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Aberrant DNA methylation pattern plays an indispensable role in the initiation and development of head and neck squamous cell carcinoma (HNSCC). It is well recognized that lymph node metastasis is closely with unfavorable prognosis of HNSCC. Therefore, exploring the methylation events accounting for the lymph node metastasis of HNSCC is very important for improving the clinical outcome of HNSCC. Methylation data, RNA-seq data and clinical data were downloaded from The Cancer Genome Atlas (TCGA) and processed using the R package TCGA-Assembler. MethylMix was use for data analysis by integrating both methylation and gene expression data on HNSCC patients with lymph node metastasis and without lymph node metastasis. Pathway analysis was performed on significantly altered genes using ConsensusPathDB. The role of our interested gene zinc figure protein 569 (ZNF569) in HNSCC was further evaluated. Our results identified many novel hypermethylated/hypomethylated genes that might be closely associated with the lymph node metastasis of HNSCC. Pathway analysis revealed that increase in methylation of genes involved in generic transcription pathway including zinc figure proteins. ZNF569 was hypermethylated in HNSCC tissues especially those with lymph node metastasis. In addition, the expression levels of ZNF569 mRNA and protein were significantly lower in HNSCC tissues and cell lines compared to their respective controls. Moreover, overexpression of ZNF569 inhibited the proliferation, migration and invasion of HNSCC cells. HNSCC patients with lower ZNF569 expression suffered a significantly shorter overall survival than those with higher ZNF569 expression. In conclusion, we have identified many novel differentially methylated genes that might be important for the lymph node metastasis of HNSCC. In addition, ZNF569 might play a tumor suppressive role in carcinogenesis of HNSCC.
Pubmed ID: 31258729
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Project exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).
View all literature mentionsCell line HEK293T is a Transformed cell line with a species of origin Homo sapiens (Human)
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