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Methylation and transcriptome analysis reveal lung adenocarcinoma-specific diagnostic biomarkers.

ABSTRACT: BACKGROUND:DNA methylation can regulate the role of long noncoding RNAs (lncRNAs) in the development of lung adenocarcinoma (LUAD). The present study aimed to identify methylation-driven lncRNAs and mRNAs as biomarkers in the prognosis of LUAD using bioinformatics analysis. METHODS:Differentially expressed RNAs were obtained using the edge R package from 535 LUAD tissues and 59 adjacent non-LUAD tissues. Differentially methylated genes were obtained using the limma R package from 475 LUAD tissues and 32 adjacent non-LUAD tissues. Methylation-driven mRNA and lncRNA were obtained using the MethylMix R package from 465 LUAD tissues with matched DNA methylation and RNA expression and 32 non-LUAD tissues with DNA methylation. Gene ontology and ConsensusPathDB pathway analysis were performed to identify functional enrichment of methylation-driven mRNAs. Univariate and multivariate Cox regression analyses were performed to identify the independent effect of each variable for predicting the prognosis of LUAD. Kaplan-Meier curve analysis of DNA methylation and gene expression might provide potential prognostic biomarkers for LUAD patients. RESULTS:A total of 99 methylation-driven mRNAs and 17 methylation-driven lncRNAs were obtained. Univariate and multivariate Cox regression analysis showed that 6 lncRNAs (FOXE1, HOXB13-AS1_2, VMO1, HIST1H3F, AJ003147.8, ASXL3) were retrieved to construct a predictive model associated with overall survival in LUAD patients. Combined DNA methylation and gene expression survival analysis revealed that 4 lncRNAs (AC023824.1, AF186192.1, LINC01354 and WASIR2) and 8 mRNAs (S1PR1, CCDC181, F2RL1, EFS, KLHDC9, MPV17L, GKN2, ITPRIPL1) might act as independent biomarkers for the prognosis of LUAD. CONCLUSIONS:Methylation-driven lncRNA and mRNA contribute to the survival of LUAD, and 4 lncRNAs and 8 mRNAs might be potential biomarkers for the prognosis of LUAD.

SUBMITTER: Li R

PROVIDER: S-EPMC6764142 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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Methylation and transcriptome analysis reveal lung adenocarcinoma-specific diagnostic biomarkers.

Li Rui R Yang Yi-E YE Yin Yun-Hong YH Zhang Meng-Yu MY Li Hao H Qu Yi-Qing YQ

Journal of translational medicine 20190927 1

<h4>Background</h4>DNA methylation can regulate the role of long noncoding RNAs (lncRNAs) in the development of lung adenocarcinoma (LUAD). The present study aimed to identify methylation-driven lncRNAs and mRNAs as biomarkers in the prognosis of LUAD using bioinformatics analysis.<h4>Methods</h4>Differentially expressed RNAs were obtained using the edge R package from 535 LUAD tissues and 59 adjacent non-LUAD tissues. Differentially methylated genes were obtained using the limma R package from ...[more]

PMID: 31558162

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Project description:Lung adenocarcinoma (LUAD) is one of the most common cancers and lethal diseases in the world. Recognition of the undetermined lung nodules at an early stage is useful for a favorable prognosis. However, there is no good method to identify the undetermined lung nodules and predict their clinical outcome. DNA methylation alteration is frequently observed in LUAD and may play important roles in carcinogenesis, diagnosis, and prediction. This study took advantage of publicly available methylation profiling resources and a machine learning method to investigate methylation differences between LUAD and adjacent non-malignant tissue. The prediction panel was first constructed using 338 tissue samples from LUAD patients including 149 non-malignant ones. This model was then validated with data from The Cancer Genome Atlas database and clinic samples. As a result, the methylation status of four CpG loci in homeobox A9 (HOXA9), keratin-associated protein 8-1 (KRTAP8-1), cyclin D1 (CCND1), and tubby-like protein 2 (TULP2) were highlighted as informative markers. A random forest classification model with an accuracy of 94.57% and kappa of 88.96% was obtained. To evaluate this panel for LUAD, the methylation levels of four CpG loci in HOXA9, KRTAP8-1, CCND1, and TULP2 of tumor samples and matched adjacent lung samples from 25 patients with LUAD were tested. In these LUAD patients, the methylation of HOXA9 was significantly upregulated, whereas the methylation of KRTAP8-1, CCND1, and TULP2 were downregulated obviously in tumor samples compared with adjacent tissues. Our study demonstrates that the methylation of HOXA9, KRTAP8-1, CCND1, and TULP2 has great potential for the early recognition of LUAD in the undetermined lung nodules. The findings also exhibit that the application of improved mathematic algorithms can yield accurate and particularly robust and widely applicable marker panels. This approach could greatly facilitate the discovery process of biomarkers in various fields.

Project description:BACKGROUND We aimed to screen and identify central genetic and molecular targets involved in advancement of lung adenocarcinoma (LUAD) and to perform an integrated analysis and clinical validation. MATERIAL AND METHODS The GEO2R technique was utilized to assess differentially expressed genes (DEGs) among the gene sets GSE75037, GSE85716, and GSE118370. Subsequently, gene Ontology (GO) analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) analytical methods were executed to determine related biofunctions and signaling pathways, which were annotated with tools from the Database for Annotation, Visualization and Integrated Discovery (DAVID) resource. Then, a protein-protein interaction (PPI) network complex consisting of all detected DEGs was built with the STRING web interface. Cytohubba and MCODE plug-ins for Cytoscape software and Gene Expression Profiling Interactive Analysis (GEPIA) were employed to identify the hub genes. Finally, the mRNA expression of the identified hub genes was quantitatively validated by The Cancer Genome Atlas (TCGA) database analysis and real-time quantitative polymerase chain reaction (RT-qPCR). RESULTS We screened 146 upregulated DEGs and 431 downregulated DEGs with the criteria of |logFC| >1 and P<0.05, and the GO analysis indicated that DEGs were implicated in mitotic nuclear division (biological process, BP), the nucleus (cellular component, CC), and protein binding (molecular function, MF) and were associated with multiple KEGG pathways, such as the p53 signaling pathway in cancer. Then, the top 8 genes that predicted significantly different outcomes in LUAD patients were filtered from the DEGs and selected as hub genes. The TCGA database analysis and RT-qPCR results demonstrated that these genes were differentially expressed with the same trends in LUAD tissues compared with normal tissues. CONCLUSIONS Overall, we propose that 8 genes (PECAM1, CDK1, MKI67, SPP1, TOP2A, CHEK1, CCNB1, and RRM2) might be novel hub genes strongly associated with the progression and prognosis of LUAD.

Project description:BackgroundThe abnormal expression of genes is an essential factor affecting the prognosis of cancer. RNA modification is a way of regulating post-transcriptional levels, including m6A, m5C, m1A RNA methylation. Studies have found that RNA methylation regulates tumorigenesis development and stem cell regeneration. However, there are few studies on lung adenocarcinoma. This study aims to explore the clinical value of RNA methylation for lung adenocarcinoma.MethodsWe summarized thirty-one RNA methylation regulators. The training set was obtained from The Cancer Genome Atlas (TCGA) database, and the test set was obtained from the Gene Expression Omnibus (GEO) database. The Wilcoxon test was used to analyze the expression of RNA methylation regulators. We constructed tumor subgroup models and risk models based on the expression of those regulators. Principal component analysis (PCA) and the receiver operating characteristic (ROC) confirmed the accuracy of the models. Real-time polymerase chain reaction (PCR) validates the results in vitro.ResultsMost RNA methylation regulators had distinct expressions in tumor tissues and adjacent tissues (P<0.05). All the models showed high predictive performance (AUC: 0.65-0.82), and the five-year survival of patients in each group was statistically different (P<0.05). The patients in the high-risk group were more likely to have a higher stage, more lymph node metastases, and distant metastases, showing a poor clinical outcome. Patients with high expression of NOP2 or HNRNP were more likely to have a poorly differentiated in vitro experiment.ConclusionsWith our study, we found that the expressions of most RNA methylation regulators were significantly different in cancer and para-cancerous tissues. Different molecular phenotypes constructed by RNA methylation regulators can be independent risk factors for the prognosis of lung adenocarcinoma. Our study demonstrates the critical role of RNA methylation in lung adenocarcinoma, and it is expected to supply a reference for the prognostic stratification and treatment strategy development of lung adenocarcinoma.

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Methylation and transcriptome analysis reveal lung adenocarcinoma-specific diagnostic biomarkers.

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Methylation and transcriptome analysis reveal lung adenocarcinoma-specific diagnostic biomarkers.

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