Molecular subtypes based on DNA methylation predict prognosis in colon adenocarcinoma patients.
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ABSTRACT:
SUBMITTER: Yang C
PROVIDER: S-EPMC6949097 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe heterogeneity of lung adenocarcinoma (LADC) makes the early diagnosis and treatment of the disease difficult. Gene silencing of DNA methylation is an important mechanism of tumorigenesis. A combination of methylation and clinical features can improve the classification of LADC heterogeneity.ResultsWe investigated the prognostic significance of 335 specimen subgroups of Lung adenocarcinoma based on the DNA methylation level. The differences in DNA methylation levels were related to the TNM stage classification, age, gender, and prognostic values. Seven subtypes were determined using 774 CpG sites that significantly affected the survival rate based on the consensus clustering. Finally, we constructed a prognostic model that performed well and further verified it in our test group.ConclusionsThis study shows that classification based on DNA methylation might aid in demonstrating heterogeneity within formerly characterized LADC molecular subtypes, assisting in the development of efficient, personalized therapy.MethodsMethylation data of lung adenocarcinoma were downloaded from the University of California Santa Cruz (UCSC) cancer browser, and the clinical patient information and RNA-seq archives were acquired from the Cancer Genome Atlas (TCGA). CpG sites were identified based on the significant correlation with the prognosis and used further to cluster the cases uniformly into several subtypes.
| S-EPMC7762488 | biostudies-literature
Project description:BackgroundEpigenetic changes are tightly linked to tumorigenesis development and malignant transformation' However, DNA methylation occurs earlier and is constant during tumorigenesis. It plays an important role in controlling gene expression in cancer cells.MethodsIn this study, we determining the prognostic value of molecular subtypes based on DNA methylation status in breast cancer samples obtained from The Cancer Genome Atlas database (TCGA).ResultsSeven clusters and 204 corresponding promoter genes were identified based on consensus clustering using 166 CpG sites that significantly influenced survival outcomes. The overall survival (OS) analysis showed a significant prognostic difference among the 7 groups (p<0.05). Finally, a prognostic model was used to estimate the results of patients on the testing set based on the classification findings of a training dataset DNA methylation subgroups.ConclusionsThe model was found to be important in the identification of novel biomarkers and could be of help to patients with different breast cancer subtypes when predicting prognosis, clinical diagnosis and management.
| S-EPMC8482718 | biostudies-literature
Project description:The marked heterogeneity of lung adenocarcinoma (LUAD) makes its diagnosis and treatment difficult. In addition, the aberrant DNA methylation profile contributes to tumor heterogeneity and alters the immune response. We used DNA methylation array data from publicly available databases to establish a predictive model for LUAD prognosis. Thirty-three methylation sites were identified as specific prognostic biomarkers, independent of patients' clinical characteristics. These methylation profiles were used to identify potential drug candidates and study the immune microenvironment of LUAD and response to immunotherapy. When compared with the high-risk group, the low-risk group had a lower recurrence rate and favorable prognosis. The tumor microenvironment differed between the two groups as reflected by the higher number of resting dendritic cells and a lower number of monocytes and resting mast cells in the low-risk group. Moreover, low-risk patients reported higher immune and stromal scores, lower tumor purity, and higher expression of HLA genes. Low-risk patients responded well to immunotherapy due to higher expression of immune checkpoint molecules and lower stemness index. Thus, our model predicted a favorable prognosis and increased overall survival for patients in the low-risk methylation group. Further, this model could provide potential drug targets to develop effective immunotherapies for LUAD.
| S-EPMC7803536 | biostudies-literature
Project description:BackgroundDue to tumor heterogeneity, the diagnosis, treatment, and prognosis of patients with lung squamous cell carcinoma (LUSC) are difficult. DNA methylation is an important regulator of gene expression, which may help the diagnosis and therapy of patients with LUSC.MethodsIn this study, we collected the clinical information of LUSC patients in the Cancer Genome Atlas (TCGA) database and the relevant methylated sequences of the University of California Santa Cruz (UCSC) database to construct methylated subtypes and performed prognostic analysis.ResultsNine hundred sixty-five potential independent prognosis methylation sites were finally identified and the genes were identified. Based on consensus clustering analysis, seven subtypes were identified by using 965 CpG sites and corresponding survival curves were plotted. The prognostic analysis model was constructed according to the methylation sites' information of the subtype with the best prognosis. Internal and external verifications were used to evaluate the prediction model.ConclusionsModels based on differences in DNA methylation levels may help to classify the molecular subtypes of LUSC patients, and provide more individualized treatment recommendations and prognostic assessments for different clinical subtypes. GNAS, FZD2, FZD10 are the core three genes that may be related to the prognosis of LUSC patients.
| S-EPMC7825161 | biostudies-literature
Project description:Ovarian cancer is one of three major malignancies of the female reproductive system. DNA methylation (MET) is closely related to ovarian cancer occurrence and development, and as such, elucidation of effective MET subtype markers may guide individualized treatment and improve ovarian cancer prognosis. To identify potential markers, we downloaded a total of 571 ovarian cancer MET samples from The Cancer Genome Atlas (TCGA), and established a Cox proportional hazards model using the MET spectrum and clinical pathological parameters. A total of 250 prognosis-related MET loci were obtained by Cox regression, and six molecular subtypes were screened by consensus clustering of CpG loci with a significant difference in both univariate and multivariate analyses. There was a remarkable MET difference between most subtypes. Cluster 2 had the highest MET level and demonstrated the best prognosis, while Clusters 4 and 5 had MET levels significantly lower than those of the other subtypes and demonstrated very poor prognosis. All Cluster 5 samples were at a high grade, while the percentage of stage IV samples in Cluster 4 was greater than in the other subtypes. We obtained five CpG loci using a coexpression network: cg27625732, cg00431050, cg22197830, cg03152385, and cg22809047. Our cluster analysis showed that prognosis in patients with hypomethylation was significantly worse than in patients with hypermethylation. These MET molecular subtypes can be used not only to evaluate ovarian cancer prognosis, but also to fully distinguish the tumor stage and histological grade in patients with ovarian cancer.
| S-EPMC7931230 | biostudies-literature
Project description:BackgroundColon adenocarcinoma (COAD) is a common gastrointestinal tumor and often occurs in the left colon with a poor prognosis. The progression of COAD is closely related to the tumor microenvironment, especially the hypoxia. Currently, few studies have reported the correlation between hypoxia-related genes and the prognosis of COAD patients. Furthermore, we constructed a prognostic model using four hypoxia-related genes to predict the prognosis of COAD patients.MethodsThe mRNA expression profiles and corresponding clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The string online analysis tool was used to construct a protein-protein interaction network (PPI) of hypoxia-related genes. Kaplan-Meier curve was used to analyze the relationship of hypoxia risk score and the overall survival of COAD patients, and the receiver operating characteristic (ROC) curve was used to assess the reliability.ResultsWe screened out four hypoxia genes, including TKTL1 (transketolase like 1), SLC2A3 (solute carrier family 2 member 3), ALDOB (aldolase, fructose-bisphosphate B) and ENO3 (enolase 3), which were used to construct a hypoxia risk model to predict the overall survival of COAD patients. Besides, we also found that the hypoxia risk score was correlated with the immunosuppression of tumor microenvironment.ConclusionThe model we constructed with four survival-related hypoxia genes, including TKTL1, SLC2A3, ALDOB and ENO3, could be used to predict the overall survival of COAD patients with high stability.
| S-EPMC8687688 | biostudies-literature
Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies and is not a clinically homogeneous disease, but subsets of patients with distinct prognosis and response to therapy can be identified by genome-wide analyses. Mutations in major PDAC driver genes were associated with poor survival. By bioinformatics analysis, we identified protocadherins among the most frequently mutated genes in PDAC suggesting an important role of these genes in the biology of this tumor. Promoter methylation of protocadherins has been suggested as a prognostic marker in different tumors, but in PDAC this epigenetic modification has not been extensively studied. Thus, we evaluated whether promoter methylation of three frequently mutated protocadherins, PCDHAC2, PCDHGC5 and PCDH10 could be used as survival predictors in PDAC patients.MethodsDNA extracted from 23 PDACs and adjacent non-neoplastic pancreatic tissues were bisulfite treated. Combined Bisulfite Restriction Analysis (COBRA) coupled to denaturing high-performance liquid chromatography (dHPLC) detection and bisulfite genomic sequencing (BGS) were used to determine the presence of methylated CpG dinucleotides in the promoter amplicons analyzed.ResultsIn an exploratory analysis, two protocadherins showed the same pattern of CpG methylation in PDAC and adjacent non-neoplastic pancreatic tissues: lack of methylation for PCDHAC2, complete methylation for PCDHGC5. Conversely, the third protocadherin analyzed, PCDH10, showed a variable degree of CpG methylation in PDAC and absence of methylation in adjacent non-neoplastic pancreatic tissues. At Kaplan-Meier analysis, high levels of PCDH10 methylation defined according to the receiver operating characteristic (ROC) curve analysis were significantly associated with worse progression-free survival (PFS) rates (P = 0.008), but not with overall survival (OS). High levels of PCDH10 methylation were a prognostic factor influencing PFS (HR = 4.0: 95% CI, 1.3-12.3; P = 0.016), but not the OS.ConclusionsIn this study, we show for the first time that the methylation status of PCDH10 can predict prognosis in PDAC patients with a significant impact on the outcome in terms of progression-free survival. High levels of PCDH10 promoter methylation could be useful to identify patients at high risk of disease progression, contributing to a more accurate stratification of PDAC patients for personalized clinical management.
| S-EPMC6518703 | biostudies-literature
Project description:Achieving cancer prognosis and molecular typing is critical for cancer treatment. Previous studies have identified some gene signatures for the prognosis and typing of cancer based on gene expression data. Some studies have shown that DNA methylation is associated with cancer development, progression, and metastasis. In addition, DNA methylation data are more stable than gene expression data in cancer prognosis. Therefore, in this work, we focused on DNA methylation data. Some prior researches have shown that gene modules are more reliable in cancer prognosis than are gene signatures and that gene modules are not isolated. However, few studies have considered cross-talk among the gene modules, which may allow some important gene modules for cancer to be overlooked. Therefore, we constructed a gene co-methylation network based on the DNA methylation data of cancer patients, and detected the gene modules in the co-methylation network. Then, by permutation testing, cross-talk between every two modules was identified; thus, the module network was generated. Next, the core gene modules in the module network of cancer were identified using the K-shell method, and these core gene modules were used as features to study the prognosis and molecular typing of cancer. Our method was applied in three types of cancer (breast invasive carcinoma, skin cutaneous melanoma, and uterine corpus endometrial carcinoma). Based on the core gene modules identified by the constructed DNA methylation module networks, we can distinguish not only the prognosis of cancer patients but also use them for molecular typing of cancer. These results indicated that our method has important application value for the diagnosis of cancer and may reveal potential carcinogenic mechanisms.
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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.
| S-EPMC8704183 | biostudies-literature
Project description:Acute myeloid leukemia (AML) is a malignancy of the stem cell precursors of the myeloid lineage. DNA methylation is an important DNA modification that regulates gene expression. Investigating AML heterogeneity based on DNA methylation could be clinically informative for improving clinical diagnosis and prognosis. The AML subtypes based on DNA methylation were identified by unsupervised consensus clustering. The association of these subtypes with gene mutation, copy number variations, immune infiltration and clinical features were further explored. Finally, univariate, LASSO and multivariate cox regression analyses were used to identify prognosis-associated genes and construct risk model for AML patients. In addition, we validated this model by using other datasets and explored the involved biological functions and pathways of its related genes. Three CpG island methylator phenotypes (CIMP-H, CIMP-M and CIMP-L) were identified using the 91 differential CpG sites. Overall survival, morphology, macrophages M0 and monocytes were distinct from each other. The most frequently mutated gene in CIMP-L was DNMT3A while which in CIMP-M that was RUNX1. In addition, the TIDE scores, used to predict the response to immune checkpoint inhibitors, were significantly different among CIMPs. The CIMP-associated prognosis risk model (CPM) using 32 key genes had convinced accuracy of prediction to forecast 0.5-year, 1-year, 3-year and 5-year survival rates. Moreover, the risk score-related genes were significantly enriched in pattern specification process, regionalization, embryonic organ morphogenesis and other critical cancer-related biological functions. We systematically and comprehensively analyzed the DNA methylation in AML. The risk model we constructed is an independent predictor of overall survival in AML and could be used as prognostic factor for AML treatment.
| S-EPMC10543573 | biostudies-literature