Dataset Information

A novel microRNA signature for pathological grading in lung adenocarcinoma based on TCGA and GEO data.

ABSTRACT: Lung adenocarcinoma (LUAD) is one of the most common types of lung cancer and its poor prognosis largely depends on the tumor pathological stage. Critical roles of microRNAs (miRNAs) have been reported in the tumorigenesis and progression of lung cancer. However, whether the differential expression pattern of miRNAs could be used to distinguish early?stage (stage I) from mid?late?stage (stages II?IV) LUAD tumors is still unclear. In this study, clinical information and miRNA expression profiles of patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus databases. TCGA?LUAD (n=470) dataset was used for model training and validation, and the GSE62182 (n=94) and GSE83527 (n=36) datasets were used as external independent test datasets. The diagnostic model was created through miRNA feature selection followed by SVM classifier and was confirmed by 5?fold cross?validation. A receiver operating characteristic curve was calculated to evaluate the accuracy and robustness of the model. Using the DX score and LIBSVM tool, a 16?miRNA signature that could distinguish LUAD pathological stages was identified. The area under the curve rates were 0.62 [95% confidence interval (CI): 0.56?0.67], 0.66 (95% CI: 0.54?0.76) and 0.63 (95% CI: 0.43?0.82) in TCGA?LUAD internal validation dataset, the GSE62182 external validation dataset, and the GSE83527 external validation dataset, respectively. Kyoto Encyclopedia of Genes and Genomes and Gene Ontology enrichment analyses suggested that the target genes of the 16?miRNA signature were mainly involved in metabolic pathways. The present findings demonstrate that a 16?miRNA signature could serve as a promising diagnostic biomarker for pathological staging in LUAD.

SUBMITTER: Yang Z

PROVIDER: S-EPMC7138293 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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A novel microRNA signature for pathological grading in lung adenocarcinoma based on TCGA and GEO data.

Yang Zhiyu Z Yin Hongkun H Shi Lei L Qian Xiaohua X

International journal of molecular medicine 20200304 5

Lung adenocarcinoma (LUAD) is one of the most common types of lung cancer and its poor prognosis largely depends on the tumor pathological stage. Critical roles of microRNAs (miRNAs) have been reported in the tumorigenesis and progression of lung cancer. However, whether the differential expression pattern of miRNAs could be used to distinguish early‑stage (stage I) from mid‑late‑stage (stages II‑IV) LUAD tumors is still unclear. In this study, clinical information and miRNA expression profiles ...[more]

PMID: 32323746

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Project description:BACKGROUNDS:Lung adenocarcinoma (LUAD) is one of the most common malignancies, and is a serious threat to human health. The aim of the present study was to assess potential biomarkers for the prognosis of LUAD through the analysis of gene expression microarrays. METHODS:The gene expression data for GSE118370 was downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between normal lung and LUAD samples were screened using the R language. The DAVID database was used to analyze the functions and pathways of DEGs. The STRING database was used to the map protein-protein interaction (PPI) networks, and these were visualized with the Cytoscape software. Finally, the prognostic analysis of the hub gene in the PPI network was performed using the Kaplan-Meier tool. RESULTS:A total of 406 downregulated and 203 upregulated DEGs were identified. The GO analysis results revealed that downregulated DEGs were significantly enriched in angiogenesis, calcium ion binding and cell adhesion. The upregulated DEGs were significantly enriched in the extracellular matrix disassembly, collagen catabolic process, chemokine-mediated signaling pathway and endopeptidase inhibitor activity. The KEGG pathway analysis revealed that downregulated DEGs were enriched in neuroactive ligand-receptor interaction, hematopoietic cell lineage and vascular smooth muscle contraction, while upregulated DEGs were enriched in phototransduction. In addition, the top 10 hub genes and the most closely interacting modules of the top 3 proteins in the PPI network were screened. Finally, the independent prognostic value of each hub gene in LUAD patients was analyzed through the Kaplan-Meier plotter. Seven hub genes (ADCY4, S1PR1, FPR2, PPBP, NMU, PF4, and GCG) were closely correlated to overall survival time. CONCLUSION:The discovery of these candidate genes and pathways reveals the etiology and molecular mechanisms of LUAD, providing ideas and guidance for the development of new therapeutic approaches to LUAD.

Project description:BackgroundLung adenocarcinoma (LUAD) is the major cause of cancer mortality. Traditional prognostic factors have limited importance after including other parameters. Thus, developing a more credible prognostic model combined with genes and clinical parameters is necessary.MethodsThe messenger RNA (mRNA) expression and clinical information from The Cancer Genome Atlas (TCGA)-LUAD datasets and microarray data from three Gene Expression Omnibus (GEO) databases were obtained. We identified differentially-expressed genes (DEGs) between lung tumor and normal tissues through integrated analysis of the three GEO datasets. Univariate and multivariate Cox regression analyses were conducted to select survival-associated DEGs and to establish a prognostic gene signature which was associated with overall survival (OS). The expression of gene proteins was assessed in 180 LUAD tissue microarrays (TMAs) by immunohistochemistry (IHC). We verified its predictive performance with a Kaplan-Meier (KM) curve, receiver operating characteristic (ROC) curve, and Harrell's concordance index (C-index) and validated it in external GEO databases. Multivariate Cox regression analysis was performed to identify the significant prognostic indicators in LUAD. Furthermore, we established a prognostic nomogram based on TCGA-LUAD dataset.ResultsA three-gene signature was constructed to predict the OS of LUAD patients. The KM analysis, ROC curve, and C-index present a good predictive ability of the gene signature in TCGA dataset [P<0.0001; C-index 0.6375; 95% confidence interval (CI): 0.5632-0.7118; area under the ROC curve (AUC) 0.674] and the external GEO datasets (P=0.05, 0.004, and 0.04, respectively). Univariate and multivariate Cox regression analyses also verified that LUAD patients with low-risk scores had a decreased risk of death compared to those with a high-risk score in TCGA database [hazard ratio (HR) =0.3898; 95% CI: 0.1938-0.7842; P<0.05]. Finally, we constructed a nomogram integrating the gene signature and clinicopathological parameters (P<0.0001; C-index 0.762; 95% CI: 0.714-0.845; AUC 0.8136). Compared with conventional staging, a nomogram can effectively improve prognosis prediction.ConclusionsThe nomogram is closely associated to the OS of LUAD patients. This consequence may be beneficial to individualized treatment and clinical decision-making.

Project description:Pyroptosis-related genes (PRGs) have been reported to be associated with prognosis of lung adenocarcinoma (LUAD). Until now, the relationship of PRGs to the prognosis of LUAD patients and its underlying mechanisms have been poorly elucidated. Using The Cancer Genome Atlas (TCGA) LUAD cohort, a prior bioinformatics analysis constructed a prognostic signature incorporating 5 PRGs (NLRP7, NLRP1, NLRP2, NOD1, and CASP6) for predicting prognosis of LUAD patients. However, it has not been validated by the Gene Expression Omnibus (GEO) LUAD cohort yet. We implemented a modified bioinformatics analysis to, respectively, construct one prognostic signature with the TCGA cohort and with the GEO cohort and attempted to perform cross-validations by the GEO cohort and the TCGA cohort alternately in turn. Univariate and multivariate Cox regression analysis screened PRGs and constructed 2 prognostic signatures with the TCGA and GEO cohorts. All LUAD samples were classified into high- and low-risk groups according to the median risk score that was generated by regression formula. Kaplan-Meier survival analysis compared the overall survival rate between the 2 risk groups, and receiver operating characteristic curve analysis evaluated predictive performance of the 2 signatures. Additionally, risk score, combined with clinicopathological features, was subjected to multivariate Cox regression analysis, to evaluate independent prognostic value of the 2 signatures. Finally, the 2 signatures received cross-validations by the GEO and TCGA cohorts, alternately. The TCGA cohort yielded a 3-gene signature (PYCARD, NLRP1, and NLRC4), whereas the GEO cohort built a 7-gene signature (SCAF11, NOD1, NLRP2, NLRP1, GPX4, CASP8, and AIM2) for predicting the prognosis of LUAD patients. Multivariate analysis proved independent prognostic value of risk score in the TCGA cohort (hazard ratio, = 1.939,; P = 8.43 × 10-4) and the GEO cohort (hazard ratio, = 2.291,; P = 4.34 × 10-9). Cross-validations confirmed prognostic value for the 7-gene signature from the GEO cohort by the TCGA cohort but not for the 3-gene signature from the TCGA cohort by the GEO cohort. We develop and validate a 7-gene prognostic signature (SCAF11, NOD1, NLRP2, NLRP1, GPX4, CASP8, and AIM2) with independent prognostic value for patients with LUAD.

Project description:BackgroundLung cancer is the leading cause of cancer morbidity and mortality worldwide, however, the individualized treatment is still unsatisfactory. DNA methylation can affect gene regulation and may be one of the most valuable biomarkers in predicting the prognosis of lung adenocarcinoma. This study was aimed to identify methylation CpG sites that may be used to predict lung adenocarcinoma prognosis.MethodsThe Cancer Genome Atlas (TCGA) database was used to detect methylation CpG sites associated with lung adenocarcinoma prognosis and construct a methylation signature model. Then, a Chinese cohort was carried out to estimate the association between methylation and lung adenocarcinoma prognosis. Biological function studies, including demethylation treatment, cell proliferative capacity, and gene expression changes in lung adenocarcinoma cell lines, were further performed.ResultsIn the TCGA set, three methylation CpG sites were selected that were associated with lung adenocarcinoma prognosis (cg14517217, cg15386964, and cg18878992). The risk of mortality was increased in lung adenocarcinoma patients with the gradual increase level of methylation signature based on three methylation sites levels (HR = 45.30, 95% CI = 26.69-66.83; p < 0.001). The C-statistic value increased to 0.77 when age, gender, and other clinical variables were added to the signature to prediction model. A similar situation was confirmed in Chinese lung adenocarcinoma cohort. In the biological function studies, the proliferative capacity of cell lines was inhibited when the cells were demethylated with 5-aza-2'-deoxycytidine (5-aza-2dC). The mRNA and protein expression levels of SEPT9 and HIST1H2BH (cg14517217 and cg15386964) were downregulated with different concentrations of 5-aza-2dC treatment, while cg18878992 showed the opposite result.ConclusionThis study is the first to develop a three-CpG-based model for lung adenocarcinoma, which is a practical and useful tool for prognostic prediction that has been validated in a Chinese population.

Dataset Information

A novel microRNA signature for pathological grading in lung adenocarcinoma based on TCGA and GEO data.

Publications

A novel microRNA signature for pathological grading in lung adenocarcinoma based on TCGA and GEO data.

Similar Datasets

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