Identification of Key Genes and Pathways in Gefitinib-Resistant Lung Adenocarcinoma using Bioinformatics Analysis.

IF 1.7 4区 生物学 Q4 EVOLUTIONARY BIOLOGY Evolutionary Bioinformatics Pub Date : 2021-06-11 eCollection Date: 2021-01-01 DOI:10.1177/11769343211023767
Kailin Mao, Fang Lin, Yingai Zhang, Hailong Zhou
{"title":"Identification of Key Genes and Pathways in Gefitinib-Resistant Lung Adenocarcinoma using Bioinformatics Analysis.","authors":"Kailin Mao,&nbsp;Fang Lin,&nbsp;Yingai Zhang,&nbsp;Hailong Zhou","doi":"10.1177/11769343211023767","DOIUrl":null,"url":null,"abstract":"<p><p>Gefitinib resistance is a serious threat in the treatment of patients with non-small cell lung cancer (NSCLC). Elucidating the underlying mechanisms and developing effective therapies to overcome gefitinib resistance is urgently needed. The differentially expressed genes (DEGs) were screened from the gene expression profile GSE122005 between gefitinib-sensitive and resistant samples. GO and KEGG analyses were performed with DAVID. The protein-protein interaction (PPI) network was established to visualize DEGs and screen hub genes. The functional roles of CCL20 in lung adenocarcinoma (LUAD) were examined using gene set enrichment analysis (GSEA). Functional analysis revealed that the DEGs were mainly concentrated in inflammatory, cell chemotaxis, and PI3K signal regulation. Ten hub genes were identified based on the PPI network. The survival analysis of the hub genes showed that CCL20 had a significant effect on the prognosis of LUAD patients. GSEA analysis showed that CCL20 high expression group was mainly enriched in cytokine-related signaling pathways. In conclusion, our analysis suggests that changes in inflammation and cytokine-related signaling pathways are closely related to gefitinib resistance in patients with lung cancer. The CCL20 gene may promote the formation of gefitinib resistance, which may serve as a new biomarker for predicting gefitinib resistance in patients with lung cancer.</p>","PeriodicalId":50472,"journal":{"name":"Evolutionary Bioinformatics","volume":"17 ","pages":"11769343211023767"},"PeriodicalIF":1.7000,"publicationDate":"2021-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1177/11769343211023767","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Evolutionary Bioinformatics","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1177/11769343211023767","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2021/1/1 0:00:00","PubModel":"eCollection","JCR":"Q4","JCRName":"EVOLUTIONARY BIOLOGY","Score":null,"Total":0}
引用次数: 4

Abstract

Gefitinib resistance is a serious threat in the treatment of patients with non-small cell lung cancer (NSCLC). Elucidating the underlying mechanisms and developing effective therapies to overcome gefitinib resistance is urgently needed. The differentially expressed genes (DEGs) were screened from the gene expression profile GSE122005 between gefitinib-sensitive and resistant samples. GO and KEGG analyses were performed with DAVID. The protein-protein interaction (PPI) network was established to visualize DEGs and screen hub genes. The functional roles of CCL20 in lung adenocarcinoma (LUAD) were examined using gene set enrichment analysis (GSEA). Functional analysis revealed that the DEGs were mainly concentrated in inflammatory, cell chemotaxis, and PI3K signal regulation. Ten hub genes were identified based on the PPI network. The survival analysis of the hub genes showed that CCL20 had a significant effect on the prognosis of LUAD patients. GSEA analysis showed that CCL20 high expression group was mainly enriched in cytokine-related signaling pathways. In conclusion, our analysis suggests that changes in inflammation and cytokine-related signaling pathways are closely related to gefitinib resistance in patients with lung cancer. The CCL20 gene may promote the formation of gefitinib resistance, which may serve as a new biomarker for predicting gefitinib resistance in patients with lung cancer.

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用生物信息学分析鉴定吉非替尼耐药肺腺癌的关键基因和途径。
吉非替尼耐药性是治疗癌症(NSCLC)患者的严重威胁。迫切需要阐明其潜在机制并开发有效的疗法来克服吉非替尼耐药性。从吉非替尼敏感和耐药样品之间的基因表达谱GSE122005中筛选差异表达基因(DEG)。采用DAVID进行GO和KEGG分析。建立了蛋白质-蛋白质相互作用(PPI)网络,以可视化DEG并筛选枢纽基因。应用基因集富集分析(GSEA)检测CCL20在肺腺癌(LUAD)中的功能作用。功能分析显示,DEG主要集中在炎症、细胞趋化性和PI3K信号调节方面。基于PPI网络鉴定了10个枢纽基因。hub基因的生存分析表明,CCL20对LUAD患者的预后有显著影响。GSEA分析显示CCL20高表达组主要富集于细胞因子相关的信号通路。总之,我们的分析表明,癌症患者炎症和细胞因子相关信号通路的变化与吉非替尼耐药性密切相关。CCL20基因可能促进吉非替尼耐药性的形成,这可能作为预测癌症患者吉非替宁耐药性的新生物标志物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Evolutionary Bioinformatics
Evolutionary Bioinformatics 生物-进化生物学
CiteScore
4.20
自引率
0.00%
发文量
25
审稿时长
12 months
期刊介绍: Evolutionary Bioinformatics is an open access, peer reviewed international journal focusing on evolutionary bioinformatics. The journal aims to support understanding of organismal form and function through use of molecular, genetic, genomic and proteomic data by giving due consideration to its evolutionary context.
期刊最新文献
Phylodynamic Investigation of Yellow Fever Virus Sheds New Insight on Geographic Dispersal Across Africa. In silico Characterization of a Hypothetical Protein (PBJ89160.1) from Neisseria meningitidis Exhibits a New Insight on Nutritional Virulence and Molecular Docking to Uncover a Therapeutic Target. Comparative Phylogenetic Analysis and Protein Prediction Reveal the Taxonomy and Diverse Distribution of Virulence Factors in Foodborne Clostridium Strains. An Effective Computational Method for Predicting Self-Interacting Proteins Based on VGGNet Convolutional Neural Network and Gray-Level Co-occurrence Matrix. Comprehensive Profiling of Transcriptome and m6A Epitranscriptome Uncovers the Neurotoxic Effects of Yunaconitine on HT22 Cells.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1