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A novel molecular and clinical staging model to predict survival for patients with esophageal squamous cell carcinoma.

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.

SUBMITTER: Wang W

PROVIDER: S-EPMC5325382 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature

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A novel molecular and clinical staging model to predict survival for patients with esophageal squamous cell carcinoma.

Wang Wei W Wang Zhiwei Z Zhao Jun J Wei Min M Zhu Xinghua X He Qi Q Ling Tianlong T Chen Xiaoyan X Cao Ziang Z Zhang Yixin Y Liu Lei L Shi Minxin M

Oncotarget 20160901 39

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 ...[more]

PMID: 27556859

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Project description: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.

Project description:Esophageal squamous cell carcinoma (ESCC) is one of the six most commonly diagnosed tumor types in the Chinese population. Gene expression profiles help to predict the prognosis of patients with ESCC. Disease recurrence as the survival endpoint has been analyzed in the majority of previous studies; therefore, the aim of the present study was to construct a robust gene signature in order to determine the overall survival (OS) of patients with ESCC. The gene expression and clinical data of patients with ESCC were downloaded from The Cancer Genome Atlas (TCGA) database. Of the selected data (172 samples from surviving patients), 72 samples were randomly selected as modeling data, and verification was conducted using the entire dataset. Data from the Gene Expression Omnibus was analyzed simultaneously, and a venn diagram was constructed to determine the intersection between these two sets of results; a total of 97 genes were found to be associated with OS. Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that these genes were primarily associated with specific pathways (Homo sapiens), including DNA replication, protein processing in endoplasmic reticulum and influenza A. A five-gene signature was identified with a robust likelihood-based survival modeling approach. Using regression coefficient modeling, a prognostic model consisting of the C-X-C motif chemokine ligand 8, DNA damage inducible transcript 3, RAB27A, member RAS oncogene family, replication factor C subunit 2 and elongation factor for RNA polymerase II 2 genes was constructed and validated. Based on these results, patients were subdivided into high and low-risk groups. Compared with the high-risk group, the OS time of patients in the low-risk group was significantly increased. Furthermore, it was determined that the five genes were all differentially expressed in ESCC tissues compared with normal tissues, indicating the potential role of these genes in ESCC initiation and progression. In another independent cohort, this five-gene signature was further confirmed and was considered as an independent prognostic biomarker for OS prediction in patients with ESCC. In conclusion, the OS of patients with ESCC may be predicted using this five-gene signature, which may be useful in identifying patients with high-risk ESCC.

Dataset Information

A novel molecular and clinical staging model to predict survival for patients with esophageal squamous cell carcinoma.

Publications

A novel molecular and clinical staging model to predict survival for patients with esophageal squamous cell carcinoma.

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