ArticleDisease markers2021
A Five-Gene Prognostic Nomogram Predicting Disease-Free Survival of Differentiated Thyroid Cancer.
Article in Disease markers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Apolipoprotein D downregulation in OSCC: multi-database validation and clinical significance.BMC medical genomics · 2026Article
- Identification of predictive pretreatment biomarkers for neoadjuvant chemotherapy response in Latino invasive breast cancer patients.Molecular medicine (Cambridge, Mass.) · 2025Article
- Identification of oxidative stress-related genes in papillary thyroid carcinoma and their common targets with resveratrol based on bioinformatics, network pharmacology, and molecular docking.Discover oncology · 2025Article
- Thyroid differentiation score-related genes and prognostic model for thyroid cancer.Translational cancer research · 2025Article
- A Six-Gene Mitochondrial Signature Predicts Prognosis in Dedifferentiated Thyroid Cancer.International journal of general medicine · 2025Article
- Analysis of metastasis‑related risk factors and clinical relevance in adult soft‑tissue sarcoma.Oncology letters · 2024Article
- Review
- PathExpSurv: pathway expansion for explainable survival analysis and disease gene discovery.BMC bioinformatics · 2023Article
- A risk prediction model mediated by genes of APOD/APOC1/SQLE associates with prognosis in cervical cancer.BMC women's health · 2022Article
- Review
- A seven-autophagy-related gene signature for predicting the prognosis of differentiated thyroid carcinoma.World journal of surgical oncology · 2022Article
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3 authors.
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Abstract
backgroundDifferentiated thyroid cancer (DTC) is the most common type of thyroid tumor with a high recurrence rate. Here, we developed a nomogram to effectively predict postoperative disease-free survival (DFS) in DTC patients.
methodsThe mRNA expressions and clinical data of DTC patients were downloaded from the Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database. Seventy percent of patients were randomly selected as the training dataset, and thirty percent of patients were classified into the testing dataset. Multivariate Cox regression analysis was adopted to establish a nomogram to predict 1-year, 3-year, and 5-year DFS rate of DTC patients.
resultsA five-gene signature comprised of TENM1, FN1, APOD, F12, and BTNL8 genes was established to predict the DFS rate of DTC patients. Results from the concordance index (C-index), area under curve (AUC), and calibration curve showed that both the training dataset and the testing dataset exhibited good prediction ability, and they were superior to other traditional models. The risk score and distant metastasis (M) of the five-gene signature were independent risk factors that affected DTC recurrence. A nomogram that could predict 1-year, 3-year, and 5-year DFS rate of DTC patients was established with a C-index of 0.801 (95% CI: 0.736, 0.866).
conclusionOur study developed a prediction model based on the gene expression and clinical characteristics to predict the DFS rate of DTC patients, which may be applied to more accurately assess patient prognosis and individualized treatment.
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