| Since the new century, biological research has deepened and next-generation sequencing technology has widely developed. Biology and bioinfo rmatics are going deep into the molecular level, and more functions of biological macromolecules are further revealed. At first, people focus on the protein and its coding RNA, however, people gradually realized non-coding RNAs play an important role, which is treated as “transcription rubbish”. As a kind of non-coding RNA, long non-coding RNA is closely related to many life process. With its importance recognized, more and more biological and bioinformatics researchers join the research of lnc RNA.This paper mainly studies the mining method of lnc RNA related to cancer patients’ living based on lnc RNAs’ expression and clinical data. Firstly, comprehensive analysis of world’s research status and detail introduction of TCGA are present. Then intergrate the data, and determine lasso as the core variable selection method. Then, calculate the weight coefficients of the lnc RNAs and the survival time of patients are evaluated. Subsequently, divide all patients evenly into training and test sets and apply survival an alysis on both of them. Finally, do the functional enrichment analysis towards GO and Pathway with the help of m RNAs to investigate the potential functions of these lnc RNAs.In addition, this paper applies the methodology on lung cancer, and mines 18 lnc RNAs related to prognosis of lung cancer patients. The result says: the 18 lnc RNAs predicted the prognosis of lung cancer patients well in both training set and test set, whose Log Rank P-values less than 0.01 with great significance. Futrher enrichments on GO and Pathway show the lnc RNAs are related to many immune nodes and paths,as well as response to oxygen-containing compound and Synthesis of Lipoxins. The evidences above indicate potential roles of the lnc RNAs dug by our methodology in the development of lung cancer and lung cancer patients’ prognosis. |
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