Subspace Clustering Algorithm Based On Cancer Data And Pan-cancer Analysis Of DNA Methylation Regulation On Gene Expressions

The malignant transformation of cancer is a multi-step process,in which numerous molecular alternations are accumulated.These molecular changes dynamically interact with the tumor micro-environment,and impact cellular functions within the tumor.To date,substantial genetic alterations have been catalogued,however,genetic alterations alone is insufficient to explain the pervasive gene expression changes and alterations to cellular functions in cancer,and it has been reported that epigenetic alterations could replace genetic changes to cause gene expression changes of tumor suppressors [1].Epigenetic alterations are heritable traits that impact the phenotype by interfering with gene expression independent of the DNA sequence [2,3].In recent decades,epigenetic alterations with functional impacts have become key targets of interest,and notably,emerging epigenetic therapies can revert specific epigenetic alterations in cancer [4-7].The concomitant challenge is the intra-tumor heterogeneity compounding the effect of inter-individual heterogeneity,which further obscures the underlying functional relationships between epigenetic modulators and gene expression in cancer.Therefore,a protocol was designed to promote the heterogeneity of cancer epigenetics in and out of tumors,and to elucidate the relationship between different epigenetic mechanisms and their synergistic effects on gene expression.In this paper,a large number of gene expression data and DNA methylation data were analyzed,and they were decomposed one by one.Then,the RMR algorithm developed in this paper was used to remove the discrete values with the greatest impact,in an attempt to fit two kinds of relevant regulatory relations.Eventually,the source of the epigenetic variation hidden in cancer was identified.At the same time in order to further understand how those genes by changing coordinated expression,this paper developed a suitable for cancer data for this subspace clustering algorithm,this algorithm adopts a bottom-up search strategy,by joining the spatial structure information,after cleaning a lot of interference find numerical data for 1 piece,these data blocks to mapped to the common effect of genes on.Through the study in this paper,we found that DNA methylation can not only promote gene expression but also inhibit gene expression.Therefore,further research into which DNA methylation can effectively inhibit cancer will help us develop new therapies for cancer and produce drugs that can inhibit tumor growth.At the same time,after the subspace clustering algorithm for gene expression data with obvious regulatory effect,this paper found the synergistic effect of gene expression,and the variation of these genes is often co-occurring and concentrated in some patients.We can trace whether these synergistic genes are related to certain biological functions.This discovery helps us to find the intrinsic link between gene mutations and provides new ideas for eventually finding more effective treatments to inhibit cancer.