Recent Insight about HE4 Role in Ovarian Cancer Oncogenesis

Ovarian cancer: Diagnosis and treatment strategies (Review)

Ovarian cancer is a malignant tumor that seriously endangers health. Early ovarian cancer symptoms are frequently challenging to detect, resulting in a large proportion of patients reaching an advanced stage when diagnosed. Conventional diagnosis relies heavily on serum biomarkers and pathological examination, but their sensitivity and specificity require improvement. Targeted therapy inhibits tumor growth by targeting certain characteristics of tumor cells, such as signaling pathways and gene mutations. However, the effectiveness of targeted therapy varies among individuals due to differences in their unique biological characteristics and requires individualized strategies. Immunotherapy is a promising treatment for ovarian cancer due to its long-lasting antitumor effect. Nevertheless, issues such as variable efficacy, immune-associated adverse effects and drug resistance remain to be resolved. The present review discusses the diagnostic strategies, rationale, treatment strategies and prospects of targeted therapy and immunotherapy for ovarian cancer.

Prediction of ovarian cancer using artificial intelligence tools

Purpose

Ovarian cancer is a common type of cancer and a leading cause of death in women. Therefore, accurate and fast prediction of ovarian tumors is crucial. One of the appropriate and precise methods for predicting and diagnosing this cancer is to build a model based on artificial intelligence methods. These methods provide a tool for predicting ovarian cancer according to the characteristics and conditions of each person.

Method

In this study, a data set included records related to 171 cases of benign ovarian tumors, and 178 records related to cases of ovarian cancer were analyzed. The data set contains the records of blood test results and tumor markers of the patients. After data preprocessing, including removing outliers and replacing missing values, the weight of the effective factors was determined using information gain indices and the Gini index. In the next step, predictive models were created using random forest (RF), support vector machine (SVM), decision trees (DT), and artificial neural network (ANN) models. The performance of these models was evaluated using the 10-fold cross-validation method using the indicators of specificity, sensitivity, accuracy, and the area under the receiver operating characteristic curve. Finally, by comparing the performance of the models, the best predictive model of ovarian cancer was selected.

Results

The most important predictive factors were HE4, CA125, and NEU. The RF model was identified as the best predictive model, with an accuracy of more than 86%. The predictive accuracy of DT, SVM, and ANN models was estimated as 82.91%, 85.25%, and 79.35%, respectively. Various artificial intelligence (AI) tools can be used with high accuracy and sensitivity in predicting ovarian cancer.

Conclusion

Therefore, the use of these tools can help specialists and patients with early, easier, and less expensive diagnosis of ovarian cancer. Future studies can leverage AI to integrate image data with serum biomarkers, thereby facilitating the creation of novel models and advancing the diagnosis and treatment of ovarian cancer.

Clinical value of serum tumor markers in assessing the efficacy of neoadjuvant chemotherapy in advanced ovarian cancer: single-center prospective clinical study

Objective

This study aimed to assess the clinical importance of various biomarkers, including NLR, CEA, CA199, CA125, CA153, and HE4, through dynamic testing to evaluate the effectiveness of neoadjuvant chemotherapy (NACT) for individuals facing advanced ovarian cancer. This provides valuable information for tailoring treatment plans to individual patients, thereby leading to a more personalized and effective management of individuals facing ovarian cancer.

Methods

The levels of NLR, CA125, CA199, CEA, CA153, and HE4 were detected before chemotherapy and after 3 courses of chemotherapy. Patients were categorized into ineffective and effective groups according to the effectiveness of NACT. To evaluate the factors influencing NACT's effectiveness in individuals facing advanced ovarian cancer, receiver operating characteristic (ROC) curves, predictive modeling, and multifactorial regression analysis were employed.

Results

In the effective group, the patients' age, maximum tumor diameter, and CEA and HE4 levels of the patients were significantly higher compared to those in the ineffective group (P <.05). Additionally, the difference in HE4 levels before and after treatment between the effective and ineffective groups was statistically significant (P<.05). Multifactorial analysis showed that age and maximum tumor diameter were independent risk factors impacting the effectiveness of NACT in individuals facing advanced ovarian cancer (P<.05). The ROC curve for predicting the effectiveness of NACT in individuals facing advanced ovarian cancer showed a sensitivity of 93.3% for NLR and a specificity of 92.3% for CA199. HE4 emerged as the most reliable predictor, demonstrating a specificity of 84.6% and a sensitivity of 75.3%. The area under the curve of the combined CA125 and HE4 assays for predicting the ineffectiveness of NACT in individuals facing advanced ovarian cancer was 0.825, showcasing a specificity of 74.2% and a sensitivity of 84.6%.

Conclusion

The predictive capacity for the effectiveness of NACT in individuals facing advanced ovarian cancer is notably high when considering the sensitivity of NLR and the specificity of CA199. Additionally, the combination of CA125 and HE4 assays can obtain a better predictive effect, which can accurately select patients suitable for NACT, determine the appropriate timing of the interval debulking surgery (IDS) surgery, and achieve a satisfactory tumor reduction effect.

Diagnostic value of cerebrospinal fluid human epididymis protein 4 for leptomeningeal metastasis in lung adenocarcinoma

Background

The diagnosis of lung adenocarcinoma (LUAD) leptomeningeal metastasis (LM) remains a clinical challenge. Human epididymis protein 4 (HE4) functions as a novel tumor biomarker for cancers. This study aimed to assess the diagnostic value of cerebrospinal fluid (CSF) HE4, and combined with CEACAM6, for LUAD LM.

Methods

The CSF HE4 protein level was measured in two independent cohorts by electrochemiluminescence. Test cohort included 58 LUAD LM patients, 22 LUAD patients without LM (Wiot-LM), and 68 normal controls. Validation cohort enrolled 50 LUAD LM patients and 40 normal controls, in parallel with Wiot-LM patients without brain metastases (19 Wiot-LM/BrM patients) or with BrM (26 BrM patients). The CSF level of CEA, CA125, CA153, CA199, CA724, NSE and ProGRP of these samples was measured by electrochemiluminescence, whereas the CSF CEACAM6 level was detected by enzyme-linked immunosorbent assay (ELISA). In addition, the serum level of these biomarkers was detected by same method as CSF.

Results

The level of HE4 or CEACAM6 in CSF samples from LUAD LM patients was significantly higher than those from normal controls and Wiot-LM patients. The HE4 or CEACAM6 level in CSF was higher than that in serum of LM patient. The CSF HE4 or CEACAM6 level for distinguished LM from Wiot-LM showed good performance by receiver-operating characteristic analysis. The better discriminative power for LM was achieved when HE4 was combined with CEACAM6. In addition, the CSF HE4 and CEACAM6 level showed little or no difference between Wiot-LM/BrM and BrM patients, the BrM would not significantly influence the HE4 or CEACAM6 level in CSF. The diagnostic power of CSF CA125, CA153, CA199, CA724, NSE and ProGRP for LUAD LM were not ideal.

Conclusion

The combination with HE4 and CEACAM6 has the promising application for the diagnosis of LUAD LM.