Prognosis And Regulation Analysis Of Long Non-coding RNA In Breast Cancer Based On TCGA Database

Breast cancer with high morbidity and mortality is the most common cancer among women worldwide.Long non-coding RNA(lncRNA)refers to a non-coding RNA that is greater than 200 nucleotides in length and does not encode a protein.Increasing studies have confirmed that lncRNAs plays an important role in breast cancer.This paper integrates the expression profiles of lncRNA and RNA of breast cancer in TCGA(The Cancer Genome Atlas Project)database and the sample information of breast cancer clinical patients.The aim of this study is to screen out the prognostic lncRNA markers of breast cancer by bioinformatics methods,and infer the relationship between lncRNA and RNA in breast cancer.Regulatory role in the process.Firstly,we collected 1052 breast cancer clinical patient samples and 13159 lncRNA expression profiles,and randomly divided all of these clinical patient samples into training sets and test sets.In the training set,we performed a univariate Cox regression analysis of the initially screened lncRNA.The obtained lncRNA was modeled using the Robust likelihood survival model and the model was iterated 1000 times.Finally,11 lncRNAs with more than 600 frequencies in all models were selected as key prognostic lncRNAs for breast cancer.The risk scores of 11 lncRNAs were established by multivariate Cox regression analysis.The risk scores of each patient were calculated and the survival analysis of these 11 key prognostic lncRNAs was performed in different data sets.These 11 lncRNAs were able to effectively classify high/low risk groups with different overall survival,suggesting that these 11 key prognostic lncRNAs may play a key role in breast cancer prognosis.Secondly,the function of lncRNA in breast cancer was analyzed by using the expression profile of mRNA.Breast cancer patients were divided into early stage(Stage I),middle stage(Stage II)and late stage(Stage III,IV).The differential expression of lncRNA and mRNA was analyzed in different stages and total samples,and the differentially expressed lncRNA and mRNA were screened.The Pearson correlation coefficients of differentially expressed lncRNA and mRNA in normal and cancer groups were calculated,and the corresponding differentially regulated pairs of lncRNA-mRNA were obtained.Screening the strong correlation regulatory pairs of lncRNA-mRNA in different groups,and analyzing the pathway enrichment of the strong correlation regulatory pairs.The results showed that most of the screened genes were related to cancer and cell metabolism.There was no significant difference between GO and KEGG pathways in different stages,indicating that these pathways may play a key role in each stage of cancer development.For the normal group and the cancer group,the enriched pathways are significantly different,indicating that the screened strong-related lncRNA-mRNA regulation has different regulatory status and regulatory functions in the normal and cancer sample tissues.Finally,the association of 11 key prognostic lncRNAs with three known key genes(Her2,ESR1,PGR)was analyzed.Through the data analysis,we can find a certain correlation between ESR1,PGR and 11 lncRNAs.These two genes may regulate these 11 key prognostic lncRNAs to some extent and affect the prognosis of breast cancer.In this paper,the lncRNA and mRNA data in the TCGA database were used to analyze the effects of lncRNA on the prognosis and regulation of breast cancer.The research methods and results used in this paper may have important implications for finding the prognosis of breast cancer lncRNA markers and revealing the regulation mechanism of breast cancer lncRNA.The 11 prognostic lncRNAs found in this article are expected to be potential markers of breast cancer prognosis.