Dataset Information

Identification and Validation of a Potential Prognostic 7-lncRNA Signature for Predicting Survival in Patients with Multiple Myeloma.

ABSTRACT: Background:An increasing number of studies have indicated that the abnormal expression of certain long noncoding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with myeloma. Methods:Gene expression data of myeloma patients were downloaded from the Gene Expression Omnibus (GEO) database (GSE4581 and GSE57317). Cox regression analysis, Kaplan-Meier, and receiver operating characteristic (ROC) analysis were performed to construct and validate the prediction model. Single sample gene set enrichment (ssGSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used to predict the function of a specified lncRNA. Results:In this study, a seven-lncRNA signature was identified and used to construct a risk score system for myeloma prognosis. This system was used to stratify patients with different survival rates in the training set into high-risk and low-risk groups. Test set, the entire test set, the external validation set, and the myeloma subtype achieved the authentication of the results. In addition, functional enrichment analysis indicated that 7 prognostic lncRNAs may be involved in the tumorigenesis of myeloma through cancer-related pathways and biological processes. The results of the immune score showed that IF_I was negatively correlated with the risk score. Compared with the published gene signature, the 7-lncRNA model has a higher C-index (above 0.8). Conclusion:In summary, our data provide evidence that seven lncRNAs could be used as independent biomarkers to predict the prognosis of myeloma, which also indicated that these 7 lncRNAs may be involved in the progression of myeloma.

SUBMITTER: Zhong Y

PROVIDER: S-EPMC7661128 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Identification and Validation of a Potential Prognostic 7-lncRNA Signature for Predicting Survival in Patients with Multiple Myeloma.

Zhong Yun Y Liu Zhe Z Li Dangchi D Liao Qinyuan Q Li Jingao J

BioMed research international 20201105

<h4>Background</h4>An increasing number of studies have indicated that the abnormal expression of certain long noncoding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with myeloma.<h4>Methods</h4>Gene expression data of myeloma patients were downloaded from the Gene Expression Omnibus (GEO) database (GSE4581 and GSE57317). Cox regression analysis, Kaplan-Meier, and receiver operating characteristic (ROC) analysis were performed to construct and validate the prediction model. ...[more]

PMID: 33204693

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Project description:Dysregulated long non-coding RNAs (lncRNAs) have been found to have oncogenic and/or tumor suppressive roles in the development and progression of cancer, implying their potentials as novel independent biomarkers for cancer diagnosis and prognosis. However, the prognostic significance of expression profile-based lncRNA signature for outcome prediction in patients with multiple myeloma (MM) has not yet been investigated.LncRNA expression profiles of a large cohort of patients with MM were obtained and analyzed by repurposing the publically available microarray data. An lncRNA-focus risk score model was developed from the training dataset, and then validated in the testing and another two independent external datasets. The time-dependent receiver operating characteristic (ROC) curve was used to evaluate the prognostic performance for survival prediction. The biological function of prognostic lncRNAs was predicted using bioinformatics analysis.Four lncRNAs were identified to be significantly associated with overall survival (OS) of patients with MM in the training dataset, and were combined to develop a four-lncRNA prognostic signature to stratify patients into high-risk and low-risk groups. Patients of training dataset in the high-risk group exhibited shorter OS than those in the low-risk group (HR?=?2.718, 95 % CI?=?1.937-3.815, p <0.001). The similar prognostic values of four-lncRNA signature were observed in the testing dataset, entire GSE24080 dataset and another two independent external datasets. Multivariate Cox regression and stratified analysis showed that the prognostic power of four-lncRNA signature was independent of clinical features, including serum beta 2-microglobulin (S?2M), serum albumin (ALB) and lactate dehydrogenase (LDH). ROC analysis also demonstrated the better performance for predicting 3-year OS. Functional enrichment analysis suggested that these four lncRNAs may be involved in known genetic and epigenetic events linked to MM.Our results demonstrated potential application of lncRNAs as novel independent biomarkers for diagnosis and prognosis in MM. These lncRNA biomarkers may contribute to the understanding of underlying molecular basis of MM.

Project description:Background: An accumulating body of evidence suggests that long non-coding RNAs (lncRNAs) can serve as potential cancer prognostic factors. However, the utility of lncRNA combinations in estimating overall survival (OS) for hepatocellular carcinoma (HCC) remains to be elucidated. This study aimed to construct a powerful lncRNA signature related to the OS for HCC to enhance prognostic accuracy. Methods: The expression patterns of lncRNAs and related clinical data of 371 HCC patients were obtained based on The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DElncRNAs) were acquired by comparing tumors with adjacent normal samples. lncRNAs displaying significant association with OS were screened through univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. All cases were classified into the validation or training group at the ratio of 3:7 to validate the constructed lncRNA signature. Data from the Gene Expression Omnibus (GEO) were used for external validation. We conducted real-time polymerase chain reaction (PCR) and assays for Transwell invasion, migration, CCK-8, and colony formation to determine the biological roles of lncRNA. Gene set enrichment analysis (GSEA) of the lncRNA model risk score was also conducted. Results: We identified 1292 DElncRNAs, among which 172 were significant in univariate Cox regression analysis. In the training group (n = 263), LASSO regression analysis confirmed 11 DElncRNAs including AC010547.1, AC010280.2, AC015712.7, GACAT3 (gastric cancer associated transcript 3), AC079466.1, AC089983.1, AC051618.1, AL121721.1, LINC01747, LINC01517, and AC008750.3. The prognostic risk score was calculated, and the constructed risk model showed significant correlation with HCC OS (log-rank P-value of 8.489e-9, hazard ratio of 3.648, 95% confidence interval: 2.238-5.945). The area under the curve (AUC) for this lncRNA model was up to 0.846. This risk model was confirmed in the validation group (n = 108), the entire cohort, and the external GEO dataset (n = 203). GACAT3 was highly expressed in HCC tissues and cell lines. Based on online databases, GACAT3 expression independently affects both OS and disease-free survival in HCC patients. Silencing GACAT3 in vitro significantly suppressed HCC cell proliferation, invasion, and migration. Moreover, pathways related to the lncRNA model risk score were confirmed by GSEA. Conclusion: The lncRNA signature established in this study can be used to predict HCC prognosis, which could provide novel clinical evidence to guide targeted HCC treatment.

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 and Validation of a Potential Prognostic 7-lncRNA Signature for Predicting Survival in Patients with Multiple Myeloma.

Publications

Identification and Validation of a Potential Prognostic 7-lncRNA Signature for Predicting Survival in Patients with Multiple Myeloma.

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