ArticleBritish journal of cancer2014
A novel staging model to classify oesophageal squamous cell carcinoma patients in China.
Article in British journal of cancer, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Clinical efficacy of different neoadjuvant therapies for resectable esophageal squamous cell carcinoma.World journal of surgical oncology · 2025Article
- Article
- Development and Validation of a New Staging System for Esophageal Squamous Cell Carcinoma Patients Based on Combined Pathological TNM, Radiomics, and Proteomics.Annals of surgical oncology · 2023Article
- Stage-Specific Survival From Esophageal Cancer in China and Implications for Control Strategies: A Systematic Review and Meta-Analyses.Gastro hep advances · 2023Review
- P300/CBP-associated factor (PCAF)-mediated acetylation of Fascin at lysine 471 inhibits its actin-bundling activity and tumor metastasis in esophageal cancer.Cancer communications (London, England) · 2021Article
- Emerging Role of Fascin-1 in the Pathogenesis, Diagnosis, and Treatment of the Gastrointestinal Cancers.Cancers · 2021Review
- Pemetrexed exerts anticancer effects by inducing GOncology letters · 2019Article
- MASAN: a novel staging system for prognosis of patients with oesophageal squamous cell carcinoma.British journal of cancer · 2018Article
- 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
- Postoperative radiation therapy of pT2-3N0M0 esophageal carcinoma-a review.Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine · 2016Review
- ATF3 suppresses ESCC via downregulation of ID1.Oncology letters · 2016Article
- Prognostic nomogram for previously untreated patients with esophageal squamous cell carcinoma after esophagectomy followed by adjuvant chemotherapy.Japanese journal of clinical oncology · 2016Article
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Authors and funding
14 authors.
Funding
Abstract
backgroundOesophageal squamous cell carcinoma (ESCC) is the predominant subtype of oesophageal carcinoma in China, with the overall 5-year survival rate of <10%. The current tumour-node-metastasis (TNM) staging system has become so complex that it is not easy to use in the life expectancy assessment. We aim to combine clinical variables and biomarkers to develop and validate a relative simple and reliable model, named the FENSAM, for ESCC prognosis.
methodsTo build the FENSAM, we analysed 22 potential prognostic factors from 461 patients, including 9 biomarkers (Ezrin, Fascin, desmocollin 2 (DSC2), pFascin, activating transcription factor 3 (ATF3), connective-tissue growth factor (CTGF), neutrophil gelatinase-associated lipocalin (NGAL), NGAL receptor (NGALR), and cysteine-rich angiogenic protein 61 (CYR61)) and other 13 clinical variables. We selected significant factors associated with survival of ESCC patients, and used them to build our FENSAM model. We then obtained the hazard risk score of the model to classify ESCC patients. In addition, we validated the model in an independent cohort of 290 patients from the same hospital. The predictive performance of the model was assessed by the Area under the Receiver Operating Characteristic Curve (AUC) and Kaplan-Meier survival analysis.
resultsWe found six markers significantly associated with survival of ESCC patients (Ezrin, Fascin, ATF3, surgery extent, N-stage, and M-stage). They were combined to create a novel four-stage FENSAM model for patients' classification. FENSAM possessed a high classification precision similar to the TNM staging system, but with a much simpler model. The efficiency of FENSAM was evaluated by different quantiles of AUC and the results of survival analysis. The validation result demonstrated the potential of the FENSAM model to improve classification accuracy for ESCC patients.
conclusionsFENSAM provides an alternative classifier for ESCC patients with a high classification precision using a simple model.
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