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基于共表达网络分析的Ⅰ期胃癌预后基因筛选

本站小编 Free考研考试/2024-01-21

摘要: 目的 基于加权基因共表达网络分析(WGCNA)筛选Ⅰ期胃癌(SIGC)的预后因子并探讨其分子机制。方法 通过WGCNA分析癌症基因组图谱(TCGA)中SIGC患者的临床数据,筛选影响SIGC预后的基因,并通过体外实验验证该基因对预后的影响。通过基因组富集分析(GSEA)进一步探索其影响胃癌预后的机制。结果 TCGA中共有胃癌数据330例,其中SIGC数据51例。脑酸可溶性蛋白1(BASP1)基因高表达患者的总生存期(OS)明显短于低表达患者(P=0.016 25)。体外研究显示,抑制BASP1表达后,胃癌细胞迁移能力降低了62.2%。BASP1高表达富集了与肿瘤密切相关的IL6-JAK-STAT3通路(NOM p-val < 0.001;FDR q-val < 0.001)。结论 BASP1是SIGC的预后不良因子。BASP1可能通过调节IL6-JAK-STAT3通路促进胃癌细胞的迁移而导致胃癌预后不良。

基于共表达网络分析的Ⅰ期胃癌预后基因筛选

礼想, 杨博文, 宫丽宝, 臧丹, 张朝旭, 张闯, 李智, 曲秀娟
中国医科大学附属第一医院肿瘤内科, 辽宁省肿瘤药物与生物治疗重点实验室, 沈阳 110001
收稿日期:2020-04-26出版日期:2021-01-30发布日期:2021-01-06
通讯作者:曲秀娟E-mail:xiujuanqu@yahoo.com
作者简介:礼想(1995-),女,硕士研究生.
基金资助:国家自然科学基金(81972331,81972751,81572374);国家重点研发计划(2017YFC1308900);辽宁省高等学校基本科研项目(LFWK201706)


关键词: 胃癌, 脑酸可溶性蛋白1, 加权基因共表达网络分析, 转移
Abstract: Objective To screen the prognostic factors of stageⅠ gastric cancer (SIGC) by weighted gene co-expression network analysis (WGCNA) and to explore the molecular mechanisms in pathogenesis. Methods Clinical data from patients with SIGC in The Cancer Genome Atlas (TCGA) were analyzed by WGCNA. Survival analysis was performed to identify genes affecting SIGC prognosis. Additionally,the effect of this gene on prognosis was confirmed by cytological experiments. We further explored the mechanism by which this gene affected the prognosis of gastric cancer (GC) through gene set enrichment analysis (GSEA). Results We analyzed 330 samples from TCGA,which included 51 SIGC samples. Overall survival in patients with high expression of brain acid soluble protein 1 (BASP1) was lower than those with low expression of BASP1 (P=0.016 25). Cytological analysis showed that the migration ability of GC cell lines after knockdown of BASP1 decreased by 62.2%. GSEA indicated that the IL6-JAK-STAT3 pathway was highly enriched (NOM p-val < 0.001;FDR q-val < 0.001). Conclusion BASP1 is a marker for poor prognosis in SIGC,and it may affect GC cell migration through regulation of the IL6-JAK-STAT3 pathway.
Key words: gastric cancer, BASP1, weighted gene co-expression network analysis, metastasis
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https://journal.cmu.edu.cn/CN/article/downloadArticleFile.do?attachType=PDF&id=2666
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