ArticlePeerJ2018
Meta-analysis of microarray datasets identify several chromosome segregation-related cancer/testis genes potentially contributing to anaplastic thyroid carcinoma.
Article in PeerJ, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- TTK Overexpression: A Negative Prognostic Biomarker in Anaplastic Thyroid Carcinoma.Cancer medicine · 2026Article
- Pig H3K4me3, H3K27ac, and gene expression profiles reveal reproductive tissue-specific activity of transposable elements.Zoological research · 2024Article
- An Integrative Multi-Omics Analysis of The Molecular Links between Aging and Aggressiveness in Thyroid Cancers.Aging and disease · 2023Article
- Ectopic expression of meiotic cohesin generates chromosome instability in cancer cell line.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- The effects of Epigallocatechin-3-gallate and Dabrafenib combination on apoptosis and the genes involved in epigenetic events in anaplastic thyroid cancer cells.Medical oncology (Northwood, London, England) · 2022Article
- Down regulated oncogeneAnnals of translational medicine · 2022Article
- Overexpression ofOncology letters · 2020Article
- Identification and Validation of Novel Genes in Anaplastic Thyroid Carcinoma via Bioinformatics Analysis.Cancer management and research · 2020Article
- TRIP13 predicts poor prognosis in clear cell renal cell carcinoma.American journal of cancer research · 2020Article
- Identification of five hub genes as monitoring biomarkers for breast cancer metastasis in silico.Hereditas · 2019Article
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimAnaplastic thyroid carcinoma (ATC) is the most lethal thyroid malignancy. Identification of novel drug targets is urgently needed. MATERIALS &
methodsWe re-analyzed several GEO datasets by systematic retrieval and data merging. Differentially expressed genes (DEGs) were filtered out. We also performed pathway enrichment analysis to interpret the data. We predicted key genes based on protein-protein interaction networks, weighted gene co-expression network analysis and genes' cancer/testis expression pattern. We also further characterized these genes using data from the Cancer Genome Atlas (TCGA) project and gene ontology annotation.
resultsCell cycle-related pathways were significantly enriched in upregulated genes in ATC. We identified
conclusionWe predicted several key genes harboring potential therapeutic value in ATC. Cell cycle-related processes, especially chromosome segregation, may be the key to tumorigenesis and treatment of ATC.
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