ReviewCancer management and research2020
Further Understanding of High-Grade Serous Ovarian Carcinogenesis: Potential Therapeutic Targets.
Review in Cancer management and research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- A decision-support tool for management of advanced epithelial ovarian cancer in a single centre in India (CT PAUSE Nomogram): a prospective Study (2022-2024).The Lancet regional health. Southeast Asia · 2026Article
- Artificial intelligence (AI) and machine learning (ML) in ovarian cancer: transforming detection, treatment, and prevention.Journal of ovarian research · 2026Review
- Combined inhibition of NAD synthesis and C-terminal binding protein cooperatively induces cell death and inhibits growth of high grade serous ovarian carcinoma.Scientific reports · 2025Article
- Main Genes Linked to the Immune Microenvironment in High-Grade Serous Ovarian Cancer.Advanced biomedical research · 2025Article
- Patient-derived ovarian cancer organoid carries immune microenvironment and blood vessel keeping high response to cisplatin.MedComm · 2024Article
- Increased expression of BRD4 isoforms long (BRD4-L) and short (BRD4-S) promotes chemotherapy resistance in high-grade serous ovarian carcinoma.Genes & cancer · 2023Article
- Role of RAS signaling in ovarian cancer.F1000Research · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
High-grade serous ovarian carcinoma (HGSOC) is the most common type of ovarian cancer and the most lethal gynecologic malignancy due to advanced stage at presentation. Recent years have witnessed progress in the therapy of HGSOC with the introduction of PARP (poly-adenosine diphosphate ribose polymerase) inhibitors and the anti-angiogenic monoclonal antibody bevacizumab to the backbone of chemotherapy or as maintenance therapy after chemotherapy. The improved molecular understanding of ovarian cancer pathogenesis, which has brought these therapies into the clinic, aspires to extend the boundaries of therapies through elucidation of other molecular aspects of ovarian carcinogenesis. This accumulating knowledge has started to be translated to additional targeted therapies that are in various stages of development. These include inhibitors of the function of other proteins involved in homologous recombination deficiency (HRD), such as WEE1 kinase, ATM/ATR kinases and CDK12 inhibitors. Despite disappointing results with immune checkpoint inhibitors monotherapy, harnessing the immune system in HGSOC with combination therapies that promote antigen production and immune cell activation is an avenue being explored. This paper examines arising HGSOC therapies based on molecular understanding of pathogenesis.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.