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A Novel Gene Prognostic Signature Based on Differential DNA Methylation in Breast Cancer.

ABSTRACT: Background: DNA methylation played essential roles in regulating gene expression. The impact of DNA methylation status on the occurrence and development of cancers has been well demonstrated. However, little is known about its prognostic role in breast cancer (BC). Materials: The Illumina Human Methylation450 array (450k array) data of BC was downloaded from the UCSC xena database. Transcriptomic data of BC was downloaded from the Cancer Genome Atlas (TCGA) database. Firstly, we used univariate and multivariate Cox regression analysis to screen out independent prognostic CpGs, and then we identified methylation-associated prognosis subgroups by consensus clustering. Next, a methylation prognostic model was developed using multivariate Cox analysis and was validated with the Illumina Human Methylation27 array (27k array) dataset of BC. We then screened out differentially expressed genes (DEGs) between methylation high-risk and low-risk groups and constructed a methylation-based gene prognostic signature. Further, we validated the gene signature with three subgroups of the TCGA-BRCA dataset and an external dataset GSE146558 from the Gene Expression Omnibus (GEO) database. Results: We established a methylation prognostic signature and a methylation-based gene prognostic signature, and there was a close positive correlation between them. The gene prognostic signature involved six genes: IRF2, KCNJ11, ZDHHC9, LRP11, PCMT1, and TMEM70. We verified their expression in mRNA and protein levels in BC. Both methylation and methylation-based gene prognostic signatures showed good prognostic stratification ability. The AUC values of 3-years, 5-years overall survival (OS) were 0.737, 0.744 in the methylation signature and 0.725, 0.715 in the gene signature, respectively. In the validation groups, high-risk patients were confirmed to have poorer OS. The AUC values of 3 years were 0.757, 0.735, 0.733 in the three subgroups of TCGA dataset and 0.635 in GSE146558 dataset. Conclusion: This study revealed the DNA methylation landscape and established promising methylation and methylation-based gene prognostic signatures that could serve as potential prognostic biomarkers and therapeutic targets.

SUBMITTER: Zhu C

PROVIDER: S-EPMC8693898 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundThe progression of breast cancer (BC) is highly dependent on the tumor microenvironment. Inflammation, stromal cells, and the immune landscape have been identified as significant drivers of BC in multiple preclinical studies. Therefore, this study aimed to clarify the predictive relevance of stromal and immune cell-associated genes in patients suffering from BC.MethodsWe employed the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm to calculate the stromal and immunological scores, which were then used to evaluate differentially expressed genes (DEGs) in BC samples using The Cancer Genome Atlas (TCGA) database. Univariate analyses were conducted to identify the DEGs linked to survival in BC patients. Next, the prognostic DEGs (with a log-rank P<0.05) were used to create a risk signature, and the least absolute shrinkage and selection operator (LASSO) regression method was used to analyze and optimize the risk signature. The following formula was used to compute the prognostic risk score values: Risk score = Gene 1 * β1 + Gene 2 * β2 +… Gene n * βn. The median prognostic risk score values were used to divide BC patients into the low-risk (LR) and high-risk (HR) groups. The patient samples of the validation cohort were then assessed using this formula. We used principal component analysis (PCA) to determine the expression patterns of the different patient groups. Gene Set Enrichment Analysis (GSEA) was used to determine whether there were significant variations between the groups in the evaluated gene sets.ResultsThe present study revealed that DEGs linked with survival were closely associated with immunological responses. A prognostic signature was constructed that consisted of 12 genes (ASCL1, BHLHE22, C1S, CLEC9A, CST7, EEF1A2, FOLR2, KLRB1, MEOX1, PEX5L, PLA2G2D, and PPP1R16B). According to their survival, BC patients were separated into LR and HR groups using the identified 12-gene signature. The immunological status and immune cell infiltration were observed differently in the LR and HR groups.ConclusionsOur results provide novel insights into several microenvironment-linked genes that influence survival outcomes in patients with BC, which suggests that these genes could be candidate therapeutic targets.

Project description:BACKGROUND:The identification of prognostic biomarkers for cancer patients is essential for cancer research. These days, DNA methylation has been proved to be associated with cancer prognosis. However, there are few methods which identify the prognostic markers based on DNA methylation data systematically, especially considering the interaction among DNA methylation sites. METHODS:In this paper, we first evaluated the stabilities of microRNA, mRNA, and DNA methylation data in prognosis of cancer. After that, a rank-based method was applied to construct a DNA methylation interaction network. In this network, nodes with the largest degrees (10% of all the nodes) were selected as hubs. Cox regression was applied to select the hubs as prognostic signature. In this prognostic signature, DNA methylation levels of each DNA methylation site are correlated with the outcomes of cancer patients. After obtaining these prognostic genes, we performed the survival analysis in the training group and the test group to verify the reliability of these genes. RESULTS:We applied our method in three cancers (ovarian cancer, breast cancer and Glioblastoma Multiforme). In all the three cancers, there are more common ones of prognostic genes selected from different samples in DNA methylation data, compared with gene expression data and miRNA expression data, which indicates the DNA methylation data may be more stable in cancer prognosis. Power-law distribution fitting suggests that the DNA methylation interaction networks are scale-free. And the hubs selected from the three networks are all enriched by cancer related pathways. The gene signatures were obtained for the three cancers respectively, and survival analysis shows they can distinguish the outcomes of tumor patients in both the training data sets and test data sets, which outperformed the control signatures. CONCLUSIONS:A computational method was proposed to construct DNA methylation interaction network and this network could be used to select prognostic signatures in cancer.

Project description:BackgroundBreast cancer is a highly heterogeneous disease resulting in diverse clinical behaviours and therapeutic responses. DNA methylation is a major epigenetic alteration that is commonly perturbed in cancers. The aim of this study is to characterize the relationship between DNA methylation and aberrant gene expression in breast cancer.MethodsWe analysed DNA methylation and gene expression profiles from breast cancer tissue and matched normal tissue in The Cancer Genome Atlas (TCGA). Genome-wide differential methylation analysis and methylation-gene expression correlation was performed. Gene expression changes were subsequently validated in the METABRIC dataset. The Oncoscore tool was used to identify genes that had previously been associated with cancer in the literature. A subset of genes that had not previously been studied in cancer was chosen for further analysis.ResultsWe identified 368 CpGs that were differentially methylated between tumor and normal breast tissue (∆β > 0.4). Hypermethylated CpGs were overrepresented in tumor tissue and were found predominantly (56%) in upstream promoter regions. Conversely, hypomethylated CpG sites were found primarily in the gene body (66%). Expression analysis revealed that 209 of the differentially-methylated CpGs were located in 169 genes that were differently expressed between normal and breast tumor tissue. Methylation-expression correlations were predominantly negative (70%) for promoter CpG sites and positive (74%) for gene body CpG sites. Among these differentially-methylated and differentially-expressed genes, we identified 7 that had not previously been studied in any form of cancer. Three of these, TDRD10, PRAC2 and TMEM132C, contained CpG sites that showed diagnostic and prognostic value in breast cancer, particularly in estrogen-receptor (ER)-positive samples. A pan-cancer analysis confirmed differential expression of these genes together with diagnostic and prognostic value of their respective CpG sites in multiple cancer types.ConclusionWe have identified 368 DNA methylation changes that characterize breast cancer tumor tissue, of which 209 are associated with genes that are differentially-expressed in the same samples. Novel DNA methylation markers were identified, of which cg12374721 (PRAC2), cg18081940 (TDRD10) and cg04475027 (TMEM132C) show promise as diagnostic and prognostic markers in breast cancer as well as other cancer types.

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A Novel Gene Prognostic Signature Based on Differential DNA Methylation in Breast Cancer.

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