ArticleOncotarget2018
Computational development of a molecular-based approach to improve risk stratification of endometrial cancer patients.
Article in Oncotarget, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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
5 citing papers in PubMed.
- Article
- Optimizing Outcomes through a Multidisciplinary Team Approach in Endometrial Cancer.Healthcare (Basel, Switzerland) · 2023Article
- Should Endometrial Cancer Treatment Be Centralized?Biology · 2022Review
- High-risk endometrial cancer proteomic profiling reveals that FBXW7 mutation alters L1CAM and TGM2 protein levels.Cancer · 2021Article
- Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Histological classification and staging are the gold standard for the prognosis of endometrial cancer (EC). However, in morphologically intermediate and doubtful cases this approach results largely insufficient, defining the need for better classification criteria. In this work we developed an algorithm that based on EC genetic alterations and in combination with the current histological classification, improves EC patients prognostic stratification, in particular in doubtful cases. A panel of 26 cancer related genes was analyzed in 89 EC patients and somatic functional mutations were investigated in association with different histology and outcome. An unsupervised hierarchical clustering analysis revealed that two groups of patients with different tumor grade and different prognosis can be distinguished by mutational profile. In particular, the mutational status of APC, CTNNB1, PIK3CA, PTEN, SMAD4 and TP53 resulted to be principal drivers of prognostic clustering. Consistently, a decisional tree generated by a data mining approach summarizes the consequential molecular criteria for patients prognostic stratification. The model proposed by this work provides the clinician with a tool able to support the prognosis of EC patients and consequently drives the choice of the most appropriated therapeutic strategy and follow up.
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Registered trials
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