Dataset Information

Identification of a 7-miRNA signature for predicting the prognosis of patients with lung adenocarcinoma.

ABSTRACT: The role of microRNAs (miRNAs) in tumor diagnosis and patients' prognosis has recently gained extensive research attention. This study was designed to analyze miRNA in lung adenocarcinoma (LUAD) using bioinformatics analysis and to identify novel biomarkers to predict overall survival (OS) for LUAD patients. Differential miRNA expression analysis was performed on LUAD, and normal tissues were extracted from The Cancer Genome Atlas (TCGA). Univariate Cox risk regression and least absolute shrinkage and selection operator (LASSO) Cox analysis were used to screen prognostic miRNAs and develop a risk score model. The prognostic performance of the system was examined utilizing the Kaplan-Meier and receiver operating characteristic (ROC) curves. Independent prognostic factors of LUAD were determined by multivariate Cox regression analysis. Nomogram was constructed according to the independent prognostic factors to evaluate the patients' one-, three- and five-year OS. A 7-miRNA signature based on miR-584-5p, miR-31-3p, miR-490-3, miR-4661-5p, miR-30e-5p, miR-582-5p, and miR-148a-3p was established. To categorize patients into high- and low-risk groups, the risk score was computed. The OS of the low-risk group was significantly longer than the high-risk group, and the signature showed high sensitivity and specificity in anticipating the one-, three- and five-year OS. The system was an independent factor in predicting the OS of LUAD patients and performed better when combined with the N stage in nomogram. A 7-miRNA signature developed in this study could accurately predict LUAD survival.

SUBMITTER: Liu R

PROVIDER: S-EPMC9039494 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of a 7-miRNA signature for predicting the prognosis of patients with lung adenocarcinoma.

Liu Ruijun R Guo Zhiyi Z Huang Jia J Li Jiantao J Tan Qiang Q Luo Qingquan Q

Experimental biology and medicine (Maywood, N.J.) 20220122 8

The role of microRNAs (miRNAs) in tumor diagnosis and patients' prognosis has recently gained extensive research attention. This study was designed to analyze miRNA in lung adenocarcinoma (LUAD) using bioinformatics analysis and to identify novel biomarkers to predict overall survival (OS) for LUAD patients. Differential miRNA expression analysis was performed on LUAD, and normal tissues were extracted from The Cancer Genome Atlas (TCGA). Univariate Cox risk regression and least absolute shrinka ...[more]

PMID: 35068222

Similar Datasets

Project description:BACKGROUND: Lung adenocarcinoma is a heterogernous disease that creates challenges for classification and management. The purpose of this study is to identify specific miRNA markers closely associated with the survival of LUAD patients from a large dataset of significantly altered miRNAs, and to assess the prognostic value of this miRNA expression profile for OS in patients with LUAD. METHODS: We obtained miRNA expression profiles and corresponding clinical information for 372 LUAD patients from The Cancer Genome Atlas (TCGA), and identified the most significantly altered miRNAs between tumor and normal samples. Using survival analysis and supervised principal components method, we identified an eight-miRNA signature for the prediction of overall survival (OS) of LUAD patients. The relationship between OS and the identified miRNA signature was self-validated in the TCGA cohort (randomly classified into two subgroups: n?=?186 for the training set and n?=?186 for the testing set). Survival receiver operating characteristic (ROC) analysis was used to assess the performance of survival prediction. The biological relevance of putative miRNA targets was also analyzed using bioinformatics. RESULTS: Sixteen of the 111 most significantly altered miRNAs were associated with OS across different clinical subclasses of the TCGA-derived LUAD cohort. A linear prognostic model of eight miRNAs (miR-31, miR-196b, miR-766, miR-519a-1, miR-375, miR-187, miR-331 and miR-101-1) was constructed and weighted by the importance scores from the supervised principal component method to divide patients into high- and low-risk groups. Patients assigned to the high-risk group exhibited poor OS compared with patients in the low-risk group (hazard ratio [HR]?=?1.99, P <0.001). The eight-miRNA signature is an independent prognostic marker of OS of LUAD patients and demonstrates good performance for predicting 5-year OS (Area Under the respective ROC Curves [AUC]?=?0.626, P?=?0.003), especially for non-smokers (AUC?=?0.686, P?=?0.023). CONCLUSIONS: We identified an eight-miRNA signature that is prognostic of LUAD. The miRNA signature, if validated in other prospective studies, may have important implications in clinical practice, in particular identifying a subgroup of patients with LUAD who are at high risk of mortality.

Project description:Lung adenocarcinomas (LUADs) harbouring epidermal growth factor receptor (EGFR) mutations are generally unable to benefit from immune checkpoint inhibitors (ICIs) due to an immunosuppressive tumour microenvironment (TME) and a lower tumour mutation burden. Currently, no gene signature can comprehensively evaluate the TME and predict the prognosis of patients with EGFR-mutant LUAD. Using the Cancer Genome Atlas database of EGFR-mutant LUAD based on the immune score derived from the ESTIMATE algorithm, we divided 80 patients with EGFR-mutant LUAD samples into high and low immune score groups with different immune microenvironments. Subsequently, we screened 396 differentially expressed immune-related genes with prognostic value. The top Gene Ontology terms were significantly enriched in biological functions related to T cell differentiation, immune response, cell cycle, and cell proliferation, which are closely related to the immune microenvironment of tumours. In addition, the KEGG pathway enrichment analysis mainly focused on cell cycle, cell adhesion molecules, and cytokine-cytokine receptor interaction, which also had a relationship with the immune response. Subsequently, we identified a three-gene signature including BTLA, BUB1B, and CENPE using the LASSO Cox regression model. The three-gene signature could accurately identify patients at risk of EGFR-mutant LUAD in the training and validation sets and high-risk patients from both the sets exhibited significantly shorter overall survival (p=0.0053 and p=0.035, respectively). CIBERSORT was used to evaluate the abundance of immune cell infiltration in the EGFR-mutant LUAD microenvironment. The immune activity of B cells and macrophages was higher in the low-risk group, while the immune activity of natural killer cells and T cells was higher in the high-risk group. Thus, the three-gene signature closely related to immunosuppressive TME could predict the risk and prognosis in patients with EGFR-mutant LUAD.

Dataset Information

Identification of a 7-miRNA signature for predicting the prognosis of patients with lung adenocarcinoma.

Publications

Identification of a 7-miRNA signature for predicting the prognosis of patients with lung adenocarcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets