Suppr超能文献

生物信息学分析表明,LINC01150可能是一种与胃癌相关的新型中性粒细胞胞外诱捕网生物标志物。

Bioinformatic analysis indicated that LINC01150 might be a novel neutrophil extracellular traps-related biomarker of gastric cancer.

作者信息

Qian Yang-Yang, Xu Min, Huang Xin-Kun, Zhu Bin

机构信息

Department of Central Laboratory, Yancheng First Hospital, Affiliated Hospital of Nanjing University Medical School, Yancheng, China.

Department of General Surgery, Yancheng First Hospital, Affiliated Hospital of Nanjing University Medical School, Yancheng, China.

出版信息

Sci Rep. 2025 Mar 6;15(1):7875. doi: 10.1038/s41598-025-92968-9.

Abstract

Gastric cancer (GC) is a highly aggressive malignancy associated with poor prognosis, particularly in its advanced stages. Neutrophil extracellular traps (NETs) have been implicated in cancer progression and immune therapy responses; however, the role of NETs-related long non-coding RNAs (lncRNAs) in GC remains poorly understood. This study used data from the Cancer Genome Atlas (TCGA) and previous research to identify NETs-related lncRNAs in GC. A prognostic signature comprising four NETs-related lncRNAs (NlncSig) was developed and validated, serving as a predictor for patient survival and response to immunotherapy. The NlncSig was correlated with poorer outcomes in high-risk patients and demonstrated that those with lower risk scores exhibited more favorable responses to immunotherapy. In vitro experiments confirmed that LINC01150 enhances GC cell proliferation, migration, and invasion. This robust NlncSig provides a reliable tool for predicting survival and immune characteristics in GC, with the potential to guide personalized therapeutic approaches and improve patient care.

摘要

胃癌(GC)是一种侵袭性很强的恶性肿瘤,预后较差,尤其是在晚期。中性粒细胞胞外陷阱(NETs)与癌症进展和免疫治疗反应有关;然而,NETs相关的长链非编码RNA(lncRNAs)在胃癌中的作用仍知之甚少。本研究利用癌症基因组图谱(TCGA)的数据和先前的研究来鉴定胃癌中与NETs相关的lncRNAs。开发并验证了一个由四个与NETs相关的lncRNAs组成的预后特征(NlncSig),作为患者生存和免疫治疗反应的预测指标。NlncSig与高危患者较差的预后相关,并表明风险评分较低的患者对免疫治疗表现出更有利的反应。体外实验证实,LINC01150可增强胃癌细胞的增殖、迁移和侵袭能力。这个强大的NlncSig为预测胃癌的生存和免疫特征提供了一个可靠的工具,有可能指导个性化治疗方法并改善患者护理。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e973/11885803/bc0508af6280/41598_2025_92968_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验