甲状腺癌
癌症
基因
鉴定(生物学)
癌症研究
甲状腺
化学
医学
内分泌学
计算生物学
生物信息学
生物
内科学
生物化学
植物
作者
N. Wu,Zuoqian Jing,Huina Lv,Qun Liu,Ming Gu,Yifan Zhong,Xing Peng,Ruiyang Ma,Yuchen Jing
标识
DOI:10.1016/j.ijbiomac.2024.134986
摘要
Endocrine tumors like thyroid carcinoma are becoming more frequent. No clinically informative predictors were found. Thus, effective gene networks and representative biomarkers can illuminate thyroid cancer prevention molecular mechanisms. TBC1D4 is an activating protein molecule that plays an important role in regulating cell metabolism and signal transduction. The aim of this study was to investigate the expression characteristics of TBC1D4 activating protein molecules and identify key module genes that prevent thyroid cancer progression. GSE65144 data were downloaded from GEO. "limma" in R found DEGs with a false discovery rate < 0.05 and a log2 fold change <1. WGCNA builds gene co-expression networks, screens key modules, and filters hub genes. Overlapping genes become hub genes. Hub genes underwent GO and KEGG pathway enrichment analysis. We used Lasso to extract hub gene expression results' distinctive genes. Key genes. GEPIA database determined expression and survival impact. A total of 3220 DEGs. Thyroid cancer was mostly associated with darkred, darkturquoise, and green modules. Venn screened 639 hub genes. Cytokine-cytokine receptor interaction was the primary KEGG enrichment. Hub genes were 14. Finally, ARHGAP6, TBC1D4, and TC2N were important genes. Through gene screening and functional enrichment analysis, we identified a group of genes related to TBC1D4 activating protein and constructed the corresponding protein interaction network.
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