Dataset Information

The prognostic value of a seven-lncRNA signature in patients with esophageal squamous cell carcinoma: a lncRNA expression analysis.

ABSTRACT: 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?

SUBMITTER: Weng NQ

PROVIDER: S-EPMC6995134 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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The prognostic value of a seven-lncRNA signature in patients with esophageal squamous cell carcinoma: a lncRNA expression analysis.

Weng Nuo-Qing NQ Chi Jun J Wen Jing J Mai Shi-Juan SJ Zhang Mei-Yin MY Huang Long L Liu Ji J Yang Xian-Zi XZ Xu Guo-Liang GL Fu Jian-Hua JH Wang Hui-Yun HY

Journal of translational medicine 20200131 1

<h4>Background</h4>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).<h4>Methods</h4>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 ...[more]

PMID: 32005248

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Project description:BackgroundAbsent in melanoma 1-like (AIM1L), also known as crystalline beta gamma domain containing 2. The relationship between AIM1L and tumors has not been fully investigated, and the biological function of AIM1L in different tumors is unknown, so we bioinformatically explored a possible relationship between AIM1L and esophageal squamous cell carcinoma (ESCC).MethodsAIM1L mRNA expression was detected by the Gene Expression Omnibus database (GSE20347, GSE161533, and GSE53625), and protein level expression was detected by immunohistochemistry. The correlation between AIM1L expression and clinical pathological characteristics was evaluated by the Wilcoxon signed rank test or chi-square test. Kaplan-Meier analysis and Cox proportional risk regression model were used to determine the prognostic value of AIM1L in ESCC patients and establish and verify a nomogram. Find genes highly related to the expression of AIM1L, conduct GO and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, and conduct GSEA analysis on the gene set. The "CIBERSORT" R package was used to explore the relationship between AIM1L and immune infiltration, and the "OncoPredict" R package was used to explore the relationship between AIM1L and drug sensitivity.ResultsCompared with the matched adjacent non-cancer tissues, the expression of AIM1L was down-regulated in ESCC tissues, and correlated with tumor grade. Kaplan-Meier survival analysis and Cox analysis showed that the low expression of AIM1L was related to the poor prognosis of ESCC patients. Enrichment analysis explained the possible function of AIM1L, GSEA determined the highly correlated signal pathway of AIM1L low expression phenotype, immune infiltration analysis determined that AIM1L was related to activated NK cells and macrophage M2, and drug sensitivity analysis determined that the low expression of AIM1L might be related to EGFR targeted drug resistance.ConclusionAIM1L may be a candidate tumor suppressor gene for ESCC and an independent molecular biomarker for the prognosis of ESCC patients.

Project description: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.

Dataset Information

The prognostic value of a seven-lncRNA signature in patients with esophageal squamous cell carcinoma: a lncRNA expression analysis.

Publications

The prognostic value of a seven-lncRNA signature in patients with esophageal squamous cell carcinoma: a lncRNA expression analysis.

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