Copy Number Variations And Expression Profile Data Analysis Of Immortalized Epithelial Cell Line Genome Expression Profile And Prognosis Analysis Of Glioma

Purpose:Understanding the early molecular alterations of lung cancer progression is very meaningful to the prevention and early diagnosis of lung cancer. However it is hard to collect precancerous lesion samples in both clinic and research works. So we select human bronchiolar epithelial cell line as a lung precancerous model, and compared with lung cancer cell line, detected and analyzed the CNVs in addition with expression patterns of it in order to find out meaningful chromosome or gene alterations. Methods: We use array-CGH and expression profile bio-chips to detect CNVs and mRNA expression data of both immortalized cell line Y-BE and lung squamous cell cancer cell line NCI-H2170, then we use Bioinformatics approaches and GO enrichment to find meaningful ones. Results:Chromosome20q of early passage Y-BE is significantly amplified; besides the same phenomenon exists in NCI-H2170. Compared with early passage Y-BE, the late Y-BE and NCI-H2170get expression alterations of genes in inflammatory or embryonic developmental biological progressions. Conclusions:20q amplification could be a significant molecular alteration of the early stage of lung cancer progression. And genes in inflammatory or embryonic developmental biological progressions may only regulate carcinogenesis dominantly in some special stages. Purpose:WHO classification is current one use in glioma grading. However, it is easy to introduce subjective attitude to histological grading, besides, histological grading is not enough in a time asked individual medical treatment. So advanced and detailed classification is needed. We use mRNA expression differentiations between different patients to reanalyze their survival times and discuss the direction and feasibility of molecular classification for glioma. Methods:We use expression profile biochip to detect216clinic samples of Glioma (contain WHO grade Ⅱ,Ⅲ,Ⅳ) and get their mRNA expression profiles. Then we use Bioinformatics approaches to get several molecular networks, from which we get a group of differentially expressed genes. Use these genes we reanalyze the survival times of all samples and samples under different WHO grades. Results:Except for the WHO grade IV, all groups took a new survival analysis with those differentially expressed genes showed significant results, WHO grade II (OS, p=4.7×10-6), WHO grade Ⅲ (OS, p=0.018) and all samples(OS, p=6.4×10-16), all of time had significant differentiations between samples inside them. Conclusions:Using differentially expressed genes to update current classification is feasible. But we need more identified clinic information to estimate our results and rectify our analytical approaches in order to refine the analysis of glioblastoma which has an advanced malignance.