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Punzón-Jiménez, P.;  Lago, V.;  Domingo, S.;  Simón, C.;  Mas, A. Diagnosis and Prognosis of HGSOC. Encyclopedia. Available online: (accessed on 19 April 2024).
Punzón-Jiménez P,  Lago V,  Domingo S,  Simón C,  Mas A. Diagnosis and Prognosis of HGSOC. Encyclopedia. Available at: Accessed April 19, 2024.
Punzón-Jiménez, Paula, Victor Lago, Santiago Domingo, Carlos Simón, Aymara Mas. "Diagnosis and Prognosis of HGSOC" Encyclopedia, (accessed April 19, 2024).
Punzón-Jiménez, P.,  Lago, V.,  Domingo, S.,  Simón, C., & Mas, A. (2022, November 29). Diagnosis and Prognosis of HGSOC. In Encyclopedia.
Punzón-Jiménez, Paula, et al. "Diagnosis and Prognosis of HGSOC." Encyclopedia. Web. 29 November, 2022.
Diagnosis and Prognosis of HGSOC

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].

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]), 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]. 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]. 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]. 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]. 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].
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]. 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]. 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]. 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]. 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]. 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] 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] 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]. 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].
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.


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