| The prostate cancer is one of the most common malignant tumor in male reproductive system, the incidence rate of which increases with age. Although the incidence rate of prostate cancer in our country is lower than that of Europe and other Asia countries, however, it has been on a rise in recent years. The morbidity and mortality of prostate cancer is the second-deadliest cancer after lung cancer. Therefore, there's a great significance to carry out the relevant research of prostate cancer for the life and health of men.In this study, two groups of RNA-seq datas of normal prostate and prostate cancer tissues which have been published by NCBI database were used as the samples. Bioinformatics methods were applied to reconstruct the transcription and predict the lncRNAs. Then, the related analysis of the predicted result was carried out.First, the gene guided method was used to reconstruct the transcriptome of samples. TopHat was used to mapping the original reads to the human reference genome, and the success rate of mapping was about 76.9 percent. Then, Cufflinks suit was used to assemble, integrate, annotate and classify the reads, and 157926 transcripts was obtained in total.Then, the lncRNAs were predicted from the result of transcriptome reconstruction. The basic definition of lncRNA that the transcript length more than 200nt and the open reading frame length less than 300nt were chosen as the first filter condition,6941 candidate transcripts of lncRNAs were got. PhyloCSF, a software of distinguish protein coding and non-coding, was used as the second filter,1776 transcripts were accepted. At last, Pfam, which is a protein data bank, was used as the last filter,1080 predicted lncRNAs were obtained.Cuffdiff and WGCNA were applied respectively to carry out the differential expression analysis and gene co-expression network analysis of the predictable results.5 up-regulation and 7 down-regulation differential expression genes,3 differential expression known lncRNAs and 2 gene modules which have a significant correlation with the prostate cancer were obtained. According to the result, we found and suspected that the differential expression lncRNA RP11-267A15.1 was associated with prostate cancer, and may produce inhibition for prostate cancer.In addition, we also analysed the differential expression of the mRNAs which included all lncRNAs and got 1715 up-regulation and 1162 down-regulation differential expression genes. Then, the gene function annotation and pathway for the result of differential expression were carried out. According to the result, these differential expression genes were likely related to gene function, such as single-multicellular organism process, system development, cell differentiation and so on, were suspected.R programming language was used to visual analysis for all of the quantitative results above and pick out the meaningful results and other information.In this study, bioinformatics methods were applied in the RNA-seq data of normal prostate tissues and prostate cancer tissues to predict the lncRNAs and other correlation analysis. The differential expression of lncRNAs, gene modules from gene co-expression network analysis, the results of gene function annotation and pathway of differential expression mRNAs were detailed in this study which was provide a powerful data basic for the research of prostate cancer and expected to provide a new theoretical basis for the further identification of specific genes in the prostate cancer. |
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