The protein-protein interaction network of intestinal gastric cancer patients reveals hub proteins with potential prognostic value.

IF 1.9
Everton Cruz Santos, Renata Binato, Priscila Valverde Fernandes, Maria Aparecida Ferreira, Eliana Abdelhay
{"title":"The protein-protein interaction network of intestinal gastric cancer patients reveals hub proteins with potential prognostic value.","authors":"Everton Cruz Santos,&nbsp;Renata Binato,&nbsp;Priscila Valverde Fernandes,&nbsp;Maria Aparecida Ferreira,&nbsp;Eliana Abdelhay","doi":"10.3233/CBM-203225","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Gastric cancer (GC) is the third leading cause of cancer worldwide. According to the Lauren classification, gastric adenocarcinoma is divided into two subtypes: diffuse and intestinal. The development of intestinal gastric cancer (IGC) can take years and involves multiple factors.</p><p><strong>Objective: </strong>To investigate the protein profile of tumor samples from patients with IGC in comparison with adjacent nontumor tissue samples.</p><p><strong>Methods: </strong>We used label-free nano-LC-MS/MS to identify proteins from the tissues samples. The results were analyzed using MetaCore™ software to access functional enrichment information. Protein-protein interactions (PPI) were predicted using STRING analysis. Hub proteins were determined using the Cytoscape plugin, CytoHubba. Survival analysis was performed using KM plotter. We identified 429 differentially expressed proteins whose pathways and processes were related to protein folding, apoptosis, and immune response.</p><p><strong>Results: </strong>The PPI network of these proteins showed enrichment modules related to the regulation of cell death, immune system, neutrophil degranulation, metabolism of RNA and chromatin DNA binding. From the PPI network, we identified 20 differentially expressed hub proteins, and assessed the prognostic value of the expression of genes that encode them. Among them, the expression of four hub genes was significantly associated with the overall survival of IGC patients.</p><p><strong>Conclusions: </strong>This study reveals important findings that affect IGC development based on specific biological alterations in IGC patients. Bioinformatics analysis showed that the pathogenesis of IGC patients is complex and involves different interconnected biological processes. These findings may be useful in research on new targets to develop novel therapies to improve the overall survival of patients with IGC.</p>","PeriodicalId":520578,"journal":{"name":"Cancer biomarkers : section A of Disease markers","volume":" ","pages":"83-96"},"PeriodicalIF":1.9000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.3233/CBM-203225","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer biomarkers : section A of Disease markers","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3233/CBM-203225","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

Abstract

Background: Gastric cancer (GC) is the third leading cause of cancer worldwide. According to the Lauren classification, gastric adenocarcinoma is divided into two subtypes: diffuse and intestinal. The development of intestinal gastric cancer (IGC) can take years and involves multiple factors.

Objective: To investigate the protein profile of tumor samples from patients with IGC in comparison with adjacent nontumor tissue samples.

Methods: We used label-free nano-LC-MS/MS to identify proteins from the tissues samples. The results were analyzed using MetaCore™ software to access functional enrichment information. Protein-protein interactions (PPI) were predicted using STRING analysis. Hub proteins were determined using the Cytoscape plugin, CytoHubba. Survival analysis was performed using KM plotter. We identified 429 differentially expressed proteins whose pathways and processes were related to protein folding, apoptosis, and immune response.

Results: The PPI network of these proteins showed enrichment modules related to the regulation of cell death, immune system, neutrophil degranulation, metabolism of RNA and chromatin DNA binding. From the PPI network, we identified 20 differentially expressed hub proteins, and assessed the prognostic value of the expression of genes that encode them. Among them, the expression of four hub genes was significantly associated with the overall survival of IGC patients.

Conclusions: This study reveals important findings that affect IGC development based on specific biological alterations in IGC patients. Bioinformatics analysis showed that the pathogenesis of IGC patients is complex and involves different interconnected biological processes. These findings may be useful in research on new targets to develop novel therapies to improve the overall survival of patients with IGC.

肠胃癌患者蛋白-蛋白相互作用网络揭示了具有潜在预后价值的枢纽蛋白。
背景:胃癌(GC)是全球第三大癌症。根据Lauren分类,胃腺癌分为两种亚型:弥漫性和肠型。肠胃癌(IGC)的发展可能需要数年时间,涉及多种因素。目的:探讨IGC患者肿瘤样本与邻近非肿瘤组织样本的蛋白质谱。方法:采用无标记纳米lc -MS/MS对组织样品进行蛋白质鉴定。使用MetaCore™软件获取功能富集信息对结果进行分析。利用STRING分析预测蛋白质-蛋白质相互作用(PPI)。使用Cytoscape插件CytoHubba测定枢纽蛋白。采用KM绘图仪进行生存分析。我们鉴定了429种差异表达蛋白,其途径和过程与蛋白质折叠、细胞凋亡和免疫反应有关。结果:这些蛋白的PPI网络显示出与细胞死亡、免疫系统、中性粒细胞脱颗粒、RNA代谢和染色质DNA结合调控相关的富集模块。从PPI网络中,我们确定了20个差异表达的枢纽蛋白,并评估了编码它们的基因表达的预后价值。其中,4个枢纽基因的表达与IGC患者的总生存率显著相关。结论:本研究揭示了基于IGC患者特异性生物学改变影响IGC发展的重要发现。生物信息学分析表明,IGC患者的发病机制是复杂的,涉及多种相互关联的生物学过程。这些发现可能有助于研究新的靶点,开发新的治疗方法,以提高IGC患者的总体生存率。
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 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信