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11 May 2020 : Animal Research  

Bioinformatics Analysis and High-Throughput Sequencing to Identify Differentially Expressed Genes in Nebulin Gene (NEB) Mutations Mice

Haoyong Wang1BCD, Xiaoyue Nie1ABE, Xin Li1BCF, Yi Fang2BF, Dandan Wang2BD, William Wang3F, Yong Hu4ACG*, Zijing Liu4DF, Cheng Cao4ACG

DOI: 10.12659/MSM.922953

Med Sci Monit 2020; 26:e922953


BACKGROUND: High-throughput sequencing of the pathological tissue of 59 patients with thyroid cancer was compared with the normal population. It was found that the mutation frequency of the Nebulin gene (NEB) at amino acid 1133 locus of thyroid cancer patients was much higher than that of the normal population, suggesting that NEB mutation may be related to thyroid cancer. Therefore, we constructed the NEB mutant mice for further investigation.

MATERIAL AND METHODS: The RNA extracted from the thyroid of wild-type and NEB mutant mice was analyzed by high-throughput sequencing, and the differential expression was analyzed by edgeR software. Several differentially expressed genes were selected for quantitative real-time PCR (qRT-PCR) verification, and these genes were analyzed with Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis.

RESULTS: A total of 624 genes were significantly enriched. Analysis of GO function and pathway significant enrichment showed that differentially expressed genes were enriched in thyroid cancer, myocardial contraction, and autoimmune thyroid disease. The qRT-PCR results were consistent with the high-throughput sequencing results.

CONCLUSIONS: Our data indicate that the expression of some cancer-driving genes and cancer suppressor genes are significantly changed in NEB mutant mice compared to wild-type mice, which suggests that NEB function plays an important role in regulating the expression of cancer-related genes in the thyroid gland.

Keywords: Genes, Neoplasm, Point Mutation, Sequence Analysis, RNA, Computational Biology, gene ontology, High-Throughput Nucleotide Sequencing, Mutation, RNA, Software

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Dinah V. Parums

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Med Sci Monit 2024; 30:e945583


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Medical Science Monitor eISSN: 1643-3750
Medical Science Monitor eISSN: 1643-3750