The prediction of tumor and normal tissues based on the DNA methylation values of ten key sites

CancerMHL: the database of integrating key DNA methylation, histone modifications and lncRNAs in cancer

The discovery of key epigenetic modifications in cancer is of great significance for the study of disease biomarkers. Through the mining of epigenetic modification data relevant to cancer, some researches on epigenetic modifications are accumulating. In order to make it easier to integrate the effects of key epigenetic modifications on the related cancers, we established CancerMHL (http://www.positionprediction.cn/), which provide key DNA methylation, histone modifications and lncRNAs as well as the effect of these key epigenetic modifications on gene expression in several cancers. To facilitate data retrieval, CancerMHL offers flexible query options and filters, allowing users to access specific key epigenetic modifications according to their own needs. In addition, based on the epigenetic modification data, three online prediction tools had been offered in CancerMHL for users. CancerMHL will be a useful resource platform for further exploring novel and potential biomarkers and therapeutic targets in cancer. Database URL: http://www.positionprediction.cn/.

M6A RNA methylation modification and tumor immune microenvironment in lung adenocarcinoma

Lung adenocarcinoma is one of the deadliest tumors. Studies have shown that N6-methyladenosine RNA methylation regulators, as a dynamic chemical modification, affect the occurrence and development of lung adenocarcinoma. To investigate the relationship between mutations and expression levels of m6A regulators in lung adenocarcinoma, we investigated the mutations and expression levels of 38 m6A regulators. We found that mutations in m6A regulatory factors did not affect the changes in expression levels, and 19 differentially expressed genes were identified. All tumor samples were classified into two subtypes based on the expression levels of 19 differentially expressed m6A-regulated genes. Survival analysis showed significant differences in survival between the two subtypes. To explore the relationship between immune cell infiltration and survival in both subtypes, we calculated the infiltration of 23 immune cells in both subtypes, and we found that the subtype with high immune cell infiltration had better survival. We found that subtypes with low tumor purity and high stromal and immune scores had better survival. The m6A-related immune genes were identified by taking the intersection of differentially expressed genes and immune genes in the two isoforms and calculating the Pearson correlation coefficients between the intersecting immune genes and the differentially expressed m6A-regulated genes. Finally, a prognostic model associated with m6A and associated with immunity was developed using prognostic genes screened from m6A-associated immune genes. The predictive power of the model was evaluated and our model was able to achieve good prediction.