • 13.

    Mirza MR, Monk BJ, Herrstedt J, Oza AM, Mahner S, Redondo A, et al. Niraparib maintenance therapy in platinum-sensitive, recurrent ovarian cancer. N Engl J Med. 2016;375(22):2154–64.

    CAS 
    PubMed 

    Google Scholar
     

  • 14.

    Moore K, Colombo N, Scambia G, Kim BG, Oaknin A, Friedlander M, et al. Maintenance olaparib in patients with newly diagnosed advanced ovarian cancer. N Engl J Med. 2018;379(26):2495–505.

    CAS 
    PubMed 

    Google Scholar
     

  • 15.

    Moore KN, Secord AA, Geller MA, Miller DS, Cloven N, Fleming GF, et al. Niraparib monotherapy for late-line treatment of ovarian cancer (QUADRA): a multicentre, open-label, single-arm, phase 2 trial. Lancet Oncol. 2019;20(5):636–48.

    CAS 
    PubMed 

    Google Scholar
     

  • 16.

    Pujade-Lauraine E, Ledermann JA, Selle F, Gebski V, Penson RT, Oza AM, et al. Olaparib tablets as maintenance therapy in patients with platinum-sensitive, relapsed ovarian cancer and a BRCA1/2 mutation (SOLO2/ENGOT-Ov21): a double-blind, randomised, placebo-controlled, phase 3 trial. Lancet Oncol. 2017;18(9):1274–84.

    CAS 
    PubMed 

    Google Scholar
     

  • 17.

    Ray-Coquard I, Pautier P, Pignata S, Pérol D, González-Martín A, Berger R, et al. Olaparib plus bevacizumab as first-line maintenance in ovarian cancer. N Engl J Med. 2019;381(25):2416–28.

    CAS 
    PubMed 

    Google Scholar
     

  • 18.

    Salwa A, Ferraresi A, Chinthakindi M, Vallino L, Vidoni C, Dhanasekaran DN, et al. BECN1 and BRCA1 deficiency sensitizes ovarian cancer to platinum therapy and confers better prognosis. Biomedicines. 2021;9(2):207.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 19.

    Zhang G, Zhang J, Zhu Y, Liu H, Shi Y, Mi K, et al. Association of somatic mutations in BRCA2 BRC domain with chemotherapy sensitivity and survival in high grade serous ovarian cancer. Exp Cell Res. 2021;406(1):112742.

    CAS 
    PubMed 

    Google Scholar
     

  • 20.

    Barbato L, Bocchetti M, Di Biase A, Regad T. Cancer stem cells and targeting strategies. Cells. 2019;8(8):926.

    CAS 
    PubMed Central 

    Google Scholar
     

  • 21.

    Batlle E, Clevers H. Cancer stem cells revisited. Nat Med. 2017;23(10):1124–34.

    CAS 
    PubMed 

    Google Scholar
     

  • 22.

    Li L, Bhatia R. Stem cell quiescence. Clin Cancer Res. 2011;17(15):4936–41.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 23.

    Muñoz-Galván S, Carnero A. Targeting cancer stem cells to overcome therapy resistance in ovarian cancer. Cells. 2020;9(6):1402.

    PubMed Central 

    Google Scholar
     

  • 24.

    Sato M, Kawana K, Adachi K, Fujimoto A, Yoshida M, Nakamura H, et al. Targeting glutamine metabolism and the focal adhesion kinase additively inhibits the mammalian target of the rapamycin pathway in spheroid cancer stem-like properties of ovarian clear cell carcinoma in vitro. Int J Oncol. 2017;50(4):1431–8.

    CAS 
    PubMed 

    Google Scholar
     

  • 25.

    Cristiano S, Leal A, Phallen J, Fiksel J, Adleff V, Bruhm DC, et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature. 2019;570(7761):385–9.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 26.

    Hossain MA, Saiful Islam SM, Quinn JMW, Huq F, Moni MA. Machine learning and bioinformatics models to identify gene expression patterns of ovarian cancer associated with disease progression and mortality. J Biomed Inform. 2019;100:103313.

    PubMed 

    Google Scholar
     

  • 27.

    Huang C, Clayton EA, Matyunina LV, McDonald LD, Benigno BB, Vannberg F, et al. Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy. Sci Rep. 2018;8(1):16444.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 28.

    Huang C, Mezencev R, McDonald JF, Vannberg F. Open source machine-learning algorithms for the prediction of optimal cancer drug therapies. PLoS One. 2017;12(10):e0186906.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 29.

    Kawakami E, Tabata J, Yanaihara N, Ishikawa T, Koseki K, Iida Y, et al. Application of artificial intelligence for preoperative diagnostic and prognostic prediction in epithelial ovarian cancer based on blood biomarkers. Clin Cancer Res. 2019;25(10):3006–15.

    CAS 
    PubMed 

    Google Scholar
     

  • 30.

    Lu TP, Kuo KT, Chen CH, Chang MC, Lin HP, Hu YH, et al. Developing a prognostic gene panel of epithelial ovarian cancer patients by a machine learning model. Cancers (Basel). 2019;11(2):270.

    CAS 
    PubMed Central 

    Google Scholar
     

  • 31.

    Mucaki EJ, Zhao JZL, Lizotte DJ, Rogan PK. Predicting responses to platin chemotherapy agents with biochemically-inspired machine learning. Signal Transduct Target Ther. 2019;4:1.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 32.

    Paik ES, Lee JW, Park JY, Kim JH, Kim M, Kim TJ, et al. Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods. J Gynecol Oncol. 2019;30(4):e65.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 33.

    Shinagare AB, Balthazar P, Ip IK, Lacson R, Liu J, Ramaiya N, et al. High-grade serous ovarian cancer: use of machine learning to predict abdominopelvic recurrence on CT on the basis of serial cancer antigen 125 levels. J Am Coll Radiol. 2018;15(8):1133–8.

    PubMed 

    Google Scholar
     

  • 34.

    Song HJ, Yang ES, Kim JD, Park CY, Kyung MS, Kim YS. Best serum biomarker combination for ovarian cancer classification. Biomed Eng Online. 2018;17(Suppl 2):152.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 35.

    Tseng CJ, Lu CJ, Chang CC, Chen GD, Cheewakriangkrai C. Integration of data mining classification techniques and ensemble learning to identify risk factors and diagnose ovarian cancer recurrence. Artif Intell Med. 2017;78:47–54.

    PubMed 

    Google Scholar
     

  • 36.

    Wang X, Han L, Zhou L, Wang L, Zhang LM. Prediction of candidate RNA signatures for recurrent ovarian cancer prognosis by the construction of an integrated competing endogenous RNA network. Oncol Rep. 2018;40(5):2659–73.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 37.

    Ikeda Y, Sato S, Yabuno A, Shintani D, Ogasawara A, Miwa M, et al. High expression of maternal embryonic leucine-zipper kinase (MELK) impacts clinical outcomes in patients with ovarian cancer and its inhibition suppresses ovarian cancer cells growth ex vivo. J Gynecol Oncol. 2020;31(6):e93.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 38.

    Schwartz LH, Litière S, de Vries E, Ford R, Gwyther S, Mandrekar S, et al. RECIST 1.1-Update and clarification: from the RECIST committee. Eur J Cancer. 2016;62:132–7.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 39.

    Dockery LE, Rubenstein AR, Ding K, Mashburn SG, Burkett WC, Davis AM, et al. Extending the platinum-free interval: the impact of omitting 2nd line platinum chemotherapy in intermediate platinum-sensitive ovarian cancer. Gynecol Oncol. 2019;155(2):201–6.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 40.

    Milanowski Ł, Hoffman-Zacharska D, Geremek M, Friedman A, Figura M, Koziorowski D. The matter of significance – Has the p.(Glu121Lys) variant of TOR1A gene a pathogenic role in dystonia or Parkinson disease? J Clin Neurosci. 2020;72:501–3.

    CAS 
    PubMed 

    Google Scholar
     

  • 41.

    Akbani R, Ng PK, Werner HM, Shahmoradgoli M, Zhang F, Ju Z, et al. A pan-cancer proteomic perspective on The Cancer Genome Atlas. Nat Commun. 2014;5:3887.

    CAS 
    PubMed 

    Google Scholar
     

  • 42.

    Bao M, Zhang L, Hu Y. Novel gene signatures for prognosis prediction in ovarian cancer. J Cell Mol Med. 2020;24(17):9972–84.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 43.

    Dellinger AE, Nixon AB, Pang H. Integrative pathway analysis using graph-based learning with applications to TCGA colon and ovarian data. Cancer Inform. 2014;13(Suppl 4):1–9.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 44.

    He Z, Zhang J, Yuan X, Liu Z, Liu B, Tuo S, et al. Network based stratification of major cancers by integrating somatic mutation and gene expression data. PLoS One. 2017;12(5):e0177662.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 45.

    Hillman RT, Chisholm GB, Lu KH, Futreal PA. Genomic rearrangement signatures and clinical outcomes in high-grade serous ovarian cancer. J Natl Cancer Inst. 2018;110(3):265–72.

    CAS 

    Google Scholar
     

  • 46.

    Lin H, Wang J, Wen X, Wen Q, Huang S, Mai Z, et al. A prognosis-predictive nomogram of ovarian cancer with two immune-related genes: CDC20B and PNPLA5. Oncol Lett. 2020;20(5):204.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 47.

    Niu Y, Sun W, Chen K, Fu Z, Chen Y, Zhu J, et al. A novel scoring system for pivotal autophagy-related genes predicts outcomes after chemotherapy in advanced ovarian cancer patients. Cancer Epidemiol Biomarkers Prev. 2019;28(12):2106–14.

    PubMed 

    Google Scholar
     

  • 48.

    Qin L, Li T, Liu Y. High SLC4A11 expression is an independent predictor for poor overall survival in grade 3/4 serous ovarian cancer. PLoS One. 2017;12(11):e0187385.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 49.

    Sun T, Yang Q. Chemoresistance-associated alternative splicing signatures in serous ovarian cancer. Oncol Lett. 2020;20(1):420–30.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 50.

    Wang R, Ye XH, Zhao XL, Liu JL, Zhang CY. Development of a five-gene signature as a novel prognostic marker in ovarian cancer. Neoplasma. 2019;66(3):343–9.

    PubMed 

    Google Scholar
     

  • 51.

    Yan S, Fang J, Chen Y, Xie Y, Zhang S, Zhu X, et al. Comprehensive analysis of prognostic gene signatures based on immune infiltration of ovarian cancer. BMC Cancer. 2020;20(1):1205.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 52.

    Saji H, Tsuboi M, Shimada Y, Kato Y, Hamanaka W, Kudo Y, et al. Gene expression profiling and molecular pathway analysis for the identification of early-stage lung adenocarcinoma patients at risk for early recurrence. Oncol Rep. 2013;29(5):1902–6.

    CAS 
    PubMed 

    Google Scholar
     

  • 53.

    Babyak MA. What you see may not be what you get: a brief, nontechnical introduction to overfitting in regression-type models. Psychosom Med. 2004;66(3):411–21.

    PubMed 

    Google Scholar
     

  • 54.

    Altman BJ, Stine ZE, Dang CV. From Krebs to clinic: glutamine metabolism to cancer therapy. Nat Rev Cancer. 2016;16(10):619–34.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 55.

    Desgrosellier JS, Cheresh DA. Integrins in cancer: biological implications and therapeutic opportunities. Nat Rev Cancer. 2010;10(1):9–22.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 56.

    Pavlova NN, Thompson CB. The emerging hallmarks of cancer metabolism. Cell Metab. 2016;23(1):27–47.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 57.

    Sancho P, Barneda D, Heeschen C. Hallmarks of cancer stem cell metabolism. Br J Cancer. 2016;114(12):1305–12.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 58.

    Yang M, Vousden KH. Serine and one-carbon metabolism in cancer. Nat Rev Cancer. 2016;16(10):650–62.

    CAS 
    PubMed 

    Google Scholar
     

  • 59.

    Tucker SL, Gharpure K, Herbrich SM, Unruh AK, Nick AM, Crane EK, et al. Molecular biomarkers of residual disease after surgical debulking of high-grade serous ovarian cancer. Clin Cancer Res. 2014;20(12):3280–8.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 60.

    Tothill RW, Tinker AV, George J, Brown R, Fox SB, Lade S, et al. Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin Cancer Res. 2008;14(16):5198–208.

    CAS 
    PubMed 

    Google Scholar
     

  • 61.

    Tischler J, Gruhn WH, Reid J, Allgeyer E, Buettner F, Marr C, et al. Metabolic regulation of pluripotency and germ cell fate through α-ketoglutarate. EMBO J. 2019;38(1):e99518.

    PubMed 

    Google Scholar
     

  • 62.

    Knijnenburg TA, Wang L, Zimmermann MT, Chambwe N, Gao GF, Cherniack AD, et al. Genomic and molecular landscape of DNA damage repair deficiency across the cancer genome atlas. Cell Rep. 2018;23(1):239–54.e6.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 63.

    Lu M, Fan Z, Xu B, Chen L, Zheng X, Li J, et al. Using machine learning to predict ovarian cancer. Int J Med Inform. 2020;141:104195.

    PubMed 

    Google Scholar
     

  • 64.

    Lu J, Li HM, Cai SQ, Zhao SH, Ma FH, Li YA, et al. Prediction of platinum-based chemotherapy response in advanced high-grade serous ovarian cancer: ADC histogram analysis of primary tumors. Acad Radiol. 2021;28(3):e77–85.

    PubMed 

    Google Scholar
     

  • 65.

    Mairinger F, Bankfalvi A, Schmid KW, Mairinger E, Mach P, Walter RF, et al. Digital immune-related gene expression signatures in high-grade serous ovarian carcinoma: developing prediction models for platinum response. Cancer Manag Res. 2019;11:9571–83.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 66.

    Murakami R, Matsumura N, Brown JB, Wang Z, Yamaguchi K, Abiko K, et al. Prediction of taxane and platinum sensitivity in ovarian cancer based on gene expression profiles. Gynecol Oncol. 2016;141(1):49–56.

    CAS 
    PubMed 

    Google Scholar
     

  • 67.

    Telli ML, Timms KM, Reid J, Hennessy B, Mills GB, Jensen KC, et al. Homologous Recombination Deficiency (HRD) score predicts response to platinum-containing neoadjuvant chemotherapy in patients with triple-negative breast cancer. Clin Cancer Res. 2016;22(15):3764–73.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 68.

    Tumiati M, Hietanen S, Hynninen J, Pietila E, Farkkila A, Kaipio K, et al. A functional homologous recombination assay predicts primary chemotherapy response and long-term survival in ovarian cancer patients. Clin Cancer Res. 2018;24(18):4482–93.

    CAS 
    PubMed 

    Google Scholar
     

  • 69.

    van Zyl B, Tang D, Bowden NA. Biomarkers of platinum resistance in ovarian cancer: what can we use to improve treatment. Endocr Relat Cancer. 2018;25(5):R303–R18.

    PubMed 

    Google Scholar
     

  • 70.

    Wu C, He L, Wei Q, Li Q, Jiang L, Zhao L, et al. Bioinformatic profiling identifies a platinum-resistant-related risk signature for ovarian cancer. Cancer Med. 2020;9(3):1242–53.

    CAS 
    PubMed 

    Google Scholar
     

  • 71.

    Yamawaki K, Mori Y, Sakai H, Kanda Y, Shiokawa D, Ueda H, et al. Integrative analyses of gene expression and chemosensitivity of patient-derived ovarian cancer spheroids link G6PD-driven redox metabolism to cisplatin chemoresistance. Cancer Lett. 2021;521:29–38.

    CAS 
    PubMed 

    Google Scholar
     

  • 72.

    Shannon NB, Tan LLY, Tan QX, Tan JW, Hendrikson J, Ng WH, et al. A machine learning approach to identify predictive molecular markers for cisplatin chemosensitivity following surgical resection in ovarian cancer. Sci Rep. 2021;11(1):16829.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 73.

    Durán RV, Oppliger W, Robitaille AM, Heiserich L, Skendaj R, Gottlieb E, et al. Glutaminolysis activates Rag-mTORC1 signaling. Mol Cell. 2012;47(3):349–58.

    PubMed 

    Google Scholar
     

  • 74.

    Jin L, Alesi GN, Kang S. Glutaminolysis as a target for cancer therapy. Oncogene. 2016;35(28):3619–25.

    CAS 
    PubMed 

    Google Scholar
     

  • 75.

    Stincone A, Prigione A, Cramer T, Wamelink MM, Campbell K, Cheung E, et al. The return of metabolism: biochemistry and physiology of the pentose phosphate pathway. Biol Rev Camb Philos Soc. 2015;90(3):927–63.

    PubMed 

    Google Scholar
     

  • 76.

    Xie H, Hanai J, Ren JG, Kats L, Burgess K, Bhargava P, et al. Targeting lactate dehydrogenase–a inhibits tumorigenesis and tumor progression in mouse models of lung cancer and impacts tumor-initiating cells. Cell Metab. 2014;19(5):795–809.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 77.

    Chen CH, Shyu MK, Wang SW, Chou CH, Huang MJ, Lin TC, et al. MUC20 promotes aggressive phenotypes of epithelial ovarian cancer cells via activation of the integrin β1 pathway. Gynecol Oncol. 2016;140(1):131–7.

    CAS 
    PubMed 

    Google Scholar
     

  • 78.

    McGrail DJ, Khambhati NN, Qi MX, Patel KS, Ravikumar N, Brandenburg CP, et al. Alterations in ovarian cancer cell adhesion drive taxol resistance by increasing microtubule dynamics in a FAK-dependent manner. Sci Rep. 2015;5:9529.

    PubMed 
    PubMed Central 

    Google Scholar
     

  • 79.

    Shah NR, Tancioni I, Ward KK, Lawson C, Chen XL, Jean C, et al. Analyses of merlin/NF2 connection to FAK inhibitor responsiveness in serous ovarian cancer. Gynecol Oncol. 2014;134(1):104–11.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 80.

    Tancioni I, Uryu S, Sulzmaier FJ, Shah NR, Lawson C, Miller NL, et al. FAK Inhibition disrupts a β5 integrin signaling axis controlling anchorage-independent ovarian carcinoma growth. Mol Cancer Ther. 2014;13(8):2050–61.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 81.

    Kim D, Langmead B, Salzberg SL. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12(4):357–60.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

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