| A large number of studies have shown that long non-coding RNAs(lncRNAs)play an important role in many biological processes.They regulate biological physiological processes and affect all aspects of cell homeostasis,and the variation or dysfunction of these lncRNAs may lead to the occurrence of some complex diseases.Due to its key role in a variety of biological processes,lncRNAs are becoming a research hotspot in the field of biology and medicine,and have potential roles in a variety of tumor diseases,and are expected to become new biomarkers and drug targets.Using computational bioinformatics to predict potential lncRNA-disease associations is of great significance for exploring the pathogenesis of disease,as well as the diagnosis,prognosis and prevention of disease.Genome-wide association studies(GWAS)have made outstanding contributions to the study of the inheritance of complex diseases and traits.Thousands of disease-related single nucleotide polymorphisms(SNPs)have been identified,more than 90% of which are outside the exons of protein-coding genes.Understanding their functional significance remains a challenge.Expression Quantitative trait locus(eQTL)analysis is a bridge between gene change and disease.It considers gene expression as a quantitative trait and studies the correlation between genetic variation and gene expression,thus linking these SNPs with gene expression.In previous studies,the mining of disease association module through SNP information combined with network mostly focused on the study of coding genes.However,the occurrence of SNPs in non-coding regions was also particularly high,especially in lncRNAs.Therefore,we mined the SNP information in the non-coding region to further analyze the influence of SNP information on lncRNAs on diseases.In this study,mutation data,expression data and clinical data from the TCGA(Cancer Genome Atlas)database were integrated,and eQTL analysis was used to establish a link between SNP and gene expression,thus migrating the study object from the SNP level to the gene level.Cancer and candidate genes were screened through eQTL analysis and differential expression analysis,and then gene co-expression network constructed with GTEX expression data was used to mine cancer-related lncRNA module based on the network model,so as to provide reference and basis for cancer diagnosis and treatment.Through GO functional annotation and KEGG pathway enrichment analysis,this paper explored the possible biological functions of the excavated cancer-associated lncRNA module,and verified the correlation between lncRNAs in the module and cancer through databases and literature.The method was applied to breast cancer,and the results showed that the extracted lncRNA module contained information highly associated with breast cancer,which proved the effectiveness of the method in this paper.Among the 25 lncRNAs excavated,the association between 2 lncRNAs and breast cancer has been verified by biological experiments,respectively CCAT2 and HOXC-AS3.The remaining lncRNAs need to be further analyzed and verified by biological experiments.The analysis of the experimental results of breast cancer shows that the mining of SNP information in lncRNA can provide a new solution for the study of complex diseases.Using calculation method to study relationship between lncRNA and cancer can reduce lncRNA research scope,reduce the cost of biological experiment personnel’s research not only can help us to deepen understanding of the pathogenesis of complex diseases at the molecular level,and can make use of lncRNA as biomarkers of disease diagnosis and prognosis,as well as potential targets for cancer treatment.This is of great significance to the diagnosis,prognosis and precision medicine of cancer. |
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