[Analysis of Gene Variation in Thymoma by Microarray].

BACKGROUND: Thymoma is the most common malignant tumor in anterior mediastinum, and its specific pathogenesis is still unclear. This limits the study of targeted drugs for thymoma. The aim of the study is to investigate the genes and signal pathways of thymoma, and provide help for the research of thymic tumor pathogenesis using the technology of second-generation genechip to analyze thymoma. METHODS: From January 2015 to December 2017, we analyzed 31 cases of thymoma by CapitaBio mRNA expression profile genechip technology, and then confirmed the genes by reverse transcription-polymerase chain reaction (RT-PCR). RESULTS: We found some genes with different expression levels between thymoma and surrounding thymus tissue. Among them, six driving genes (FANCI, CAPD3, NCAPG, OXCT1, EPHA1 and MCM2) were significantly abnormal in thymoma. Some specific genes affected by copy-number variation were detected: E2F2, EphA1, CCL25 and MCM2 were significantly up-regulated, while IL-6, CD36, FABP4, SH2D1A and MYOC genes were significantly down-regulated. KEGG database analysis showed that the expression of 10 signaling pathway genes was generally up-regulated or down-regulated, such as systemic lupus erythematosus, viral oncogenes, primary immunodeficiency, cell cycle genes and p53 signaling pathway, which may be related to occurrence of thymoma. CONCLUSIONS: We found a variety of genes abnormally expressed in thymoma, which will provide reference for the study of pathogenesis and biomarkers of thymoma in the future.

Analysis of Gene Variation in Thymoma by Microarray / 中国肺癌杂志

BACKGROUND@#Thymoma is the most common malignant tumor in anterior mediastinum, and its specific pathogenesis is still unclear. This limits the study of targeted drugs for thymoma. The aim of the study is to investigate the genes and signal pathways of thymoma, and provide help for the research of thymic tumor pathogenesis using the technology of second-generation genechip to analyze thymoma.@*METHODS@#From January 2015 to December 2017, we analyzed 31 cases of thymoma by CapitaBio mRNA expression profile genechip technology, and then confirmed the genes by reverse transcription-polymerase chain reaction (RT-PCR).@*RESULTS@#We found some genes with different expression levels between thymoma and surrounding thymus tissue. Among them, six driving genes (FANCI, CAPD3, NCAPG, OXCT1, EPHA1 and MCM2) were significantly abnormal in thymoma. Some specific genes affected by copy-number variation were detected: E2F2, EphA1, CCL25 and MCM2 were significantly up-regulated, while IL-6, CD36, FABP4, SH2D1A and MYOC genes were significantly down-regulated. KEGG database analysis showed that the expression of 10 signaling pathway genes was generally up-regulated or down-regulated, such as systemic lupus erythematosus, viral oncogenes, primary immunodeficiency, cell cycle genes and p53 signaling pathway, which may be related to occurrence of thymoma.@*CONCLUSIONS@#We found a variety of genes abnormally expressed in thymoma, which will provide reference for the study of pathogenesis and biomarkers of thymoma in the future.

A new method for mining information of co-expression network based on multi-cancers integrated data.

BACKGROUND: Gene co-expression network is a favorable method to reveal the nature of disease. With the development of cancer, the way to build gene co-expression networks based on cancer data has been become a hot spot. However, there are still a limited number of current node measurement methods and node mining strategies for multi-cancers network construction. METHODS: In this paper, we introduce a new method for mining information of co-expression network based on multi-cancers integrated data, named PMN. We construct the network by combining the different types of relevant measures (linear and nonlinear rules) for different nodes based on integrated gene expression data of multi-cancers from The Cancer Genome Atlas (TCGA). For mining genes, we combine different properties (local and global characteristics) of the nodes. RESULTS: We uncover more suspicious abnormally expressed genes and shared pathways of different cancers. And we have also found some proven genes and pathways; of course, there are some suspicious factors and molecules that need clinical validation. CONCLUSIONS: The results demonstrate that our method is very effective in excavating gene co-expression genes of multi-cancers.

Network analysis based on low-rank method for mining information on integrated data of multi-cancers.

The noise problem of cancer sequencing data has been a problem that can't be ignored. Utilizing considerable way to reduce noise of these cancer data is an important issue in the analysis of gene co-expression network. In this paper, we apply a sparse and low-rank method which is Robust Principal Component Analysis (RPCA) to solve the noise problem for integrated data of multi-cancers from The Cancer Genome Atlas (TCGA). And then we build the gene co-expression network based on the integrated data after noise reduction. Finally, we perform nodes and pathways mining on the denoising networks. Experiments in this paper show that after denoising by RPCA, the gene expression data tend to be orderly and neat than before, and the constructed networks contain more pathway enrichment information than unprocessed data. Moreover, learning from the betweenness centrality of the nodes in the network, we find some abnormally expressed genes and pathways proven that are associated with many cancers from the denoised network. The experimental results indicate that our method is reasonable and effective, and we also find some candidate suspicious genes that may be linked to multi-cancers.