Aberrant CpG-methylation affects genes expression predicting survival in lung adenocarcinoma.
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ABSTRACT:
SUBMITTER: He W
PROVIDER: S-EPMC6246931 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Epigenetic silencing associated with aberrant methylation of promoter region CpG islands is one mechanism leading to loss of tumor suppressor function in human cancer. Profiling of CpG island methylation indicates that some genes are more frequently methylated than others, and that each tumor type is associated with a unique set of methylated genes. However, little is known about why certain genes succumb to this aberrant event. To address this question, we used Restriction Landmark Genome Scanning to analyze the susceptibility of 1,749 unselected CpG islands to de novo methylation driven by overexpression of DNA cytosine-5-methyltransferase 1 (DNMT1). We found that although the overall incidence of CpG island methylation was increased in cells overexpressing DNMT1, not all loci were equally affected. The majority of CpG islands (69.9%) were resistant to de novo methylation, regardless of DNMT1 overexpression. In contrast, we identified a subset of methylation-prone CpG islands (3.8%) that were consistently hypermethylated in multiple DNMT1 overexpressing clones. Methylation-prone and methylation-resistant CpG islands were not significantly different with respect to size, C+G content, CpG frequency, chromosomal location, or promoter association. We used DNA pattern recognition and supervised learning techniques to derive a classification function based on the frequency of seven novel sequence patterns that was capable of discriminating methylation-prone from methylation-resistant CpG islands with 82% accuracy. The data indicate that CpG islands differ in their intrinsic susceptibility to de novo methylation, and suggest that the propensity for a CpG island to become aberrantly methylated can be predicted based on its sequence context.
| S-EPMC218745 | biostudies-literature
Project description:BackgroundDNA methylation alteration is frequently observed in Lung adenocarcinoma (LUAD) and may play important roles in carcinogenesis, diagnosis, and prognosis. Thus, this study aimed to construct a reliable methylation-based nomogram, guiding prognostic classification screening and personalized medicine for LUAD patients.MethodThe DNA methylation data, gene expression data and corresponding clinical information of lung adenocarcinoma samples were extracted from The Cancer Genome Atlas (TCGA) database. Differentially methylated sites (DMSs) and differentially expressed genes (DEGs) were obtained and then calculated correlation by pearson correlation coefficient. Functional enrichment analysis and Protein-protein interaction network were used to explore the biological roles of aberrant methylation genes. A prognostic risk score model was constructed using univariate Cox and LASSO analysis and was assessed in an independent cohort. A methylation-based nomogram that included the risk score and the clinical risk factors was developed, which was evaluated by concordance index and calibration curves.ResultWe identified a total of 1362 DMSs corresponding to 471 DEGs with significant negative correlation, including 752 hypermethylation sites and 610 hypomethylation sites. Univariate cox regression analysis showed that 59 DMSs were significantly associated with overall survival. Using LASSO method, we constructed a three-DMSs signature that was independent predictive of prognosis in the training cohort. Patients in high-risk group had a significant shorter overall survival than patients in low-risk group classified by three-DMSs signature (log-rank p = 1.9E-04). Multivariate cox regression analysis proved that the three-DMSs signature was an independent prognostic factor for LUAD in TCGA-LUAD cohort (HR = 2.29, 95%CI: 1.47-3.57, P = 2.36E-04) and GSE56044 cohort (HR = 2.16, 95%CI: 1.19-3.91, P = 0.011). Furthermore, a nomogram, combining the risk score with clinical risk factors, was developed with C-indexes of 0.71 and 0.70 in TCGA-LUAD and GSE56044 respectively.ConclusionsThe present study established a robust three-DMSs signature for the prediction of overall survival and further developed a nomogram that could be a clinically available guide for personalized treatment of LUAD patients.
| S-EPMC8273993 | biostudies-literature
Project description:BackgroundThe highest rate of cancer-related deaths worldwide is from lung adenocarcinoma (LUAD) annually. Metabolism was associated with tumorigenesis and cancer development. Metabolic-related genes may be important biomarkers and metabolic therapeutic targets for LUAD.Materials and methodsIn this study, the gleaned cohort included LUAD RNA-SEQ data from the Cancer Genome Atlas (TCGA) and corresponding clinical data (n = 445). The training cohort was utilized to model construction, and data from the Gene Expression Omnibus (GEO, GSE30219 cohort, n = 83; GEO, GSE72094, n = 393) were regarded as a testing cohort and utilized for validation. First, we used a lasso-penalized Cox regression analysis to build a new metabolic-related signature for predicting the prognosis of LUAD patients. Next, we verified the metabolic gene model by survival analysis, C-index, receiver operating characteristic (ROC) analysis. Univariate and multivariate Cox regression analyses were utilized to verify the gene signature as an independent prognostic factor. Finally, we constructed a nomogram and performed gene set enrichment analysis to facilitate subsequent clinical applications and molecular mechanism analysis.ResultPatients with higher risk scores showed significantly associated with poorer survival. We also verified the signature can work as an independent prognostic factor for LUAD survival. The nomogram showed better clinical application performance for LUAD patient prognostic prediction. Finally, KEGG and GO pathways enrichment analyses suggested several especially enriched pathways, which may be helpful for us investigative the underlying mechanisms.
| S-EPMC7520091 | biostudies-literature
Project description:The lack of clinically useful biomarkers compromise the personalized management of lung adenocarcinomas (ADCs); epigenetic events and DNA methylation in particular have exhibited potential value as biomarkers. By comparing genome-wide DNA methylation data of paired lung ADCs and normal tissues from 6 public datasets, cancer-specific CpG island (CGI) methylation changes were identified with a pre-specified criterion. Correlations between DNA methylation and expression data for each gene were assessed by Pearson correlation analysis. A prognostically relevant CGI methylation signature was constructed by risk-score analysis, and was validated using a training-validation approach. Survival data were analyzed by log-rank test and Cox regression model. In total, 134 lung ADC-specific CGI CpGs were identified, among which, a panel of 9 CGI loci were selected as prognostic candidates, and were used to construct a risk-score signature. The novel CGI methylation signature was identified to classify distinct prognostic subgroups across different datasets, and was demonstrated to be a potent independent prognostic factor for overall survival time of patients with lung ADCs. In addition, it was identified that cancer-specific CGI hypomethylation of RPL39L, along with the corresponding gene expression, provided optimized prognostication of lung ADCs. In summary, cancer-specific CGI methylation aberrations are optimal candidates for novel biomarkers of lung ADCs; the 9-CpG methylation panel and hypomethylation of RPL39L exhibited particularly promising significance.
| S-EPMC6607393 | biostudies-literature
Project description:The early symptoms of lung adenocarcinoma patients are inapparent, and the clinical diagnosis of lung adenocarcinoma is primarily through X-ray examination and pathological section examination, whereas the discovery of biomarkers points out another direction for the diagnosis of lung adenocarcinoma with the development of bioinformatics technology. However, it is not accurate and trustworthy to diagnose lung adenocarcinoma due to omics data with high-dimension and low-sample size (HDLSS) features or biomarkers produced by utilizing only single omics data. To address the above problems, the feature selection methods of biological analysis are used to reduce the dimension of gene expression data (GSE19188) and DNA methylation data (GSE139032, GSE49996). In addition, the Cartesian product method is used to expand the sample set and integrate gene expression data and DNA methylation data. The classification is built by using a deep neural network and is evaluated on K-fold cross validation. Moreover, gene ontology analysis and literature retrieving are used to analyze the biological relevance of selected genes, TCGA database is used for survival analysis of these potential genes through Kaplan-Meier estimates to discover the detailed molecular mechanism of lung adenocarcinoma. Survival analysis shows that COL5A2 and SERPINB5 are significant for identifying lung adenocarcinoma and are considered biomarkers of lung adenocarcinoma.
| S-EPMC9280023 | biostudies-literature
Project description:Lung adenocarcinoma (LACA) is the leading cause of cancer-associated death worldwide. The present study intended to identify DNA methylation patterns that may serve as diagnostic and prognostic biomarkers for LACA. Data on DNA methylation and the survival data of the patients of LACA were obtained from The Cancer Genome Atlas. Kaplan-Meier curves and receiver operating characteristic curve analysis were utilized to build diagnostic and prognostic models. A total of 13 CpG sites were identified and validated as the optimal diagnostic and prognostic signature for overall survival. It was concluded that the CpG-based signature is a reliable predictor for the diagnosis and prognosis of patients with LACA.
| S-EPMC6909784 | biostudies-literature
Project description:BackgroundDue to the heterogeneity of cancer, identifying differentially methylated (DM) CpG sites between a set of cancer samples and a set of normal samples cannot tell us which patients have methylation aberrations in a particular DM CpG site.MethodsWe firstly showed that the relative methylation-level orderings (RMOs) of CpG sites within individual normal lung tissues are highly stable but widely disrupted in lung adenocarcinoma tissues. This finding provides the basis of using the RankComp algorithm, previously developed for differential gene expression analysis at the individual level, to identify DM CpG sites in each cancer tissue compared with its own normal state. Briefly, through comparing with the highly stable normal RMOs predetermined in a large collection of samples for normal lung tissues, the algorithm finds those CpG sites whose hyper- or hypo-methylations may lead to the disrupted RMOs of CpG site pairs within a disease sample based on Fisher's exact test.ResultsEvaluated in 59 lung adenocarcinoma tissues with paired adjacent normal tissues, RankComp reached an average precision of 94.26% for individual-level DM CpG sites. Then, after identifying DM CpG sites in each of the 539 lung adenocarcinoma samples from TCGA, we found five and 44 CpG sites hypermethylated and hypomethylated in above 90% of the disease samples, respectively. These findings were validated in 140 publicly available and eight additionally measured paired cancer-normal samples. Gene expression analysis revealed that four of the five genes, HOXA9, TAL1, ATP8A2, ENG and SPARCL1, each harboring one of the five frequently hypermethylated CpG sites within its promoters, were also frequently down-regulated in lung adenocarcinoma.ConclusionsThe common DNA methylation aberrations in lung adenocarcinoma tissues may be important for lung adenocarcinoma diagnosis and therapy.
| S-EPMC5299650 | biostudies-literature
Project description:Understanding the different genetic landscape between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is important for understanding the underlying molecular mechanism, which may facilitate the development of effective and precise treatments. Although previous studies have identified a number of differentially expressed genes (DEGs) responsible for lung cancer, it is unknown which of these genes are causal. The present study integrated DNA methylation, RNA sequencing, clinical characteristics and survival outcomes of patients with LUAD and LUSC from The Cancer Genome Atlas. DEGs were first identified using edgeR by comparing tumor and normal tissue, and differentially methylated probes (DMPs) were assessed using ChAMP. Candidate genes for further time-to-event instrumental variable analysis were selected as the intersecting genes between DEGs and the genes including DMP CpG sites within the transcription start site (TSS1500), with DMPs in TSS1500 region being the instrumental variables. Extensive sensitivity analyses were conducted to assess the robustness of the results. The present study identified 906 DEGs for LUAD, among which 538 also had DMPs in the TSS1500 region. In addition, 1,543 DEGs were identified for LUSC, among which 1,053 also had DMPs in the TSS1500 region. Time-to-event instrumental variable analysis detected eight potential causal genes for LUAD survival, including aryl hydrocarbon receptor nuclear translocator like 2, semaphorin 3G, serum deprivation-response protein, chloride intracellular channel protein 5, LIM zinc finger domain containing 2, epithelial membrane protein 2, carbonic anhydrase 7 and LOC116437. The results also identified that phosphatidylinositol-3,4,5-trisphosphate-dependent Rac exchange factor 2 may be a potential causal gene for LUSC. Therefore, the results of the present study suggested that there was molecular heterogeneity between these two lung cancer subtypes. Such analysis framework can be extended to other cancer genomics research.
| S-EPMC7291670 | biostudies-literature
Project description:BackgroundTo explore the necroptosis-related genes (NRGs) signature and its predictive values in lung adenocarcinoma (LUAD).MethodsThe training cohort consisted of tumor samples from The Cancer Genome Atlas, and the validation set comprised data from the Gene Expression Omnibus. Univariate and multivariate Cox regression analyses were applied to identify the prognostic NRG signature as an independent molecular indicator. Correlation analysis was used for the association assessment between the NRG signature and immune checkpoint molecules.ResultsNRGs involved in necroptosis and immune NOD-like receptor signaling. The NRG signature based on eight NRGs can divide tumors into high-risk and low-risk groups, which was significantly associated with worse survival. Multivariate Cox regression analysis showed that this NRG signature remained an independent prognostic indicator. Stratification analyses demonstrated that this NRG signature was still effective for predicting survival in each stratum of age, gender, and tumor stage. The ROC curve showed a good predictive ability using the NRG signature in the validation cohort (AUC = 0.81). The NRG signature was related to immune checkpoint molecules PD - 1, PD-L1, and PD-L2.ConclusionsThe NRG signature could be a novel predictor of the prognosis and may become a potential therapeutic target in LUAD.
| S-EPMC10685572 | biostudies-literature
Project description:S100A16 is a conserved member of the S100 protein family in mammals. Its upregulation was observed in many tumors and is related to malignant transformation. In this study, we explored the independent prognostic value of S100A16 in terms of overall survival (OS) and recurrence-free survival (RFS) by performing a retrospective study, using data in The Cancer Genome Atlas (TCGA)-lung adenocarcinoma (LUAD). Besides, by using deep sequencing data in TCGA-LUAD, we also explored the association between S100A16 expression and its DNA methylation and copy number alterations (CNAs). Results showed that the primary LUAD tissues (N = 514) had significantly elevated S100A16 expression compared with the normal lung tissues (N = 59). Based on OS data of 502 primary LUAD cases, we found that high S100A16 expression was correlated with inferior OS. The following univariate and multivariate analysis confirmed that increased S100A16 expression was an independent prognostic indicator of unfavorable OS (HR: 1.197, 95%CI: 1.050-1.364, p = 0.007) and RFS (HR: 1.206, 95%CI: 1.045-1.393, p = 0.011). By examining the DNA methylation data in TCGA-LUAD, we found that some S100A16 DNA CpG sites were generally hypermethylated in normal tissues, but not in LUAD tissues. Regression analysis identified a moderately negative correlation between S100A16 expression and its DNA methylation. In comparison, although DNA amplification (+1/+2) was frequent (378/511, 74%) in LUAD patients, it was not associated with increased S100A16 expression. Based on findings above, we infer that aberrant S100A16 expression might be modulated by its DNA hypomethylation and serves as an independent prognostic indicator of unfavorable OS and RFS in LUAD.
| S-EPMC5945035 | biostudies-literature