Diagnosis and Prognosis of HGSOC: Comparison
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Please note this is a comparison between Version 2 by Dean Liu and Version 1 by PAULA PUNZÓN JIMÉNEZ.

High-grade serous ovarian carcinoma (HGSOC) represents the most common form of epithelial ovarian carcinoma. The absence of specific symptoms leads to late-stage diagnosis, making HGSOC one of the gynecological cancers with the worst prognosis. The cellular origin of HGSOC, the role of reproductive hormones, the genetic and chromosomal traits, or the pathways mainly involved in the physiopathology of this cancer, as well as when evaluating prognosis and response to therapy in  patients. Despite the growing knowledge on this field, the detection of HGSOC is still based on traditional methods such as carbohydrate antigen 125 (CA125) detection and ultrasound, and the combined use of these methods has yet to support significant reductions in overall mortality rates, which opens new avenues to guide research towards the early diagnosis of HGSOC.

  • ovarian cancer
  • high-grade serous ovarian carcinoma
  • early diagnosis

1. Introduction

Ovarian cancer (OC), situated among the most aggressive and deadly gynecological malignancies, is the fifth leading cause of cancer-related death in females in developed countries, with a total of 313,959 new cases diagnosed in 2020 worldwide (66,693 in European countries) and 12,810 predicted annual deaths and 19,880 predicted diagnoses in 2022 in the US [1,2,3]. OC refers to a heterogeneous set of neoplasms subdivided according to genetics, histological evaluations, and tissue of origin [4,5,6]. The main subtypes include sex-cord stromal OC, germ-cell OC, and epithelial ovarian carcinoma (EOC) as the most common subtype, accounting for up to 95% of all cases [7,8]. EOC can be subdivided into five histological subtypes: mucinous, endometrioid, clear-cell, low-grade (LGSOC), and high-grade (HGSOC). HGSOC, the most common histological subtype, constitutes 70% of diagnosed EOC cases [9,10] and is often first diagnosed at advanced stages (III and IV), where the tumor has spread to the abdomen or outside the abdominal cavity [11,12], due to the lack of specific symptoms. A late diagnosis dramatically reduces therapeutic responses and overall survival rates in affected patients [13]; therefore, a strong, urgent rationale supports the design of early detection strategies, as tumor detection at stage I (i.e., confined to the ovaries) or II (i.e., confined to the pelvic area) could improve overall five-year survival rates [4,11,12,14]. On the other hand, the differential diagnosis of ovarian masses represents a challenge for clinicians [15]. A diagnosis can be made intraoperatively or weeks after surgery, which leads to a poor optimization of the hospital’s surgical resources and the need for re-interventions, with consequent associated costs to the health system [16,17].

Ovarian cancer (OC), situated among the most aggressive and deadly gynecological malignancies, is the fifth leading cause of cancer-related death in females in developed countries, with a total of 313,959 new cases diagnosed in 2020 worldwide (66,693 in European countries) and 12,810 predicted annual deaths and 19,880 predicted diagnoses in 2022 in the US [1][2][3]. OC refers to a heterogeneous set of neoplasms subdivided according to genetics, histological evaluations, and tissue of origin [4][5][6]. The main subtypes include sex-cord stromal OC, germ-cell OC, and epithelial ovarian carcinoma (EOC) as the most common subtype, accounting for up to 95% of all cases [7][8]. EOC can be subdivided into five histological subtypes: mucinous, endometrioid, clear-cell, low-grade (LGSOC), and high-grade (HGSOC). HGSOC, the most common histological subtype, constitutes 70% of diagnosed EOC cases [9][10] and is often first diagnosed at advanced stages (III and IV), where the tumor has spread to the abdomen or outside the abdominal cavity [11][12], due to the lack of specific symptoms. A late diagnosis dramatically reduces therapeutic responses and overall survival rates in affected patients [13]; therefore, a strong, urgent rationale supports the design of early detection strategies, as tumor detection at stage I (i.e., confined to the ovaries) or II (i.e., confined to the pelvic area) could improve overall five-year survival rates [4][11][12][14]. On the other hand, the differential diagnosis of ovarian masses represents a challenge for clinicians [15]. A diagnosis can be made intraoperatively or weeks after surgery, which leads to a poor optimization of the hospital’s surgical resources and the need for re-interventions, with consequent associated costs to the health system [16][17].

Current clinical approaches that guide physicians in managing EOC remain primarily based on imaging, histological evaluation, serum markers such as CA125, or predictive models, which do not display sufficient sensitivity and sensibility [18,19]; however, a growing trend exists in exploring novel/alternative molecular tools [20].

Current clinical approaches that guide physicians in managing EOC remain primarily based on imaging, histological evaluation, serum markers such as CA125, or predictive models, which do not display sufficient sensitivity and sensibility [18][19]; however, a growing trend exists in exploring novel/alternative molecular tools [20].

2. Current Molecular Approaches for the Diagnosis and Prognosis of HGSOC

Difficulties in the early detection of HGSOC, before the disease develops to advanced stages, can be attributed to the lack of specific symptoms, which are usually missed or attributed to other pathologies [21]. In clinical practice, the diagnosis of EOC is based on four main techniques: pelvic palpation examination (PPE), imaging (which includes transvaginal ultrasound or sonography, magnetic resonance imaging, computed tomography, and positron-emission computed tomography [22,23,24][22][23][24]), serum levels of specific proteins, and surgery (either laparoscopic or laparotomic) [25]; however, there is an urgent need to develop alternative techniques for early-stage HGSOC identification. Accordingly, new diagnostic methods based on cellular techniques or molecular approaches—such as gene expression profiling via NGS—are under development.

2.1. Molecular Markers and Algorithm Decisions for the Diagnosis of HGSOC: Carbohydrate Antigen 125 (CA125), Alone or in Combination with Other Imaging Techniques or Biomarkers

Initial studies found elevated levels of CA125—a transmembrane cell-surface protein encoded by the MUC16 gene—in HGSOC compared to healthy ovarian tissue [26,27][26][27]. CA125 remains the most-used molecular marker; however, the use of CA125 suffers from several drawbacks. Elevated CA125 levels occur in only 23–50% of stage I-II cases, and CA125 cannot be detected in all advanced HGSOC cases [28,29,30,31,32][28][29][30][31][32]. Moreover, female patients who smoke or develop inflammatory processes, as well as those with physiological or benign conditions (e.g., menstruation, pregnancy, uterine fibroids), might display altered serum CA125 levels, thereby increasing false positive rates and HGSOC misdiagnoses [33,34,35,36,37,38,39,40,41,42,43,44][33][34][35][36][37][38][39][40][41][42][43][44]. This fact highlights the need to conduct further studies to elucidate the relationship between these variables and CA125 levels. Therefore, CA125 levels alone cannot discriminate early HGSOC cases with sufficient sensitivity and specificity. To solve this problem, clinical trials and triage algorithms have explored the power of combining CA125 with imaging techniques for the early diagnosis of HGSOC. Additionally, distinct predictive indices based on biomarkers and ultrasound have been developed to differentiate the nature of adnexal masses, which have the advantage of eliminating inter- and intra-observer variability, estimating the probability of mass malignancy, and increasing efficacy and efficiency [45]. The Risk of Malignancy Index (RMI) algorithm combines CA125 levels, ultrasound results, and menopausal status [46]; however, despite the proposal of several combinations of the formula [47], the RMI possesses lower sensitivity than transvaginal sonography (TVS) alone [32,48][32][48]. For this reason, clinical trials such as the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial evaluated CA125 levels combined with TVS; however, the results failed to provide evidence of improvements in mortality rates or early-stage HGSOC detection rates [30,31,49,50][30][31][49][50]. Other strategies based on the Risk of Ovarian Cancer Algorithm (ROCA) [51] aimed to stratify risk according to CA125 levels; however, the use of ROCA in the Normal Risk Ovarian Screening Study (NROSS) [52] or the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS) [53] clinical trials failed to suffice for the early detection of OC due to a high number of false positives and the associated failure to reduce HGSOC-associated mortality rates [14,54][14][54]. Conversely, a study by Srivastava et al. that combined CA125 with additional protein biomarkers suggested some clinical impact [20]. The glycoprotein HE4 (human epididymis 4) displays elevated levels in HGSOC and endometrioid EOCs, presumably at advanced tumor stages [55,56][55][56]. Meanwhile, other biomarkers examined in combination with CA125 and HE4 include mesothelin [57], CEA and VCAM-1 [58], glycodelin [59], IL-6 and E-cadherin [60], and transthyretin [61]. The Assessment of Different Neoplasias in the Adnexa (ADNEX) model represents an alternative strategy that considers six ultrasound and three clinical predictors (including CA125 levels) to discriminate between benign, borderline, invasive, and metastatic ovarian tumors [62]. Alternative strategies such as the Risk of Ovarian Malignancy Algorithm (ROMA), OVA1™, or OVERA™ deserve special consideration given their U.S. Food and Drug Administration (FDA) approval and their relevance to routine clinical practice. ROMA, which displays high performance in menopausal patients, evaluates HE4 and CA125 levels and stratifies patients with adnexal masses into low or high risk of malignancy [63,64][63][64]. HE4 levels are not usually modified by benign pathologies or external factors, and they display greater specificity in differentiating between malignant and benign tumors; furthermore, HE4 combined with CA125 outperforms the specificity and sensitivity in detecting cases missed when using CA125 alone [32,65,66,67,68][32][65][66][67][68]. OVA1™ comprises a multivariate index assay (MIA) test that examines five biomarkers (i.e., transthyretin, apolipoprotein A-1, 2-microglobulin, transferrin, and CA125) and scores the likelihood of malignancy of a pre-detected adnexal mass prior to surgical intervention [69,70,71][69][70][71]. OVERA™ examines levels of CA125, HE4, apolipoprotein A-1, FSH (follicle-stimulating hormone), and transferrin, achieving a sensitivity of 91% and specificity of 61% for HGSOC screening [72]. Other recently proposed protein panels have included CA125, vitamin-K-dependent protein Z, C-reactive protein, and LCAT [73]; CA125, HE4, CA72-4, and MMP-7 [74]; or CA125, HE4, FOLR1, KLK11, WISP1, MDK, CXCL13, MSLN, and ADAM8 [75]. The most recent strategies rely on the detection of autoantibody levels in combination with CA125 [76,77][76][77] and involve the detection of anti-TP53 [78], anti-HSF1, or anti-CCDC155 [79].

2.2. Gene Expression Profiling and Gene Panels

While ongoing gene expression profiling studies have suggested that HGSOC represents a highly heterogeneous pathology [6], expression analyses can differentiate between HGSCO and other types of EOC. For example, Sallum et al. reported a differential WT1, TP53, and P16 expression profile that distinguishes HGSCO from LGSOC [80], while Li et al. found that 11 differentially expressed genes (DEGs) could discriminate between borderline cases and HGSOC tumors [81]. Different gene expression profiles reported by studies such as the Cancer Genome Atlas (TCGA) project [82,83][82][83] can be associated with pathological outcomes [84]. For instance, high expression of HOX, FAP (myofibroblast markers), and ANGPTL1/2 (markers of microvascular pericytes) or high expression of HMGA2, SOX11 (transcription factors), MCM2, and PCNA (proliferation markers) is correlated with worse prognosis [85,86,87][85][86][87]. Additionally, those studies linked improved survival rates and patient prognoses to MUC gene (MUC16) expression or CXCL11, CXCL10 (chemokine ligands), and CXCR3 (receptor) expression [85,86,87][85][86][87]. Gene expression panels such as NR5A1, GATA4, FOXL2, TP53, and BMP7 possess different profiles when comparing primary OC tumors and their metastases, and could eventually be used to predict patient survival [88]. Additionally, the expression of homologous recombination repair (HRR) genes (e.g., BRCA1, ATR, FANCD2, BRIP1, BARD1, and RAD51) is associated with a better prognosis in HGSOC cases, whereas the expression of epithelial-to-mesenchymal genes (e.g., GATA4, GATA6, FOXC2, KLF6, and TWIST2) is associated with a worse prognosis [89]. Studies have also assessed differential gene expression in HGSOC to guide the optimal therapeutic choice and measure expected responses [84]. A meta-analysis by Matondo et al. that included 1020 patients identified a prognostic signature regulated by HIF1α and TP53 in therapy-unresponsive patients as an indicator of a worse overall prognosis [90]. Lee et al. also identified several DEGs in patients who underwent complete gross resection or neoadjuvant chemotherapy with either positive or negative responses [91]. Most recently, Buttarelli et al. identified a 10-gene signature (including genes such as CTNNBL1, CKB, GNG11, IGFBP7, and PLCG2) for classifying wild-type BRCA HGSOC patients into sensitive- and resistant-to-therapy groups [92]. Novel bioinformatic approaches are currently attempting to refine gene expression signatures that predict therapeutic responses [93] and differentiate between HGSOC and other cancer types [94]. For example, co-expression network analyses have identified UBE2Q1 as a prognostic biomarker associated with poor relapse-free overall survival in HGSOC patients [95]. Overall, using DEG analysis in early diagnosis has the potential to improve the management and survival of HGSOC patients.

References

  1. Common Cancer Sites—Cancer Stat Facts. Available online: https://seer.cancer.gov/statfacts/html/common.html (accessed on 10 September 2022).
  2. Siegel, R.L.; Miller, K.D.; Fuchs, H.E.; Jemal, A. Cancer Statistics, 2021. CA Cancer J. Clin. 2021, 71, 7–33.
  3. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249.
  4. Xie, W.; Sun, H.; Li, X.; Lin, F.; Wang, Z.; Wang, X. Ovarian Cancer: Epigenetics, Drug Resistance, and Progression. Cancer Cell Int. 2021, 21, 434.
  5. Köbel, M.; Kang, E.Y. The Evolution of Ovarian Carcinoma Subclassification. Cancers 2022, 14, 416.
  6. Lisio, M.A.; Fu, L.; Goyeneche, A.; Gao, Z.H.; Telleria, C. High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints. Int. J. Mol. Sci. 2019, 20, 952.
  7. Lheureux, S.; Braunstein, M.; Oza, A.M. Epithelial Ovarian Cancer: Evolution of Management in the Era of Precision Medicine. CA Cancer J. Clin. 2019, 69, 280–304.
  8. Torre, L.A.; Trabert, B.; DeSantis, C.E.; Miller, K.D.; Samimi, G.; Runowicz, C.D.; Gaudet, M.M.; Jemal, A.; Siegel, R.L. Ovarian Cancer Statistics, 2018. CA Cancer J. Clin. 2018, 68, 284–296.
  9. Hirst, J.; Crow, J.; Godwin, A. Ovarian Cancer Genetics: Subtypes and Risk Factors. In Ovarian Cancer—From Pathogenesis to Treatment; IntechOpen: London, UK, 2018.
  10. Prat, J. Ovarian Carcinomas: Five Distinct Diseases with Different Origins, Genetic Alterations, and Clinicopathological Features. Virchows Archiv 2012, 460, 237–249.
  11. Berek, J.S.; Renz, M.; Kehoe, S.; Kumar, L.; Friedlander, M. Cancer of the Ovary, Fallopian Tube, and Peritoneum: 2021 Update. Int. J. Gynaecol. Obstet. 2021, 155, 61–85.
  12. Peres, L.C.; Cushing-Haugen, K.L.; Köbel, M.; Harris, H.R.; Berchuck, A.; Rossing, M.A.; Schildkraut, J.M.; Doherty, J.A. Invasive Epithelial Ovarian Cancer Survival by Histotype and Disease Stage. J. Natl. Cancer Inst. 2019, 111, 60–68.
  13. Bowtell, D.D.; Böhm, S.; Ahmed, A.A.; Aspuria, P.J.; Bast, R.C.; Beral, V.; Berek, J.S.; Birrer, M.J.; Blagden, S.; Bookman, M.A.; et al. Rethinking Ovarian Cancer II: Reducing Mortality from High-Grade Serous Ovarian Cancer. Nat. Rev. Cancer 2015, 15, 668–679.
  14. Bast, R.C.; Lu, Z.; Han, C.Y.; Lu, K.H.; Anderson, K.S.; Drescher, C.W.; Skates, S.J. Biomarkers and Strategies for Early Detection of Ovarian Cancer. Cancer Epidemiol. Biomark. Prev. 2020, 29, 2504–2512.
  15. Chacón, E.; Dasí, J.; Caballero, C.; Alcázar, J.L. Risk of Ovarian Malignancy Algorithm versus Risk Malignancy Index-I for Preoperative Assessment of Adnexal Masses: A Systematic Review and Meta-Analysis. Gynecol. Obstet. Investig. 2019, 84, 591–598.
  16. Ratnavelu, N.D.; Brown, A.P.; Mallett, S.; Scholten, R.J.; Patel, A.; Founta, C.; Galaal, K.; Cross, P.; Naik, R. Intraoperative Frozen Section Analysis for the Diagnosis of Early Stage Ovarian Cancer in Suspicious Pelvic Masses. Cochrane Database Syst. Rev. CDS 2016, 2016, CD010360.
  17. Querleu, D.; Planchamp, F.; Chiva, L.; Fotopoulou, C.; Barton, D.; Cibula, D.; Aletti, G.; Carinelli, S.; Creutzberg, C.; Davidson, B.; et al. European Society of Gynaecological Oncology (ESGO) Guidelines for Ovarian Cancer Surgery. Int. J. Gynecol. Cancer 2017, 27, 1534–1542.
  18. Zhang, M.; Cheng, S.; Jin, Y.; Zhao, Y.; Wang, Y. Roles of CA125 in Diagnosis, Prediction, and Oncogenesis of Ovarian Cancer. Biochim. Biophys. Acta Rev. Cancer 2021, 1875, 188503.
  19. Charkhchi, P.; Cybulski, C.; Gronwald, J.; Wong, F.O.; Narod, S.A.; Akbari, M.R. CA125 and Ovarian Cancer: A Comprehensive Review. Cancers 2020, 12, 3730.
  20. Srivastava, A.; Gupta, A.; Patidar, S. Review of Biomarker Systems as an Alternative for Early Diagnosis of Ovarian Carcinoma. Clin. Transl. Oncol. 2021, 23, 1967–1978.
  21. Matulonis, U.A.; Sood, A.K.; Fallowfield, L.; Howitt, B.E.; Sehouli, J.; Karlan, B.Y. Ovarian Cancer. Nat. Rev. Dis. Primers 2016, 2, 16061.
  22. Shinagare, A.B.; Sadowski, E.A.; Park, H.; Brook, O.R.; Forstner, R.; Wallace, S.K.; Horowitz, J.M.; Horowitz, N.; Javitt, M.; Jha, P.; et al. Ovarian Cancer Reporting Lexicon for Computed Tomography (CT) and Magnetic Resonance (MR) Imaging Developed by the SAR Uterine and Ovarian Cancer Disease-Focused Panel and the ESUR Female Pelvic Imaging Working Group. Eur. Radiol. 2022, 32, 3220–3235.
  23. Sokalska, A.; Timmerman, D.; Testa, A.C.; van Holsbeke, C.; Lissoni, A.A.; Leone, F.P.G.; Jurkovic, D.; Valentin, L. Diagnostic Accuracy of Transvaginal Ultrasound Examination for Assigning a Specific Diagnosis to Adnexal Masses. Ultrasound Obstet. Gynecol. 2009, 34, 462–470.
  24. Jung, S.E.; Lee, J.M.; Rha, S.E.; Byun, J.Y.; Jung, J.I.; Hahn, S.T. CT and MR Imaging of Ovarian Tumors with Emphasis on Differential Diagnosis. Radiographics 2002, 22, 1305–1325.
  25. Liberto, J.M.; Chen, S.-Y.; Shih, I.-M.; Wang, T.-H.; Wang, T.-L.; Pisanic, T.R. Current and Emerging Methods for Ovarian Cancer Screening and Diagnostics: A Comprehensive Review. Cancers 2022, 14, 2885.
  26. Bast, R.C.; Feeney, M.; Lazarus, H.; Nadler, L.M.; Colvin, R.B.; Knapp, R.C. Reactivity of a Monoclonal Antibody with Human Ovarian Carcinoma. J. Clin. Investig. 1981, 68, 1331–1337.
  27. Köbel, M.; Kalloger, S.E.; Boyd, N.; McKinney, S.; Mehl, E.; Palmer, C.; Leung, S.; Bowen, N.J.; Ionescu, D.N.; Rajput, A.; et al. Ovarian Carcinoma Subtypes Are Different Diseases: Implications for Biomarker Studies. PLoS Med. 2008, 5, 1749–1760.
  28. Van Haaften-day, C.; Shen, Y.; Xu, F.; Yu, Y.; Berchuck, A.; Havrilesky, L.J.; De Bruijn, H.W.A.; Hacker, N.F. OVX1, Macrophague-Colony Stimulating Factor, and CA-125-II as Tumor Markers for Epithelial. A Critical Appraisal. Cancer 2001, 92, 2837–2844.
  29. Urban, N.; McIntosh, M.W.; Andersen, M.; Karlan, B.Y. Ovarian Cancer Screening. Hematol. Oncol. Clin. N. Am. 2003, 17, 989–1005.
  30. Kobayashi, H.; Yamada, Y.; Sado, T.; Sakata, M.; Yoshida, S.; Kawaguchi, R.; Kanayama, S.; Shigetomi, H.; Haruta, S.; Tsuji, Y.; et al. A Randomized Study of Screening for Ovarian Cancer: A Multicenter Study in Japan. Int. J. Gynecol. Cancer 2008, 18, 414–420.
  31. Jacobs, I.J.; Skates, S.J.; MacDonald, N.; Menon, U.; Rosenthal, A.N.; Davies, A.P.; Woolas, R.; Jeyarajah, A.R.; Sibley, K.; Lowe, D.G.; et al. Screening for Ovarian Cancer: A Pilot Randomised Controlled Trial. Lancet 1999, 353, 1207–1210.
  32. Dochez, V.; Caillon, H.; Vaucel, E.; Dimet, J.; Winer, N.; Ducarme, G. Biomarkers and Algorithms for Diagnosis of Ovarian Cancer: CA125, HE4, RMI and ROMA, a Review. J. Ovarian Res. 2019, 12, 28.
  33. Kafali, H.; Artuc, H.; Demir, N. Use of CA125 Fluctuation during the Menstrual Cycle as a Tool in the Clinical Diagnosis of Endometriosis; a Preliminary Report. Eur. J. Obstet. Gynecol. Reprod. Biol. 2004, 116, 85–88.
  34. Kokot, I.; Piwowar, A.; Jędryka, M.; Sołkiewicz, K.; Kratz, E.M. Diagnostic Significance of Selected Serum Inflammatory Markers in Women with Advanced Endometriosis. Int. J. Mol. Sci. 2021, 22, 2295.
  35. Wang, Z.; Zhou, F.; Xiao, X.; Ying, C. Serum Levels of Human Epididymis Protein 4 Are More Stable than Cancer Antigen 125 in Early and Mid-Term Pregnancy. J. Obstet. Gynaecol. Res. 2018, 44, 2053–2058.
  36. Hirsch, M.; Duffy, J.M.N.; Davis, C.J.; Nieves Plana, M.; Khan, K.S. Diagnostic Accuracy of Cancer Antigen 125 for Endometriosis: A Systematic Review and Meta-Analysis. BJOG 2016, 123, 1761–1768.
  37. Szecsi, P.B.; Andersen, M.R.; Bjørngaard, B.; Hedengran, K.K.; Stender, S. Cancer Antigen 125 after Delivery in Women with a Normal Pregnancy: A Prospective Cohort Study. Acta Obstet. Gynecol. Scand. 2014, 93, 1295–1301.
  38. Machado-Lopez, A.; Simón, C.; Mas, A. Molecular and Cellular Insights into the Development of Uterine Fibroids. Int. J. Mol. Sci. 2021, 22, 8483.
  39. Hu, X.; Zhang, J.; Cao, Y. Factors Associated with Serum CA125 Level in Women without Ovarian Cancer in the United States: A Population-Based Study. BMC Cancer 2022, 22, 544.
  40. Ataseven, H.; Öztürk, Z.A.; Arhan, M.; Yüksel, O.; Köklü, S.; Ibiş, M.; Başar, Ö.; Yilmaz, F.M.; Yüksel, I. Cancer Antigen 125 Levels in Inflammatory Bowel Diseases. J. Clin. Lab. Anal. 2009, 23, 244–248.
  41. Johnson, C.C.; Kessel, B.; Riley, T.L.; Ragard, L.R.; Williams, C.R.; Xu, J.L.; Buys, S.S. The Epidemiology of CA-125 in Women without Evidence of Ovarian Cancer in the Prostate, Lung, Colorectal and Ovarian Cancer (PLCO) Screening Trial. Gynecol. Oncol. 2008, 110, 383–389.
  42. Fortner, R.T.; Vitonis, A.F.; Schock, H.; Hüsing, A.; Johnson, T.; Fichorova, R.N.; Fashemi, T.; Yamamoto, H.S.; Tjønneland, A.; Hansen, L.; et al. Correlates of Circulating Ovarian Cancer Early Detection Markers and Their Contribution to Discrimination of Early Detection Models: Results from the EPIC Cohort. J. Ovarian Res. 2017, 10, 20.
  43. Sasamoto, N.; Babic, A.; Rosner, B.A.; Fortner, R.T.; Vitonis, A.F.; Yamamoto, H.; Fichorova, R.N.; Titus, L.J.; Tjønneland, A.; Hansen, L.; et al. Development and Validation of Circulating CA125 Prediction Models in Postmenopausal Women. J. Ovarian Res. 2019, 12, 116.
  44. Pauler, D.K.; Menon, U.; McIntosh, M.; Symecko, H.L.; Skates, S.J.; Jacobs, I.J. Factors Influencing Serum CA125II Levels in Healthy Postmenopausal Women. Cancer Epidemiol. Biomark. Prev. 2001, 10, 489–493.
  45. Lycke, M.; Kristjansdottir, B.; Sundfeldt, K. A Multicenter Clinical Trial Validating the Performance of HE4, CA125, Risk of Ovarian Malignancy Algorithm and Risk of Malignancy Index. Gynecol. Oncol. 2018, 151, 159–165.
  46. Jacobs, I.; Oram, D.; Fairbanks, J.; Turner, J.; Frost, C.; Grudzinskas, J.G. A Risk of Malignancy Index Incorporating CA 125, Ultrasound and Menopausal Status for the Accurate Preoperative Diagnosis of Ovarian Cancer. Maturitas 1991, 13, 177.
  47. Campos, C.; Sarian, L.O.; Jales, R.M.; Hartman, C.; Araújo, K.G.; Pitta, D.; Yoshida, A.; Andrade, L.; Derchain, S. Performance of the Risk of Malignancy Index for Discriminating Malignant Tumors in Women with Adnexal Masses. J. Med. Ultrasound 2016, 35, 143–152.
  48. Meys, E.M.J.; Kaijser, J.; Kruitwagen, R.F.P.M.; Slangen, B.F.M.; van Calster, B.; Aertgeerts, B.; Verbakel, J.Y.; Timmerman, D.; van Gorp, T. Subjective Assessment versus Ultrasound Models to Diagnose Ovarian Cancer: A Systematic Review and Meta-Analysis. Eur. J. Cancer 2016, 58, 17–29.
  49. Buys, S.S.; Partridge, E.; Black, A.; Johnson, C.C.; Lamerato, L.; Isaacs, C.; Reding, D.J.; Greenlee, R.T.; Yokochi, L.A.; Kessel, B.; et al. Effect of Screening on Ovarian Cancer Mortality: The Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Randomized Controlled Trial. JAMA 2011, 305, 2295–2302.
  50. Pinsky, P.F.; Yu, K.; Kramer, B.S.; Black, A.; Buys, S.S.; Partridge, E.; Gohagan, J.; Berg, C.D.; Prorok, P.C. Extended Mortality Results for Ovarian Cancer Screening in the PLCO Trial with Median 15 Years Follow-Up. Gynecol. Oncol. 2016, 143, 270–275.
  51. Skates, S.J. OCS: Development of the Risk of Ovarian Cancer Algorithm (ROCA) and ROCA Screening Trials. Int. J. Gynecol. Cancer 2012, 22, S24–S26.
  52. Lu, K.H.; Skates, S.; Hernandez, M.A.; Bedi, D.; Bevers, T.; Leeds, L.; Moore, R.; Granai, C.; Harris, S.; Newland, W.; et al. A 2-Stage Ovarian Cancer Screening Strategy Using the Risk of Ovarian Cancer Algorithm (ROCA) Identifies Early-Stage Incident Cancers and Demonstrates High Positive Predictive Value. Cancer 2013, 119, 3454–3461.
  53. Jacobs, I.J.; Menon, U.; Ryan, A.; Gentry-Maharaj, A.; Burnell, M.; Kalsi, J.K.; Amso, N.N.; Apostolidou, S.; Benjamin, E.; Cruickshank, D.; et al. Ovarian Cancer Screening and Mortality in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS): A Randomised Controlled Trial. Lancet 2016, 387, 945–956.
  54. Henderson, J.T.; Webber, E.M.; Sawaya, G.F. Screening for Ovarian Cancer Updated Evidence Report and Systematic Review for the US Preventive Services Task Force. JAMA 2018, 319, 595–606.
  55. Blackman, A.; Mitchell, J.; Rowswell-Turner, R.; Singh, R.; Kim, K.K.; Eklund, E.; Skates, S.; Bast, R.C.; Messerlian, G.; Miller, M.C.; et al. Analysis of Serum HE4 Levels in Various Histologic Subtypes of Epithelial Ovarian Cancer and Other Malignant Tumors. Tumour Biol. 2021, 43, 355–365.
  56. Drapkin, R.; von Horsten, H.H.; Lin, Y.; Mok, S.C.; Crum, C.P.; Welch, W.R.; Hecht, J.L. Human Epididymis Protein 4 (HE4) Is a Secreted Glycoprotein That Is Overexpressed by Serous and Endometrioid Ovarian Carcinomas. Cancer Res. 2005, 65, 2162–2169.
  57. Anderson, G.L.; McIntosh, M.; Wu, L.; Barnett, M.; Goodman, G.; Thorpe, J.D.; Bergan, L.; Thornquist, M.D.; Scholler, N.; Kim, N.; et al. Assessing Lead Time of Selected Ovarian Cancer Biomarkers: A Nested Case-Control Study. J. Natl. Cancer Inst. 2010, 102, 26–38.
  58. Yurkovetsky, Z.; Skates, S.; Lomakin, A.; Nolen, B.; Pulsipher, T.; Modugno, F.; Marks, J.; Godwin, A.; Gorelik, E.; Jacobs, I.; et al. Development of a Multimarker Assay for Early Detection of Ovarian Cancer. J. Clin. Oncol. 2010, 28, 2159–2166.
  59. Blyuss, O.; Gentry-Maharaj, A.; Fourkala, E.O.; Ryan, A.; Zaikin, A.; Menon, U.; Jacobs, I.; Timms, J.F. Serial Patterns of Ovarian Cancer Biomarkers in a Prediagnosis Longitudinal Dataset. Biomed. Res. Int. 2015, 2015, 681416.
  60. Chanhee, H.; Bellone, S.; Siegel, E.R.; Altwerger, G.; Menderes, G.; Bonazzoli, E.; Takata, T.; Petinella, F.; Bianchi, A.; Riccio, F.; et al. A Novel Multiple Biomarker Panel for the Early Detection of High-Grade Serous Ovarian Carcinoma. Gynecol. Oncol. 2018, 149, 585–591.
  61. Zheng, X.; Chen, S.; Li, L.; Liu, X.; Liu, X.; Dai, S.; Zhang, P.; Lu, H.; Lin, Z.; Yu, Y.; et al. Evaluation of HE4 and TTR for Diagnosis of Ovarian Cancer: Comparison with CA-125. J. Gynecol. Obstet. Hum. Reprod. 2018, 47, 227–230.
  62. van Calster, B.; van Hoorde, K.; Valentin, L.; Testa, A.C.; Fischerova, D.; van Holsbeke, C.; Savelli, L.; Franchi, D.; Epstein, E.; Kaijser, J.; et al. Evaluating the Risk of Ovarian Cancer before Surgery Using the ADNEX Model to Differentiate between Benign, Borderline, Early and Advanced Stage Invasive, and Secondary Metastatic Tumours: Prospective Multicentre Diagnostic Study. BMJ 2014, 349, g5920.
  63. Moore, R.G.; McMeekin, D.S.; Brown, A.K.; DiSilvestro, P.; Miller, M.C.; Allard, W.J.; Gajewski, W.; Kurman, R.; Bast, R.C.; Skates, S.J. A Novel Multiple Marker Bioassay Utilizing HE4 and CA125 for the Prediction of Ovarian Cancer in Patients with a Pelvic Mass. Gynecol. Oncol. 2009, 112, 40–46.
  64. Cui, R.; Wang, Y.; Li, Y.; Li, Y. Clinical Value of ROMA Index in Diagnosis of Ovarian Cancer: Meta-Analysis. Cancer Manag. Res. 2019, 11, 2545–2551.
  65. Elorriaga, M.Á.; Neyro, J.L.; Mieza, J.; Cristóbal, I.; Llueca, A. Biomarkers in Ovarian Pathology: From Screening to Diagnosis. Review of the Literature. J. Pers. Med. 2021, 11, 1115.
  66. Goff, B.A.; Agnew, K.; Neradilek, M.B.; Gray, H.J.; Liao, J.B.; Urban, R.R. Combining a Symptom Index, CA125 and HE4 (Triple Screen) to Detect Ovarian Cancer in Women with a Pelvic Mass. Gynecol. Oncol. 2017, 147, 291–295.
  67. Furrer, D.; Grégoire, J.; Turcotte, S.; Plante, M.; Bachvarov, D.; Trudel, D.; Têtu, B.; Douville, P.; Bairati, I. Performance of Preoperative Plasma HE4 and CA-125 Levels in Predicting Ovarian Cancer Mortality in Women with Epithelial Ovarian Cancer (EOC). PLoS ONE 2019, 14, e0218621.
  68. Qing, X.; Liu, L.; Mao, X. A Clinical Diagnostic Value Analysis of Serum CA125, CA199, and HE4 in Women with Early Ovarian Cancer: Systematic Review and Meta-Analysis. Comput. Math. Methods Med. 2022, 2022, 9339325.
  69. Bristow, R.E.; Smith, A.; Zhang, Z.; Chan, D.W.; Crutcher, G.; Fung, E.T.; Munroe, D.G. Ovarian Malignancy Risk Stratification of the Adnexal Mass Using a Multivariate Index Assay. Gynecol. Oncol. 2013, 128, 252–259.
  70. Fung, E.T. A Recipe for Proteomics Diagnostic Test Development: The OVA1 Test, from Biomarker Discovery to FDA Clearance. Clin. Chem. 2010, 56, 327–329.
  71. Ueland, F.R.; Desimone, C.P.; Seamon, L.G.; Miller, R.A.; Goodrich, S.; Podzielinski, I.; Sokoll, L.; Smith, A.; van Nagell, J.R.; Zhang, Z. Effectiveness of a Multivariate Index Assay in the Preoperative Assessment of Ovarian Tumors. Obstet. Gynecol. 2011, 117, 1289–1297.
  72. Coleman, R.L.; Herzog, T.J.; Chan, D.W.; Munroe, D.G.; Pappas, T.C.; Smith, A.; Zhang, Z.; Wolf, J. Validation of a Second-Generation Multivariate Index Assay for Malignancy Risk of Adnexal Masses. Am. J. Obstet. Gynecol. 2016, 215, 82.e1–82.e11.
  73. Russell, M.R.; Graham, C.; D’Amato, A.; Gentry-Maharaj, A.; Ryan, A.; Kalsi, J.K.; Whetton, A.D.; Menon, U.; Jacobs, I.; Graham, R.L.J. Diagnosis of Epithelial Ovarian Cancer Using a Combined Protein Biomarker Panel. Br. J. Cancer 2019, 121, 483–489.
  74. Simmons, A.R.; Fourkala, E.O.; Gentry-Maharaj, A.; Ryan, A.; Sutton, M.N.; Baggerly, K.; Zheng, H.; Lu, K.H.; Jacobs, I.; Skates, S.; et al. Complementary Longitudinal Serum Biomarkers to CA125 for Early Detection of Ovarian Cancer. Cancer Prev. Res. 2019, 12, 391–399.
  75. Mukama, T.; Fortner, R.T.; Katzke, V.; Hynes, L.C.; Petrera, A.; Hauck, S.M.; Johnson, T.; Schulze, M.; Schiborn, C.; Rostgaard-Hansen, A.L.; et al. Prospective Evaluation of 92 Serum Protein Biomarkers for Early Detection of Ovarian Cancer. Br. J. Cancer 2022, 126, 1301–1309.
  76. Ma, Y.; Wang, X.; Qiu, C.; Qin, J.; Wang, K.; Sun, G.; Jiang, D.; Li, J.; Wang, L.; Shi, J.; et al. Using Protein Microarray to Identify and Evaluate Autoantibodies to Tumor-Associated Antigens in Ovarian Cancer. Cancer Sci. 2021, 112, 537–549.
  77. Nebgen, D.R.; Lu, K.H.; Bast, R.C. Novel Approaches to Ovarian Cancer Screening. Curr. Oncol. Rep. 2019, 21, 75.
  78. Yang, W.L.; Gentry-Maharaj, A.; Simmons, A.; Ryan, A.; Fourkala, E.O.; Lu, Z.; Baggerly, K.A.; Zhao, Y.; Lu, K.H.; Bowtell, D.; et al. Elevation of TP53 Autoantibody before CA125 in Preclinical Invasive Epithelial Ovarian Cancer. Clin. Cancer Res. 2017, 23, 5912–5922.
  79. Wilson, A.L.; Moffitt, L.R.; Duffield, N.; Rainczuk, A.; Jobling, T.W.; Plebanski, M.; Stephens, A.N. Autoantibodies against HSF1 and CCDC155 as Biomarkers of Early-Stage, High-Grade Serous Ovarian Cancer. Cancer Epidemiol. Biomark. Prev. 2018, 27, 183–192.
  80. Sallum, L.F.; Andrade, L.; Ramalho, S.; Ferracini, A.C.; de Andrade Natal, R.; Borsarelli, A.; Brito, C.; Sarian, L.O.; Derchain, S. WT1, P53 and P16 Expression in the Diagnosis of Low-and High-Grade Serous Ovarian Carcinomas and Their Relation to Prognosis. Oncotarget 2018, 9, 15818–15827.
  81. Li, Y.; Jaiswal, S.K.; Kaur, R.; Alsaadi, D.; Liang, X.; Drews, F.; DeLoia, J.A.; Krivak, T.; Petrykowska, H.M.; Gotea, V.; et al. Differential Gene Expression Identifies a Transcriptional Regulatory Network Involving ER-Alpha and PITX1 in Invasive Epithelial Ovarian Cancer. BMC Cancer 2021, 21, 768.
  82. Bell, D.; Berchuck, A.; Birrer, M.; Chien, J.; Cramer, D.W.; Dao, F.; Dhir, R.; Disaia, P.; Gabra, H.; Glenn, P.; et al. Integrated Genomic Analyses of Ovarian Carcinoma. Nature 2011, 474, 609–615.
  83. Tothill, R.W.; Tinker, A.V.; George, J.; Brown, R.; Fox, S.B.; Lade, S.; Johnson, D.S.; Trivett, M.K.; Etemadmoghadam, D.; Locandro, B.; et al. Novel Molecular Subtypes of Serous and Endometrioid Ovarian Cancer Linked to Clinical Outcome. Clin. Cancer Res. 2008, 14, 5198–5208.
  84. Testa, U.; Petrucci, E.; Pasquini, L.; Castelli, G.; Pelosi, E. Ovarian Cancers: Genetic Abnormalities, Tumor Heterogeneity and Progression, Clonal Evolution and Cancer Stem Cells. Medicines 2018, 5, 16.
  85. Verhaak, R.G.W.; Tamayo, P.; Yang, J.Y.; Hubbard, D.; Zhang, H.; Creighton, C.J.; Fereday, S.; Lawrence, M.; Carter, S.L.; Mermel, C.H.; et al. Prognostically Relevant Gene Signatures of High-Grade Serous Ovarian Carcinoma. J. Clin. Investig. 2013, 123, 517–525.
  86. Konecny, G.E.; Wang, C.; Hamidi, H.; Winterhoff, B.; Kalli, K.R.; Dering, J.; Ginther, C.; Chen, H.W.; Dowdy, S.; Cliby, W.; et al. Prognostic and Therapeutic Relevance of Molecular Subtypes in High-Grade Serous Ovarian Cancer. J. Natl. Cancer Inst. 2014, 106, dju249.
  87. Shilpi, A.; Kandpal, M.; Ji, Y.; Seagle, B.L.; Shahabi, S.; Davuluri, R.V. Platform-Independent Classification System to Predict Molecular Subtypes of High-Grade Serous Ovarian Carcinoma. JCO Clin. Cancer Inform. 2019, 3, 1–9.
  88. Sallinen, H.; Janhonen, S.; Pölönen, P.; Niskanen, H.; Liu, O.H.; Kivelä, A.; Hartikainen, J.M.; Anttila, M.; Heinäniemi, M.; Ylä-Herttuala, S.; et al. Comparative Transcriptome Analysis of Matched Primary and Distant Metastatic Ovarian Carcinoma. BMC Cancer 2019, 19, 1121.
  89. Sohn, M.H.; Kim, S.I.; Shin, J.Y.; Kim, H.S.; Chung, H.H.; Kim, J.W.; Lee, M.; Seo, J.S. Classification of High-Grade Serous Ovarian Carcinoma by Epithelial-to-Mesenchymal Transition Signature and Homologous Recombination Repair Genes. Genes 2021, 12, 1103.
  90. Matondo, A.; Jo, Y.H.; Shahid, M.; Choi, T.G.; Nguyen, M.N.; Nguyen, N.N.Y.; Akter, S.; Kang, I.; Ha, J.; Maeng, C.H.; et al. The Prognostic 97 Chemoresponse Gene Signature in Ovarian Cancer. Sci. Rep. 2017, 7, 9689.
  91. Lee, S.; Zhao, L.; Rojas, C.; Bateman, N.W.; Yao, H.; Lara, O.D.; Celestino, J.; Morgan, M.B.; Nguyen, T.V.; Conrads, K.A.; et al. Molecular Analysis of Clinically Defined Subsets of High-Grade Serous Ovarian Cancer. Cell Rep. 2020, 31, 107502.
  92. Buttarelli, M.; Ciucci, A.; Palluzzi, F.; Raspaglio, G.; Marchetti, C.; Perrone, E.; Minucci, A.; Giacò, L.; Fagotti, A.; Scambia, G.; et al. Identification of a Novel Gene Signature Predicting Response to First-Line Chemotherapy in BRCA Wild-Type High-Grade Serous Ovarian Cancer Patients. J. Exp. Clin. Cancer Res. 2022, 41, 50.
  93. McGrail, D.J.; Lin, C.C.J.; Garnett, J.; Liu, Q.; Mo, W.; Dai, H.; Lu, Y.; Yu, Q.; Ju, Z.; Yin, J.; et al. Improved Prediction of PARP Inhibitor Response and Identification of Synergizing Agents through Use of a Novel Gene Expression Signature Generation Algorithm. NPJ Syst. Biol. Appl. 2017, 3, 8.
  94. Talhouk, A.; George, J.; Wang, C.; Budden, T.; Tan, T.Z.; Chiu, D.S.; Kommoss, S.; Leong, H.S.; Chen, S.; Intermaggio, M.P.; et al. Development and Validation of the Gene Expression Predictor of High-Grade Serous Ovarian Carcinoma Molecular SubTYPE (PrOTYPE). Clin. Cancer Res. 2020, 26, 5411–5423.
  95. Topno, R.; Singh, I.; Kumar, M.; Agarwal, P. Integrated Bioinformatic Analysis Identifies UBE2Q1 as a Potential Prognostic Marker for High Grade Serous Ovarian Cancer. BMC Cancer 2021, 21, 220.
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