Correlation analysis and clinical significance of CA125, HE4, DDI, and FDP in type II epithelial ovarian cancer

HER2 puzzle pieces: Non-Coding RNAs as keys to mechanisms, chemoresistance, and clinical outcomes in Ovarian cancer

Ovarian cancer (OC) presents significant challenges, characterized by limited treatment options and therapy resistance often attributed to dysregulation of the HER2 signaling pathway. Non-coding RNAs (ncRNAs) have emerged as key players in regulating gene expression in OC. This comprehensive review underscores the pivotal role of ncRNAs in modulating HER2 signaling, with a specific focus on their mechanisms, impact on chemoresistance, and prognostic/diagnostic implications. MicroRNAs, long non-coding RNAs, and circular RNAs have been identified as essential regulators in the modulation of the HER2 pathway. By directly targeting key components of the HER2 axis, these ncRNAs influence its activation and downstream signaling cascades. Dysregulated ncRNAs have been closely associated with chemoresistance, leading to treatment failures and disease progression in OC. Furthermore, distinct expression profiles of ncRNAs hold promise as reliable prognostic and diagnostic markers, facilitating personalized treatment strategies and enhancing disease outcome assessments. A comprehensive understanding of how ncRNAs intricately modulate HER2 signaling is imperative for the development of targeted therapies and the improvement of patient outcomes. The integration of ncRNA profiles into clinical practice has the potential to enhance prognostic and diagnostic accuracy in the management of ovarian cancer. Further research efforts are essential to validate the clinical utility of ncRNAs and elucidate their precise roles in the regulation of HER2 signaling. In conclusion, ncRNAs play a crucial role in governing HER2 signaling in ovarian cancer, impacting chemoresistance and providing valuable prognostic and diagnostic insights. The exploration of ncRNA-mediated HER2 modulation offers promising avenues for the development of personalized treatment approaches, ultimately advancing patient care and outcomes in OC.

Aptamers Versus Vascular Endothelial Growth Factor (VEGF): A New Battle against Ovarian Cancer

Cancer is one of the diseases that causes a high mortality as it involves unregulated and abnormal cell growth proliferation that can manifest in any body region. One of the typical ovarian cancer symptoms is damage to the female reproductive system. The death rate can be reduced through early detection of the ovarian cancer. Promising probes that can detect ovarian cancer are suitable aptamers. Aptamers, i.e., so-called chemical antibodies, have a strong affinity for the target biomarker and can typically be identified starting from a random library of oligonucleotides. Compared with other probes, ovarian cancer targeting using aptamers has demonstrated superior detection effectiveness. Various aptamers have been selected to detect the ovarian tumor biomarker, vascular endothelial growth factor (VEGF). The present review highlights the development of particular aptamers that target VEGF and detect ovarian cancer at its earliest stages. The therapeutic efficacy of aptamers in ovarian cancer treatment is also discussed.

Molecular Biomarkers for the Early Detection of Ovarian Cancer

Ovarian cancer is the deadliest gynecological cancer, leading to over 152,000 deaths each year. A late diagnosis is the primary factor causing a poor prognosis of ovarian cancer and often occurs due to a lack of specific symptoms and effective biomarkers for an early detection. Currently, cancer antigen 125 (CA125) is the most widely used biomarker for ovarian cancer detection, but this approach is limited by a low specificity. In recent years, multimarker panels have been developed by combining molecular biomarkers such as human epididymis secretory protein 4 (HE4), ultrasound results, or menopausal status to improve the diagnostic efficacy. The risk of ovarian malignancy algorithm (ROMA), the risk of malignancy index (RMI), and OVA1 assays have also been clinically used with improved sensitivity and specificity. Ongoing investigations into novel biomarkers such as autoantibodies, ctDNAs, miRNAs, and DNA methylation signatures continue to aim to provide earlier detection methods for ovarian cancer. This paper reviews recent advancements in molecular biomarkers for the early detection of ovarian cancer.

Value of the risk of ovarian malignancy algorithm index in predicting the recurrence of epithelial ovarian cancer

Aim: This study aimed to assess the predictive and diagnostic value of the risk of ovarian malignancy algorithm (ROMA) index for epithelial ovarian cancer (EOC) recurrence. Materials & methods: The clinical features and follow-up data of 159 EOC cases were studied. The ROMA index was calculated by serum CA125 and HE4 levels with menopausal status. Recurrence-free survival was evaluated for an end point. Results: The ROMA was strongly associated with clinical characteristics. The ROMA index above the cutoff value (34.71%) was significantly associated with recurrence-free survival. The ROMA index had a significantly higher sensitivity (90.59%) than CA125 (84.71%) and HE4 (80.80%) for recurrence diagnosis, and its optimal cutoff value was 17.07%. Conclusion: The primary ROMA index is a predictive factor in EOC recurrence and has better performance in the diagnosis of EOC recurrence.

Kinetics of HE4 and CA125 as prognosis biomarkers during neoadjuvant chemotherapy in advanced epithelial ovarian cancer

BACKGROUND

Ovarian cancer (OC) is considered the most lethal gynecological cancer, of which more than 65% cases are diagnosed in advanced stages, requiring platinum-based neoadjuvant chemotherapy (NACT).

METHODS

A prospective-longitudinal study was conducted among women with advanced epithelial ovarian cancer (AEOC), III and IV stages, and treated with NACT, at the National Cancer Institute - Mexico, from July 2017 to July 2018. Serum samples were obtained for quantification of CA125 and HE4 using ELISA at the first and in each of the three NACT cycles. The therapeutic response was evaluated through standard tomography. We determined whether CA125 and HE4, alone or in combination, were associated with TR to NACT during follow up.

RESULTS

53 patients aged 38 to 79 years were included, 92.4% presented papillary serous subtype OC. Higher serum HE4 levels were observed in patients with non-tomographic response (6.89 vs 5.19 pmol/mL; p = 0.031), specially during the second (p = 0.039) and third cycle of NACT (p = 0.031). Multivariate-adjusted models showed an association between HE4 levels and TR, from the second treatment cycle (p = 0.042) to the third cycle (p = 0.033). Changes from baseline HE4 levels during the first cycle was negative associated with TR. No associations were found between CA125 and TR.

CONCLUSIONS

Serum HE4 levels were independently associated with TR among patients with AOEC treated with NACT, also a reduction between baseline HE4 and first chemotherapy levels was also independently associated with the TR. These findings might be relevant for predicting a lack of response to treatment.

Diagnostic Accuracy of the ADNEX Model for Ovarian Cancer at the 15% Cut-Off Value: A Systematic Review and Meta-Analysis

Objectives

To evaluate the diagnostic accuracy of the ADNEX model for ovarian cancer at the 15% cut-off value.

Methods

Studies on the identified diagnosis of the ADNEX model for ovarian cancer published in PubMed, Embase, the Cochrane Library and Web of Science databases from January 1st, 2014 to February 20th, 2021 were searched. Two researchers independently screened the retrieved studies and extracted the basic features and parameter data. The quality of the eligible studies was evaluated by Quality Assessment of Diagnostic Accuracy Studies-2, and the result was summarized by Review Manager 5.3. Meta-Disc 1.4 and STATA 16.0 were used in statistical analysis. Heterogeneity of this meta-analysis was calculated. Meta-regression was performed to investigate the potential sources of heterogeneity. Sensitivity analysis and Deek's funnel plot analysis were conducted to evaluate the stability and publication bias, respectively.

Results

280 studies were initially retrieved through the search strategy, and 10 eligible studies were ultimately included. The random-effects model was selected for data synthesis. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio and the area under the summary receiver operating characteristic curve were 0.92 (95% CI: 0.89-0.94), 0.82 (95% CI: 0.78-0.86), 5.2 (95% CI: 4.1-6.4), 0.10 (95% CI: 0.07-0.13), 54.0 (95% CI: 37.0-77.0) and 0.95 (95% CI: 0.91-0.95). Meta-regression based on study design, country, enrollment and blind method was not statistically significant. This meta-analysis was stable with no obvious publication bias.

Conclusions

The ADNEX model at the 15% cut-off had high diagnostic accuracy in identifying ovarian cancer.