ArticleGland surgery2019
Predicting the likelihood of early recurrence based on mRNA sequencing of pituitary adenomas.
Article in Gland surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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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.
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Who cites it
4 citing papers in PubMed.
- Prognostic Factors for Recurrence in Pituitary Adenomas: Recent Progress and Future Directions.Diagnostics (Basel, Switzerland) · 2022Review
- Nomogram predictive model of post-operative recurrence in non-functioning pituitary adenoma.Gland surgery · 2021Article
- Article
- Comprehensive analysis of the immunological landscape of pituitary adenomas: implications of immunotherapy for pituitary adenomas.Journal of neuro-oncology · 2020Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThere is no comprehensive and objective method existing for predicting early recurrence of pituitary adenomas (PAs). The most advanced gene sequencing technology can be applied to build a prognostic model that can effectively predict early recurrence of PAs.
methodsIn this study, using mRNA-Seq data, the corresponding postoperative early recurrence status, and other clinical features of 107 PA samples were obtained and randomly divided into the training and validation groups. Cox regression and receiver operating characteristic (ROC) analysis accompanied by the risk score method was used to build a seven-gene prediction model.
resultsArea under curve values was 0.857 in the training group, 0.936 in the validation group, and 0.848 in all patients. Patients with low-risk scores had a significantly lower probability of early postoperative recurrence compared to those acquiring high-risk scores in the training group, validation group, and all patient (P<0.0001) groups. In addition, 6 out of these 7 significant genes were highly correlated to the early recurrence of PAs.
conclusionsThis prediction model derived from mRNA-Seq data may help in identifying the early recurrence of PAs, consequently aiding in the classification of patients with PAs and the administration of the appropriate therapeutic and follow-up strategy for these patients.
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