ArticleOncotarget2016
A novel molecular and clinical staging model to predict survival for patients with esophageal squamous cell carcinoma.
Article in Oncotarget, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Overexpression of UBE2C in esophageal squamous cell carcinoma tissues and molecular analysis.BMC cancer · 2021Article
- Establishment of a Novel Risk Score System of Immune Genes Associated With Prognosis in Esophageal Carcinoma.Frontiers in oncology · 2021Article
- Identification of crucial genes associated with lung adenocarcinoma by bioinformatic analysis.Medicine · 2020Observational
- The expression of ubiquitin-conjugating enzyme E2C and KAI1 in ovarian carcinoma and their clinical significance.Medicine · 2019Observational
- Protein-coding genes combined with long noncoding RNA as a novel transcriptome molecular staging model to predict the survival of patients with esophageal squamous cell carcinoma.Cancer communications (London, England) · 2018Article
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Authors and funding
12 authors.
Funding
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Abstract
Current prognostic factors fail to accurately determine prognosis for patients with esophageal squamous cell carcinoma (ESCC) after surgery. Here, we constructed a survival prediction model for prognostication in patients with ESCC. Candidate molecular biomarkers were extracted from the Gene Expression Omnibus (GEO), and Cox regression analysis was performed to determine significant prognostic factors. The survival prediction model was constructed based on cluster and discriminant analyses in a training cohort (N=205), and validated in a test cohort (N=207). The survival prediction model consisting of two genes (UBE2C and MGP) and two clinicopathological factors (tumor stage and grade) was developed. This model could be used to accurately categorize patients into three groups in the test cohort. Both disease-free survival and overall survival differed among the diverse groups (P<0.05). In summary, we have developed and validated a predictive model that is based on two gene markers in conjunction with two clinicopathological variables, and which can accurately predict outcomes for ESCC patients after surgery.
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