Dataset Information

A five-long non-coding RNA signature with the ability to predict overall survival of patients with lung adenocarcinoma.

ABSTRACT: An increasing number of studies have indicated that the abnormal expression of certain long non-coding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with lung adenocarcinoma (LUAD). The aim of the present study was to establish an lncRNA signature to predict the survival of patients with LUAD. The gene expression profiles and associated clinical information of patients with LUAD were downloaded from The Cancer Genome Atlas database. The cohort was randomly sub-divided into training and verification cohorts. Univariate Cox regression analysis was performed on differentially expressed lncRNAs in the training cohort to select candidate lncRNAs closely associated with survival. Next, a risk score (RS) model consisting of 5 lncRNAs was established by multivariate Cox regression analysis on candidate lncRNAs. Using the median RS obtained from the training cohort as a cut-off point, patients were classified into high- and low-risk groups. Kaplan-Meier survival analysis revealed a significant difference in OS between high- and low-risk groups. The survival prediction ability of the 5-lncRNA signature was further tested in the verification and total cohorts. The results proved that the 5-lncRNA signature had good reliability and stability in survival prediction for patients with LUAD. The univariate Cox regression analysis for the 5-lncRNA signature in each cohort indicated that the 5-lncRNA signature was closely associated with survival. Multivariate Cox regression analysis and stratification analysis proved that the prognostic signature was an independent predictor of survival for patients with LUAD. In addition, functional enrichment analysis indicated that the 5 prognostic lncRNAs may be involved in the tumorigenesis of LUAD through cancer-associated pathways and biological processes. Taken together, the present study provided a 5-lncRNA signature that may serve as an independent survival predictor for patients with LUAD.

SUBMITTER: Zeng L

PROVIDER: S-EPMC6862666 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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A five-long non-coding RNA signature with the ability to predict overall survival of patients with lung adenocarcinoma.

Zeng Lizhong L Wang Wei W Chen Yang Y Lv Xin X Yuan Jingyan J Sun Ruiying R Yang Shuanying S

Experimental and therapeutic medicine 20191025 6

An increasing number of studies have indicated that the abnormal expression of certain long non-coding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with lung adenocarcinoma (LUAD). The aim of the present study was to establish an lncRNA signature to predict the survival of patients with LUAD. The gene expression profiles and associated clinical information of patients with LUAD were downloaded from The Cancer Genome Atlas database. The cohort was randomly sub-divided into tr ...[more]

PMID: 31777562

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Project description:BackgroundImmunotherapy has changed the treatment landscape for lung cancer. This study aims to construct a tumor mutation-related model that combines long non-coding RNA (lncRNA) expression levels and tumor mutation levels in tumor genomes to detect the possibilities of the lncRNA signature as an indicator for predicting the prognosis and response to immunotherapy in lung adenocarcinoma (LUAD).MethodsWe downloaded the tumor mutation profiles and RNA-seq expression database of LUAD from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs were extracted based on the cumulative number of mutations. Cox regression analyses were used to identify the prognostic lncRNA signature, and the prognostic value of the five selected lncRNAs was validated by using survival analysis and the receiver operating characteristic (ROC) curve. We used qPCR to validate the expression of five selected lncRNAs between human lung epithelial and human lung adenocarcinoma cell lines. The ImmuCellAI, immunophenoscore (IPS) scores and Tumor Immune Dysfunction and Exclusion (TIDE) analyses were used to predict the response to immunotherapy for this mutation related lncRNA signature.ResultsA total of 162 lncRNAs were detected among the differentially expressed lncRNAs between the Tumor mutational burden (TMB)-high group and the TMB-low group. Then, five lncRNAs (PLAC4, LINC01116, LINC02163, MIR223HG, FAM83A-AS1) were identified as tumor mutation-related candidates for constructing the prognostic prediction model. Kaplan‒Meier curves showed that the overall survival of the low-risk group was significantly better than that of the high-risk group, and the results of the GSE50081 set were consistent. The expression levels of PD1, PD-L1 and CTLA4 in the low-risk group were higher than those in the high-risk group. The IPS scores and TIDE scores of patients in the low-risk group were significantly higher than those in the high-risk group.ConclusionOur findings demonstrated that the five lncRNAs (PLAC4, LINC01116, LINC02163, MIR223HG, FAM83A-AS1) were identified as candidates for constructing the tumor mutation-related model which may serve as an indicator of tumor mutation levels and have important implications for predicting the response to immunotherapy in LUAD.

Project description:BackgroundLung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer-related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis.MethodThe information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters.ResultA total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year.ConclusionA series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.

Project description:Background: Lung adenocarcinoma (LUAD) is a malignant tumor with high heterogeneity and poor prognosis. Ferroptosis, a form of regulated cell-death-related iron, has been proven to trigger inflammation-associated immunosuppression in the tumor microenvironment, which promotes tumor growth. Therefore, the clinical prognostic value of ferroptosis-related genes in LUAD needs to be further explored. Method: In this study, we downloaded the mRNA expression profiles and corresponding clinical data of LUAD patients from the Cancer Genome Atlas database. The least absolute shrinkage and selection operator (LASSO) Cox regression model was utilized to construct ferroptosis-related gene signature. Based on these, we established the nomograms for prognosis prediction and validated the model in the GSE72094 dataset. The cell type was identified using the CIBERSORT algorithm for estimating relative subsets of RNA transcripts, which was then used to screen significant tumor immune-infiltrating cells associated with the LUAD prognosis prediction model. Subsequently, we applied co-expression analysis to reveal the relationship between ferroptosis-related genes and significant immune cells. Results: The univariate COX regression analysis showed that 20 genes were associated with the overall survival (OS) as prognostic differentially expressed genes (DEGs) (FDR <0.05). Patients were divided into two risk groups using a 13-gene signature, with the high-risk group having a significantly worse OS than their low-risk counterparts (p < 0.001). We used receiver operating characteristic (ROC) curve analysis to confirm the predictive capacity of the signature. Besides, we identified seven pairs of ferroptosis-related genes and tumor-infiltrating immune cells associated with the prognosis of LUAD patients. Conclusion: In this study, we construct a ferroptosis-related gene signature that can be used for prognostic prediction in LUAD. In addition, we reveal a potential connection between ferroptosis and tumor-infiltrating immune cells.

Dataset Information

A five-long non-coding RNA signature with the ability to predict overall survival of patients with lung adenocarcinoma.

Publications

A five-long non-coding RNA signature with the ability to predict overall survival of patients with lung adenocarcinoma.

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