Identification of New Differentially Methylated Genes That Have Potential Functional Consequences in Prostate Cancer

Many differentially methylated genes have been identified in prostate cancer (PCa), primarily using candidate gene-based assays. Recently, several global DNA methylation profiles have been reported in PCa, however, each of these has weaknesses in terms of ability to observe global DNA methylation alterations in PCa. We hypothesize that there remains unidentified aberrant DNA methylation in PCa, which may be identified using higher resolution assay methods. We used the newly developed Illumina HumanMethylation450 BeadChip in PCa (n = 19) and adjacent normal tissues (n = 4) and combined these with gene expression data for identifying new DNA methylation that may have functional consequences in PCa development and progression. We also confirmed our methylation results in an independent data set. Two aberrant DNA methylation genes were validated among an additional 56 PCa samples and 55 adjacent normal tissues. A total 28,735 CpG sites showed significant differences in DNA methylation (FDR adjusted P<0.05), defined as a mean methylation difference of at least 20% between PCa and normal samples. Furthermore, a total of 122 genes had more than one differentially methylated CpG site in their promoter region and a gene expression pattern that was inverse to the direction of change in DNA methylation (e.g. decreased expression with increased methylation, and vice-versa). Aberrant DNA methylation of two genes, AOX1 and SPON2, were confirmed via bisulfate sequencing, with most of the respective CpG sites showing significant differences between tumor samples and normal tissues. The AOX1 promoter region showed hypermethylation in 92.6% of 54 tested PCa samples in contrast to only three out of 53 tested normal tissues. This study used a new BeadChip combined with gene expression data in PCa to identify novel differentially methylated CpG sites located within genes. The newly identified differentially methylated genes may be used as biomarkers for PCa diagnosis.     

Genome-wide differentially methylated genes in prostate cancer tissues from African-American and Caucasian men

Increasing evidence suggests that aberrant DNA methylation changes may contribute to prostate cancer (PCa) ethnic disparity. To comprehensively identify DNA methylation alterations in PCa disparity, we used the Illumina 450K methylation platform to interrogate the methylation status of 485,577 CpG sites focusing on gene-associated regions of the human genome. Genomic DNA from African-American (AA; 7 normal and 3 cancers) and Caucasian (Cau; 8 normal and 3 cancers) was used in the analysis. Hierarchical clustering analysis identified probe-sets unique to AA and Cau samples, as well as common to both. We selected 25 promoter-associated novel CpG sites most differentially methylated by race (fold change > 1.5-fold; adjusted P < 0.05) and compared the β-value of these sites provided by the Illumina, Inc. array with quantitative methylation obtained by pyrosequencing in 7 prostate cell lines. We found very good concordance of the methylation levels between β-value and pyrosequencing. Gene expression analysis using qRT-PCR in a subset of 8 genes after treatment with 5-aza-2′-deoxycytidine and/or trichostatin showed up-regulation of gene expression in PCa cells. Quantitative analysis of 4 genes, SNRPN, SHANK2, MST1R, and ABCG5, in matched normal and PCa tissues derived from AA and Cau PCa patients demonstrated differential promoter methylation and concomitant differences in mRNA expression in prostate tissues from AA vs. Cau. Regression analysis in normal and PCa tissues as a function of race showed significantly higher methylation prevalence for SNRPN (P = 0.012), MST1R (P = 0.038), and ABCG5 (P < 0.0002) for AA vs. Cau samples. We selected the ABCG5 and SNRPN genes and verified their biological functions by Western blot analysis and siRNA gene knockout effects on cell proliferation and invasion in 4 PCa cell lines (2 AA and 2 Cau patients-derived lines). Knockdown of either ABCG5 or SNRPN resulted in a significant decrease in both invasion and proliferation in Cau PCa cell lines but we did not observe these remarkable loss-of-function effects in AA PCa cell lines. Our study demonstrates how differential genome-wide DNA methylation levels influence gene expression and biological functions in AA and Cau PCa.     

A large-scale transcriptome analysis identified ELANE and PRTN3 as novel methylation prognostic signatures for clear cell renal cell carcinoma

DNA methylation was involved in the progress of many types of cancer including clear cell renal cell carcinomas (ccRCCs). This study aimed to identify the prognostic DNA methylation biomarkers for the ccRCCs by a large-scale RNA-seq analysis. The DNA methylation data and the corresponding clinical information of the patients with ccRCCs were extracted from TCGA database and randomly divided into the training group and the validation group. The differentially expressed CpG sites and the survival-related CpG sites were further identified, which was combined into CpG sites pair and followed by screening the survival-related pairs. The C-index and the forward search algorithms were constructed to identify the prognostic signatures for the patients with ccRCCs. The prognostic signatures were verified by the validation dataset and the protein–protein interactions (PPI) network analysis was performed on the CPG sites of the signature. A total of 9,861 differentially expressed CPG sites were identified and 567 CpG sites were found to relate to the overall survival (OS) of the patients with ccRCCs. Besides, 1,146 CPG sites pairs were found to be related to the OS of the ccRCCs samples and the signature composed of seven CpG sites pairs were obtained to predict the prognosis of patients with ccRCCs and the results were verified in the validation dataset. Besides, the PPI network analysis showed that ELANE and PRTN3 gene may be associated with the invasion and metastasis of ccRCCs and could function as potential prognostic and therapeutic signatures for ccRCCs.     

DNA methylation profiles in primary cutaneous melanomas are associated with clinically significant pathologic features

DNA methylation studies have elucidated a methylation signature distinguishing primary melanomas from benign nevi and provided new insights about genes that may be important in melanoma development. However, it is unclear whether methylation differences among primary melanomas are related to tumor pathologic features with known clinical significance. We utilized the Illumina GoldenGate Cancer Panel array to investigate the methylation profiles of 47 primary cutaneous melanomas. Arraywide methylation patterns revealed a positive association of methylation with Breslow thickness and mutated BRAF, a negative association with mitotic rate, and a weak association with ulceration. Hierarchical clustering on CpG sites exhibiting the most variable methylation (n = 235) divided the melanoma samples into three clusters, including a highly methylated cluster that was positively associated with Breslow thickness and an intermediately methylated cluster associated with Breslow thickness and mitotic rate. Our findings provide support for the existence of methylation‐defined subsets in melanomas with increased methylation associated with Breslow thickness.     

A two-CpG-based prognostic signature for oral squamous cell carcinoma overall survival

Oral squamous cell carcinoma (OSCC) represents one of the most common head and neck cancer that with dire prognosis due partly to the lack of reliable prognostic biomarker. Here, we aimed to develop a CpG site–based prognostic signature through which we could accurately predict overall survival (OS) of patients with OSCC. We obtained OSCC-related DNA methylation and gene expression data sets from the public accessible Gene Expression Omnibus. Correlations between methylation level of CpG sites and OS of patients with OSCC were assessed by univariate Cox regression analysis followed by robust likelihood-based survival analysis on those CpG sites with permutation P < 0.05 for further screening the optimal CpG sites for OSCC OS prediction based on the risk score formula that composed of the methylation level of optimal CpG sites weighted by their regression coefficients. Besides, differential expression genes (DEGs) and differential methylation genes (DMGs) in OSCC samples compared with normal samples were obtained and shared genes were considered as vital genes in OSCC tumorgenesis and progression. As a result, two CpG sites including cg17892178 and cg17378966 that located in NID2 and IDO1, respectively, were identified as the optimal prognostic signatures for OSCC OS. In addition, 12 overlapping genes between DEGs and DMGs that closely associated with inflammation or blood and tissue development–related biological processes were obtained. In conclusions, this study should provide valuable signatures for OSCC diagnosis and treatment.     

A ten-gene methylation signature as a novel biomarker for improving prediction of prognosis and indicating gene targets in endometrial cancer

Endometrial cancer (EC) is a common female reproductive tumor worldwide. Nonetheless, the pathogenesis of EC still remains ambiguous and associated epigenetic mechanism still to be explored. The goal of this study is to investigate whether gene methylation signature is associated with overall survival (OS) for EC patients. In this study, a 10-gene methylation risk model was built and the OS in high- and low-risk groups was significant different. The area under the ROC curve (AUC) of this model was 0.856 at 5 years survival. The nomogram could accurately predict the OS in EC patients, with concordance index and AUC at 5 year survival reached 0.796 and 0.792, respectively. Furthermore, we verified the nomogram with 24 patients in our center and the Kaplan-Meier survival curve also proved to be significantly different (p < 0.01). WGCNA revealed a key gene group for the model and further bioinformatics analysis indicated 6 genes as the hub genes in the module. Knockdown of MMP12 inhibited the proliferation, invasion and metastasis of EC cells. After all, a methylation signature and a nomogram based on this signature were constructed, and they could both predict survival in patients with EC. Moreover, WGCNA model identified MMP12 as a potential target for the treatment of EC.     

Development and validation of a novel 15‐CpG‐based signature for predicting prognosis in triple‐negative breast cancer

DNA methylation is an important biological regulatory mechanism that changes gene expression without altering the DNA sequence. Increasing studies have revealed that DNA methylation data play a vital role in the field of oncology. However, the methylation site signature in triple‐negative breast cancer (TNBC) remains unknown. In our research, we analysed 158 TNBC samples and 98 noncancerous samples from The Cancer Genome Atlas (TCGA) in three phases. In the discovery phase, 86 CpGs were identified by univariate Cox proportional hazards regression (CPHR) analyses to be significantly correlated with overall survival (P < 0.01). In the training phase, these candidate CpGs were further narrowed down to a 15‐CpG‐based signature by conducting least absolute shrinkage and selector operator (LASSO) Cox regression in the training set. In the validation phase, the 15‐CpG‐based signature was verified using two different internal sets and one external validation set. Furthermore, a nomogram comprising the CpG‐based signature and TNM stage was generated to predict the 1‐, 3‐ and 5‐year overall survival in the primary set, and it showed excellent performance in the three validation sets (concordance indexes: 0.924, 0.974 and 0.637). This study showed that our nomogram has a precise predictive effect on the prognosis of TNBC and can potentially be implemented for clinical treatment and diagnosis.     

Joint analysis of WES and RNA-Seq identify signature genes related to metastasis in prostate cancer

Prostate cancer (PCa) has a certain degree of heritability, and metastasis occurs as cancer progresses. However, its underlying mechanism remains largely unknown. We sequenced four cases of cancer without metastasis, four metastatic cancer, and four benign hyperplasia tissues as controls. A total of 1839 damaging mutations were identified. Pathway analysis, gene clustering, and weighted gene co-expression network analysis were employed to find characteristics associated with metastasis. Chr19 had the most mutation density and 1p36 had the highest mutation frequency across the genome. These mutations occurred in 1630 genes, including the most frequently mutated genes TTN and PLEC, and dozens of metastasis-related genes, such as FOXA1, NCOA1, CD34, and BRCA2. Ras signalling and arachidonic acid metabolism were uniquely enriched in metastatic cancer. Gene programmes 10 and 11 showed the signatures indicating the occurrence of metastasis better. A module (135 genes) was specifically associated with metastasis. Of them, 67.41% reoccurred in program 10, with 26 genes further retained as the signature genes related to PCa metastasis, including AGR3, RAPH1, SOX14, DPEP1, and UBL4A. Our study provides new molecular perspectives on PCa metastasis. The signature genes and pathways could be served as potential therapeutic targets for metastasis or cancer progression.     

The Relationship Between Insulin Resistance and CpG Island Methylation of LMNA Gene in Polycystic Ovary Syndrome

We sought to investigate the relationship between the changes of CpG island methylation status of LMNA gene and insulin resistance in polycystic ovary syndrome (PCOS) patients. The genome-wide methylation microarray screening was done in three PCOS cases of insulin resistance and one case of a normal woman. The PCOS insulin resistance-related genes were identified as indicated by the results of gene chip screening. Then, 24 cases of insulin-resistant PCOS patients and 24 cases of normal individuals were studied to identify the effects of the candidate genes using genome-wide study of DNA from the peripheral blood analyzed by MassARRAY^®EpiTYPER? DNA methylation analysis technique. We found that the methylation status of CpG island in the promoter area of LMNA gene was changed. The 20 CG sites in CpG island of LMNA gene were examined using case control experiment among which 12 CpG sites differed significantly (P < 0.05) between two groups while the remaining eight CpG sites differed non-significantly. We, therefore, concluded that the changes in the hypermethylation status of CpG island of LMNA gene were related to the insulin resistance in PCOS patients, indicating that this gene may be involved in the regulation of PCOS-associated insulin resistance.     

Methylation profiling identified novel differentially methylated markers including OPCML and FLRT2 in prostate cancer

To develop new methods to distinguish indolent from aggressive prostate cancers (PCa), we utilized comprehensive high-throughput array-based relative methylation (CHARM) assay to identify differentially methylated regions (DMRs) throughout the genome, including both CpG island (CGI) and non-CGI regions in PCa patients based on Gleason grade. Initially, 26 samples, including 8 each of low [Gleason score (GS) 6] and high (GS ≥7) grade PCa samples and 10 matched normal prostate tissues, were analyzed as a discovery cohort. We identified 3,567 DMRs between normal and cancer tissues, and 913 DMRs distinguishing low from high-grade cancers. Most of these DMRs were located at CGI shores. The top 5 candidate DMRs from the low vs. high Gleason comparison, including OPCML, ELAVL2, EXT1, IRX5, and FLRT2, were validated by pyrosequencing using the discovery cohort. OPCML and FLRT2 were further validated in an independent cohort consisting of 20 low-Gleason and 33 high-Gleason tissues. We then compared patients with biochemical recurrence (n=70) vs. those without (n=86) in a third cohort, and they showed no difference in methylation at these DMR loci. When GS 3+4 cases and GS 4+3 cases were compared, OPCML-DMR methylation showed a trend of lower methylation in the recurrence group (n=30) than in the no-recurrence (n=52) group. We conclude that whole-genome methylation profiling with CHARM revealed distinct patterns of differential DNA methylation between normal prostate and PCa tissues, as well as between different risk groups of PCa as defined by Gleason scores. A panel of selected DMRs may serve as novel surrogate biomarkers for Gleason score in PCa.