Trial reportMedicine2020
A novel RNA sequencing-based prognostic nomogram to predict survival for patients with cutaneous melanoma: Clinical trial/experimental study.
Trial report in Medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed, 26 citations in OpenAlex.
- Review
- Development and validation of a nomogram for predicting overall survival in patients with lower extremity melanoma: based on the Surveillance, Epidemiology, and End Results (SEER) database.Archives of dermatological research · 2025Article
- LncRNA MALAT1 in Keratinocyte function: A review of recent advances.Non-coding RNA research · 2024Review
- Noncoding RNA circuitry in melanoma onset, plasticity, and therapeutic response.Pharmacology & therapeutics · 2023Review
- Ferroptosis-related lncRNA signature predicts prognosis and immunotherapy efficacy in cutaneous melanoma.Frontiers in surgery · 2022Article
- Article
- Article
- Comprehensive Review on the Clinical Relevance of Long Non-Coding RNAs in Cutaneous Melanoma.International journal of molecular sciences · 2021Review
- Prognostic Implications of Metabolism Related Gene Signature in Cutaneous Melanoma.Frontiers in oncology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPlenty of evidence has suggested that long non-coding RNAs (lncRNAs) have played a vital part may act as prognostic biomarkers in a variety of cancers. The aim of this study was to screen survival-related lncRNAs and to construct a lncRNA-based prognostic model in patients with cutaneous melanoma (CM).
methodsWe obtained lncRNAs expression profiles and clinicopathological data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases. A lncRNA-based prognostic model was established in training set. The established prognostic model was evaluated, and validated in the validation set. Then, a prognostic nomogram combining the lncRNA-based risk score and clinicopathological characteristics was developed in training set, and assessed in the validation set. The accuracy of the model was evaluated by the discrimination and calibration plots.
resultsA total of 212 lncRNAs were identified to be differentially expressed in CM. After univariate analysis, LASSO penalized regression analysis, and multivariate analysis, 3 lncRNAs were used to construct risk score model. The proposed risk score model could divide patients into high-risk and low-risk groups with significantly different survival in both training set and validation set. The ROC curve showed good performance in survival prediction in both sets. Furthermore, the nomogram for predicting 3-, 5-, and 10-year OS was established based on lncRNA-based risk score and clinicopathologic factors. The prognostic accuracy of the risk model was confirmed by the discrimination and calibration plots in both training set and validation set.
conclusionsWe established a novel three lncRNA-based risk score model and nomogram to predict overall survival of CM. The proposed nomogram may provide information for individualized treatment in CM patients.
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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.