| Long non-coding RNAs(lnc RNAs)are a class of non-coding RNAs that do not encode proteins.They are involved in many biological processes and play an important role in them.Not only that,lnc RNA is also closely related to the occurrence and development of the disease.Therefore,the research of lnc RNA has received extensive attention in the industry and has become a hot spot in genetic research.Identifying lnc RNA associated with disease will provide new opportunities for the discovery and diagnosis of complex diseases such as cancer.However,the screening of lnc RNA molecules associated with diseases from tens of thousands of lnc RNAs will cost enormous human and material resources,posing great challenges for research.Using computational methods to identify lnc RNA associated with cancer can greatly reduce the scope of experimental screening and provide guidance for biological experiments.Genome-wide association study(GWAS)are powerful tools for genetic studies of complex diseases and traits.It is designed to detect single nucleotide polymorphisms(SNPs)associated with trait variation.By integrating GWAS data and biological networks,network-based analysis methods and techniques to carry out in-depth mining of GWAS data,researching genome-related information of various human diseases or traits,and identifying genes associated with complex diseases including lnc RNA is an emerging research field.Research has shown that network-assisted analysis can enhance our interpretation and prioritization of candidate genes and biomarkers.In this paper,the gene co-expression network and GWAS data are integrated to construct a heterogeneous network carrying SNP information.A network-based computational method is proposed to identify genes associated with complex diseases including lnc RNA,and a sub-network associated with the disease is obtained,and the disease is further selected.The lnc RNA provides a powerful support for the screening of disease-associated genes.The expression data of the coding gene and lnc RNA are obtained by GTEx Portal database.Based on the clustering characteristics analysis of complex network,a reasonable coding gene-lnc RNA heterogeneous network is constructed.The SNP information associated with the cancer is converted into the weight information of the corresponding node in the heterogeneous network,thereby obtaining a network with equal weights between the edges and the points.The genetic problem associated with identifying the disease is first converted into a sub-network found in the network associated with the disease,and the optimization problem corresponding to the problem can be solved by the maximum flow/minimum cut method.The Push-Relabel algorithm is used to obtain the minimum cut set as a sub-network associated with the disease,based on which the genes associated with the disease,including lnc RNA,can be further screened.This paper verifies the method and results by the following aspects.Firstly,based on the GO function annotation and KEGG enrichment analysis to verify the biological function of the sub-network,and further verify the association between lnc RNA and disease in the sub-network through the existing database and literature.The analysis of breast cancer indicate that the sub-network contains information highly correlated with breast cancer,and the association of 10 lnc RNAs with breast cancer has been verified by other independent experiments.The prediction information of SNP(rs6983267(G / T))located in lnc RNA CCAT2 is consistent with the results reported in the literature.The analysis of prostate cancer indicate that the sub-network associated with prostate cancer contained four lnc RNAs,and the association is confirmed by other independent experiments.In summary,the research methods and techniques proposed in this paper have achieved good prediction results.It is hoped that in the association study between cancer and genes,new lnc RNA molecules highly associated with cancer will be discovered,and potential lnc RNA biomarkers will be explored to explore SNP pairs.The effects of lnc RNA and the association between lnc RNA and disease are important. |
