纵向单细胞RNA模型有助于预测EGFR-TKI耐药性。

IF 2.9 3区 医学 Q2 ONCOLOGY
Guoxin Hou, Zhimin Lu, Dongqiang Zeng, Qican Chen, Subing Cheng, Binbin Song
{"title":"纵向单细胞RNA模型有助于预测EGFR-TKI耐药性。","authors":"Guoxin Hou, Zhimin Lu, Dongqiang Zeng, Qican Chen, Subing Cheng, Binbin Song","doi":"10.1093/carcin/bgaf038","DOIUrl":null,"url":null,"abstract":"<p><p>Resistance is inevitable and a major challenge in treating lung adenocarcinoma (LUAD) patients with EGFR mutations. This study aimed to investigate the mechanism of EGFR-TKI resistance in LUAD using longitudinal single-cell RNA sequencing (scRNA-seq) data. We collected tumour samples of LUAD patients before and after EGFR inhibitor treatment and performed single-cell RNA sequencing. We used machine learning models for cell annotation and classified cells into subgroups. The inferCNV algorithm was used for CNV score calculation and tumour cell identification, and metabolic analysis was done using a gene-scoring approach. EGFR resistance score (ERscore), a gene signature derived from resistant tumour cells, was established to evaluate the predictiveness to EGFR-TKI inhibitors. The investigation classified subgroups of cells and identified three tumour cell types as critical cells mediating EGFR-TKI resistance. Our data also analysed the metabolic aspects of EGFR-TKI resistance using a single-cell approach. It showed that some tumour cell subtypes had a consistent metabolic profile, significantly up-regulating purine metabolism, oxidative phosphorylation, glycogen, and lipid metabolism. An assessment system called ERscore was established to evaluate the association between EGFR-TKI resistance and tumour ecosystem. The analysis showed a significant correlation between the ERscore and EGFR-TKI resistance, lung cancer phenotype, and prognosis. The findings suggest that the molecular mechanisms driving EGFR-TKI resistance in lung cancer may also contribute to poorer prognosis, particularly in lung adenocarcinomas with high EGFR mutation rates. Overall, the study provides important insights into the mechanisms of EGFR-TKI resistance in lung cancer at the single-cell level.</p>","PeriodicalId":9446,"journal":{"name":"Carcinogenesis","volume":" ","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Longitudinal single-cell RNA model aids prediction of EGFR-TKI resistance.\",\"authors\":\"Guoxin Hou, Zhimin Lu, Dongqiang Zeng, Qican Chen, Subing Cheng, Binbin Song\",\"doi\":\"10.1093/carcin/bgaf038\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Resistance is inevitable and a major challenge in treating lung adenocarcinoma (LUAD) patients with EGFR mutations. This study aimed to investigate the mechanism of EGFR-TKI resistance in LUAD using longitudinal single-cell RNA sequencing (scRNA-seq) data. We collected tumour samples of LUAD patients before and after EGFR inhibitor treatment and performed single-cell RNA sequencing. We used machine learning models for cell annotation and classified cells into subgroups. The inferCNV algorithm was used for CNV score calculation and tumour cell identification, and metabolic analysis was done using a gene-scoring approach. EGFR resistance score (ERscore), a gene signature derived from resistant tumour cells, was established to evaluate the predictiveness to EGFR-TKI inhibitors. The investigation classified subgroups of cells and identified three tumour cell types as critical cells mediating EGFR-TKI resistance. Our data also analysed the metabolic aspects of EGFR-TKI resistance using a single-cell approach. It showed that some tumour cell subtypes had a consistent metabolic profile, significantly up-regulating purine metabolism, oxidative phosphorylation, glycogen, and lipid metabolism. An assessment system called ERscore was established to evaluate the association between EGFR-TKI resistance and tumour ecosystem. The analysis showed a significant correlation between the ERscore and EGFR-TKI resistance, lung cancer phenotype, and prognosis. The findings suggest that the molecular mechanisms driving EGFR-TKI resistance in lung cancer may also contribute to poorer prognosis, particularly in lung adenocarcinomas with high EGFR mutation rates. Overall, the study provides important insights into the mechanisms of EGFR-TKI resistance in lung cancer at the single-cell level.</p>\",\"PeriodicalId\":9446,\"journal\":{\"name\":\"Carcinogenesis\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2025-09-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Carcinogenesis\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1093/carcin/bgaf038\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Carcinogenesis","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1093/carcin/bgaf038","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

耐药是不可避免的,也是治疗EGFR突变肺腺癌(LUAD)患者的主要挑战。本研究旨在利用纵向单细胞RNA测序(scRNA-seq)数据探讨EGFR-TKI在肺腺癌中的耐药机制。我们收集了EGFR抑制剂治疗前后LUAD患者的肿瘤样本,并进行了单细胞RNA测序。我们使用机器学习模型进行细胞注释,并将细胞分类为子组。使用intercnv算法进行CNV评分计算和肿瘤细胞鉴定,使用基因评分方法进行代谢分析。EGFR耐药评分(ERscore)是一种来自耐药肿瘤细胞的基因标记,用于评估对EGFR- tki抑制剂的预测性。该研究对细胞亚群进行了分类,并确定了三种肿瘤细胞类型作为介导EGFR-TKI抗性的关键细胞。我们的数据还使用单细胞方法分析了EGFR-TKI耐药的代谢方面。结果表明,一些肿瘤细胞亚型具有一致的代谢谱,显著上调嘌呤代谢、氧化磷酸化、糖原和脂质代谢。我们建立了一个名为ERscore的评估系统来评估EGFR-TKI耐药性与肿瘤生态系统之间的关系。分析显示,ERscore与EGFR-TKI耐药、肺癌表型和预后之间存在显著相关性。研究结果表明,肺癌中驱动EGFR- tki耐药的分子机制也可能导致预后较差,特别是在EGFR突变率高的肺腺癌中。总的来说,该研究在单细胞水平上为肺癌EGFR-TKI耐药机制提供了重要的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Longitudinal single-cell RNA model aids prediction of EGFR-TKI resistance.

Resistance is inevitable and a major challenge in treating lung adenocarcinoma (LUAD) patients with EGFR mutations. This study aimed to investigate the mechanism of EGFR-TKI resistance in LUAD using longitudinal single-cell RNA sequencing (scRNA-seq) data. We collected tumour samples of LUAD patients before and after EGFR inhibitor treatment and performed single-cell RNA sequencing. We used machine learning models for cell annotation and classified cells into subgroups. The inferCNV algorithm was used for CNV score calculation and tumour cell identification, and metabolic analysis was done using a gene-scoring approach. EGFR resistance score (ERscore), a gene signature derived from resistant tumour cells, was established to evaluate the predictiveness to EGFR-TKI inhibitors. The investigation classified subgroups of cells and identified three tumour cell types as critical cells mediating EGFR-TKI resistance. Our data also analysed the metabolic aspects of EGFR-TKI resistance using a single-cell approach. It showed that some tumour cell subtypes had a consistent metabolic profile, significantly up-regulating purine metabolism, oxidative phosphorylation, glycogen, and lipid metabolism. An assessment system called ERscore was established to evaluate the association between EGFR-TKI resistance and tumour ecosystem. The analysis showed a significant correlation between the ERscore and EGFR-TKI resistance, lung cancer phenotype, and prognosis. The findings suggest that the molecular mechanisms driving EGFR-TKI resistance in lung cancer may also contribute to poorer prognosis, particularly in lung adenocarcinomas with high EGFR mutation rates. Overall, the study provides important insights into the mechanisms of EGFR-TKI resistance in lung cancer at the single-cell level.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Carcinogenesis
Carcinogenesis 医学-肿瘤学
CiteScore
9.20
自引率
2.10%
发文量
95
审稿时长
1 months
期刊介绍: Carcinogenesis: Integrative Cancer Research is a multi-disciplinary journal that brings together all the varied aspects of research that will ultimately lead to the prevention of cancer in man. The journal publishes papers that warrant prompt publication in the areas of Biology, Genetics and Epigenetics (including the processes of promotion, progression, signal transduction, apoptosis, genomic instability, growth factors, cell and molecular biology, mutation, DNA repair, genetics, etc.), Cancer Biomarkers and Molecular Epidemiology (including genetic predisposition to cancer, and epidemiology), Inflammation, Microenvironment and Prevention (including molecular dosimetry, chemoprevention, nutrition and cancer, etc.), and Carcinogenesis (including oncogenes and tumor suppressor genes in carcinogenesis, therapy resistance of solid tumors, cancer mouse models, apoptosis and senescence, novel therapeutic targets and cancer drugs).
×
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