Dataset Information

Identification of a 6-lncRNA prognostic signature based on microarray re-annotation in gastric cancer.

ABSTRACT: Gastric cancer (GC) remains an important malignancy worldwide with poor prognosis. Long noncoding RNAs (lncRNAs) can markedly affect cancer progression. Moreover, lncRNAs have been proposed as diagnostic or prognostic biomarkers of GC. Therefore, the current study aimed to explore lncRNA-based prognostic biomarkers for GC. LncRNA expression profiles from the Gene Expression Omnibus (GEO) database were first downloaded. After re-annotation of lncRNAs, a univariate Cox analysis identified 177 prognostic lncRNA probes in the training set GSE62254 (n = 225). Multivariate Cox analysis of each lncRNA with clinical characteristics as covariates identified a total of 46 prognostic lncRNA probes. Robust likelihood-based survival and least absolute shrinkage and selection operator (LASSO) models were used to establish a 6-lncRNA signature with prognostic value. Receiver operating characteristic (ROC) curve analyses were employed to compare survival prediction in terms of specificity and sensitivity. Patients with high-risk scores exhibited a significantly worse overall survival (OS) than patients with low-risk scores (log-rank test P-value <.0001), and the area under the ROC curve (AUC) for 5-year survival was 0.77. A nomogram and forest plot were constructed to compare the clinical characteristics and risk scores by a multivariable Cox regression analysis, which suggested that the 6-lncRNA signature can independently make the prognosis evaluation of patients. Single-sample GSEA (ssGSEA) was used to determine the relationships between the 6-lncRNA signature and biological functions. The internal validation set GSE62254 (n = 75) and the external validation set GSE57303 (n = 70) were successfully used to validate the robustness of our 6-lncRNA signature. In conclusion, based on the above results, the 6-lncRNA signature can effectively make the prognosis evaluation of GC patients.

SUBMITTER: Ma B

PROVIDER: S-EPMC6943089 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Identification of a 6-lncRNA prognostic signature based on microarray re-annotation in gastric cancer.

Ma Bin B Li Yongmin Y Ren Yupeng Y

Cancer medicine 20191119 1

Gastric cancer (GC) remains an important malignancy worldwide with poor prognosis. Long noncoding RNAs (lncRNAs) can markedly affect cancer progression. Moreover, lncRNAs have been proposed as diagnostic or prognostic biomarkers of GC. Therefore, the current study aimed to explore lncRNA-based prognostic biomarkers for GC. LncRNA expression profiles from the Gene Expression Omnibus (GEO) database were first downloaded. After re-annotation of lncRNAs, a univariate Cox analysis identified 177 prog ...[more]

PMID: 31743579

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Project description:Background: This study aimed to explore the biological functions and prognostic role of Epithelial-mesenchymal transition (Epithelial-mesenchymal transition)-related lncRNAs in colorectal cancer (CRC). Methods: The Cancer Genome Atlas database was applied to retrieve gene expression data and clinical information. An EMT-related lncRNA risk signature was constructed relying on univariate Cox regression, Least Absolute Shrinkage and Selector Operation (LASSO) and multivariate Cox regression analysis of the EMT-related lncRNA expression data and clinical information. Then, an individualized prognostic prediction model based on the nomogram was developed and the predictive accuracy and discriminative ability of the nomogram were determined by the receiver operating characteristic curve and calibration curve. Finally, a series of analyses, such as functional analysis and unsupervised cluster analysis, were conducted to explore the influence of independent lncRNAs on CRC. Results: A total of 581 patients were enrolled and an eleven-EMT-related lncRNA risk signature was identified relying on the comprehensive analysis of the EMT-related lncRNA expression data and clinical information in the training cohort. Then, risk scores were calculated to divide patients into high and low-risk groups, and the Kaplan-Meier curve analysis showed that low-risk patients tended to have better overall survival (OS). Multivariate Cox regression analysis indicated that the EMT-related lncRNA signature was significantly associated with prognosis. The results were subsequently confirmed in the validation dataset. Then, we constructed and validated a predictive nomogram for overall survival based on the clinical factors and risk signature. Functional characterization confirmed this signature could predict immune-related phenotype and was associated with immune cell infiltration (i.e., macrophages M0, M1, Tregs, CD4 memory resting cells, and neutrophils), tumor mutation burden (TMB). Conclusions: Our study highlighted the value of the 11-EMT-lncRNA signature as a predictor of prognosis and immunotherapeutic response in CRC.

Dataset Information

Identification of a 6-lncRNA prognostic signature based on microarray re-annotation in gastric cancer.

Publications

Identification of a 6-lncRNA prognostic signature based on microarray re-annotation in gastric cancer.

Similar Datasets

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