Dataset Information

Prognostic value of a five-lncRNA signature in esophageal squamous cell carcinoma.

ABSTRACT: Background:The aim of this study was to identify prognostic long non-coding RNAs (lncRNAs) and develop a multi-lncRNA signature for suvival prediction in esophageal squamous cell carcinoma (ESCC). Methods:The clinical and gene expression data from Gene Expression Omnibus database (GSE53624, n?=?119) were obtianed as training set. A total of 98 paired ESCC tumor and normal tissues were detected by RNA sequencing and used as test set. Another 84 ESCC tissues were used for real-time quantitative PCR(qRT-PCR) and as an independent validation cohort. Survival analysis, Cox regression and Kaplan-Meier analysis were performed. Results:We screened a prognostic marker of ESCC from the GSE53624 dataset and named it as the five-lncRNA signature including AC007179.1, MORF4L2-AS1, RP11-488I20.9, RP13-30A9.2, RP4-735C1.6, which could classify patients into high- and low-risk groups with significantly different survival(median survival: 1.75 years vs. 4.01 years, log rank P?

SUBMITTER: Zhang L

PROVIDER: S-EPMC7419219 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Prognostic value of a five-lncRNA signature in esophageal squamous cell carcinoma.

Zhang Lan L Li Pan P Liu Enjie E Xing Chenju C Zhu Di D Zhang Jianying J Wang Weiwei W Jiang Guozhong G

Cancer cell international 20200810

<h4>Background</h4>The aim of this study was to identify prognostic long non-coding RNAs (lncRNAs) and develop a multi-lncRNA signature for suvival prediction in esophageal squamous cell carcinoma (ESCC).<h4>Methods</h4>The clinical and gene expression data from Gene Expression Omnibus database (GSE53624, n = 119) were obtianed as training set. A total of 98 paired ESCC tumor and normal tissues were detected by RNA sequencing and used as test set. Another 84 ESCC tissues were used for real-time ...[more]

PMID: 32831646

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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.

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.

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: Cuproptosis has been recognized as a novel regulatory cell death, which has been confirmed to promote the occurrence and development of tumors. However, whether cuproptosis-related lncRNA has an impact on the prognosis of squamous cell carcinoma of the head and neck (HNSCC) is still unclear. Methods: In total, 501 HNSCC tumor samples and 44 normal were downloaded from the TCGA database. Cuproptosis-related lncRNAs were obtained by co-expressed analysis. We got prognostic lncRNA that was associated with cuproptosis by using univariate Cox regression analysis and LASSO Cox regression. Then we constructed and validated the prognostic signature of HNSCC and analyzed the immune landscape of the signature. Results: The Prognostic Signature is based on 10 cuproptosis-related lncRNAs including AC090587.1, AC004943.2, TTN-AS1, AL162458.1, AC106820.5, AC012313.5, AL132800.1, WDFY3-AS2, CDKN2A-DT, and AL136419.3. The results of overall survival, risk score distribution, and survival status in the low-risk group were better than those in the high-risk group. In addition, all immune checkpoint genes involved were significantly different between the two risk groups (p < 0.05). The risk score was positively correlated with Eosinophils. M0 and M2 phenotype macrophages, mast cells activated, NK cells activated, and negatively related with B cells naive, mast cells resting, plasma cells, CD8T cells, T cells follicular helper, T cells regulatory (Tregs). Consensus clustering was identified in molecular subtypes of HNSC. More high-risk samples concentrated in Cluster1, which had a higher Tumor Immune Dysfunction and Exclusion (TIDE) score and Single Nucleotide Polymorphisms (SNP) alternation than Cluster2. Conclusion: Our study elucidated the correlation between cuproptosis-related lncRNA with prognosis and immune landscape of HNSCC, which may provide references for further research on the exploration of the mechanism and functions of the prognosis for HNSCC.

Dataset Information

Prognostic value of a five-lncRNA signature in esophageal squamous cell carcinoma.

Publications

Prognostic value of a five-lncRNA signature in esophageal squamous cell carcinoma.

Similar Datasets

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