Predicting Prognosis and Platinum Resistance in Ovarian Cancer: Role of Immunohistochemistry Biomarkers.
Ghofraan Abdulsalam AtallahNirmala Chandralega KampanKah Teik ChewNorfilza Mohd MokhtarReena Rahayu Md ZinMohamad Nasir Bin ShafieeNor Haslinda Binti Abd AzizPublished in: International journal of molecular sciences (2023)
Ovarian cancer is a lethal reproductive tumour affecting women worldwide. The advancement in presentation and occurrence of chemoresistance are the key factors for poor survival among ovarian cancer women. Surgical debulking was the mainstay of systemic treatment for ovarian cancer, which was followed by a successful start to platinum-based chemotherapy. However, most women develop platinum resistance and relapse within six months of receiving first-line treatment. Thus, there is a great need to identify biomarkers to predict platinum resistance before enrolment into chemotherapy, which would facilitate individualized targeted therapy for these subgroups of patients to ensure better survival and an improved quality of life and overall outcome. Harnessing the immune response through immunotherapy approaches has changed the treatment way for patients with cancer. The immune outline has emerged as a beneficial tool for recognizing predictive and prognostic biomarkers clinically. Studying the tumour microenvironment (TME) of ovarian cancer tissue may provide awareness of actionable targets for enhancing chemotherapy outcomes and quality of life. This review analyses the relevance of immunohistochemistry biomarkers as prognostic biomarkers in predicting chemotherapy resistance and improving the quality of life in ovarian cancer.
Keyphrases
- polycystic ovary syndrome
- immune response
- locally advanced
- end stage renal disease
- stem cells
- pregnancy outcomes
- chronic kidney disease
- free survival
- neoadjuvant chemotherapy
- squamous cell carcinoma
- newly diagnosed
- metabolic syndrome
- prognostic factors
- pregnant women
- skeletal muscle
- health insurance
- insulin resistance
- toll like receptor
- drug delivery
- adipose tissue
- big data
- deep learning
- patient reported