DNA methylation patterns-based subtype distinction and identification of soft tissue sarcoma prognosis

Soft tissue sarcomas (STSs) are heterogeneous at the clinical with a variable tendency of aggressive behavior. In this study, we constructed a specific DNA methylation-based classification to identify the distinct prognosis-subtypes of STSs based on the DNA methylation spectrum from the TCGA database. Eventually, samples were clustered into 4 subgroups, and their survival curves were distinct from each other. Meanwhile, the samples in each subgroup reflected differentially in several clinical features. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was also conducted on the genes of the corresponding promoter regions of the above-described specific methylation sites, revealing that these genes were mainly concentrated in certain cancer-associated biological functions and pathways. In addition, we calculated the differences among clustered methylation sites and performed the specific methylation sites with LASSO algorithm. The selection operator algorithm was employed to derive a risk signature model, and a prognostic signature based on these methylation sites performed well for risk stratification in STSs patients. At last, a nomogram consisted of clinical features and risk score was developed for the survival prediction. This study declares that DNA methylation-based STSs subtype classification is highly relevant for future development of personalized therapy as it identifies the prediction value of patient prognosis.

DNA methylation-based classification and identification of renal cell carcinoma prognosis-subgroups

BACKGROUND

Renal cell carcinoma (RCC) is the most common kidney cancer and includes several molecular and histological subtypes with different clinical characteristics. The combination of DNA methylation and gene expression data can improve the classification of tumor heterogeneity, by incorporating differences at the epigenetic level and clinical features.

METHODS

In this study, we identified the prognostic methylation and constructed specific prognosis-subgroups based on the DNA methylation spectrum of RCC from the TCGA database.

RESULTS

Significant differences in DNA methylation profiles among the seven subgroups were revealed by consistent clustering using 3389 CpGs that indicated that were significant differences in prognosis. The specific DNA methylation patterns reflected differentially in the clinical index, including TNM classification, pathological grade, clinical stage, and age. In addition, 437 CpGs corresponding to 477 genes of 151 samples were identified as specific hyper/hypomethylation sites for each specific subgroup. A total of 277 and 212 genes corresponding to DNA methylation at promoter sites were enriched in transcription factor of GKLF and RREB-1, respectively. Finally, Bayesian network classifier with specific methylation sites was constructed and was used to verify the test set of prognoses into DNA methylation subgroups, which was found to be consistent with the classification results of the train set. DNA methylation-based classification can be used to identify the distinct subtypes of renal cell carcinoma.

CONCLUSIONS

This study shows that DNA methylation-based classification is highly relevant for future diagnosis and treatment of renal cell carcinoma as it identifies the prognostic value of each epigenetic subtype.

Data-Driven-Based Approach to Identifying Differentially Methylated Regions Using Modified 1D Ising Model

Background

DNA methylation is essential for regulating gene expression, and the changes of DNA methylation status are commonly discovered in disease. Therefore, identification of differentially methylation patterns, especially differentially methylated regions (DMRs), in two different groups is important for understanding the mechanism of complex diseases. Few tools exist for DMR identification through considering features of methylation data, but there is no comprehensive integration of the characteristics of DNA methylation data in current methods.

Results

Accounting for the characteristics of methylation data, such as the correlation characteristics of neighboring CpG sites and the high heterogeneity of DNA methylation data, we propose a data-driven approach for DMR identification through evaluating the energy of single site using modified 1D Ising model. Applied to both simulated and publicly available datasets, our approach is compared with other popular methods in terms of performance. Simulated results show that our method is more sensitive than competing methods. Applied to the real data, our method can identify more common DMRs than DMRcate, ProbeLasso, and Wang's methods with a high overlapping ratio. Also, the necessity of integrating the heterogeneity and correlation characteristics in identifying DMR is shown through comparing results with only considering mean or variance signals and without considering relationship of neighboring CpG sites, respectively. Through analyzing the number of DMRs identified in real data located in different genomic regions, we find that about 90% DMRs are located in CGI which always regulates the expression of genes. It may help us understand the functional effect of DNA methylation on disease.

SMYD3 promoter hypomethylation is associated with the risk of colorectal cancer

AIM

SMYD3 encodes histone lysine methyltransferase. The goal of our study was to investigate the association between SMYD3 methylation and colorectal cancer (CRC).

MATERIALS & METHODS

SMYD3 methylation was measured by quantitative methylation-specific PCR method in 117 pairs of CRC tumor and para-tumor tissues.

RESULTS

Significantly lower SMYD3 methylation was observed in CRC tumor tissues than para-tumor tissues (p = 0.002). Further subgroup analysis by clinical features showed that significantly lower SMYD3 methylation were only observed in the CRC patients with tumors of moderately and well differentiation, positive lymph node metastasis, and stage III + IV.

CONCLUSION

Our work reported for the first time that SMYD3 promoter hypomethylation was associated with CRC.

SSTR2 promoter hypermethylation is associated with the risk and progression of laryngeal squamous cell carcinoma in males

Background

Somatostatin receptor 2 (SSTR2) encodes somatostatin receptor that can inhibit the cell proliferation of solid tumors. Promoter hypermethylation is likely to silence the expression of SSTR2. The goal of our study was to investigate the association between SSTR2 promoter methylation and the risk and progression of laryngeal carcinoma.

Methods

In the current study, tumor tissues and their adjacent non-tumor tissues were collected from a total of 87 laryngeal squamous cell carcinoma (LSCC) male patients. DNA methylation levels of nine SSTR2 promoter CpGs were measured using the bisulphite pyrosequencing technology.

Results

Our results revealed that there was a significantly increased SSTR2 promoter methylation in LSCC tissues than in their adjacent non-cancerous tissues (adjusted P = 0.003). Breakdown analysis by age indicated that the significant association was mainly contributed by patients younger than 60 (adjusted P = 0.039) but not in patients older than 60. Meanwhile, the significant association was observed in the patients with moderately (adjusted P = 0.037) and well differentiated tissues (adjusted P = 0.028), as well as the patients with histological stage IV (adjusted P = 0.031). Multivariate Cox analysis suggested that SSTR2 promoter methylation was an independent prognostic factor of LSCC (HR = 1.127, 95 % CI = 1.034–1.228).

Conclusions

In conclusion, SSTR2 promoter hypermethylation might be associated with the risk and progression of LSCC in males.