Dataset Information

The Prognostic Significance of Metabolic Syndrome and a Related Six-lncRNA Signature in Esophageal Squamous Cell Carcinoma.

ABSTRACT: Background: Metabolic syndrome (MetS) is associated with the development of esophageal squamous cell carcinoma (ESCC), and long non-coding RNAs (lncRNAs) are involved in a variety of mechanisms of MetS and tumor. This study will explore the prognostic effect of MetS and the associated lncRNA signature on ESCC. Methods: Our previous RNA-chip data (GSE53624, GSE53622) for 179 ESCC patients were reanalyzed according to MetS. The recurrence-free survival (RFS) was collected for these patients. The status of the MetS-related tumor microenvironment was analyzed with the CIBERSORT and ESTIMATE algorithms. A lncRNA signature was established with univariate and multivariate Cox proportional hazards regression (PHR) analysis and verified using the Kaplan-Meier survival curve analysis and time-dependent receiver operating characteristic (ROC) curves. A clinical predictive model was constructed based on multiple risk factors, evaluated using C-indexes and calibration curves, and verified using data from the GEO and TCGA databases. Results: The results showed that MetS was an independent risk factor for ESCC patients conferring low OS and RFS. Tumor microenvironment analysis indicated that patients with MetS have high stromal scores and M2 macrophage infiltration. A six-lncRNA signature was established by 60 ESCC patients randomly selected from GSE53624 and identified with an effective predictive ability in validation cohorts (59 patients from GSE53624 and 60 patients from GSE53622), subgroup analysis, and ESCC patients from TCGA. MetS and the six-lncRNA signature could be regarded as independent risk factors and enhanced predictive ability in the clinical predictive model. Conclusions: Our results indicated that MetS was associated with poor prognosis in ESCC patients, and the possible mechanism was related to changes in the tumor microenvironment. MetS and the six-lncRNA signature could also serve as independent risk factors with available clinical application value.

SUBMITTER: Liu Y

PROVIDER: S-EPMC7040247 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

The Prognostic Significance of Metabolic Syndrome and a Related Six-lncRNA Signature in Esophageal Squamous Cell Carcinoma.

Liu Yu Y Wang Liyu L Liu Hengchang H Li Chunxiang C He Jie J

Frontiers in oncology 20200218

<b>Background:</b> Metabolic syndrome (MetS) is associated with the development of esophageal squamous cell carcinoma (ESCC), and long non-coding RNAs (lncRNAs) are involved in a variety of mechanisms of MetS and tumor. This study will explore the prognostic effect of MetS and the associated lncRNA signature on ESCC. <b>Methods:</b> Our previous RNA-chip data (GSE53624, GSE53622) for 179 ESCC patients were reanalyzed according to MetS. The recurrence-free survival (RFS) was collected for these p ...[more]

PMID: 32133283

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Project description:BACKGROUND:Long non-coding RNAs (lncRNAs) have been reported to be prognostic biomarkers in many types of cancer. We aimed to identify a lncRNA signature that can predict the prognosis in patients with esophageal squamous cell carcinoma (ESCC). METHODS:Using a custom microarray, we retrospectively analyzed lncRNA expression profiles in 141 samples of ESCC and 81 paired non-cancer specimens from Sun Yat-Sen University Cancer Center (Guangzhou, China), which were used as a training cohort to identify a signature associated with clinical outcomes. Then we conducted quantitative RT-PCR in another 103 samples of ESCC from the same cancer center as an independent cohort to verify the signature. RESULTS:Microarray analysis showed that there were 338 lncRNAs significantly differentially expressed between ESCC and non-cancer esophagus tissues in the training cohort. From these differentially expressed lncRNAs, we found 16 lncRNAs associated with overall survival (OS) of ESCC patients using Cox regression analysis. Then a 7-lncRNA signature for predicting survival was identified from the 16 lncRNAs, which classified ESCC patients into high-risk and low-risk groups. Patients with high-risk have shorter OS (HR: 3.555, 95% CI 2.195-5.757, p?<?0.001) and disease-free survival (DFS) (HR: 2.537, 95% CI 1.646-3.909, p?<?0.001) when compared with patients with low-risk in the training cohort. In the independent cohort, the 7 lncRNAs were detected by qRT-PCR and used to compute risk score for the patients. The result indicates that patients with high risk also have significantly worse OS (HR?=?2.662, 95% CI 1.588-4.464, p?<?0.001) and DFS (HR 2.389, 95% CI 1.447-3.946, p?<?0.001). The univariate and multivariate Cox regression analyses indicate that the signature is an independent factor for predicting survival of patients with ESCC. Combination of the signature and TNM staging was more powerful in predicting OS than TNM staging alone in both the training (AUC: 0.772 vs 0.681, p?=?0.002) and independent cohorts (AUC: 0.772 vs 0.660, p?=?0.003). CONCLUSIONS:The 7-lncRNA signature is a potential prognostic biomarker in patients with ESCC and may help in treatment decision when combined with the TNM staging system.

Dataset Information

The Prognostic Significance of Metabolic Syndrome and a Related Six-lncRNA Signature in Esophageal Squamous Cell Carcinoma.

Publications

The Prognostic Significance of Metabolic Syndrome and a Related Six-lncRNA Signature in Esophageal Squamous Cell Carcinoma.

Similar Datasets

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